Papers

• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Machine actionable metadata models - This paper discussed the use of JSON Schema to define human and machine readable metadata models.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Blaze: Compiling JSON Schema for 10x Faster Validation - This paper introduces Blaze, a JSON Schema validator compiles complex schemas to an efficient representation in seconds to minutes, adding minimal overhead at build time. Blaze incorporates several unique optimizations to reduce the validation time by an average of approximately 10x compared existing validators on a variety of datasets. In some cases, Blaze achieves a reduction in validation time of multiple orders of magnitude compared to the next fastest validator. We also demonstrate that several popular validators produce incorrect results in some cases, while Blaze maintains strict adherence to the JSON Schema specification..
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Schema extraction and structural outlier detection for JSON-based nosql data stores - Rather than designing the schema up front, extracting a schema in hindsight can be seen as a reverse-engineering step. Based on the extracted schema information, we propose set of similarity measures that capture the degree of heterogeneity of JSON data and which reveal structural outliers in the data.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• JSONSchemaBench: A Rigorous Benchmark of Structured Outputs for Language Models - This paper introduces JSONSchemaBench, a benchmark for constrained decoding comprising 10K real-world JSON schemas that encompass a wide range of constraints with varying complexity. We pair the benchmark with the existing official JSON Schema Test Suite and evaluate six state-of-the-art constrained decoding frameworks, including Guidance, Outlines, Llamacpp, XGrammar, OpenAI, and Gemini. Through extensive experiments, we gain insights into the capabilities and limitations of constrained decoding on structured generation with real-world JSON schemas..
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Elimination of annotation dependencies in validation for Modern JSON Schema - This paper proves that the elimination of annotation dependent keywords cannot, in general, avoid an exponential increase of the schema dimension. We provide an algorithm to eliminate these keywords that, despite the theoretical lower bound, behaves quite well in practice, as we verify with an extensive set of experiments..
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.
• Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas - This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions.
• What Are Real JSON Schemas Like? - A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
• Example-Driven Web API Specification Discovery - In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented.
• τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases - This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages.
• Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework - This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created.
• JSON Schema Inference Approaches - In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison.