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awesome-jsonschema

A curated list of awesome JSON Schema resources, tutorials, tools, and more
https://github.com/sourcemeta/awesome-jsonschema

Last synced: 5 days ago
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  • Papers

    • 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.
    • 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.
    • Negation-Closure for JSON Schema - Examines how JSON Schema handles negation, demonstrates that the language lacks negation closure, explores how recent schema drafts address this limitation, and proposes enrichments to the language. Includes an algebraic reformulation of JSON Schema and a prototype system for generating schema witnesses.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.

  • Podcasts

    • API Lifecycles, Specifications, and Standards with Kin Lane - Thomas Betts talks with Kin Lane about managing your API lifecycle using standards and specifications, including OpenAPI, AsyncAPI, and JSON Schema. These specifications and the tooling based on them can help reduce communication problems, by creating documentation, generating code, and automating testing.

  • Registries

    • Sourcemeta Schemas - An immutable collection of curated and versioned modern JSON Schema definitions.
    • Intelligence.AI Schemas - A schema registry by Intelligence.AI.
    • Intelligence.AI Schemas - A schema registry by Intelligence.AI.
    • AsyncAPI Schemas - This repository contains all the JSON Schema documents for validating AsyncAPI documents.
    • Conda Schemas - Conda file formats and schemas.
    • KrakenD Schemas - This repository contains the source code used to publish KrakenD's configuration schemas.
    • OCSF Schemas - This repository defines the Open Cybersecurity Schema Framework (OCSF) schema. OCSF is a framework for creating schemas and it also delivers a cybersecurity event schema built with the framework.
    • SchemaStore - The largest collection of independent JSON schemas in the world meant as a universal JSON schema store, where schemas for popular JSON documents can be found.
    • Apicurio Registry - A runtime server system for storing and managing API designs and schemas including OpenAPI, AsyncAPI, Avro, and JSON Schema with configurable content rules for evolution control.
    • Sourcemeta One - A self-hosted JSON Schema microservice that transforms Git repositories into searchable, discoverable schema catalogs with a web explorer, editor integration, schema health checks, and a rich HTTP API.
    • JSON Schema in RDF - This document introduces an RDF vocabulary for JSON Schema definitions. This vocabulary provides a stable namespace IRI for JSON Schema keywords, as well as simple axioms, defined against schema.org's meta-model.
    • W3C Web of Things - The Web of Things (WoT) seeks to counter the fragmentation of the IoT by using and extending existing, standardized Web technologies. WoT models data using JSON Schema.
    • Agent2Agent Protocol (A2A) - An open protocol by Google enabling communication and interoperability between agentic applications. A2A uses JSON-RPC 2.0 over HTTP and JSON Schema for defining Agent Cards and message structures.
    • AsyncAPI - AsyncAPI is an open source initiative that seeks to improve the current state of Event-Driven Architectures (EDA). The AsyncAPI specification supports data modeling using JSON Schema.
    • OpenAPI - The OpenAPI Specification embeds and extends JSON Schema for defining API requests and responses.
    • REST API Linked Data Keywords - An Internet-Draft proposing JSON Schema keywords to attach semantic information to OpenAPI and JSON Schema documents, enabling contract-first API design with RDF type information and JSON-LD context.
    • Semantic Definition Format (SDF) - An IETF specification for modeling Internet of Things devices and their interactions through Properties, Actions, and Events. SDF uses JSON to represent definitions and incorporates JSON Schema for data validation.

  • Specifications

    • AsyncAPI - AsyncAPI is an open source initiative that seeks to improve the current state of Event-Driven Architectures (EDA). The AsyncAPI specification supports data modeling using JSON Schema.
    • OpenAPI - The OpenAPI specification embeds and extends JSON Schema for defining API request and responses.
    • RAML - The RAML specification supports modeling API data using JSON Schema.
    • JSON Schema Core 2020-12 - JSON Schema defines the media type "application/schema+json", a JSON-based format for describing the structure of JSON data. JSON Schema asserts what a JSON document must look like, ways to extract information from it, and how to interact with it. The "application/schema-instance+json" media type provides additional feature-rich integration with "application/schema+json" beyond what can be offered for "application/json" documents.
    • JSON Schema Validation 2020-12 - JSON Schema (application/schema+json) has several purposes, one of which is JSON instance validation. This document specifies a vocabulary for JSON Schema to describe the meaning of JSON documents, provide hints for user interfaces working with JSON data, and to make assertions about what a valid document must look like.
    • Relative JSON Pointers - JSON Pointer is a syntax for specifying locations in a JSON document, starting from the document root. This document defines an extension to the JSON Pointer syntax, allowing relative locations from within the document.
    • JSON Hyper-Schema - JSON Schema is a JSON-based format for describing JSON data using various vocabularies. This document specifies a vocabulary for annotating JSON documents with hyperlinks. These hyperlinks include attributes describing how to manipulate and interact with remote resources through hypermedia environments such as HTTP, as well as determining whether the link is usable based on the instance value.

  • Tools

    • TypedWebhook.tools - An online webhook testing tool that is able to generate JSON Schema definitions out of incoming data.
    • QuickType.io - An online JSON Schema code-generation utility with diverse programming language support.
    • JSONschema.Net - An online tool that generates JSON schema from JSON documents.
    • JSONSchema.dev - An online JSON Schema validator created by the JSON Schema specification lead.
    • JSONBuddy - A JSON editor and validator desktop application for Windows.
    • JSON Schema Validator and Generator - An online JSON Schema validator that can generate JSON Schema from JSON documents and generate random JSON documents from JSON Schema.
    • JSON Schema Validator - An online JSON Schema validator with support for JSON Schema Draft 3, Draft 4, Draft 6, Draft 7 and Draft 2019-09.
    • Hyperjump JSON Schema Validator - An online JSON Schema validator that supports Draft 04, 06, 07, 2019-09, and 2020-12.
    • AlterSchema - Convert a JSON Schema definition between specification versions.
    • JSON Schema Viewer - An online tool to visualize JSON Schema definitions.
    • JSONschema.Net - An online tool that generates JSON schema from JSON documents.
    • JSON Schema Viewer - An online tool to visualize JSON Schema definitions.

  • Videos

    • JSON Schema in Production: #3 Mads Kristensen at Microsoft - Mads shares the story of bringing JSON Schema support into Visual Studio back in version 2013, the first editor supporting JSON Schema. What's called .Net Core today needed tooling for their "project.json" file, similar to a "package.json" file. JSON Schema ended up playing a key role in the autocompletion and validation infastructure ontop of a basic JSON editor.
    • JSON Schema in Production - #6 Suresh Srinivas at OpenMetadata - We talk to Suresh Srinivas, Co-Founder at Collate, building OpenMetadata. (Previously engineering leadership at Uber and Founder of Hortonworks.).
    • JSON Schema in Production - #5 Jens Neuse at Wundergraph - Jens Neuse, Founder of Wundergraph shares how Wundergraph compiles GraphQL definitions into JSON RPC and create JSON Schema definitions for each JSON RPC endpoint. Input data is validated using JSON Schema at the middleware-level before executing the corresponding GraphQL query.
    • JSON Schema in Production - #4 David Brownman at Zapier - David shares the story of using JSON Schema at Zapier to validate and document their intergration payloads, enabling a better developer experience for their 4000+ intergration partners..
    • JSON Schema in Production - #2 Kin Lane at F5 - Kin shares the challenges of building a company wide index of data structures (or Schema Registry), aiming to help understand previous, and avoid future, unexpected breaking changes that really impact customers.
    • JSON Schema in Production - #1 Chuck Reeves at Zones - Ben talks to Chuck Reeves, previously at Zones, about how they used JSON Schema to validate data and keep it in a consistent format across their application. Chuck shares how using a public API gateway to validate incoming data saved developers time and compute effort, making sure only valid data was passed downstream to serverless functions or other services.
    • JSON Schema In Production - You Can Use It Today - Ben Hutton, Postman - Hear the about the journey of organizations that use JSON Schema in production today, and learn about how JSON Schema continues to deliver value.
    • So you think you understand JSON Schema? - Ben Hutton - In this session you'll learn some key fundamentals, intracacies that even catch out the experienced, and how to develop your own interoperable JSON Schema Vocabulary.
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