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https://github.com/huawei-noah/streamDM

Stream Data Mining Library for Spark Streaming
https://github.com/huawei-noah/streamDM

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Stream Data Mining Library for Spark Streaming

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README 

        
# streamDM for Spark Streaming



streamDM is a new open source software for mining big data streams using [Spark Streaming](https://spark.apache.org/streaming/), started at [Huawei Noah's Ark

Lab](http://www.noahlab.com.hk/). streamDM is licensed under Apache Software License v2.0.



## Big Data Stream Learning 



Big Data stream learning is more challenging than batch or offline learning,

since the data may not keep the same distribution over the lifetime of the

stream. Moreover, each example coming in a stream can only be processed once, or

they need to be summarized with a small memory footprint, and the learning

algorithms must be very efficient. 



### Spark Streaming



[Spark Streaming](https://spark.apache.org/streaming/) is an extension of the

core [Spark](https://spark.apache.org)  API that enables stream processing from

a variety of sources. Spark is a extensible and programmable framework for

massive distributed processing of datasets, called Resilient Distributed

Datasets (RDD). Spark Streaming receives input data streams and divides the data

into batches, which are then processed by the Spark engine to generate the

results.



Spark Streaming data is organized into a sequence of DStreams, represented

internally as a sequence of RDDs.



### Included Methods



In this current release of StreamDM v0.2, we have implemented:



* [SGD Learner](http://huawei-noah.github.io/streamDM/docs/SGD.html) and [Perceptron](http://huawei-noah.github.io/streamDM/docs/SGD.html#perceptron)

* [Naive Bayes](http://huawei-noah.github.io/streamDM/docs/NB.html)

* [CluStream](http://huawei-noah.github.io/streamDM/docs/CluStream.html)

* [Hoeffding Decision Trees](http://huawei-noah.github.io/streamDM/docs/HDT.html)

* [Bagging](http://huawei-noah.github.io/streamDM/docs/Bagging.html)

* [Stream KM++](http://huawei-noah.github.io/streamDM/docs/StreamKM.html)



we also implemented following [data generators](http://huawei-noah.github.io/streamDM/docs/generators.html):



* HyperplaneGenerator

* RandomTreeGenerator

* RandomRBFGenerator

* RandomRBFEventsGenerator



We have also implemented [SampleDataWriter](http://huawei-noah.github.io/streamDM/docs/SampleDataWriter.html), which can call data generators 

to create sample data for simulation or test.



In the next release of streamDM, we are going to add:



* Classification: Random Forests

* Multi-label: Hoeffding Tree ML, Random Forests ML

* Frequent Itemset Miner: IncMine



For future works, we are considering:

* Regression: Hoeffding Regression Tree, Bagging, Random Forests

* Clustering: Clustree, DenStream

* Frequent Itemset Miner: IncSecMine



## Going Further



For a quick introduction to running StreamDM, refer to the [Getting

Started](http://huawei-noah.github.io/streamDM/docs/GettingStarted.html) document. The StreamDM [Programming

Guide](http://huawei-noah.github.io/streamDM/docs/Programming.html) presents a detailed view of StreamDM. The full API

documentation can be consulted [here](http://huawei-noah.github.io/streamDM/api/index.html). 



## Environment

* Spark 2.3.2

* Scala 2.11

* SBT 0.13

* Java 8+



## Mailing lists

### User support and questions mailing list:

[email protected]

### Development related discussions:

[email protected]