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Debellor: Open-Source Data Mining Platform with Stream Architecture

Debellor is an advanced data mining platform designed for efficient processing of large datasets through its innovative stream architecture. Developed in Java and available under the GPL license, Debellor supports multi-threading, extends various data types, and features a comprehensive suite of algorithms from Rseslib and Weka. Version 0.6 introduces numerous powerful functionalities, including the processing and visualizing capabilities of complex algorithms. Future releases promise exciting features like multi-input cells and meta-learning capabilities. Learn more and download at www.debellor.org.

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Debellor: Open-Source Data Mining Platform with Stream Architecture

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  1. DebellorData Mining Platform with Stream Architecture Marcin Wojnarski Warsaw University, Poland

  2. Outline • Debellor – data mining platform • Motivation • Main features • Architecture: • Cell • data streaming • multi-threading • Available in ver. 0.6 • Future releases • Summary

  3. Debellor • Language:Java • Licence:open source (GPL) • Download:www.debellor.org • Debello – to conquer (latin).Debellor – conqueror of data

  4. Debellor – data mining platform Rseslib LibSVM Debellor Weka TA-Lib own… own…

  5. Motivation Demand for more complex algorithms. Necessity to combine elementary algorithms.

  6. Visualize Load Preprocess Preprocess Predict Save Load Motivation • Data Processing Network (DPN)

  7. Classifier A Classifier B Voting Classifier C Motivation • Committee of algorithms

  8. Motivation • Nested algorithms RBF neural network K-means

  9. Requirements Versatile Efficient Simple

  10. Features of Debellor • All types of data processing algorithms • Extendible data types • Stream architecture  large data sets • Multi-threading • Immutability of data objects  safety

  11. Debellor

  12. Algorithm= Cell Cell cell = new RseslibClassifier("C45"); cell.set("pruning", "true"); cell

  13. Cell – data source cell.open(); Sample s1 = cell.next(), s2 = cell.next(), ... cell.close(); cell

  14. Cell – data receiver cell.setSource(anotherCell); anotherCell cell

  15. Trainable Cell cell.setSource(…); cell.learn(); EMPTY cell TRAINED cell

  16. A B A B Data Streaming BATCH STREAM It’s the cell who is responsible for asking for data

  17. Benefits of streaming training of k-means X X crash!

  18. Multi-threading Thread_1 A B

  19. Multi-threading A.newThread(); Thread_2 Thread_1 A B

  20. Available in version 0.6 • Rseslib algorithms: • classifiers (~20 algorithms) • Weka algorithms: • ARFF reader • classifiers (~60) • filters (47) • Debellor algorithms: • Train&Test evaluation • k-means for large data (stream-based) • Data types: • numeric andsymbolic features • vectors of features, vectors of vectors of …

  21. Future releases • Multi-input & multi-output cells • Composite cells (e.g. meta-learning) • Serialization and copying • …

  22. Summary • Platform • Stream architecture • Extendible • Multi-threaded • Weka & Rseslib partially integrated

  23. Home www.debellor.org

  24. Thank You

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