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Polystore and Tensor Data Model for Logical Data Independence and Impedance Mismatch in Big Data Analytics

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Transactions on Large-Scale Data- and Knowledge-Centered Systems XLII

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 11860))

Abstract

This paper presents a Tensor based Data Model (TDM) for polystore systems meant to address two major closely related issues in big data analytics architectures, namely logical data independence and data impedance mismatch. The TDM is an expressive model that subsumes traditional data models, it allows to link different data models of various data stores, and which also facilitates data transformations by using operators with clearly defined semantics. Our contribution is twofold. Firstly, it is the addition of the notion of a schema for the tensor mathematical object using typed associative arrays. Secondly, it is the definition of a set of operators to manipulate data through the TDM. In order to validate our approach we first show how our TDM model is inserted into a given polystore architecture. We then describe some use cases of real analyses using our TDM and its operators in the context of the French Presidential Election in 2017.

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Notes

  1. 1.

    https://kafka.apache.org/.

  2. 2.

    https://flink.apache.org/.

  3. 3.

    Single Instruction Multiple Data.

  4. 4.

    https://www.vertica.com/docs/9.2.x/HTML/Content/Authoring/FlexTables/FlexTableHandbook.htm.

  5. 5.

    https://docs.arangodb.com/3.4/Manual/index.html.

  6. 6.

    https://orientdb.com/graph-database/.

  7. 7.

    https://bigdata.uni-saarland.de/projects/octopusdb.php.

  8. 8.

    http://wp.sigmod.org/?p=1629.

  9. 9.

    https://spark.apache.org/sql/.

  10. 10.

    https://drill.apache.org/.

  11. 11.

    http://forward.ucsd.edu/.

  12. 12.

    https://hive.apache.org/.

  13. 13.

    https://neo4j.com/developer/graph-algorithms/.

  14. 14.

    https://www.vertica.com/product/database-machine-learning/.

  15. 15.

    https://www.paradigm4.com/.

  16. 16.

    https://www.tensorflow.org/.

  17. 17.

    http://deeplearning.net/software/theano/.

  18. 18.

    https://keras.io/.

  19. 19.

    https://spark.apache.org/mllib/.

  20. 20.

    https://amplab.cs.berkeley.edu/software/.

  21. 21.

    http://www.alluxio.org/.

  22. 22.

    https://azure.microsoft.com/en-us/services/data-lake-analytics/.

  23. 23.

    https://www.ibm.com/analytics/data-lake.

  24. 24.

    https://github.com/nicolewhite/RNeo4j.

  25. 25.

    https://github.com/RevolutionAnalytics/rhbase.

  26. 26.

    The notation | is the restriction applied to sets, \(A|B=A-(A-B)\).

  27. 27.

    expr is a logical expression to compare values of \(\varvec{\mathcal {X}}\) to constants. Its form is as follows: expr  :  : = <condition\(>\vert<\)condition> <logical operator> <condition\(> \vert \lnot<\)condition\(> \vert \) (<condition>)

    Logical operators are \(\{\wedge , \vee \}\) condition>  :  : = values of \(\varvec{\mathcal {X}}\) (implicit) <comparison operator> constantComparison operators are \(\{<,\le , =,\ne ,\ge ,>\}\).

  28. 28.

    expr allows to compare keys of the dimensions with constants. Its shape is the same as for the operator \(\sigma \) except for

    <condition>  :  : = name of a dimension <comparison operator> constant.

  29. 29.

    https://botometer.iuni.iu.edu/.

  30. 30.

    https://github.com/ginestrab/Multiplex-PageRank.

  31. 31.

    https://github.com/AnnabelleGillet/Multiplex-PageRank.

  32. 32.

    https://github.com/scalanlp/breeze.

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Acknowledgement

This research was partially supported by the project I-SITE UBFC COCKTAIL. We thank George Becker for comments that have greatly improved the manuscript and Arnaud Da Costa for the maintenance of the server infrastructure.

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    Éric Leclercq, Annabelle Gillet, Thierry Grison & Marinette Savonnet

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Leclercq, É., Gillet, A., Grison, T., Savonnet, M. (2019). Polystore and Tensor Data Model for Logical Data Independence and Impedance Mismatch in Big Data Analytics. In: Hameurlain, A., Wagner, R. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XLII. Lecture Notes in Computer Science(), vol 11860. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-60531-8_3

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