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FACTORBASE: multi-relational structure learning with SQL all the way

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Abstract

FactorBase is a new SQL-based framework that leverages a relational database management system to support multi-relational model discovery. A multi-relational statistical model provides an integrated analysis of the heterogeneous and interdependent data resources in the database. We adopt the BayesStore design philosophy: Statistical models are stored and managed as first-class citizens inside a database (Wang et al., in: PVLDB, pp 340–351, 2008). Whereas previous systems like BayesStore support multi-relational inference, FactorBase supports multi-relational learning. A case study on six benchmark databases evaluates how our system supports a challenging machine learning application, namely learning a first-order Bayesian network model for an entire database. Model learning in this setting has to examine a large number of potential statistical associations across data tables. Our implementation shows how the SQL constructs in FactorBase facilitate the fast, modular, and reliable development of scalable model learning systems.

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Notes

  1. A par-factor can also include constraints on possible groundings.

  2. The schema assumes that all relationships are binary.

  3. Essentially, the same concept is called a slot chain in PRM modeling [9].

  4. www.grouplens.org, 1M version.

  5. www.imdb.com, July 2013.

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Acknowledgements

This research was supported by a Discovery Grant to Oliver Schulte by the Natural Sciences and Engineering Research Council of Canada. Zhensong Qian was supported by a grant from the China Scholarship Council. We are indebted to anonymous reviewers for the Journal of Data Science and Analytics for helpful comments that improved the paper presentation substantially.

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  1. Simon Fraser University, Burnaby, Canada

    Oliver Schulte & Zhensong Qian

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  1. Oliver Schulte
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Correspondence to Zhensong Qian.

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Appendix: The random variable database layout

Appendix: The random variable database layout

We provide details about the Schema Analyzer. A complete SQL script that implements the Schema Analyzer is available [37]. Table 21 shows the relational schema of the Random Variable Database. Figure 9 shows dependencies between the tables of this schema.

Table 21 Schema for random variable database
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Fig. 9
figure 9

Table dependencies in the random variable database \({ VDB }\)

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Schulte, O., Qian, Z. FACTORBASE: multi-relational structure learning with SQL all the way. Int J Data Sci Anal 7, 289–309 (2019). https://doi.org/10.1007/s41060-018-0130-1

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