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

FACTORBASE: multi-relational structure learning with SQL all the way 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. Keywords Relational learning · Bayesian networks · Model selection · Relational database management systems 1 Introduction tistical objects, such as factor tables, cross-table sufficient statistics, parameter estimates, and model selection scores. Data science brings together ideas from different fields for Multi-relational data have a complex structure that inte- extracting value from large complex datasets. The system http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Data Science and Analytics Springer Journals

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

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer International Publishing AG, part of Springer Nature
Subject
Computer Science; Data Mining and Knowledge Discovery; Database Management; Artificial Intelligence (incl. Robotics); Computational Biology/Bioinformatics; Business Information Systems
ISSN
2364-415X
eISSN
2364-4168
D.O.I.
10.1007/s41060-018-0130-1
Publisher site
See Article on Publisher Site

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. Keywords Relational learning · Bayesian networks · Model selection · Relational database management systems 1 Introduction tistical objects, such as factor tables, cross-table sufficient statistics, parameter estimates, and model selection scores. Data science brings together ideas from different fields for Multi-relational data have a complex structure that inte- extracting value from large complex datasets. The system

Journal

International Journal of Data Science and AnalyticsSpringer Journals

Published: Jun 2, 2018

References

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