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
International Journal of Data Science and Analytics – Springer Journals
Published: Jun 2, 2018
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