Analyzing SQL Query Logs using
                                   Multi-Relational Graphs

                                           Andreas M. Wahl and Richard Lenz

                           Computer Science 6 (Data Management), FAU Erlangen-Nürnberg
                                       {andreas.wahl|richard.lenz}@fau.de
                     Analytical SQL queries are a valuable source of information. They contain
                 expert knowledge that cannot be inferred from schemas or content alone. Con-
                 sider, for example, data lake scenarios, where relational and semi-structured
                 data sources are combined in a single storage and processing environment. Data
                 lakes often lack a structured curation process [1]. Neither global schemas nor
                 vocabularies might be established and data sources might be disparate. SQL
                 provides effective mechanisms to apply these curation steps during querying in
                 a demand-driven way (e.g. by using aliases, joins, casts, user-defined functions,
                 conditional expressions). Hence, the resulting SQL query logs constitute a dy-
                 namic documentation of the data lake and the knowledge gathered by its users
                 through previous pay-as-you-go integration tasks. This knowledge includes the
                 purpose of data sources, their semantics, vocabularies, associations with other
                 data sources, and their temporal and social usage context.
                     To leverage this knowledge, we have developed an extensible framework for
                 analyzing SQL query logs. Query logs are mapped to a multi-relational [3] graph
                 model. We store query texts and corresponding abstract syntax trees to enable
                 meta-querying for syntactic features. However, as SQL allows expressing queries
                 with many different language constructs and the use of aliases, wildcards and
                 unqualified attributes, meta-querying for semantic features requires a different
                 query representation. We convert each query to a corresponding relational al-
                 gebra tree and normalize it using algebraic transformation rules. Each tree is
                 interlinked with a schema lineage tree, which captures attribute lineage and
                 output schemas of each relational operator. Metadata about users, physical time
                 and logical order allows to inspect the social and temporal context of each query.
                 Meta-queries are specified using domain-specific graph traversal expressions.
                     Our framework can be used for a broad range of application scenarios. It
                 facilitates collaborative data science by locating relevant queries. Other use cases
                 include maintenance and monitoring tasks, schema evolution mechanisms and
                 existing log mining algorithms. We rely on Apache TinkerPop [2] to abstract
                 from vendor-specific graph implementations. TinkerPop enables both interactive
                 meta-querying and complex distributed computations on our graph model.
                 References
                 1. Heudecker, N., White, A.: The data lake fallacy: All water and little substance.
                    Gartner Inc. (2014)
                 2. Rodriguez, M.A.: The gremlin graph traversal machine and language (invited talk).
                    In: DBPL’15 (2015)
                 3. Rodriguez, M.A., Neubauer, P.: A path algebra for multi-relational graphs. In:
                    ICDEW’11 (2011)




Copyright © 2017 by the paper’s authors. Copying permitted only for private and academic purposes.
In: M. Leyer (Ed.): Proceedings of the LWDA 2017 Workshops: KDML, FGWM, IR, and FGDB.
Rostock, Germany, 11.-13. September 2017, published at http://ceur-ws.org