Exploiting similarity-aware grouping in decision support systems
Decision Support Systems (DSS) are information systems that support decision making processes. In many scenarios these systems are built on top of data managed by DBMSs and make extensive use of its underlying grouping and aggregation capabilities, i.e., Group-by operation. Unfortunately, the standard grouping operator has the inherent limitation of being based only on equality, i.e., all the tuples in a group share the same values of the grouping attributes. Similarity-based Group-by (SGB) has been recently proposed as an extension aimed to overcome this limitation. SGB allows fast formation of groups with similar objects under different grouping strategies and the pipelining of results for further processing. This demonstration presents how SGB can be effectively used to build useful DSSs. The presented DSS has been built around the data model and queries of the TPC-H benchmark intending to be representative of complex business analysis applications. The system provides intuitive dashboards that exploit similarity aggregation queries to analyze: (1) customer clustering, (2) profit and revenue, (3) marketing campaigns, and (4) discounts. The presented DSS runs on top of PostgreSQL whose query engine is extended with similarity grouping operators.
DSS, group share, DBMS, SGB, DSS, TPC-H, complex business analysis, PostgreSQL
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