MayBMS: Managing Incomplete Information with Probabilistic World-Set Decompositions

Managing incomplete information is important in many real world applications. In this demonstration we present MayBMS - a system for representing and managing finite sets of possible worlds - that successfully combines expressiveness and efficiency. Some features of MayBMS are: completeness of the representation system for finite world-sets; space-efficient representation of large world-sets; scalable evaluation and support for full relational algebra queries; and probabilistic extension of the representation system and the query language. MayBMS is implemented on top of PostgreSQL. It models incomplete data using the so-called world-set decompositions (WSDs) (Ruggles et al., 2004). For this demonstration, we introduce a probabilistic extension of world-sets and WSDs, where worlds or correlations between worlds have probabilities. The main idea underlying probabilistic WSDs is to use relational factorization combined with probabilistic independence in order to efficiently decompose large world-sets into a set of independent smaller relations. Queries in MayBMS can be expressed in an SQL-like language with special constructs that deal with incompleteness and probabilities. MayBMS rewrites and optimizes user queries into a sequence of relational queries on world-set decompositions.