We study two succinct representation systems for relational data based on relational algebra expressions with unions, Cartesian products, and singleton relations: f-representations, which employ algebraic factorisation using distributivity of product over union, and d-representations, which are f-representations where further succinctness is brought by explicit sharing of repeated subexpressions. In particular we study such representations for results of conjunctive queries. We derive tight asymptotic bounds for representation sizes and present algorithms to compute representations within these bounds. We compare the succinctness of f-representations and d-representations for results of equi-join queries, and relate them to fractional edge covers and fractional hypertree decompositions of the query hypergraph. Recent work showed that f-representations can significantly boost the performance of query evaluation in centralised and distributed settings and of machine learning tasks.

A common approach to data analysis involves understanding and manipulating succinct representations of data. In earlier work, we put forward a succinct representation system for relational data called factorised databases and reported on the main-memory query engine FDB for select-project-join queries on such databases. In this paper, we extend FDB to support a larger class of practical queries with aggregates and ordering. This requires novel optimisation and evaluation techniques. We show how factorisation coupled with partial aggregation can effectively reduce the number of operations needed for query evaluation. We also show how factorisations of query results can support enumeration of tuples in desired orders as efficiently as listing them from the unfactorised, sorted results. We experimentally observe that FDB can outperform off-the-shelf relational engines by orders of magnitude.

We introduce a representation system for relational data based on algebraic factorisation using distributivity of product over union and commutativity of product and union.

Factorised databases are relational databases that use compact factorised representations at the physical layer to reduce data redundancy and boost query performance. This paper introduces FDB, an in-memory query engine for select-project-join queries on factorised databases. Key components of FDB are novel algorithms for query optimisation and evaluation that exploit the succinctness brought by data factorisation. Experiments show that for data sets with many-to-many relationships FDB can outperform relational engines by orders of magnitude.

FDB is an in-memory query engine for factorised databases, which are relational databases that use compact factorised representations at the physical layer to reduce data redundancy and boost query performance. We demonstrate FDB using real data sets from IMDB, DBLP, and the NELL repository of facts learned from Web pages. The users can inspect factorisations as well as plans used by FDB to compute factorised results of select-project-join queries on factorised databases.