BE-tree: an index structure to efficiently match boolean expressions over high-dimensional discrete space

BE-Tree is a novel dynamic tree data structure designed to efficiently index Boolean expressions over a high-dimensional discrete space. BE-Tree copes with both high-dimensionality and expressiveness of Boolean expressions by introducing a novel two-phase space-cutting technique that specifically utilizes the discrete and finite domain properties of the space. Furthermore, BE-Tree employs self-adjustment policies to dynamically adapt the tree as the workload changes. We conduct a comprehensive evaluation to demonstrate the superiority of BE-Tree in comparison with state-of-the-art index structures designed for matching Boolean expressions.

[1]  Jennifer Widom,et al.  Practical Applications of Triggers and Constraints: Success and Lingering Issues (10-Year Award) , 2000, VLDB.

[2]  Hao Zhang,et al.  Path sharing and predicate evaluation for high-performance XML filtering , 2003, TODS.

[3]  Marcos K. Aguilera,et al.  Matching events in a content-based subscription system , 1999, PODC '99.

[4]  Hans-Arno Jacobsen,et al.  GPX-matcher: a generic boolean predicate-based XPath expression matcher , 2011, EDBT/ICDT '11.

[5]  Beng Chin Ooi,et al.  The Claremont report on database research , 2008, SGMD.

[6]  Michael Freeston A general solution of the n-dimensional B-tree problem , 1995, SIGMOD '95.

[7]  Sergei Vassilvitskii,et al.  Efficiently evaluating complex boolean expressions , 2010, SIGMOD Conference.

[8]  Hector Garcia-Molina,et al.  Index structures for selective dissemination of information under the Boolean model , 1994, TODS.

[9]  Johannes Gehrke,et al.  Cayuga: a high-performance event processing engine , 2007, SIGMOD '07.

[10]  Peter Scheuermann,et al.  Active Database Systems , 2008, Wiley Encyclopedia of Computer Science and Engineering.

[11]  Helmut Veith,et al.  Efficient filtering in publish-subscribe systems using binary decision diagrams , 2001, Proceedings of the 23rd International Conference on Software Engineering. ICSE 2001.

[12]  Jonathan Goldstein,et al.  When Is ''Nearest Neighbor'' Meaningful? , 1999, ICDT.

[13]  Eric N. Hanson,et al.  A predicate matching algorithm for database rule systems , 1990, SIGMOD '90.

[14]  Hans-Arno Jacobsen,et al.  Efficient event processing through reconfigurable hardware for algorithmic trading , 2010, Proc. VLDB Endow..

[15]  Hans-Arno Jacobsen,et al.  A Unified Approach to Routing, Covering and Merging in Publish/Subscribe Systems Based on Modified Binary Decision Diagrams , 2005, 25th IEEE International Conference on Distributed Computing Systems (ICDCS'05).

[16]  Antonin Guttman,et al.  R-trees: a dynamic index structure for spatial searching , 1984, SIGMOD '84.

[17]  Hans-Arno Jacobsen,et al.  Efficient matching for state-persistent publish/subscribe systems , 2003, CASCON.

[18]  Charles L. Forgy,et al.  Rete: a fast algorithm for the many pattern/many object pattern match problem , 1991 .

[19]  Sergei Vassilvitskii,et al.  Indexing Boolean Expressions , 2009, Proc. VLDB Endow..

[20]  Lan Huang,et al.  Scalable trigger processing , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[21]  Badrish Chandramouli,et al.  High-performance dynamic pattern matching over disordered streams , 2010, Proc. VLDB Endow..

[22]  Dennis Shasha,et al.  Filtering algorithms and implementation for very fast publish/subscribe systems , 2001, SIGMOD '01.

[23]  Nesime Tatbul,et al.  DejaVu: declarative pattern matching over live and archived streams of events , 2009, SIGMOD Conference.

[24]  A. Guttmma,et al.  R-trees: a dynamic index structure for spatial searching , 1984 .

[25]  Divesh Srivastava,et al.  Benchmarking declarative approximate selection predicates , 2007, SIGMOD '07.

[26]  Hans-Peter Kriegel,et al.  The X-tree : An Index Structure for High-Dimensional Data , 2001, VLDB.

[27]  Klaus R. Dittrich,et al.  Event matching in symmetric subscription systems , 2002, CASCON.