Multi-objective Query Processing for Database Systems

Query processing in database systems has developed beyond mere exact matching of attribute values. Scoring database objects and retrieving only the top k matches or Pareto-optimal result sets (skyline queries) are already common for a variety of applications. Specialized algorithms using either paradigm can avoid naive linear database scans and thus improve scalability. However, these paradigms are only two extreme cases of exploring viable compromises for each user's objectives. To find the correct result set for arbitrary cases of multi-objective query processing in databases we will present a novel algorithm for computing sets of objects that are nondominated with respect to a set of monotonic objective functions. Naturally containing top k and skyline retrieval paradigms as special cases, this algorithm maintains scalability also for all cases in between. Moreover, we will show the algorithm's correctness and instance-optimality in terms of necessary object accesses and how the response behavior can be improved by progressively producing result objects as quickly as possible, while the algorithm is still running.

[1]  Donald Kossmann,et al.  The Skyline operator , 2001, Proceedings 17th International Conference on Data Engineering.

[2]  Donald Kossmann,et al.  Shooting Stars in the Sky: An Online Algorithm for Skyline Queries , 2002, VLDB.

[3]  Ralph L. Keeney,et al.  Decisions with multiple objectives: preferences and value tradeoffs , 1976 .

[4]  Wolf-Tilo Balke,et al.  On Real-Time Top k Querying for Mobile Services , 2002, OTM.

[5]  M. Lacroix,et al.  Preferences; Putting More Knowledge into Queries , 1987, VLDB.

[6]  H. T. Kung,et al.  On the Average Number of Maxima in a Set of Vectors and Applications , 1978, JACM.

[7]  Luis Gravano,et al.  Evaluating top-k queries over Web-accessible databases , 2002, Proceedings 18th International Conference on Data Engineering.

[8]  Thomas S. Huang,et al.  Supporting Ranked Boolean Similarity Queries in MARS , 1998, IEEE Trans. Knowl. Data Eng..

[9]  Felix Naumann,et al.  Quality-driven Integration of Heterogenous Information Systems , 1999, VLDB.

[10]  Wolf-Tilo Balke,et al.  Efficient Distributed Skylining for Web Information Systems , 2004, EDBT.

[11]  Moni Naor,et al.  Optimal aggregation algorithms for middleware , 2001, PODS.

[12]  Mihalis Yannakakis,et al.  Multiobjective query optimization , 2001, PODS '01.

[13]  Bernhard Seeger,et al.  An optimal and progressive algorithm for skyline queries , 2003, SIGMOD '03.

[14]  Beng Chin Ooi,et al.  Efficient Progressive Skyline Computation , 2001, VLDB.

[15]  Werner Kießling,et al.  Personalized Keyword Search with Partial-Order Preferences , 2002, SBBD.

[16]  Werner Kießling,et al.  Foundations of Preferences in Database Systems , 2002, VLDB.

[17]  Marco Patella,et al.  The M2-tree: Processing Complex Multi-Feature Queries with Just One Index , 2000, DELOS.

[18]  F. B. Vernadat,et al.  Decisions with Multiple Objectives: Preferences and Value Tradeoffs , 1994 .

[19]  Jan Chomicki,et al.  Querying with Intrinsic Preferences , 2002, EDBT.

[20]  Wolf-Tilo Balke,et al.  Personalized services for mobile route planning: a demonstration , 2003, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405).

[21]  Surya Nepal,et al.  Query processing issues in image (multimedia) databases , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[22]  Amihai Motro,et al.  VAGUE: a user interface to relational databases that permits vague queries , 1988, TOIS.

[23]  R. L. Keeney,et al.  Decisions with Multiple Objectives: Preferences and Value Trade-Offs , 1977, IEEE Transactions on Systems, Man, and Cybernetics.