By Topic

Probe Minimization by Schedule Optimization: Supporting Top-K Queries with Expensive Predicates

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Seung-Won Hwang ; Dept. of Comput. Sci. & Eng., Pohang Univ. of Sci. & Technol. ; Chang, K.C.-C.

This paper addresses the problem of evaluating ranked top-k queries with expensive predicates. As major DBMSs now all support expensive user-defined predicates for Boolean queries, we believe such support for ranked queries can be even more important: first, ranked queries often need to model user-specific concepts of preference, relevance, or similarity, which call for dynamic user-defined functions. Second, middleware systems must incorporate external predicates for integrating autonomous sources typically accessible only by per-object queries. Third, ranked queries often accompany Boolean ranking conditions, which may turn predicates into expensive ones, as the index structure on the predicate built on the base table may be no longer effective in retrieving the filtered objects in order. Fourth, fuzzy joins are inherently expensive, as they are essentially user-defined operations that dynamically associate multiple relations. These predicates, being dynamically defined or externally accessed, cannot rely on index mechanisms to provide zero-time sorted output, and must instead require per-object probe to evaluate. To enable probe minimization, we develop the problem as cost-based optimization of searching over potential probe schedules. In particular, we decouple probe scheduling into object and predicate scheduling problems and develop an analytical object scheduling optimization and a dynamic predicate scheduling optimization, which combined together form a cost-effective probe schedule

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:19 ,  Issue: 5 )