NDLI: PREFER: a system for the efficient execution of multi-parametric ranked queries

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PREFER: a system for the efficient execution of multi-parametric ranked queries

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PREFER: a system for the efficient execution of multi-parametric ranked queries

Content Provider	ACM Digital Library
Author	Papakonstantinou, Yannis Hristidis, Vagelis Koudas, Nick
Abstract	Users often need to optimize the selection of objects by appropriately weighting the importance of multiple object attributes. Such optimization problems appear often in operations' research and applied mathematics as well as everyday life; e.g., a buyer may select a home as a weighted function of a number of attributes like its distance from office, its price, its area, etc.We capture such queries in our definition of preference queries that use a weight function over a relation's attributes to derive a score for each tuple. Database systems cannot efficiently produce the top results of a preference query because they need to evaluate the weight function over all tuples of the relation. PREFER answers preference queries efficiently by using materialized views that have been pre-processed and stored.We first show how the result of a preference query can be produced in a pipelined fashion using a materialized view. Then we show that excellent performance can be delivered given a reasonable number of materialized views and we provide an algorithm that selects a number of views to precompute and materialize given space constraints.We have implemented the algorithms proposed in this paper in a prototype system called PREFER, which operates on top of a commercial database management system. We present the results of a performance comparison, comparing our algorithms with prior approaches using synthetic datasets. Our results indicate that the proposed algorithms are superior in performance compared to other approaches, both in preprocessing (preparation of materialized views) as well as execution time.
Starting Page	259
Ending Page	270
Page Count	12
File Format	PDF
ISBN	1581133324
DOI	10.1145/375663.375690
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2001-05-01
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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