Linear programming: methods and applications (5th ed.)
Linear programming: methods and applications (5th ed.)
On the average number of maxima in a set of vectors
Information Processing Letters
The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
On the Average Number of Maxima in a Set of Vectors and Applications
Journal of the ACM (JACM)
The onion technique: indexing for linear optimization queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Proceedings of the 17th International Conference on Data Engineering
Efficient Progressive Skyline Computation
Proceedings of the 27th International Conference on Very Large Data Bases
Algorithms and applications for answering ranked queries using ranked views
The VLDB Journal — The International Journal on Very Large Data Bases
Progressive skyline computation in database systems
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
RankSQL: query algebra and optimization for relational top-k queries
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Stratified computation of skylines with partially-ordered domains
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Toward a generalized theory of uncertainty (GTU): an outline
Information Sciences—Informatics and Computer Science: An International Journal
Towards robust indexing for ranked queries
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Answering top-k queries using views
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
IO-Top-k: index-access optimized top-k query processing
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
On Efficient Processing of Subspace Skyline Queries on High Dimensional Data
SSDBM '07 Proceedings of the 19th International Conference on Scientific and Statistical Database Management
Is there a need for fuzzy logic?
Information Sciences: an International Journal
Computational Geometry: Algorithms and Applications
Computational Geometry: Algorithms and Applications
Ranking queries on uncertain data: a probabilistic threshold approach
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Efficient search for the top-k probable nearest neighbors in uncertain databases
Proceedings of the VLDB Endowment
Fundamentals of Data Structures in C
Fundamentals of Data Structures in C
Efficient and generic evaluation of ranked queries
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Supporting efficient distributed skyline computation using skyline views
Information Sciences: an International Journal
Subspace top-k query processing using the hybrid-layer index with a tight bound
Data & Knowledge Engineering
Hi-index | 0.07 |
A top-k query returns k tuples with the highest (or the lowest) scores from a relation. The score is computed by combining the values of one or more attributes. We focus on top-k queries having monotone linear score functions. Layer-based methods are well-known techniques for top-k query processing. These methods construct a database as a single list of layers. Here, the ith layer has the tuples that can be the top-i tuple. Thus, these methods answer top-k queries by reading at most k layers. Query performance, however, is poor when the number of tuples in each layer (simply, the layer size) is large. In this paper, we propose a new layer-ordering method, called the Partitioned-Layer Index (simply, the PL Index), that significantly improves query performance by reducing the layer size. The PL Index uses the notion of partitioning, which constructs a database as multiple sublayer lists instead of a single layer list subsequently reducing the layer size. The PL Index also uses the convex skyline, which is a subset of the skyline, to construct a sublayer to further reduce the layer size. The PL Index has the following desired properties. The query performance of the PL Index is quite insensitive to the weights of attributes (called the preference vector) of the score function and is approximately linear in the value of k. The PL Index is capable of tuning query performance for the most frequently used value of k by controlling the number of sublayer lists. Experimental results using synthetic and real data sets show that the query performance of the PL Index significantly outperforms existing methods except for small values of k (say, k=