A statistical interpretation of term specificity and its application in retrieval
Document retrieval systems
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
PREFER: a system for the efficient execution of multi-parametric ranked queries
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Optimizing Multi-Feature Queries for Image Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Query Processing Issues in Image(Multimedia) Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Towards Efficient Multi-Feature Queries in Heterogeneous Environments
ITCC '01 Proceedings of the International Conference on Information Technology: Coding and Computing
Algorithms and applications for answering ranked queries using ranked views
The VLDB Journal — The International Journal on Very Large Data Bases
Evaluating top-k queries over web-accessible databases
ACM Transactions on Database Systems (TODS)
Answering top-k queries using views
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Efficient top-k aggregation of ranked inputs
ACM Transactions on Database Systems (TODS)
Best position algorithms for top-k queries
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Efficiently answering top-k typicality queries on large databases
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
A survey of top-k query processing techniques in relational database systems
ACM Computing Surveys (CSUR)
Information Systems for Federated Biobanks
Transactions on Large-Scale Data- and Knowledge-Centered Systems I
Maintenance of top-k materialized views
Distributed and Parallel Databases
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
| Hi-index | 0.00 |
Efficient retrieval of the most relevant (i.e. top-k) tuples is an important requirement in information systems which access large amounts of data. In general answering a top-k query request means to retrieve the k-objects which score best for an objective function given with an input query. Such queries are frequent where users specify a set of restrictions defining their ideal solution and want to retrieve results which are closest to these ideals. Within this work we show how the well known Threshold Algorithm (short TA) of Fagin et al. [8] can be improved both in time and memory requirements. We do so by dynamically creating intelligent index structures out of the query restrictions posed by the user. The further we present a powerful extension: user preference queries where weighted preferences on query restrictions influence the objective function used to select the top-k objects from a relation or a database. Therefore we integrated the capability to deal with user preference queries into our top-k query answering approach. Users may define restrictions, assign weights and retrieve the top-k objects matching the given preferences best. We prototypically implemented our approach and evaluated it by validating its results against the results achieved with TA.