Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Query Processing Issues in Image(Multimedia) Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Continuous monitoring of top-k queries over sliding windows
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Ranking queries on uncertain data: a probabilistic threshold approach
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A survey of top-k query processing techniques in relational database systems
ACM Computing Surveys (CSUR)
Managing and Mining Uncertain Data
Managing and Mining Uncertain Data
Semantics of Ranking Queries for Probabilistic Data and Expected Ranks
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
On the semantics and evaluation of top-k queries in probabilistic databases
ICDEW '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering Workshop
Top-k queries on uncertain data: on score distribution and typical answers
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
A unified approach to ranking in probabilistic databases
Proceedings of the VLDB Endowment
Handling ER-topk query on uncertain streams
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Hi-index | 0.00 |
Ranking query that is widely used in various applications is a fundamental kind of queries in the database management field. Although most of the existing work on ranking query focuses on getting top-k high-score tuples from a data set, this paper focuses on getting top-k critical categories from a data set, where each category is a data item in the nominal attribute or a combination of data items from more than one nominal attribute. To describe each category precisely, we use a data distribution that comes from the score attribute to represent each category, so that the set consisting of all categories can be treated as a probabilistic data set. In this paper, we devise a novel method to handle this issue. Analysis in theorem and experimental results show the effectiveness and efficiency of the proposed method.