Handling ER-topk query on uncertain streams

Authors:
Cheqing Jin;Ming Gao;Aoying Zhou
Affiliations:
Shanghai Key Laborary of Trustworthy Computing, Software Engineering Institute, East China Normal University, China;Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, China;Shanghai Key Laborary of Trustworthy Computing, Software Engineering Institute, East China Normal University, China
Venue:
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Year:
2011

Citing 19
Cited 1

Introduction to Algorithms

Introduction to Algorithms
Trio: a system for data, uncertainty, and lineage

VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Sketching probabilistic data streams

Proceedings of the 2007 ACM SIGMOD international conference on Management of data
From complete to incomplete information and back

Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Estimating statistical aggregates on probabilistic data streams

Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Time-decaying sketches for sensor data aggregation

Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing
Efficient aggregation algorithms for probabilistic data

SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Efficient query evaluation on probabilistic databases

The VLDB Journal — The International Journal on Very Large Data Bases
Ranking queries on uncertain data: a probabilistic threshold approach

Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Finding frequent items in probabilistic data

Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Sliding-window top-k queries on uncertain streams

Proceedings of the VLDB Endowment
Managing and Mining Uncertain Data

Managing and Mining Uncertain Data
Exponentially Decayed Aggregates on Data Streams

ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
A Framework for Clustering Uncertain Data Streams

ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Semantics of Ranking Queries for Probabilistic Data and Expected Ranks

ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Ranking with Uncertain Scores

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

Getting critical categories of a data set

WAIM'11 Proceedings of the 12th international conference on Web-age information management

Quantified Score

Hi-index	0.00

Visualization

Abstract

It is critical to manage uncertain data streams nowadays because data uncertainty widely exists in many applications, such as Web and sensor networks. The goal of this paper is to handle top-k query on uncertain data streams. Since the volume of a data stream is unbounded whereas the memory resource is limited, it is challenging to devise one-pass solutions that is both time- and space efficient. We have devised two structures to handle this issue, namely domGraph and probTree. The domGraph stores all candidate tuples, and the probTree is helpful to compute the expected rank of a tuple. The analysis in theory and extensive experimental results show the effectiveness and efficiency of the proposed solution.