The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
The Impact of Data Quality Information on Decision Making: An Exploratory Analysis
IEEE Transactions on Knowledge and Data Engineering
Using Data Quality Measures in Decision-Making Algorithms
IEEE Expert: Intelligent Systems and Their Applications
AIMQ: a methodology for information quality assessment
Information and Management
Managing Data Quality in Cooperative Information Systems
On the Move to Meaningful Internet Systems, 2002 - DOA/CoopIS/ODBASE 2002 Confederated International Conferences DOA, CoopIS and ODBASE 2002
Managing Data Quality and Integrity in Federated Databases
Proceedings of the IFIP TC11 Working Group 11.5, Second Working Conference on Integrity and Internal Control in Information Systems: Bridging Business Requirements and Research Results
Data Quality in Web Information Systems
ER '02 Proceedings of the 21st International Conference on Conceptual Modeling
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 7 - Volume 7
Sensor: the atomic computing particle
ACM SIGMOD Record
Report on the Dagstuhl Seminar
ACM SIGMOD Record
Information Systems - Special issue: Data quality in cooperative information systems
Completeness of integrated information sources
Information Systems - Special issue: Data quality in cooperative information systems
IEEE Intelligent Systems
Effective change detection using sampling
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Quality-driven query answering for integrated information systems
Quality-driven query answering for integrated information systems
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In the field of sensor networks, data integration and collaboration, and intelligence gathering efforts, information on the quality of data sources are important but are often not available. We describe a technique to rank data sources by observing and comparing their behavior (i.e., the data produced by data sources) to rank. Intuitively, our measure characterizes data sources that agree with accurate or high-quality data sources as likely accurate. Furthermore, our measure includes a temporal component that takes into account a data source's past accuracy in evaluating its current accuracy. Initial experimental results based on simulation data to support our hypothesis demonstrate high precision and recall on identifying the most accurate data sources.