Data Quality Inference Raymond K. Pon and Alfonso F. Cárdenas   View Paper (PDF)   Return to Paper Session 3: Statistics, clustering Abstract 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. ©2006 Association for Computing Machinery