Uncertainty accompanies our life processes and covers almost all fields of scientific studies.
Two general categories of uncertainty, namely, aleatory uncertainty and epistemic …

Uncertain data are data with uncertainty information, which exist widely in database
applications. In recent years, uncertainty in data has brought challenges in almost all …

The availability of Big Data sets has led many organizations to shift their emphases from
supporting transaction-oriented data processing to supporting data-centric analytics and …

The dramatic growth in the number of application domains that naturally generate
probabilistic, uncertain data has resulted in a need for efficiently supporting complex …

Data Stream Management Systems (DSMS) provide real-time data processing in an
effective way, but there is always a tradeoff between data quality (DQ) and performance. We …

Ranking is a fundamental operation in data analysis and decision support and plays an
even more crucial role if the dataset being explored exhibits uncertainty. This has led to …

Uncertain data streams, where data are incomplete and imprecise, have been observed in
many environments. Feeding such data streams to existing stream systems produces results …

Uncertain data streams, where data is incomplete, imprecise, and even misleading, have
been observed in many environments. Feeding such data streams to existing stream …

Data Stream Management Systems (DSMS) have been proposed to address the challenges
of applications which produce continuous, rapid streams of data that have to be processed …

We present the design and development of a data stream system that captures data
uncertainty from data collection to query processing to final result generation. Our system …