We consider the problem of aggregation for uncertain and imprecise data. For such data, we define aggregation operators and use them to provide information on properties and patterns of data attributes. The aggregates that we define use the Kullback-Leibler information divergence between the aggregated probability distribution and the individual tuple data values. We are thus able to provide a ...

Various applications rely on a continuous processing of data streams originating from a network of interconnected and collaborated sensors. The processing of those streams has turned out to be a difficult task as sensors only have limited resources and the data they produce is inherently uncertain and unreliable. In order to bridge the gap from raw, uncertain sensor readings to a meaningful mod...

Applications requiring the handling of uncertain data have led to the development of database management systems extending the scope of relational databases to include uncertain (probabilistic) data as a native data type. New automatic query optimizations having the ability to estimate the cost of execution of a given query plan, as available in existing databases, need to be developed. For pro...

Abstract We consider a scenario where server is wirelessly connected to countless sensor nodes that continuously measure data. The task of the monitor sensors’ More precisely, at each time step calculates function defined over current measurements sensors. Since sensors only have small computational power and tight battery constraints, communication between should be as possible, i.e., we aim m...

Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems (CPS) present novel challenges to Big Data platforms for performing online analytics. Ubiquitous sensors from IoT deployments are able to generate data streams at high velocity, that include information from a variety of domains, and accumulate to large volumes on disk. Complex Event Processing (CEP) is recognized as a...