With more and more data generated, it has become a big challenge for traditional
architectures and infrastructures to process large amounts of data within an acceptable time …

Machine learning (ML) is widely used to derive useful information from large-scale data
(such as user activities, pictures, and videos) generated at increasingly rapid rates, all over …

Uncertain data have already widely existed in many practical applications recently, such as
sensor networks, RFID networks, location-based services, and mobile object management …

In the data stream scenario, input arrives very rapidly and there is limited memory to store
the input. Algorithms have to work with one or few passes over the data, space less than …

Previous approaches for computing duplicate-sensitive aggregates in wireless sensor
networks have used a tree topology, in order to conserve energy and to avoid double …

Wireless sensor networks are proving to be useful in a variety of settings. A core challenge
in these networks is to minimize energy consumption. Prior database research has …

Runtime verification is an area of formal methods that studies the dynamic analysis of
execution traces against formal specifications. Typically, the two main activities in runtime …

Emerging large-scale monitoring applications require continuous tracking of complex
dataanalysis queries over collections of physicallydistributed streams. Effective solutions …

We consider the problem of tracking quantiles and range countings in wireless sensor
networks. The quantiles and range countings are two important aggregations to characterize …

Monitoring data streams in a distributed system is the focus of much research in recent
years. Most of the proposed schemes, however, deal with monitoring simple aggregated …