This paper is written for (social science) researchers seeking to analyze the wealth of social
media now available. It presents a comprehensive review of software tools for social …

Big data processing systems are evolving to be more stream oriented where each data
record is processed as it arrives by distributed and low-latency computational frameworks on …

Various research communities have independently arrived at stream processing as a
programming model for efficient and parallel computing. These communities include digital …

Stream processing has been an active research field for more than 20 years, but it is now
witnessing its prime time due to recent successful efforts by the research community and …

RDF and SPARQL are established standards for data interchange and querying on the Web.
While they have been shown to be useful and applicable in many scenarios, they are not …

Stream processing is gaining importance as more data becomes available in the form of
continuous streams and companies compete to promptly extract insights from them. In such …

Stream processing has been an active research field for more than 20 years, but it is now
witnessing its prime time due to recent successful efforts by the research community and …

In spite of the omnipresence of parallel (multi-core) systems, the predominant strategy to
evaluate window-based stream joins is still strictly sequential, mostly just straightforward …

Stream processing addresses the needs of real-time applications. Transaction processing
addresses the coordination and safety of short atomic computations. Heretofore, these two …

Stream Processing Engines (SPEs) execute long-running queries on unbounded data
streams. They follow an interpretation-based processing model and do not perform runtime …