Shared Arrangements: practical inter-query sharing for streaming dataflows

Current systems for data-parallel, incremental processing and view maintenance over high-rate streams isolate the execution of independent queries. This creates unwanted redundancy and overhead in the presence of concurrent incrementally maintained queries: each query must independently maintain the same indexed state over the same input streams, and new queries must build this state from scratch before they can begin to emit their first results. This paper introduces shared arrangements: indexed views of maintained state that allow concurrent queries to reuse the same in-memory state without compromising data-parallel performance and scaling. We implement shared arrangements in a modern stream processor and show order-of-magnitude improvements in query response time and resource consumption for interactive queries against high-throughput streams, while also significantly improving performance in other domains including business analytics, graph processing, and program analysis

A Reasoner for Calendric and Temporal Data

Calendric and temporal data are omnipresent in countless Web and Semantic Web applications and Web services. Calendric and temporal data are probably more than any other data a subject to interpretation, in almost any case depending on some cultural, legal, professional, and/or locational context. On the current Web, calendric and temporal data can hardly be interpreted by computers. This article contributes to the Semantic Web, an endeavor aiming at enhancing the current Web with well-defined meaning and to enable computers to meaningfully process data. The contribution is a reasoner for calendric and temporal data. This reasoner is part of CaTTS, a type language for calendar definitions. The reasoner is based on a \theory reasoning" approach using constraint solving techniques. This reasoner complements general purpose \axiomatic reasoning" approaches for the Semantic Web as widely used with ontology languages like OWL or RDF

A Reasoner for Calendric and Temporal Data

Calendric and temporal data are omnipresent in countless Web and Semantic Web applications and Web services. Calendric and temporal data are probably more than any other data a subject to interpretation, in almost any case depending on some cultural, legal, professional, and/or locational context. On the current Web, calendric and temporal data can hardly be interpreted by computers. This article contributes to the Semantic Web, an endeavor aiming at enhancing the current Web with well-defined meaning and to enable computers to meaningfully process data. The contribution is a reasoner for calendric and temporal data. This reasoner is part of CaTTS, a type language for calendar definitions. The reasoner is based on a "theory reasoning" approach using constraint solving techniques. This reasoner complements general purpose "axiomatic reasoning" approaches for the Semantic Web as widely used with ontology languages like OWL or RDF