Bernstein
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Current research project
| Title / Titel | Parallelized probabilistic detection of complex events on triple data streams | ||||
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| Abstract (PDF, 14 KB) | |||||
| Summary / Zusammenfassung | The amount of data available (e.g., on on the Semantic Web and Linked Open Data Cloud (LOD)) is growing at an astounding speed. An increasing number of these data-sources are dynamic (i.e. their content changes over time) or even represent continually updating phenomena (such as the stock exchange, sensor networks, social networks, or the continuous arrival of intelligence data). In many cases they also contain some temporal information either explicitly given through temporal constraints (e.g. limiting a triple’s validity by giving a start date and some live span) or implicitly (i.e. defining its validity from the moment it is made available until it is superseded by an update). One possibility to process these kinds of data are stream-processing systems that guarantee to correctly answer queries within acceptable time-frames whilst (i) continuously consuming triples and (ii) utilizing a limited amount of computational resources (i.e. space and time). The usual approach to address these requirements is the definition of a time window over which the queries are evaluated: newly arriving triples evict the oldest ones from the window of consideration. This approach allows for continuous updates since a triple consumption process can continuously update the window (often defined as a “logically circular” region of memory) and a query process can evaluate queries over the window. It also clearly limits memory consumption via the size of the window. The time-window approach is, however, severely limited as it (1) cannot cope with queries that will only match in time-spans longer than the chosen window, (2) cannot evaluate queries with temporal constraints as all triples within the window are usually regarded as being true without any temporal constraint, and (3) does not account for possibly-varying validity-spans of different parts of a query (just consider a query that involves both TBox -- and ABox -- related triple- patterns, where the former can be assumed to be long-living and the latter can change continuously). The goal of this project to find well-founded approaches the the scalable detection of complex events in scalable triple-streams. Weitere Informationen |
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| Publications / Publikationen | Weitere Informationen | ||||
| Keywords / Suchbegriffe | triples, semantic web, linked data, stream processing | ||||
| Project leadership and contacts / Projektleitung und Kontakte |
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| Funding source(s) / Unterstützt durch |
Other Public Sources (e.g. Federal or Cantonal Agencies) |
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| Duration of Project / Projektdauer | Jan 2012 to Dec 2013 |