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Distributed Heterogeneous Stream Reasoning

supported by the Austrian Science Funds (FWF) under project number P26471.


Contents


Project team

Motivation and Background

With the development of the Web and data interlinkage, distributed computation has become essential for modern information systems. An increasing involvement of sensors, sensor networks and mobile devices, generates a trend towards pushing rather than pulling in data processing, and streamed data has become important. While simple stream processing tasks facilitate access to data streams, reasoning on streamed data recently gains special interest, as it enables improved computation results (such as query answers, decisions, problem solutions).

Consider a webservice that provides live recommendations for exploring Vienna based on the user’s profile, recent changes of her positions based on the GPS data from her smartphone, current traffic status, weather data, the city map and an ontology of objects on the map, etc. The service should respect all this information to dynamically suggest the most suitable route. In order to do so, reasoning needs to be performed in a streaming manner, taking continuously changing, streamed data into account. Compared to the state of the art in stream processing, advanced techniques are needed to cope with incomplete information (e.g., if a stream is off, or data, such as a classification, is missing), as well as with the distributedness and heterogeneity of the sources. To reason about the behavior of the system and verify the admissibility of the output, query answering should have a clear formal semantics. Moreover, a model-based approach enabling advanced AI techniques can add value to computation results, e.g., multiple choices, preference-/assumption-based conclusions. In other words, “thinking” components are needed rather than simple reactive entities. Current streaming engines are not able to offer such advanced features as they build on operational semantics of streamed data at either single nodes or distributed nodes with homogeneous processing models. The operational approach makes it difficult to predict the overall system behavior, which are very sensible to low-level streaming features (e.g., the streaming rate).

Goal of the project

This project will tackle the above issues and contribute a strong model-based semantic foundation to distributed heterogeneous stream reasoning, optimized algorithms for practicable realization and a prototype implementation and evaluation toolbox. These achievements and project results will make advanced stream reasoning a reality and enable novel applications where streaming data is essential.

Publications

2015

Minh Dao-Tran, Harald Beck, and Thomas Eiter.
Towards Comparing RDF Stream Processing Semantics.
In 1st Workshop on High-Level Declarative Stream Processing (HiDeSt), September 22, 2015, Dresden, Germany, 2015.
[bib | paper | slides]

Minh Dao-Tran and Danh Le-Phuoc.
Towards Enriching CQELS with Complex Event Processing and Path Navigation.
In 1st Workshop on High-Level Declarative Stream Processing (HiDeSt), September 22, 2015, Dresden, Germany, 2015.
[bib | paper]

Harald Beck, Minh Dao-Tran, Thomas Eiter.
Answer Update for Rule-based Stream Reasoning.
24th International Joint Conference on Artificial Intelligence (IJCAI), July 25-31, 2015, Buenos Aires, Argentinia.
[
bib | paper | slides]

Danh Le-Phuoc, Minh Dao-Tran, Anh Le Tuan, Manh Nguyen Duc, and Manfred Hauswirth.
DEBS Grand Challenge: RDF Stream Processing with CQELS Framework for Real-time Analysis.
In ACM International Conference on Distributed Event-Based Systems (DEBS), June 29-July 3, 2015, Oslo, Norway, 2015.
[bib | paper | slides]

Danh Le-Phuoc, Minh Dao-Tran, Chan Le Van, Anh Le Tuan, Manh Nguyen Duc, Tuan Tran Nhat, and Manfred Hauswirth.
Platform-Agnostic Execution Framework Towards RDF Stream Processing.
In RDF Stream Processing Workshop, May 31, Portoroz, Slovenia, 2015.
[bib | paper | slides]

Harald Beck, Minh Dao-Tran, and Thomas Eiter.
Semantics and Complexity of RDF Stream Processing & Reasoning: Expression of Interest.
In RDF Stream Processing Workshop, May 31, Portoroz, Slovenia, 2015.
[bib | paper | slides]

Minh Dao-Tran, Thomas Eiter, Michael Fink, and Thomas Krennwallner.
Distributed Evaluation of Nonmonotonic Multi-Context Systems.
Journal of Artificial Intelligence Research, 52:543-600, 2015.
[bib | paper]

Harald Beck, Minh Dao-Tran, Thomas Eiter, and Michael Fink.
LARS: A Logic-based Framework for Analyzing Reasoning over Streams.
29th AAAI Conference, January 25-30, 2015, Austin, Texas, USA.
[
bib | paper | slides]

2014

Harald Beck, Minh Dao-Tran, Thomas Eiter, and Michael Fink.
Towards a Logic-Based Framework for Analyzing Stream Reasoning.
3rd International Workshop on Ordering and Reasoning, October 19-20, 2014, Riva del Garda, Trentino, Italy.
Best Paper.
[
bib | paper | extended version | slides]

Harald Beck, Minh Dao-Tran, Thomas Eiter, and Michael Fink.
Towards Ideal Semantics for Analyzing Stream Reasoning.
International Workshop on Reactive Concepts in Knowledge Representation, August 19, 2014, Prague, Czech Republic.
[
bib | paper | slides]

Cooperations

References

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S. E.-D. Bairakdar, M. Dao-Tran, T. Eiter, M. Fink, and T. Krennwallner. Decomposition of distributed nonmonotonic multi-context systems. In JELIA, pages 24-37, 2010.
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S. E.-D. Bairakdar, M. Dao-Tran, T. Eiter, M. Fink, and T. Krennwallner. The DMCS solver for distributed nonmonotonic multi-context systems. In JELIA, pages 352-355, 2010.
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M. Dao-Tran, T. Eiter, M. Fink, and T. Krennwallner. Distributed nonmonotonic multi-context systems. In KR, 2010.
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T. Eiter, M. Fink, P. Sch&uumlller, and A. Weinzierl. Finding explanations of inconsistency in multi-context systems. In KR, 2010.
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T. Eiter, G. Ianni, R. Schindlauer, and H. Tompits. dlvhex: A prover for semantic-web reasoning under the answer-set semantics. In Web Intelligence, pages 1073-1074, 2006.
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T. Eiter, T. Krennwallner, M. Prandtstetter, C. Rudloff, P. Schneider, and M. Straub. Semantically Enriched Multi-Modal Routing. In 19th ITS World Congress, 2012.
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M. Gebser, T. Grote, R. Kaminski, P. Obermeier, O. Sabuncu, and T. Schaub. Stream reasoning with answer set programming. In KR, 2012.
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M. Gebser, T. Grote, R. Kaminski, and T. Schaub. Reactive answer set programming. In LPNMR, pages 54-66, 2011.
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M. Gebser, O. Sabuncu, and T. Schaub. An incremental answer set programming based system for finite model computation. AI Commun., 24(2):195-212, 2011.
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O. Kennedy, Y. Ahmad, and C. Koch. DBToaster: Agile views for a dynamic data management system. In CIDR, pages 284-295, 2011.
[koch10]
C. Koch. Incremental query evaluation in a ring of databases. In PODS, pages 87-98, 2010.
[pdp11]
D. L. Phuoc, M. Dao-Tran, J. X. Parreira, and M. Hauswirth. A native and adaptive approach for unified processing of linked streams and linked data. In ISWC, pages 370-388, 2011.
[pdp12]
D. L. Phuoc, M. Dao-Tran, M.-D. Pham, P. Boncz, T. Eiter, and M. Fink. Linked stream data processing engines: Facts and figures. In ISWC, 2012
[pph12]
D. L. Phuoc, J. X. Parreira, and M. Hauswirth. Linked stream data processing. In Reasoning Web, 2012.
[rp11]
Y. Ren and J. Z. Pan. Optimising ontology stream reasoning with truth maintenance system. In CIKM, pages 831-836, 2011.
[vchf09]
E. D. Valle, S. Ceri, F. van Harmelen, and D. Fensel. It’s a streaming world! reasoning upon rapidly changing information. IEEE Intelligent Systems, 24:83-89, 2009.

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