Encyclopedia of Big Data Technologies

2019 Edition
| Editors: Sherif Sakr, Albert Y. Zomaya

Semantic Stream Processing

  • Danh Le-Phuoc
  • Manfred HauswirthEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-3-319-77525-8_287



Semantic stream processing (SSP) refers to a set of models, principles, and techniques for analyzing and processing stream data by exploiting semantic structures which are explicitly or implicitly embedded in stream data elements. Such “semantic streams” are represented as sequences of temporal graphs linking human-machine understandable semantics and computational primitives. Semantic stream processing approaches leverage reasoning capabilities through formally defined rules to automate and optimize their continuous processing flows formulated in high-level abstract concepts and relationships.


Billions of sensors being distributed across the globe are continuously streaming data about the physical world around us. The stream data generated by networks of sensors enables us to detect and identify a multitude of things, from simple phenomena to complex events and situations. However, the lack of integration and...

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  1. Alani H, Szomszor M, Cattuto C, Van den Broeck W, Correndo G, Barrat A (2009) Live social semantics. Springer, Berlin/Heidelberg, pp 698–714.  https://doi.org/10.1007/978-3-642-04930-9_44Google Scholar
  2. Anantharam P, Barnaghi P, Thirunarayan K, Sheth A (2015) Extracting city traffic events from social streams. ACM Trans Intell Syst Technol 6(4):43:1–43:27.  https://doi.org/10.1145/2717317CrossRefGoogle Scholar
  3. Anantharam P, Thirunarayan K, Marupudi S, Sheth A, Banerjee T (2016) Understanding city traffic dynamics utilizing sensor and textual observations. In: Proceedings of the thirtieth AAAI conference on artificial intelligence (AAAI’16). AAAI Press, pp 3793–3799. http://dl.acm.org/citation.cfm?id=3016387.3016438
  4. Anicic D, Fodor P, Rudolph S, Stühmer R, Stojanovic N, Studer R (2010) A rule-based language for complex event processing and reasoning. In: Proceedings of the fourth international conference on web reasoning and rule systems (RR’10). Springer, Berlin/Heidelberg, pp 42–57CrossRefGoogle Scholar
  5. Arias Fisteus J, Fernández García N, Sánchez Fernández L, Fuentes-Lorenzo D (2014) Ztreamy. Web Semant 25(C):16–23.  https://doi.org/10.1016/j.websem.2013.11.002CrossRefGoogle Scholar
  6. Baier S, Ma Y, Tresp V (2017) Improving visual relationship detection using semantic modeling of scene descriptions. In: d’Amato C, Fernández M, Tamma VAM, Lécué F, Cudré-Mauroux P, Sequeda JF, Lange C, Heflin J (eds) The semantic web – ISWC 2017 – proceedings of 16th international semantic web conference, Vienna, 21–25 Oct 2017, Part I. Lecture notes in computer science, vol 10587, pp 53–68.  https://doi.org/10.1007/978-3-319-68288-4_4Google Scholar
  7. Balduini M, Celino I, Dell’Aglio D, Della Valle E, Huang Y, Lee T, Kim SH, Tresp V (2012) Bottari: an augmented reality mobile application to deliver personalized and location-based recommendations by continuous analysis of social media streams. Web Semant 16:33–41.  https://doi.org/10.1016/j.websem.2012.06.004CrossRefGoogle Scholar
  8. Balduini M, Della Valle E, Dell’Aglio D, Tsytsarau M, Palpanas T, Confalonieri C (2013) Social listening of City scale events using the streaming linked data framework. Springer, Berlin/Heidelberg, pp 1–16.  https://doi.org/10.1007/978-3-642-41338-4_1Google Scholar
  9. Barbieri D, Braga D, Ceri S, Valle ED, Huang Y, Tresp V, Rettinger A, Wermser H (2010a) Deductive and inductive stream reasoning for semantic social media analytics. IEEE Intell Syst 25(6):32–41.  https://doi.org/10.1109/MIS.2010.142CrossRefGoogle Scholar
  10. Barbieri DF, Braga D, Ceri S, Grossniklaus M (2010b) An execution environment for C-SPARQL queries. In: EDBT 2010, pp 441–452CrossRefGoogle Scholar
  11. Barnaghi P, Wang W, Henson C, Taylor K (2012) Semantics for the internet of things: early progress and back to the future. Int J Semant Web Inf Syst 8(1):1–21.  https://doi.org/10.4018/jswis.2012010101CrossRefGoogle Scholar
  12. Barnaghi P, Wang W, Dong L, Wang C (2013) A linked-data model for semantic sensor streams. In: 2013 IEEE international conference on green computing and communications and IEEE internet of things and IEEE Cyber, physical and social computing, pp 468–475.  https://doi.org/10.1109/GreenCom-iThings-CPSCom.2013.95
  13. Bazoobandi HR, Beck H, Urbani J (2017) Expressive stream reasoning with laser. CoRR abs/1707.08876. http://arxiv.org/abs/1707.08876Google Scholar
  14. Beck H, Dao-Tran M, Eiter T, Fink M (2015) Lars: a logic-based framework for analyzing reasoning over streams. In: Proceedings of the twenty-ninth AAAI conference on artificial intelligence (AAAI’15). AAAI Press, pp 1431–1438. http://dl.acm.org/citation.cfm?id=2887007.2887205
  15. Bordes A, Chopra S, Weston J (2014) Question answering with subgraph embeddings. CoRR abs/1406.3676. http://arxiv.org/abs/1406.3676
  16. Calbimonte JP, Corcho O, Gray AJG (2010) Enabling ontology-based access to streaming data sources. In: Proceedings of the 9th international semantic web conference on the semantic web – volume part I (ISWC’10). Springer, Berlin/Heidelberg, pp 96–111Google Scholar
  17. Chang X, Yang Y, Xing EP, Yu YL (2015) Complex event detection using semantic saliency and nearly-isotonic SVM. In: Proceedings of the 32nd international conference on international conference on machine learning, vol 37, JMLR.org (ICML’15), pp 1348–1357. http://dl.acm.org/citation.cfm?id=3045118.3045262
  18. Chen J, Lécué F, Pan JZ, Chen H (2017) Learning from ontology streams with semantic concept drift. In: Sierra C (ed) Proceedings of the twenty-sixth international joint conference on artificial intelligence (IJCAI 2017), Melbourne, 19–25 Aug 2017, ijcai.org, pp 957–963.  https://doi.org/10.24963/ijcai.2017/133
  19. Cox S, Little C (2017) Time ontology in owl. https://www.w3.org/TR/owl-time/. Online; Accessed 21 Mar 2018
  20. Dell’Aglio D, Valle ED, Calbimonte J, Corcho Ó (2014) RSP-QL semantics: a unifying query model to explain heterogeneity of RDF stream processing systems. Int J Semant Web Inf Syst 10(4):17–44.  https://doi.org/10.4018/ijswis.2014100102CrossRefGoogle Scholar
  21. Dell’Aglio D, Dao-Tran M, Calbimonte JP, Le-Phuoc D, Della Valle E (2016) A query model to capture event pattern matching in RDF stream processing query languages. Springer, Cham, pp 145–162.  https://doi.org/10.1007/978-3-319-49004-5_10Google Scholar
  22. Dell’Aglio D, Della Valle E, van Harmelen F, Bernstein A (2017) Stream reasoning: a survey and outlook. Data Sci 01(1–2):59–83Google Scholar
  23. Eiter T, Parreira JX, Schneider P (2017) Spatial ontology-mediated query answering over mobility streams. Springer, Cham, pp 219–237.  https://doi.org/10.1007/978-3-319-58068-5_14Google Scholar
  24. Forgy CL (1982) Rete: a fast algorithm for the many pattern/many object pattern match problem. Artif Intell 19(1):17–37.  https://doi.org/10.1016/0004-3702(82)90020-0. http://www.sciencedirect.com/science/article/pii/0004370282900200CrossRefGoogle Scholar
  25. Gulisano V, Jerzak Z, Katerinenko R, Strohbach M, Ziekow H (2017) The DEBS 2017 grand challenge. In: Proceedings of the 11th ACM international conference on distributed and event-based systems (DEBS’17). ACM, New York, pp 271–273.  https://doi.org/10.1145/3093742.3096342CrossRefGoogle Scholar
  26. Haller A, Janowicz K, Cox S, Phuoc DL, Taylor K, Lefrançoi M (2017) Semantic sensor network ontology, w3c recomendation. https://www.w3.org/TR/vocab-ssn/. Online; Accessed 21 Mar 2018
  27. Jang Y, Song Y, Yu Y, Kim Y, Kim G (2017) TGIF-QA: toward spatio-temporal reasoning in visual question answering. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR 2017), Honolulu, 21–26 July 2017, pp 1359–1367.  https://doi.org/10.1109/CVPR.2017.149
  28. Johnson J, Hariharan B, van der Maaten L, Hoffman J, Fei-Fei L, Zitnick CL, Girshick RB (2017) Inferring and executing programs for visual reasoning. In: IEEE international conference on computer vision (ICCV 2017), Venice, 22–29 Oct 2017. IEEE Computer Society, pp 3008–3017.  https://doi.org/10.1109/ICCV.2017.325
  29. Kaebisc S, Kamiya T (2018) Web of things (wot) thing description. https://www.w3.org/TR/wot-thing-description/. Online; Accessed 21 Mar 2018
  30. Kharlamov E, Kotidis Y, Mailis T, Neuenstadt C, Nikolaou C, Özçep Ö, Svingos C, Zheleznyakov D, Brandt S, Horrocks I, Ioannidis Y, Lamparter S, Möller R (2016) Towards analytics aware ontology based access to static and streaming data. Springer, Cham, pp 344–362.  https://doi.org/10.1007/978-3-319-46547-0_31Google Scholar
  31. Kharlamov E, Mailis T, Mehdi G, Neuenstadt C, Özçep Ö, Roshchin M, Solomakhina N, Soylu A, Svingos C, Brandt S, Giese M, Ioannidis Y, Lamparter S, Möller R, Kotidis Y, Waaler A (2017) Semantic access to streaming and static data at siemens. Web Semant Sci Serv Agents World Wide Web 44(Suppl C):54–74.  https://doi.org/10.1016/j.websem.2017.02.001. http://www.sciencedirect.com/science/article/pii/S1570826817300124; Industry and In-use Applications of Semantic TechnologiesCrossRefGoogle Scholar
  32. Komazec S, Cerri D, Fensel D (2012) Sparkwave: continuous schema-enhanced pattern matching over RDF data streams. In: Proceedings of the 6th ACM international conference on distributed event-based systems (DEBS’12). ACM, New York, pp 58–68.  https://doi.org/10.1145/2335484.2335491CrossRefGoogle Scholar
  33. Krishna R, Zhu Y, Groth O, Johnson J, Hata K, Kravitz J, Chen S, Kalantidis Y, Li LJ, Shamma DA, Bernstein M, Fei-Fei L (2016) Visual genome: connecting language and vision using crowdsourced dense image annotations. https://arxiv.org/abs/1602.07332
  34. Le-Phuoc D (2017) Operator-aware approach for boosting performance in RDF stream processing. Web Semant Sci Serv Agents World Wide Web 42(Suppl C):38–54.  https://doi.org/10.1016/j.websem.2016.04.001. http://www.sciencedirect.com/science/article/pii/S1570826816300014CrossRefGoogle Scholar
  35. Le-Phuoc D, Dao-Tran M, Parreira JX, Hauswirth M (2011) A native and adaptive approach for unified processing of linked streams and linked data. In: Proceedings of 10th international semantic web conference, pp 370–388CrossRefGoogle Scholar
  36. Le-Phuoc D, Nguyen-Mau HQ, Parreira JX, Hauswirth M (2012a) A middleware framework for scalable management of linked streams. Web Semant Sci Serv Agents World Wide Web 16(Suppl C):42–51.  https://doi.org/10.1016/j.websem.2012.06.003. http://www.sciencedirect.com/science/article/pii/S1570826812000728; the Semantic Web Challenge 2011CrossRefGoogle Scholar
  37. Le-Phuoc D, Xavier Parreira J, Hauswirth M (2012b) Linked stream data processing. Springer, Berlin/Heidelberg, pp 245–289.  https://doi.org/10.1007/978-3-642-33158-9_7Google Scholar
  38. Le-Phuoc D, Quoc HNM, Van CL, Hauswirth M (2013) Elastic and scalable processing of linked stream data in the cloud. In: ISWC 2013 (1), pp 280–297.  https://doi.org/10.1007/978-3-642-41335-3_18Google Scholar
  39. Margara A, Urbani J, van Harmelen F, Bal H (2014) Streaming the web: reasoning over dynamic data. Web Semant Sci Serv Agents World Wide Web 25(Suppl C):24–44.  https://doi.org/10.1016/j.websem.2014.02.001. http://www.sciencedirect.com/science/article/pii/S1570826814000067CrossRefGoogle Scholar
  40. Puschmann D, Barnaghi P, Tafazolli R (2017) Adaptive clustering for dynamic IoT data streams. IEEE Internet Things J 4(1):64–74.  https://doi.org/10.1109/JIOT.2016.2618909Google Scholar
  41. Rea N, Dahyot R, Kokaram A (2004) Semantic event detection in sports through motion understanding. Springer, Berlin/Heidelberg, pp 88–97.  https://doi.org/10.1007/978-3-540-27814-6_14Google Scholar
  42. Ren X, Curé O, Ke L, Lhez J, Belabbess B, Randriamalala T, Zheng Y, Kepeklian G (2017) Strider: an adaptive, inference-enabled distributed RDF stream processing engine. Proc VLDB Endow 10(12):1905–1908.  https://doi.org/10.14778/3137765.3137805CrossRefGoogle Scholar
  43. Rinne M, Solanki M, Nuutila E (2016) Rfid-based logistics monitoring with semantics-driven event processing. In: Proceedings of the 10th ACM international conference on distributed and event-based systems (DEBS’16). ACM, New York, pp 238–245.  https://doi.org/10.1145/2933267.2933300Google Scholar
  44. Ronca A, Kaminski M, Cuenca Grau B, Motik B, Horrocks I. Stream reasoning in temporal datalog. In: Proceedings of the 32nd AAAI conference on artificial intelligence (AAAI 2018). AAAI Press, New OrleansGoogle Scholar
  45. Sheth A (2009) Citizen sensing, social signals, and enriching human experience. IEEE Internet Comput 13(4):87–92.  https://doi.org/10.1109/MIC.2009.77CrossRefGoogle Scholar
  46. Sheth A, Henson C, Sahoo SS (2008a) Semantic sensor web. IEEE Internet Comput 12(4):78–83.  https://doi.org/10.1109/MIC.2008.87CrossRefGoogle Scholar
  47. Sheth AP, Henson CA, Sahoo SS (2008b) Semantic sensor web. IEEE Internet Comput 12(4): 78–83CrossRefGoogle Scholar
  48. Sheu P, Yu H, Ramamoorthy CV, Joshi AK, Zadeh LA (2010) Semantic computing. Wiley-IEEE Press, New YorkzbMATHCrossRefGoogle Scholar
  49. Unger C, Bühmann L, Lehmann J, Ngonga Ngomo AC, Gerber D, Cimiano P (2012) Template-based question answering over RDF data. In: Proceedings of the 21st international conference on world wide web (WWW’12). ACM, New York, pp 639–648.  https://doi.org/10.1145/2187836.2187923CrossRefGoogle Scholar
  50. Wang T, Li J, Diao Q, Hu W, Zhang Y, Dulong C (2006) Semantic event detection using conditional random fields. In: Proceedings of the 2006 conference on computer vision and pattern recognition workshop (CVPRW’06), IEEE Computer Society, Washington, DC, pp 109–114.  https://doi.org/10.1109/CVPRW.2006.190CrossRefGoogle Scholar
  51. Whitehouse K, Zhao F, Liu J (2006) Semantic streams: a framework for composable semantic interpretation of sensor data. In: Proceedings of the third European conference on wireless sensor networks (EWSN’06). Springer, Berlin/Heidelberg, pp 5–20.  https://doi.org/10.1007/11669463_4CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Open Distributed SystemsTechnical University of BerlinBerlinGermany
  2. 2.Fraunhofer FOKUSBerlinGermany