Skip to main content
Log in

Enhancing Situation Awareness Using Semantic Web Technologies and Complex Event Processing

  • Published:
Annals of Data Science Aims and scope Submit manuscript

Abstract

Data fusion techniques combine raw data of multiple sources and collect associated data to achieve more specific inferences than what could be attained with a single source. Situational awareness is one of the levels of the JDL, a matured information fusion model. The aim of situational awareness is to understand the developing relationships of interests between entities within a specific time and space. The present research shows how semantic web technologies, i.e. ontology and semantic reasoner, can be used to describe situations and increase awareness of the situation. As the situation awareness level receives data streams from numerous distributed sources, it is necessary to manage data streams by applying data stream processor engines such as Esper. In addition, in this research, complex event processing, a technique for achieving related situational in real-time, has been used, whose main aim is to generate actionable abstractions from event streams, automatically. The proposed approach combines Complex Event Processing and semantic web technologies to achieve better situational awareness. To show the functionality of the proposed approach in practice, some simple examples are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. http://esper.codehaus.org.

  2. http://www.w3.org/TR/rdf-primer.

  3. http://www.w3.org/TR/rdf-schema.

  4. https://jena.apache.org/.

  5. https://www.w3.org/TR/sparql11-overview/.

References

  1. White A (1987) Data Fusion Lexicon, Joint Directors of Laboratories, Technical Panel for C3. Data Fusion SubPanel, Naval Ocean Systems Center, San Diego

  2. Llinas J, Bowman C, Rogova G, Steinberg A, Waltz E, White F (2004) Revisiting the JDL data fusion model II. Space and Naval Warefare Systems Command, San Diego

    Google Scholar 

  3. Nakamura EF, Loureiro AA, Frery AC (2007) Information fusion for wireless sensor networks: methods, models, and classifications. ACM Comput Surv (CSUR) 39(3):9

    Article  Google Scholar 

  4. Llinas J, Hall DL (1998) An introduction to multi-sensor data fusion. In: Proceedings of the 1998 IEEE international symposium on, circuits and systems, 1998. ISCAS’98, vol 6. IEEE, pp 537–540

  5. Boyd JR (1987) A discourse on winning and losing. Air University document MU43947, briefing, p 1

  6. Bedworth M, O’Brien J (2000) The Omnibus model: A new model of data fusion? IEEE Aerosp Electron Syst Mag 15(4):30–36

    Article  Google Scholar 

  7. Kokar MM, Bedworth MD, Frankel CB (2000) Reference model for data fusion systems. In: Proceedings of SPIE, the international society for optical engineering, vol 4051, pp 191–202

  8. Frankel CB, Bedworth MD (2000) Control, estimation and abstraction in fusion architectures: lessons from human information processing. In: Proceedings of the 3rd international conference on information fusion, 2000. FUSION 2000, vol 1. IEEE, pp MOC5-3

  9. Endsley MR (1988) Design and evaluation for situation awareness enhancement. In: Proceedings of the human factors society annual meeting, vol 32(2). SAGE Publications, Los Angeles, pp 97–101

  10. Cugola G, Margara A (2012) Processing flows of information: from data stream to complex event processing. ACM Comput Surv (CSUR) 44(3):15

    Article  Google Scholar 

  11. Blasch EP, Plano S (2003) Level 5: user refinement to aid the fusion process. In: Multisensor, multisource information fusion: architectures, algorithms, and applications 2003, vol 5099. International Society for Optics and Photonics, pp 288–298

  12. Lambert DA (2001) Situations for situation awareness. In: Proceedings of fusion

  13. Lambert DA (2003) An exegesis of data fusion. Stud Fuzziness Soft Comput 127:68–75

    Article  Google Scholar 

  14. Blasch E, Steinberg A, Das S, Llinas J, Chong C, Kessler O et al (2013) Revisiting the JDL model for information exploitation. In: 16th international conference on information fusion (FUSION), 2013. IEEE, pp 129–136

  15. Synnergren J, Gamalielsson J, Olsson B (2007) Mapping of the JDL data fusion model to bioinformatics. In: IEEE international conference on systems, man and cybernetics, 2007, ISIC. IEEE, pp 1506–1511

  16. Swart I, Irwin B, Grobler MM (2016) Adaptation of the JDL model for multi-sensor national cyber security data fusion. Int J Cyber Warf Terror (IJCWT) 6(3):17–30

    Article  Google Scholar 

  17. Giacobe NA (2010) Application of the JDL data fusion process model for cyber security. In: Proceedings of SPIE, vol 7710, p 77100R

  18. Cruz IF, Xiao H (2005) The role of ontologies in data integration. Eng Intell Syst Electr Eng Commun 13(4):245

    Google Scholar 

  19. Wache H, Voegele T, Visser U, Stuckenschmidt H, Schuster G, Neumann H, Hübner S (2001) Ontology-based integration of information—a survey of existing approaches. In: IJCAI-01 workshop: ontologies and information sharing, vol 2001, pp 108–117

  20. Arens Y, Hsu CN, Knoblock CA (1996) Query processing in the sims information mediator. Adv Plan Technol 32:78–93

    Google Scholar 

  21. Mena E, Illarramendi A, Kashyap V, Sheth AP (2000) OBSERVER: An approach for query processing in global information systems based on interoperation across pre-existing ontologies. Distrib Parallel Databases 8(2):223–271

    Article  Google Scholar 

  22. Matheus CJ, Kokar MM, Baclawski K (2003) A core ontology for situation awareness. In: Proceedings of the 6th international conference on information fusion, vol 1, pp 545–552

  23. Matheus C, Kokar M, Baclawski K, Letkowski J (2005) An application of semantic web technologies to situation awareness. Semant Web-ISWC 2005:944–958

    Google Scholar 

  24. Noughabi HA, Kahani M, Behkamal B (2013) SemFus: semantic fusion framework based on JDL. In: Innovations and advances in computer, information, systems sciences, and engineering. Springer, New York, pp 583–594

  25. Golab L, Özsu MT (2010) Data stream management. Synth Lect Data Manag 2(1):1–73

    Article  Google Scholar 

  26. Luckham D (2008) The power of events: an introduction to complex event processing in distributed enterprise systems. In: International workshop on rules and rule markup languages for the semantic web. Springer, Berlin pp 3–3

  27. ESPERTECH (2015) Event processing with Esper and NEsper. http://esper.codehaus.org

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Havva Alizadeh Noughabi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Noughabi, H.A., Kahani, M. & Shakibamanesh, A. Enhancing Situation Awareness Using Semantic Web Technologies and Complex Event Processing. Ann. Data. Sci. 5, 487–496 (2018). https://doi.org/10.1007/s40745-018-0148-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40745-018-0148-1

Keywords

Navigation