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빅 데이터 분석정보 공유를 위한 다차원 이벤트 데이터베이스의 동기화 효과 연구

A Study on Synchronization Effect of A Multi-dimensional Event Database for Big Data Information Sharing

  • Lee, Choon Y. (Dept. of Management Information System, Kookmin University)
  • 투고 : 2017.09.01
  • 심사 : 2017.10.20
  • 발행 : 2017.10.28

초록

효과적인 데이터 분석 및 활용을 위해서는 빅 데이터를 내부 데이터와 유연하게 연계할 수 있는 방안이 필요하다. 빅 데이터 분석 정보를 내부 정보시스템과 연계시키기 위한 방안으로서 본 연구는 다차원 이벤트 온톨리지를 제시하였다. 이를 위해서 먼저 빅 데이터 분석 정보를 이벤트 모형을 사용하여 온톨리지로 표현하고, 다차원 데이터베이스 또한 OWL-DL 온톨리지로 변환하여 표현하였다. 다차원 이벤트 온톨리지에서 빅 데이터 분석정보들은 차원 계층구조를 통하여 다차원 데이터베이스에 저장된 모든 개체들에게 공유되는데, 본 연구에서는 이를 이벤트의 하향공유, 상향 공유 및 복합 이벤트 공유로 구분한다. 이들 정보공유 유형별로 빅 데이터 분석 정보의 공유 및 활용 방안들을 제시하였으며, Protege를 사용하여 시험적으로 구현하였다. 본 연구는 외부의 빅 데이터 분석 정보를 내부의 다차원 데이터베이스와 연계하는 방안을 실험적으로 제시하였다는 점에서 의의를 가진다고 할 수 있다.

As external data have become important corporate information resources, there are growing needs to combine them with internal data. This paper proposes an ontology-based scheme to combine external data with multi-dimensional databases, which shall be called multi-dimensional event ontology. In the ontology, external data are represented as events. Event characteristics such as actors, places, times, targets are linked to dimensions of a multi-dimensional database. By mapping event characteristics to database dimensions, external event data are shared via multi-dimensional hierarchies. This paper proposes rules to synchronize information sharing in multi-dimensional event ontology such as upward event information sharing, downward event information sharing and complex event information sharing. These rules are implemented using Protege. This study has a value in suggesting Big Data information sharing processes using an event database framework.

키워드

참고문헌

  1. C. Y. Lee, "An Evaluation of an Information Sharing Workflow Using Data Provenance Semantics", Journal of Digital Convergence, Vol. 11, No. 6, pp. 175-185, 2013. https://doi.org/10.14400/JDPM.2013.11.6.175
  2. C. Y. Lee, Network Enterprise and Future Management Doctrines, SERI Research Essay 74, SERI, 2007.
  3. IBM, Integrating and Governing Big Data, White Paper, 2014.
  4. M. Maleszka, B. Mianowska, N. Nguyen, "A framework for data warehouse federations building", IEEE International Conference on Systems, Man, and Cybernatics, pp.2897-2902. 2012.
  5. L. Patil, "Ontology-based exchange of product data semantics", IEEE Transactions on Automation Science and Engineering, Vol. 2, No. 3, pp.213-225, 2005, https://doi.org/10.1109/TASE.2005.849087
  6. V. A. Martins, J. P. C. L. da Costa, R. T. de Sousa Junior "Architecture of a collaborative business intelligence environment based on an ontology repository and distributed data services", KMIS 2012 - International Conference on Knowledge Management and Information Sharing, pp.99-106, 2012.
  7. A. Mikroyannidis, B. Theodoulidis, "Ontologymanagement and evolution for business intelligence", International Journal of Information Management, Vol 30, No. 6, pp.559-566, 2010. https://doi.org/10.1016/j.ijinfomgt.2009.10.002
  8. N. Choi, I. Song, H. Han, "A survey on ontology mapping", ACM Sigmod Record, Vol 35, No. 3, pp34-41, 2006. https://doi.org/10.1145/1168092.1168097
  9. Y. Kalfoglou, M. Schorlemmer, "Ontology mapping: the state of the art", The Knowledge Engineering Review, Vol. 18, Issue 1, pp.1-31, 2003. https://doi.org/10.1017/S0269888903000651
  10. B. Neumayer, S. Anderlik, and M. Schrefl, "Towards ontology-based OLAP: datalog-based reasoning over multidimensional ontologies", Proceedings of the fifteenth international workshop on Data warehousing and OLAP, ACM, pp. 41-48, 2012.
  11. R. Kern, T. Stolarczyk, N. Nguyen, "A formal framework for query decomposition and knowledge integration in data warehouse federations", Expert Systems with Applications, Vol. 40, Issue 7, pp.2592-2606, 2013. https://doi.org/10.1016/j.eswa.2012.10.060
  12. N. Prat, J. Akoka, I. Comyn-Wattiau, "Transforming multidimensional models into OWL-DL ontologies", Research Challenges in Information Science, 2012 Sixth International Conference on. IEEE, pp. 1-12, 2012.
  13. N. Prat, I. Magdiche, J. Akoka, "Multidimensional models meet the semantic web: defining and reasoning on OWL-DL ontologies for OLAP", Proceedings of the fifteenth international workshop on Data warehousing and OLAP. ACM, pp. 17-24, 2012.
  14. A. Paschke, "A semantic design pattern language for complex event processing", AAAI Spring Symposium: Intelligent Event Processing, pp.54-60, 2009.
  15. K. Teymourian, G. Coskun, A. Paschke, "Modular upper-level ontologies for semantic complex event processing", WoMO. pp.81-93, 2010.
  16. J. Park, XML Topic Maps: Creating and Using Topic maps for the Web, Addison Wesley, 2003.
  17. M. Horridge, A Practical Guide To Building OWL Ontologies Using Pretege 4 and CO-ODE Tools, Edition 1.3, The University of Manchester, 2011.
  18. http://protege.stanford.edu/
  19. SAS, How to Use an Uncommon-Sense Approach to Big Data Quality, SAS Conclusions Paper, 2012.
  20. S. Kim, "Study on Big Data Utilization Plans of Medical Institutions", Journal of Digital Convergence, Vol. 12, No. 2, pp. 397-407, 2014. https://doi.org/10.14400/JDC.2014.12.2.397