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A Study on the Application of SE Approach to the Design of Health Monitoring Pilot Platform utilizing Big Data in the Nuclear Power Plant (NPP)
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 Title & Authors
A Study on the Application of SE Approach to the Design of Health Monitoring Pilot Platform utilizing Big Data in the Nuclear Power Plant (NPP)
Cha, Jae-Min; Shin, Junguk; Son, Choong-Yeon; Hwang, Dong-Sik; Yeom, Choong Sub;
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 Abstract
With the era of big data, the big data has been expected to have a large impact in the NPP safety areas. Although high interests of the big data for the NPP safety, only a limited researches concerning this issue are revealed. Especially, researches on the logical/physical structure and systematic design methods for the big data platform for the NPP safety were not dealt with. In this research, we design a new big data pilot platform for the NPP safety especially focusing on health monitoring and early warning services. For this, we propose a tailored design process based on SE approaches to manage inherent high complexities of the platform design. The proposed design process is consist of several steps from elicitate stakeholders to integration test via define operational concept and scenarios, and system requirements, design a conceptual functional architecture, select alternative physical modules for the derived functions and assess the applicability of the alternative modules, design a conceptual physical architecture, implement and integrate the physical modules. From the design process, this paper covers until the conceptual physical architecture design. In the following paper, the rest of the design process and results of the field test will be shown.
 Keywords
Nuclear Power Plant;Big Data;Early Warning;Platform;Systems Engineering;
 Language
Korean
 Cited by
 References
1.
Alam, J. R., Sajid, A., Talib, R., and Niaz, M., A Review on the Role of Big Data in Business, 2014.

2.
Apache Chuckwa, Retrieved Sep. 1, 2015, from http://chukwa.apache.org/

3.
Apache Flume, Retrieved Sep. 1, 2015, from http://flume.apache.org/

4.
Apache HBase, Retrieved Sep. 1, 2015, from http://hbase.apache.org/

5.
Apache Mahout, Retrieved Sep. 1, 2015, from http://mahout.apache.org/

6.
Apache Sqoop, Retrieved Sep. 1, 2015, from http://sqoop.apache.org/

7.
Baaziz, A. and Quoniam, L., How to use Big Data technologies to optimize operations in Upstream Petroleum Industry, 21st World Petroleum Congress, 2014.

8.
Beyer, M. A. and Laney, D., The import ance of 'big data': a definition. Stamford, CT: Gartner, 2012.

9.
Brule, M., Tapping the power of Big Data for the oil and gas industry, IBM Software White Paper for Petroleum Industry, 2013.

10.
BSR-AIAA G-043A-201, Guide to the Preparation of Operational Concept Documents, 1992.

11.
Cameron, D., Big Data in Exploration and Production: Silicon Snake-Oil, Magic Bullet, or Useful Tool?, In SPE Intelligent Energy Conference & Exhibition, Society of Petroleum Engineers, 2014.

12.
Cassandra, Retrieved Sep. 1, 2015, from http://cassandra.apache.org/

13.
Cloudera, Retrieved Sep. 1, 2015, from http://www.cloudera.com/

14.
D3.js, Retrieved Sep. 1, 2015, from http://d3js.org/

15.
Dijcks, J. P., Oracle: Big data for the enterprise, Oracle White Paper, 2012.

16.
ANSI/EIA-632, Processes for Engineeri ng a System, American National Standard Institute(ANSI)/Electronic Industries Alliance (EIA), 2003.

17.
Facebook Scribe, Retrieved Sep. 1, 2015, from http://github.com/facebookarchive/scribe/

18.
Feblowitz, J., The Big Deal About Big Data in Upstream Oil and Gas, IDC Energy Insights, 2012.

19.
Flot, Retrieved Sep. 1, 2015, from http:// www.flotcharts.org/

20.
FluentD, Retrieved Sep. 1, 2015, from http://www.fluentd.org/

21.
Gantz, J. and Reinsel, D., Extracting value from chaos, IDC iview, 1142: 9-10, 2011.

22.
Google Chart, Retrieved Sep. 1, 2015, from http://developers.google.com/chart/

23.
gRaphael, Retrieved Sep. 1, 2015, from http://raphaeljs.com/

24.
Hems, A., Soofi, A., and E. Perez, How innovative oil and gas companies are using big data to outmaneuver the competition, 2013.

25.
Hortonworks, Retrieved Sep. 1, 2015, from http://www.hortonworks.com/

26.
IEEE 1220, IEEE Standard for Application and Management of the Systems Engineering Process, IEEE Computer Society, 2005.

27.
INCOSE, What is systems engineering, Retrieved Sep. 1, 2015, from http://incose.org/practice/whatissystemseng.aspx/

28.
ISO/IEC 15288, Systems and Software Engineering - System Life Cycle Processes, International Organization for Standardization, 2015.

29.
Kang, S. K., Development of Big Data Demonstration Platform to Improve the Safety of NPP (2014520400090) 1st yearly report, Korea Institute of Energy Technology Evaluation and Planning (KETEP), 2015.

30.
Kei, F., Masaya, K., Isao, H., Satoshi, T., Takayuki, Y., and Toshiyuki, O., Failure sign monitoring system for large-scale plants applying system invariant analysis technology (SIAT), NEC Technical Journal, 9(1), 2014.

31.
Kim, M., Risk Communication about Nuclear Power in Korea: One-Year Descriptive Analysis on Twitter, Science Education International, 24(3), 324-343, 2013.

32.
Kim, D. S., & Kim, J. W., Public Opinion Sensing and Trend Analysis on Social Media: A Study on Nuclear Power on Twitter, Int. J. of Multimedia and Ubiquitous Engineering, 9(11), 373-384, 2014.

33.
Korea Atomic Industrial Forum, The 18th survey on the status of nuclear industry in 2012, Research Program for Nuclear Policy 2013-032251, 2014.

34.
Lurie, A., 39 Data Visualization Tools for Big Data. In Cloud Computing, Retrieved from Sep. 1, 2015, http://blog.profitbricks.com/39-data-visualization-tools-for-big-data/

35.
Manyika, J., Chui, M. Brown, B., Bughin, J. Dobbs, R., Roxburgh, C., and Byers, A. H., Big Data: the next Frontier for Innovation, Competition and Productivity, McKinsey Global Institute, 2011.

36.
MapR, Retrieved Sep. 1, 2015, from http://www.mapr.com/

37.
MongoDB, Retrieved Sep. 1, 2015, from http://www.mongodb.org/

38.
NIA, Classification and current status of big data technology, National Information Society Agency (NIA) Report, 2013.

39.
NIA, Work process and technology application manual on each big data usage phases ver. 1.0: Providing considerations for work process and technology application on each big data collection.store.application, National Information Society Agency (NIA) Report, 2014.

40.
NIST, Draft NIST Big Data Interoperability Framework: Vol. 2, Big Data Taxonomies, 2014.

41.
NodeBox, Retrieved Sep. 1, 2015, from http://www.nodebox.net/

42.
Polymaps, Retrieved Sep. 1, 2015, from http://polymaps.org/

43.
Processing, Retrieved Sep. 1, 2015, from http://processing.org/

44.
Redis, Retrieved Sep. 1, 2015, from http://redis.io/

45.
RHadoop, Retrieved Sep. 1, 2015, from http://github.com/RevolutionAnalytics/RHadoop

46.
RHIPE, Retrieved Sep. 1, 2015, from http://github.com/tesseradata/RHIPE

47.
RHive, Retrieved Sep. 1, 2015, from http://github.com/nexr/RHive

48.
Ryu, J.-H., Kang, S.-H., Cha, J.-M., ICT application of plant industry technology, KIC News, Vol. 18, No. 5, 2015.

49.
Sagiroglu, S., and Sinanc, D., Big data: A Review, Proc. of Collaboration Tech. and Systems (CTS), pp. 42-47, 2013.

50.
Seshadri, M., Big Data Science Challenging, The Oil Industry, CTO Global Services, EMC Corporation, 2013.

51.
SocialCompare, Javascript Graphs and Charts libraries, Retrieved April 15, 2015, from http://socialcompare.com/en/comparison/javascript-graphs-and-charts-libraries/

52.
Spark MLib, Retrieved Sep. 1, 2015, from http://spark.apache.org/mllib/

53.
Suda, B., The 37 best tools for data visualization, Retrieved Nov. 11, 2014, from http://www.creativebloq.com/design-tools/data-visualization-712402/

54.
Truck, M. and Zilis, S., The Big Data Landscape 3.0, 2014.

55.
TTAK.KO-10.0700, Functional Requirement Analysis for Big Data Service in Cloud Computing, TTA, 2013.

56.
TTAK.KO-10.0705, Framework for Big Data Service in Cloud Computing, TTA, 2013.

57.
TTAK.KO-10.0778, Big Data Real-time Processing Technology - Part 1: Overview, TTA, 2014.

58.
Ward, J. S., and Barker, A., Undefined by data: a survey of big data definitions, arXiv preprint arXiv:1309.5821, 2013.