AIRCC's International Journal of Computer Science and Information Technology

Open Access Open Access  Restricted Access Subscription Access

Importance of Process Mining for Big Data Requirements Engineering


Affiliations
1 Department of Electrical and Computer Engineering and Computer Science, University of Detroit Mercy, Detroit, MI, 48221, United States
 

Requirements engineering (RE), as a part of the project development life cycle, has increasingly been recognized as the key to ensure on-time, on-budget, and goal-based delivery of software projects. RE of big data projects is even more crucial because of the rapid growth of big data applications over the past few years. Data processing, being a part of big data RE, is an essential job in driving big data RE process successfully. Business can be overwhelmed by data and underwhelmed by the information so, data processing is very critical in big data projects. Employing traditional data processing techniques lacks the invention of useful information because of the main characteristics of big data, including high volume, velocity, and variety. Data processing can be benefited by process mining, and in turn, helps to increase the productivity of the big data projects. In this paper, the capability of process mining in big data RE to discover valuable insights and business values from event logs and processes of the systems has been highlighted. Also, the proposed big data requirements engineering framework, named REBD, helps software requirements engineers to eradicate many challenges of big data RE.

Keywords

Big Data, Requirements Engineering, Requirements Elicitation, Data Processing, Knowledge Discovery, Process Mining.
User
Notifications
Font Size

  • M. Volk, N. Jamous, and K. Turowski, “Ask the right questions: requirements engineering for the execution of big data projects,” presented at the 23rd Americas Conference on Information Systems, SIGITPROJMGMT, Boston, MA, Aug. 2017, pp 1-10.

  • J. Dick, E. Hull, and K. Jackson, Requirements Engineering, 4th edition, Springer International Publishing, 2017.

  • P.A. Laplante, Requirements Engineering for Software and Systems, 3rd Edition. Auerbach Publications (T&F), 20171024. VitalBook file, 2017.

  • D. Arruda, “Requirements engineering in the context of big data applications,” ACM SIGSOFT Software Engineering Notes, 43(1): 1-6, Mar. 2018.

  • A.G. Khan, et al., “Does software requirement elicitation and personality make any relation?” Journal of Advanced Research in Dynamical and Control Systems. 11. 1162-1168, 2019.

  • T.A. Bahill and S.J. Henderson, “Requirements development, verification, and validation exhibited in famous failures,” Systems Engineering, 8(1): 1–14, 2005.

  • Pulse of the Profession 2018: Success in Disruptive Times, 2018.

  • C. Gopal, et al., “Worldwide big data and analytics software forecast, 2019–2023,” IDC Market Analysis, US44803719, Sept. 2019.

  • I. Noorwali, D. Arruda, and N. H. Madhavji, “Understanding quality requirements in the context of big data systems,” presented at the 2nd International Workshop on Big Data Software Engineering (BIGDSE), Austin, USA, May 2016, pp. 76-79.

  • D. Arruda and N.H. Madhavji, “State of requirements engineering research in the context of big data applications,” presented at the 24th International Working Conference on Requirements Engineering: Foundation for Software Quality (REFSQ), Utrecht, The Netherlands, Mar. 2018, pp 307-323.

  • M. Volk, D. Staegemann, M. Pohl, and K. Turowski, “Challenging big data engineering: positioning of current and future development,” presented at the 4th International Conference on Internet of Things, Big Data and Security (IoTBDS), Heraklion, Greece, May. 2019, pp 351-358.

  • H.H. Altarturi, K. Ng, M.I.H. Ninggal, A.S.A. Nazri, and A.A.A. Ghani, “A requirement engineering model for big data software,” presented at the 2nd International Conference on Big Data Analysis (ICBDA), Beijing, China, Mar. 2017, pp 111-117.

  • K.J. Cios, W.Pedrycz, R.W.Swiniarski, L. Kurgan, Data Mining: a Knowledge Discovery Approach. Springer, 2010.

  • W. van der Aalst, Process Mining in Action: Principles, Use Cases and Outlook, Springer, 1st ed. 2020.

  • M. Ghasemi, “What requirements engineering can learn from process mining,” presented at the 1st International Workshop on Learning from other Disciplines for Requirements Engineering (D4RE), Banff, Canada, Aug. 2018, pp 8-11.

  • S. Kourla, E. Putti, and M. Maleki, “REBD: A Conceptual Framework for Big Data Requirements Engineering,” 7th International Conference on Computer Science and Information Technology (CSIT 2020), Helsinki, Finland, June 2020.

  • D. Arruda, N.H. Madhavji, and I. Noorwali, “A validation study of a requirements engineering artefact model for big data software development projects,” presented at the 14th International Conference on Software Technologies (ICSOFT), Prague, Czech Republic, Jul. 2019, pp 106-116.

  • B. Jan, et al., “Deep learning in big data analytics: a comparative study,” Journal of Computer Electrical Engineering, 75(1): 275-287, 2019.

  • A. Haldorai, A. Ramum, and C. Chow, “Editorial: big data innovation for sustainable cognitive computing,” Mobile Netw Application Journal, 24(1): 221-226, 2019.

  • R.H. Hariri, E.M. Fredericks, and K.M. Bowers, “Uncertainty in big data analytics: survey, opportunities, and challenges,” Journal of Big Data, 6(1), 2019.

  • J. Eggers and A. Hein, “Turning Big Data Into Value: A Literature Review on Business Value Realization From Process Mining,” presented at the Twenty-Eighth European Conference on Information Systems (ECIS2020), Marrakesh, Morocco, Jun. 2020.

  • K.K Azumah, S. Kosta, and L.T. Sørensen, “Load Balancing in Hybrid Clouds Through Process Mining Monitoring,” Internet and Distributed Computing Systems, Springer, Cham, 2019, pp 148-157.

  • S. Ramachandran, S. Dodda, and L. Santapoor, “Overcoming Social Issues in Requirements Engineering,” in Meghanathan N., Kaushik B.K., Nagamalai D. (eds) Advanced Computing, Springer, Berlin, Heidelberg, 2011, pp 310-324.

  • M. Batra and A. Bhatnagar, “Requirements Elicitation Technique Selection: A Five Factors Approach,” International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, 8(5C):1332-3141, India, May 2019.

  • K. Lyko, M. Nitzschke, and AC. Ngonga Ngomo, “Big data acquisition,” in New Horizons for a Data-Driven Economy, Springer, Cham, 2016, pp 35-61.

  • M. Suhaib, “Conflicts Identification among Stakeholders in Goal Oriented Requirements Engineering Process,” International Journal of Innovative Technology and Exploring Engineering (IJITEE), 2019.

  • C. Yao, “The Effective Application of Big Data Analysis in Supply Chain Management,” IOP Conference Series: Materials Science and Engineering Mar. 2020.

  • X. Han, X. Wang, and H. Fan, “Requirements analysis and application research of big data in power network dispatching and planning,” presented at the 3rd Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing Shi, China, Oct. 2017, pp 663-668.

  • J. Kelly, M.E. Jennex, K. Abhari, A. Durcikova, and E. Frost, “Data in the Wild: A KM Approach to Collecting Census Data Without Surveying the Population and the Issue of Data Privacy,” in Knowledge Management, Innovation, and Entrepreneurship in a Changing World, IGI Global, 2020, pp. 286-312.

  • D. Pandey, U. Suman, and A. Ramani, “A Framework for Modelling Software Requirements,” International Journal of Computer Science Issues. 8(3):164, 2011.

  • O. Marbán, G. Mariscal, and J. Segovia, “A Data Mining & Knowledge Discovery Process Model,” in book Data Mining and Knowledge Discovery in Real Life Applications, IntechOpen, 2009.

  • O. Dogan, J.L. Bayo-Monton, C. Fernandez-Llatas, and B. Oztaysi, “Analyzing of gender behaviors from paths using process mining: A shopping mall application,” Sensors, 19(3), 557, 2019.

  • NH. Madhavji, A. Miranskyy, and K. Kontogiannis, “Big picture of big data software Engineering: with example research challenges,” presented at the IEEE/ACM 1st International Workshop on Big Data Software Engineering (BIGDSE), Florence, Italy, May 2015, pp 11-14.


Abstract Views: 228

PDF Views: 115




  • Importance of Process Mining for Big Data Requirements Engineering

Abstract Views: 228  |  PDF Views: 115

Authors

Sandhya Rani Kourla
Department of Electrical and Computer Engineering and Computer Science, University of Detroit Mercy, Detroit, MI, 48221, United States
Eesha Putti
Department of Electrical and Computer Engineering and Computer Science, University of Detroit Mercy, Detroit, MI, 48221, United States
Mina Maleki
Department of Electrical and Computer Engineering and Computer Science, University of Detroit Mercy, Detroit, MI, 48221, United States

Abstract


Requirements engineering (RE), as a part of the project development life cycle, has increasingly been recognized as the key to ensure on-time, on-budget, and goal-based delivery of software projects. RE of big data projects is even more crucial because of the rapid growth of big data applications over the past few years. Data processing, being a part of big data RE, is an essential job in driving big data RE process successfully. Business can be overwhelmed by data and underwhelmed by the information so, data processing is very critical in big data projects. Employing traditional data processing techniques lacks the invention of useful information because of the main characteristics of big data, including high volume, velocity, and variety. Data processing can be benefited by process mining, and in turn, helps to increase the productivity of the big data projects. In this paper, the capability of process mining in big data RE to discover valuable insights and business values from event logs and processes of the systems has been highlighted. Also, the proposed big data requirements engineering framework, named REBD, helps software requirements engineers to eradicate many challenges of big data RE.

Keywords


Big Data, Requirements Engineering, Requirements Elicitation, Data Processing, Knowledge Discovery, Process Mining.

References