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Software Quality Journal - CfP: Special Issue on Development Methodologies for Big Data Analytics Systems – plan-driven, lightweight and agile approaches

Guest Editors

Manuel Mora, Autonomous University of Aguascalientes, Mexico

Jorge Marx Gómez, University of Oldenburg, Germany

Alena Buchalcevova, Prague University of Economics and Business, Czech Republic

Fen Wang, Central Washington University, USA

CONTEXT

Big Data Analytics (BDA) systems are software systems developed to provide valuable insights to decision-makers exploiting Big Data sources (Chen & Zhang, 2014) and Analytics processing mechanisms (Larson & Chang, 2016; Delen & Ram, 2018; Davoudian & Liu, 2020; Phillips-Wren et al. 2021). BDA systems can be developed for descriptive, predictive or prescriptive purposes (Delen & Ram, 2018).

BDA systems are the main outcomes of the new Analytics Data Science discipline (Cao, 2017a; 2017b; Weihs & Ikstadt, 2018; Arabnia et al., 2020), that emerged as a result of the convergence of Statistics, Computer Science, and Business Intelligent Analytics with the practical aim to provide concepts, models, methods and tools required for exploiting the wide variety, volume, and velocity of available business internal and external data – i.e. Big Data – to lately provide decision-making value to decision-makers (Mikalef et al., 2018). “Through Data Science, one can identify relevant issues, collect data from various data sources, integrate the data, conclude solutions, and communicate the results to improve and enhance organizations’ decisions and deliver value to users and organizations” (Arabnia et al., 2020; pp. V).

BDA systems have been mainly developed and used for large business organizations due to the nature of the implicated human, technological, organizational, and data resources required for such developments (Maroufkhani et al., 2020; Davenport & Bean, 2022). However, despite several specific cases of successful BDA systems have been reported in the literature (Davenport, 2006) in diverse domains such as Healthcare, Logistics, Finance, Marketing, Retail, and Education in the last decade (Watson, 2014),  it has been identified that the systematic development of BDA systems is not usually pursued by organizations.  Furthermore, whereas the adaptation of a few comprehensive development methodologies for Data Analytics systems (Martinez et al; 2021) such as CRISP-DM, SEMMA, and KDD have been relatively useful, still frequent failed BDA system development projects are reported (Davenport & Malone, 2021). As Davenport and Malore (2021; p. 2) indicate “It is becoming increasingly clear that deployment—getting analytical and artificial intelligence (AI) systems fully and successfully implemented within organizations—is becoming one of the most critical disciplines at all phases of a business data science project”. Similarly Martinez et al. (2021; p. 4) reported the existence of a critical problem for Big Data Analytics Systems projects as “the lack of a coherent methodology” as well as the need of counting with an “explicit process methodology for developing data science projects”. Furthermore, from a business management perspective, a core survey collected answers from  3,000 respondents working in 29 types of industries located in 112 countries, and identified that only 10% of them claimed financial benefits despite the large investments done in Big Data projects (Ransbotham et al., 2020).

From a Software Systems Engineering perspective, the utilization of software processes and development methodologies – plan-driven, agile, hybrid, and lightweight types – are necessary to fit the expected “Iron Triangle” metrics of schedule, budget, and quality (Humphrey, 2005; Agarwal & Rathod, 2006; Humphrey et al., 2007). Hence, initial top research has realized the need to incorporate software and systems engineering development methods to comply with the business expectations of BDA systems (Delen & Ram, 2018; Martinez-Plumed et al., 2019; Davoudian & Liu, 2020; Haakman et al., 2021). As Delen and Ram (2018) indicate, innovative and best-practices development approaches for designing and building BDA systems are required as well as the development of new and improved algorithms. Haakman et al. (2021; p. 2) claim a similar statement that “like normal software applications, these projects need to be planned, tested, debugged, deployed, maintained, and integrated into complex systems”. . Related initial research efforts have been reported to introduce quality models for Machine Learning components included in some BDA systems (Siebert et al., 2021). Studies on open source and proprietary technological platforms and tools for developing BDA systems are also required given the demand of minimal IT resources required for the adequate performance of such systems (Pääkkönen & Pakkala, 2015; Gökalp et al., 2017).

Consequently, this special issue pursues to advance on the first relevant research problem – i.e. lack of systematic development methodologies - through the study of development methodologies based on plan-driven, lightweight and agile approaches (Beck, 1999; Boehm & Turner, 2003; Sutherland, 2010; ISO/IEC, 2011; Delen & Ram, 2018; Martinez-Plumed et al., 2019; Davoudian & Liu, 2020; Haakman et al., 2021; De Silva & Alahakoon, 2022). Given that lightweight and agile development practices are usually performed by small development teams – between 3 to 10 people- and/or very small entities (VSE) from 5 up to 25 people and mainly address projects of short-term scope – between 1 to 6 months-, and small budgets, these plausible practices are highly suitable to be used for small and medium-sized business (SMBs). Consequently, SMBs can also take advantage of their available Big Data sources for SMBs contexts.

Hence, due to the global relevance and business impact of BDA systems, the vast availability of Big Data sources, the richness and complexity of Analytics processing mechanisms, the affordability of open source and proprietary technological platforms for BDA systems, and the availability of general-purpose plan-driven, lightweight and agile development methodologies, we expect that researchers addressing the convergence of Big Data Analytics and Systems and Software Engineering sciences submit their high-quality contributions to this special issue.

TOPICS OF SUBMISSIONS

This call for papers invites researchers from the disciplines of Analytics Data Science and Software and Systems Engineering to submit high-quality conceptual, simulation-based and empirical research manuscripts on plan-driven, lightweight, and agile software system development methodologies for BDA systems. High-quality articles that develop and evaluate methods, methodologies, platforms and tools for helping business organizations to develop systematically BDA systems are asked.

The main expected topics of submissions are:

  • Review of plan-driven development methodologies for Big Data Analytics Systems such as CRISP-DM, SEMMA, KDD, and generic ones that are also used for Big Data Analytics Systems such as RUP, MBASE, and MSF. 
  • Review of emergent agile, and lightweight development methodologies for Big Data Analytics Systems based on Scrum, XP, ISO/IEC 29110, Microsoft TDSP, and combinations from them. 
  • Comparative reviews of Big Data Analytics Systems Projects using plan-driven, agile, or lightweight development methodologies. 
  • Review, comparison and introduction of specific Software Systems development techniques and tools for project management, risk analysis, configuration management, requirements, architectural design, detailed design, coding and testing, deployment, and maintenance stages. 
  • Cases Studies of Big Data Analytics Systems Projects using plan-driven, agile, or lightweight development methodologies in real-world applications in diverse domains such as Healthcare, Marketing, Financial, Education, Sports, Retail, Logistics, and Manufacturing, among others.
  • Review and comparative studies of open source and proprietary technological platforms and development tools for BDA systems.
  • Review of challenges, current problems and limitations, trends, and future directions on Big Data Analytics Systems Projects.

REFERENCES

Agarwal, N., & Rathod, U. (2006). Defining ‘success’ for software projects: An exploratory revelation. International Journal of Project Management, 24(4), 358-370.

Arabnia, H. R., Daimi, K., Stahlbock, R., Soviany, C., Heilig, L., & Brüssau, K. (Eds.). (2020). Principles of Data Science. Springer.

Beck, K. (1999). Embracing change with extreme programming. Computer, 32(10), 70-77.

Cao, L. (2017a). Data science: challenges and directions. Communications of the ACM, 60(8), 59-68.

Cao, L. (2017b). Data science: a comprehensive overview. ACM Computing Surveys (CSUR), 50(3), 1-42.

Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347.

Boehm, B., & Turner, R. (2003). Using risk to balance agile and plan-driven methods. Computer, 36(6), 57-66.

Davenport, T. H. (2006). Competing on analytics. Harvard Business Review, 84(1), 98-107.

Davenport, T., & Malone, K. (2021). Deployment as a Critical Business Data Science Discipline. Harvard Data Science Review. https://doi.org/10.1162/99608f92.90814c32

Davenport, T. & Bean, R. (2022). The Quest to Achieve Data-Driven Leadership: A Progress Report on the State of Corporate Data Initiatives – Foreword. Special Report, New Advantage Partners.

Davoudian, A., & Liu, M. (2020). Big data systems: A software engineering perspective. ACM Computing Surveys (CSUR), 53(5), 1-39.

Delen, D., & Ram, S. (2018). Research challenges and opportunities in business analytics. Journal of Business Analytics, 1(1), 2-12.

De Silva, D., & Alahakoon, D. (2022). An artificial intelligence life cycle: From conception to production. Patterns, 100489.

Gökalp, M. O., Kayabay, K., Zaki, M., Koçyiğit, A., Eren, P. E., & Neely, A. (2017). Big-Data Analytics Architecture for Businesses: a comprehensive review on new open-source big-data tools. Cambridge Service Alliance: Cambridge, UK.

Haakman, M., Cruz, L., Huijgens, H., & van Deursen, A. (2021). AI lifecycle models need to be revised. Empirical Software Engineering, 26(5), 1-29.

Humphrey, W. S. (2005). The software process: Global goals. In Software Process Workshop (pp. 35-42). Springer, Berlin, Heidelberg.   

Humphrey, W. S., Konrad, M. D., Over, J. W., & Peterson, W. C. (2007). Future directions in process improvement. Crosstalk–The Journal of Defense Software Engineering, 20(2), 17-22.

Informs.org (2019). Why most big data analytics projects fail. ----------------------------------------------------------------https://pubsonline.informs.org/do/10.1287/orms.2019.06.08/full/

ISO/IEC (2011). ISO/IEC TR 29110-5-1-2:2011 Software Engineering - Lifecycle Profiles for Very Small Entities (VSES) - Part 5-1-2: Management and Engineering Guide: Generic Profile Group: Basic Profile. ISO - International Organization for Standardization.

Laney, D. (2001). 3-D Data Management: Controlling Data Volume, Velocity and Variety. META Group Research File 949.

Larson, D., & Chang, V. (2016). A review and future direction of agile, business intelligence, analytics and data science. International Journal of Information Management, 36(5), 700-710.

Maroufkhani, P., Ismail, W. K. W., & Ghobakhloo, M. (2020). Big data analytics adoption model for small and medium enterprises. Journal of Science and Technology Policy Management, 11(4), 483-513.

Martínez-Plumed, F., Contreras-Ochando, L., Ferri, C., Orallo, J. H., Kull, M., Lachiche, N., ... & Flach, P. A. (2019). CRISP-DM twenty years later: From data mining processes to data science trajectories. IEEE Transactions on Knowledge and Data Engineering, 33(8), 3048-3061.

Martinez, I., Viles, E., & Olaizola, I. G. (2021). Data science methodologies: Current challenges and future approaches. Big Data Research, 24, 100183.

Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2018). Big data analytics capabilities: a systematic literature review and research agenda. Information Systems and e-Business Management, 16(3), 547-578.

Pääkkönen, P., & Pakkala, D. (2015). Reference architecture and classification of technologies, products and services for big data systems. Big data research, 2(4), 166-186.

Phillips-Wren, G., Daly, M., & Burstein, F. (2021). Reconciling business intelligence, analytics and decision support systems: More data, deeper insight. Decision Support Systems, 146, 113560.

Ransbotham, S., Khodabandeh, S., Kiron, D., Candelon, F., Chu, M., & LaFountain, B. (2020). Expanding AI’s impact with organizational learning. MIT Sloan Management Review and Boston Consulting Group, 1-15.

Siebert, J., Joeckel, L., Heidrich, J., Trendowicz, A., Nakamichi, K., Ohashi, K., Namba, I., Yamamoto, R., & Aoyama, M. (2021). Construction of a quality model for machine learning systems. Software Quality Journal, 30(2), 307-335.

Sutherland, J. (2010). Jeff Sutherland’s Scrum Handbook. Boston: Scrum Training Institute.

Watson, H. J. (2014). Tutorial: Big data analytics: Concepts, technologies, and applications. Communications of the Association for Information Systems, 34(1), 1247-1268.

Weihs, C., & Ickstadt, K. (2018). Data science: the impact of statistics. International Journal of Data Science and Analytics, 6(3), 189-194.

IMPORTANT DATES

First submission deadline: August 31, 2023

SUBMISSION GUIDELINES

All submitted papers must be original and unpublished research in concordance with the quality standards of the Software Quality Journal.  All manuscripts and any supplementary material should be submitted via the Software Quality Journal submission website at: https://www.springer.com/journal/11219

Guide for prospective authors can be reached at: https://www.springer.com/journal/11219/submission-guidelines

SHORT BIOS OF GUEST EDITORS

Prof. Dr. Manuel Mora is a full-time Professor in the Information Systems Department at the Autonomous University of Aguascalientes (UAA), Mexico. Dr. Mora holds an M.Sc. in Computer Sciences (Artificial Intelligence area, 1989) from Monterrey Tech (ITESM), and an Eng.D. in Engineering (Systems Engineering area, 2003) from the National Autonomous University of Mexico (UNAM). He has published over 90 research papers in international top conferences, research books, and journals such as IEEE-TSMC, European Journal of Operational Research, International Journal of Information Management, Engineering Management Journal, Int. Journal of Information Technology and Decision Making, Information Technology for Development, Int. Journal in Software Engineering and Knowledge Engineering,  Computer Standards & Interfaces, Software and Systems Modeling, Expert Systems, Software Quality Journal, and Journal of Organizational Computing and Electronic Commerce. Dr. Mora has also co-edited 6 research-oriented books for Springer and IGI publishers. Dr. Mora is a senior member of ACM (since 2008), an SNI at Level II, and serves in the ERB of several international journals indexed by the Emergent Source Citation Index focused on decision-making support systems (DMSS) and IT services systems. Website: https://www.researchgate.net/profile/Manuel-Mora-7

Prof. Dr. Jorge Marx Gómez studied Computer Engineering and Industrial Engineering at the University of Applied Sciences Berlin (Technische Fachhochschule Berlin). He was a lecturer and researcher at the Otto-von-Guericke-Universität Magdeburg (Germany) where he also obtained a Ph.D. degree in Business Information Systems with the work Computer-based Approaches to Forecast Returns of Scrapped Products to Recycling. From 2002 till 2003 he was a visiting professor for Business Informatics at the Technical University of Clausthal (TU Clausthal, Germany). In 2004 he received his habilitation for the work Automated Environmental Reporting through Material Flow Networks at the Otto-von-Guericke-Universität Magdeburg. In 2005 he became a full professor and chair of Business Information Systems at the Carl von Ossietzky University Oldenburg (Germany). His research interests include Very Large Business Applications, Business Information Systems, Federated ERP-Systems, Business Intelligence, Data Warehousing, Interoperability, and Environmental Management Information Systems. Website: https://uol.de/en/vlba/team/prof-dr-ing-habil-jorge-marx-gomez

Prof. Dr. Alena Buchalcevova graduated in 1981 at Prague University of Economics, Faculty of Management in the specialization Information Systems Management. Since then she has been working at the faculty and at present she is associate professor at the Department of Information technologies. Her research field is object-oriented analysis, design and programming, software process improvement, software quality assurance, management of business informatics, Enterprise Architecture, and Green ICT. She is a programme committee member of several international information systems and software engineering conferences (Software development and object technologies, Information System Development, CONFENIS, EuroSPI, WESOA, IBIMA) and reviewer of several journals (Journal of Systems Integration, Journal of Systemics, Cybernetics and Informatics, Information Systems Frontiers, Malaysian Journal of Computer Science, Software Quality Journal ). Since 2008 she has been involved in the working group WG24 within ISO/IEC JTC1/SC7. She is an author or co-author of 10 books, many journal articles, and conference papers. Website: https://nb.vse.cz/~buchalc/eindex.htm

Prof. Dr. Fen Wang is a Full Professor in the Information Technology & Administrative Management Department at Central Washington University (CWU). Before joining CWU, Prof. Wang was an Assistant Professor and Director of the Management Information Systems (MIS) program at the Eastern Nazarene College in Massachusetts. Prof. Wang holds a B.S. in MIS, an M.S., and a Ph.D. in Information Systems from the University of Maryland Baltimore County. Prof. Wang has brought over ten years of professional and research experience in information technology management to her students. Her research focuses on intelligent decision support technologies and E-business strategies. These efforts have resulted in contributions to the applied literature on information technologies that have been well-received in the research community. Prof. Wang has published over thirty papers in internationally-circulated journals and book series, including the International Journal of E-Business Research (IJEBR), International Journal of Decision Support System Technology (IJDSST), Intelligent Decision Technologies (IDT), Information Technology for Development (ITFD), and the Encyclopedia of E-Commerce, E-Government and Mobile Commerce.  Prof. Wang has also consulted for a variety of public and private organizations on IT management and applications. Website: https://www.researchgate.net/profile/Fen-Wang-23

Submitted papers should present original, unpublished work, relevant to one of the topics of the Special Issue.  All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least two independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process. 

Before submitting, it is also recommended that you visit the following webpages to familiarize yourself with various aspects of the editor role: Springer Nature Code of Conduct (this opens in a new tab) and  Springer Nature publishing and editorial policies (this opens in a new tab).

Peer review policy 

The Software Quality Journal adheres to the standard Peer Review Policy, Process and Guidance (this opens in a new tab) as outlined by Springer under Editorial Policies (this opens in a new tab) in the Information for Journal Authors (this opens in a new tab) web page.

  • All special issue papers must be prepared in accordance with the Journal guidelines: https://www.springer.com/journal/11219/submission-guidelines.Submitted papers should present original, unpublished work, relevant to one of the topics of the special issue. 
  • All manuscripts will be subject to the Journal’s rigorous peer review policy, by at least two independent reviewers. This evaluation will cover the following aspects, but will not be limited to: relevance, significance of contribution to the field, technical quality, scholarship, and quality of presentation. 
  • Conference-based special issue papers are reviewed by the Program Chairs and Program Committee members of the respective conference, with help from external reviewers selected by them. Conference-based special issue papers are expected to have 30-40% new material to be publishable in the journal.
  • It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the peer review process.


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