[AISWorld] Call for Chapters - Springer book on "Development Methodologies for Big Data Analytics Systems – plan-driven, agile, hybrid, lightweight approaches"

Mon May 16 18:49:33 EDT 2022

Call for Book Chapters<http://x3620a-labdc.uaa.mx:8080/web/org-cagesti/avisos-libros>

"Development Methodologies for Big Data Analytics Systems
– plan-driven, agile, hybrid, lightweight approaches"
<http://x3620a-labdc.uaa.mx:8080/web/org-cagesti/avisos-libros>

Springer Nature - Book Series:
Transactions on Computational Science & Computational Intelligence
Series Editor:  Prof. Hamid R. Arabnia
https://www.springer.com/series/11769

Chapter proposal (400-500 words) period: May 31, 2022 – June 30, 2022
Full chapter submission deadline: August 31, 2022

Book Co-Editors:
Prof. Manuel Mora, Autonomous University of Aguascalientes, Mexico
Prof. Fen Wang, Central Washington University, USA
Prof. Jorge Marx Gómez, University of Oldenburg, Germany
Prof. Hector Duran-Limon, University of Guadalajara, Mexico

CONTEXT
Big Data Analytics (BDA) systems are software systems developed to provide valuable insights to decision-makers exploiting Big Data sources (Laney, 2001; Davoudian & Liu, 2020). 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).
BDA systems are the main outcomes of the new Data Science discipline (Cao, 2017; 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). Additionally, it has been recently identified that the systematic development of BDA systems has not been usually pursued by organizations, and despite the adaptation of a few comprehensive development methodologies for Data Analytics systems (Martinez et al; 2021) such as CRISP-DM, SEMMA, and KDD, many failed BDA system development projects are frequent (Davenport & Malone, 2021). From a Systems and Software 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 for comply the business expectations of BDA systems (Martinez-Plumed et al., 2019; Haakman et al., 2021).
This co-edited book pursues to advance on this relevant current research problem through the study of development methodologies based on plan-driven, agile, hybrid, and lightweight approaches (Beck, 1999; Boehm & Turner, 2003; Sutherland, 2010; ISO/IEC, 2011; Martinez-Plumed et al., 2019; Haakman et al., 2021). Given that agile and lightweight development practices require 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, and the availability of plan-driven, agile, and hybrid lightweight 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 co-edited book.

TOPICS OF INTEREST
This call for book chapters invites researchers from the disciplines of Data Science and Software Engineering to submit high-quality conceptual and/or empirical research chapters on plan-driven, agile, hybrid, and/or lightweight development methodologies for BDA systems suitable to be used for large-, medium-, and small-sized business organizations. This book will be organized into five sections:
•    Section I – Foundations on Big Data Analytics Systems
o    Topics: Big Data Analytics foundations; Big Data Science foundations; Big Data Analytics Systems Frameworks; Big Data Analytics Systems Architectures; Big Data Analytics Tools and Platforms; Big Data Analytics Computational Techniques.
•    Section II – Plan-Driven Development Methodologies for Big Data Analytics Systems
o    Topics: Review of specific plan-driven methodologies such as CRISP-DM, SEMMA, KDD, and generic ones used for Big Data Analytics Systems as RUP, MBASE, and MSF.
•    Section III – Emergent Agile, Hybrid, and Lightweight Development Methodologies for Big Data Analytics Systems
o    Topics: Review of specific agile, hybrid, and lightweight methodologies based on Scrum, XP, ISO/IEC 29110, and Microsoft TDSP and combinations from them.
•    Section IV – Cases Studies of Big Data Analytics Systems Projects
o    Topics: Real-world applications in diverse domains such as Healthcare, Marketing, Financial, Education, Sports, Retail, Logistics, Manufacturing, among others.
•    Section V – Challenges and Future Directions on Big Data Analytics Systems Projects
o    Topics: Review of challenges, current problems and limitations, trends, and future directions.

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. (2017). Data science: challenges and directions. Communications of the ACM, 60(8), 59-68.
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.
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.
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.
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.
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
Chapter proposal period:                   May 31, 2022 – June 30, 2022
Full chapter submission deadline:      August 31, 2022
First chapter review decision:           September 30, 2022
Chapter resubmission deadline:        October 31, 2022
Final chapter review decision:          November 30, 2022
Camera-ready chapter submission:  December 15, 2022
Book publication:                             During 2023

SUBMISSION PROCESS
• Please submit your initial chapter proposal including title, authors, and their affiliations, abstract (300-400 words), and a list of main 7-10 references. Please submit it to Prof. Manuel Mora at jose.mora at edu.uaa.mx, during the period of May 31, 2022 – June 30, 2022. Book co-editors will review it and recommend its full chapter development in the case of a satisfactory alignment with the planned content of this book. We kindly ask it to avoid duplicated topic submissions.
• Please submit your full chapter – among 5,500-7,500 words- to Prof. Manuel Mora at jose.mora at edu.uaa.mx, on or before August 31, 2022. All submitted chapters will be reviewed in a blind mode by three evaluators – two assigned adequately from the same set of book chapter authors and one from the book editors-. The editorial decision can be: accepted with minor changes, conditioned to major changes, or rejected.
• Conditioned chapters will have an additional opportunity for being improved and re-evaluated. In the second evaluation, a definitive editorial decision of the chapter – either accepted or rejected -will be reported.
• All the accepted chapters must be submitted according to the Editorial publishing format rules timely using the MathPhys style of references. A zip package of instructions for authors will be emailed once received the chapter proposal.

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Prof. Dr. José Manuel Mora Tavarez
Depto. de Sistemas de Información
Centro de Ciencias Básicas
Universidad Autónoma de Aguascalientes
Ave. Universidad 940
Aguascalientes, AGS. México 20131
Email: jose.mora at edu.uaa.mx
<https://www.researchgate.net/profile/Manuel_Mora>
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<https://scholar.google.com.mx/citations?user=97rTgbkAAAAJ&hl=en&oi=sra>
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