A Comprehensive Review of the Main Heavyweight and Lightweight SDLC for Big Data Analytics Systems (BDAS)
Big Data Analytics Systems (BDAS) are software systems developed with descriptive, predictive, or prescriptive decision-making purposes, and currently are implemented in diverse domains – i.e. Healthcare, Finance, and Manufacturing, among others -. BDAS emerged by the joint availability o...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11028107/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850220123221131264 |
|---|---|
| author | David Alejandro Montoya-Murillo Sergio Galvan-Cruz Manuel Mora Estela Lizbeth Munoz Andrade |
| author_facet | David Alejandro Montoya-Murillo Sergio Galvan-Cruz Manuel Mora Estela Lizbeth Munoz Andrade |
| author_sort | David Alejandro Montoya-Murillo |
| collection | DOAJ |
| description | Big Data Analytics Systems (BDAS) are software systems developed with descriptive, predictive, or prescriptive decision-making purposes, and currently are implemented in diverse domains – i.e. Healthcare, Finance, and Manufacturing, among others -. BDAS emerged by the joint availability of Analytics techniques and affordable sources of massive internal and external datasets. However, despite the advanced technological progress – i.e. on BDAS algorithms, languages and platforms-, their development has been mainly through uncomplete ad-hoc or old rigorous heavyweight System Development Life Cycles (SDLC). Nowadays, the business competitive environment demands lightweight and agile SDLC, and initial ones have emerged for BDAS. However, studies comparing heavyweight vs lightweight BDAS SDLC are still scarce in the literature. This research, thus, addresses this knowledge gap, and report a comparative review between the main heavyweight (KDD, SEMMA, CRISP-DM, BDPL) and emergent lightweight (ASUM, TDSP, DDSL) SDLC for BDAS, selected through a Selective Systematic Literature Review (SSLR) research method for the 2000-2023 period. For this aim, a Pro Forma of the ISO/IEC 29110 standard – Basic Profile –is used to examine their conceptual structure – i.e. roles, phases-activities, products -. This comparative review provides theoretical and practical insights for discriminating both approaches for BDAS development and calls for further conceptual and empirical research. |
| format | Article |
| id | doaj-art-c464bfb65efd4af48e8359a9460a757f |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c464bfb65efd4af48e8359a9460a757f2025-08-20T02:07:10ZengIEEEIEEE Access2169-35362025-01-011310132810136710.1109/ACCESS.2025.357797011028107A Comprehensive Review of the Main Heavyweight and Lightweight SDLC for Big Data Analytics Systems (BDAS)David Alejandro Montoya-Murillo0https://orcid.org/0009-0005-9834-9853Sergio Galvan-Cruz1https://orcid.org/0000-0001-7453-3115Manuel Mora2https://orcid.org/0000-0003-1631-5931Estela Lizbeth Munoz Andrade3https://orcid.org/0000-0003-4182-5044Basic Sciences Center, Autonomous University of Aguascalientes, Aguascalientes, MexicoBasic Sciences Center, Autonomous University of Aguascalientes, Aguascalientes, MexicoBasic Sciences Center, Autonomous University of Aguascalientes, Aguascalientes, MexicoBasic Sciences Center, Autonomous University of Aguascalientes, Aguascalientes, MexicoBig Data Analytics Systems (BDAS) are software systems developed with descriptive, predictive, or prescriptive decision-making purposes, and currently are implemented in diverse domains – i.e. Healthcare, Finance, and Manufacturing, among others -. BDAS emerged by the joint availability of Analytics techniques and affordable sources of massive internal and external datasets. However, despite the advanced technological progress – i.e. on BDAS algorithms, languages and platforms-, their development has been mainly through uncomplete ad-hoc or old rigorous heavyweight System Development Life Cycles (SDLC). Nowadays, the business competitive environment demands lightweight and agile SDLC, and initial ones have emerged for BDAS. However, studies comparing heavyweight vs lightweight BDAS SDLC are still scarce in the literature. This research, thus, addresses this knowledge gap, and report a comparative review between the main heavyweight (KDD, SEMMA, CRISP-DM, BDPL) and emergent lightweight (ASUM, TDSP, DDSL) SDLC for BDAS, selected through a Selective Systematic Literature Review (SSLR) research method for the 2000-2023 period. For this aim, a Pro Forma of the ISO/IEC 29110 standard – Basic Profile –is used to examine their conceptual structure – i.e. roles, phases-activities, products -. This comparative review provides theoretical and practical insights for discriminating both approaches for BDAS development and calls for further conceptual and empirical research.https://ieeexplore.ieee.org/document/11028107/Big data analytics systems (BDAS)ISO/IEC 29110 standard—basic profilesystem development life cycle (SDLC)lightweight and heavyweight SDLCKDDSEMMA |
| spellingShingle | David Alejandro Montoya-Murillo Sergio Galvan-Cruz Manuel Mora Estela Lizbeth Munoz Andrade A Comprehensive Review of the Main Heavyweight and Lightweight SDLC for Big Data Analytics Systems (BDAS) IEEE Access Big data analytics systems (BDAS) ISO/IEC 29110 standard—basic profile system development life cycle (SDLC) lightweight and heavyweight SDLC KDD SEMMA |
| title | A Comprehensive Review of the Main Heavyweight and Lightweight SDLC for Big Data Analytics Systems (BDAS) |
| title_full | A Comprehensive Review of the Main Heavyweight and Lightweight SDLC for Big Data Analytics Systems (BDAS) |
| title_fullStr | A Comprehensive Review of the Main Heavyweight and Lightweight SDLC for Big Data Analytics Systems (BDAS) |
| title_full_unstemmed | A Comprehensive Review of the Main Heavyweight and Lightweight SDLC for Big Data Analytics Systems (BDAS) |
| title_short | A Comprehensive Review of the Main Heavyweight and Lightweight SDLC for Big Data Analytics Systems (BDAS) |
| title_sort | comprehensive review of the main heavyweight and lightweight sdlc for big data analytics systems bdas |
| topic | Big data analytics systems (BDAS) ISO/IEC 29110 standard—basic profile system development life cycle (SDLC) lightweight and heavyweight SDLC KDD SEMMA |
| url | https://ieeexplore.ieee.org/document/11028107/ |
| work_keys_str_mv | AT davidalejandromontoyamurillo acomprehensivereviewofthemainheavyweightandlightweightsdlcforbigdataanalyticssystemsbdas AT sergiogalvancruz acomprehensivereviewofthemainheavyweightandlightweightsdlcforbigdataanalyticssystemsbdas AT manuelmora acomprehensivereviewofthemainheavyweightandlightweightsdlcforbigdataanalyticssystemsbdas AT estelalizbethmunozandrade acomprehensivereviewofthemainheavyweightandlightweightsdlcforbigdataanalyticssystemsbdas AT davidalejandromontoyamurillo comprehensivereviewofthemainheavyweightandlightweightsdlcforbigdataanalyticssystemsbdas AT sergiogalvancruz comprehensivereviewofthemainheavyweightandlightweightsdlcforbigdataanalyticssystemsbdas AT manuelmora comprehensivereviewofthemainheavyweightandlightweightsdlcforbigdataanalyticssystemsbdas AT estelalizbethmunozandrade comprehensivereviewofthemainheavyweightandlightweightsdlcforbigdataanalyticssystemsbdas |