Sprint Planning and Al/Ml: How to Balance Iterations with Data Complexity
DOI:
https://doi.org/10.47941/jts.1817Keywords:
Agile, Neural Networks, Complexity, Machine Learning, Phenomenon, Integration StrategiesAbstract
Purpose: This study aims to bridge the gap in modern software development by integrating agile methodologies with artificial intelligence and machine learning (AI/ML). It seeks to understand how agile sprint planning can effectively interact with the complexities of data inherent in AI/ML projects.
Methodology: The approach taken in this research draws upon a hermeneutic philosophy, which is inference-based, alongside a descriptive methodology. It investigates effective strategies for data preparation, model building, and validation, utilizing the iterative architecture of agile sprinting. The study also incorporates a critical examination to address significant limitations encountered in the integration process.
Findings: The findings highlight the essential role of cross-departmental teams and identify various technological tools that facilitate the smooth integration of agile methodologies with AI/ML projects. A rigorous examination also emphasizes the necessity for ongoing validation through evidence to manage the complexities effectively.
Unique Contribution to Theory, Practice, and Policy (Recommendations): The study offers a comprehensive framework and practical recommendations for businesses aiming to handle data-driven AI/ML initiatives in agile environments. It provides a strategic management approach that aligns more successfully with the demands of data production and agile processes, thus contributing significantly to both theoretical perspectives and practical applications in software development. These contributions are pivotal for informing policy on the integration of cutting-edge technologies in agile settings.
Downloads
References
Mattsson, U., 2023. Controlling Privacy and the Use of Data Assets-Volume 2: What is the New
World Currency–Data or Trust?. CRC Press.
Thiée, L.W., 2021. A systematic literature review of machine learning canvases. INFORMATIK
Mapetu, J.P.B., Chen, Z. and Kong, L., 2019. Low-time complexity and low-cost binary particle
swarm optimization algorithm for task scheduling and load balancing in cloud computing. Applied
Intelligence, 49, pp.3308-3330.
El Emam, K., Mosquera, L. and Hoptroff, R., 2020. Practical synthetic data generation:
balancing privacy and the broad availability of data. O'Reilly Media.
RM, S.P., Bhattacharya, S., Maddikunta, P.K.R., Somayaji, S.R.K., Lakshmanna, K., Kaluri,
R., Hussien, A. and Gadekallu, T.R., 2020. Load balancing of energy cloud using wind driven and
firefly algorithms in internet of everything. Journal of parallel and distributed computing, 142,
pp.16-26.
Ghasedi, K., Wang, X., Deng, C. and Huang, H., 2019. Balanced self-paced learning for
generative adversarial clustering network. In Proceedings of the IEEE/CVF conference on
computer vision and pattern recognition (pp. 4391-4400).
Ghasedi, K., Wang, X., Deng, C. and Huang, H., 2019. Balanced self-paced learning for
generative adversarial clustering network. In Proceedings of the IEEE/CVF conference on
computer vision and pattern recognition (pp. 4391-4400).
Xu, Z., Shen, D., Nie, T. and Kou, Y., 2020. A hybrid sampling algorithm combining MSMOTE and ENN based on Random forest for medical imbalanced data. Journal of Biomedical
Informatics, 107, p.103465.
Shan, W., Qiao, Z., Heidari, A.A., Chen, H., Turabieh, H. and Teng, Y., 2021. Double adaptive
weights for stabilization of moth flame optimizer: Balance analysis, engineering cases, and medical
diagnosis. Knowledge-Based Systems, 214, p.106728.
Deng, W., Xu, J., Song, Y. and Zhao, H., 2021. Differential evolution algorithm with wavelet
basis function and optimal mutation strategy for complex optimization problem. Applied Soft
Computing, 100, p.106724.
Fischer, D., Brettel, M. and Mauer, R., 2020. The three dimensions of sustainability: A delicate
balancing act for entrepreneurs made more complex by stakeholder expectations. Journal of
Business Ethics, 163, pp.87-106.
Song, S., Wang, P., Heidari, A.A., Wang, M., Zhao, X., Chen, H., He, W. and Xu, S., 2021.
Dimension decided Harris hawks optimization with Gaussian mutation: Balance analysis and
diversity patterns. Knowledge-Based Systems, 215, p.106425.
Van Chien, T., Björnson, E. and Larsson, E.G., 2020. Joint power allocation and load
balancing optimization for energy-efficient cell-free massive MIMO networks. IEEE Transactions
on Wireless Communications, 19(10), pp.6798-6812.
Barcelo, A., Queralt, A. and Cortes, T., 2023. Enhancing iteration performance on distributed
task-based workflows. Future Generation Computer Systems, 149, pp.359-375.
Li, G., Shi, L., Chen, Y., Chi, Y. and Wei, Y., 2022. Settling the sample complexity of modelbased offline reinforcement learning. arXiv preprint arXiv:2204.05275.
Chen, Y., Ning, Y., Slawski, M. and Rangwala, H., 2020, December. Asynchronous online
federated learning for edge devices with non-iid data. In 2020 IEEE International Conference on
Big Data (Big Data) (pp. 15-24). IEEE.
Hu, J., Chen, H., Heidari, A.A., Wang, M., Zhang, X., Chen, Y. and Pan, Z., 2021. Orthogonal
learning covariance matrix for defects of grey wolf optimizer: Insights, balance, diversity, and
feature selection. Knowledge-Based Systems, 213, p.106684.
Neelakandan, S. and Paulraj, D., 2021. An automated exploring and learning model for data
prediction using balanced CA-SVM. Journal of Ambient Intelligence and Humanized Computing,
, pp.4979-4990.
Zhao, S., Song, J. and Ermon, S., 2019, July. Infovae: Balancing learning and inference in
variational autoencoders. In Proceedings of the aaai conference on artificial intelligence (Vol. 33,
No. 01, pp. 5885-5892).
Dimakopoulou, M., Zhou, Z., Athey, S. and Imbens, G., 2019, July. Balanced linear contextual
bandits. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp.
-3453).
El-Kenawy, E.S. and Eid, M., 2020. Hybrid gray wolf and particle swarm optimization for
feature selection. Int. J. Innov. Comput. Inf. Control, 16(3), pp.831-844.
Nicholls, H.L., John, C.R., Watson, D.S., Munroe, P.B., Barnes, M.R. and Cabrera, C.P., 2020.
Reaching the end-game for GWAS: machine learning approaches for the prioritization of complex
disease loci. Frontiers in genetics, 11, p.350.
Ezugwu, A.E., Ikotun, A.M., Oyelade, O.O., Abualigah, L., Agushaka, J.O., Eke, C.I. and
Akinyelu, A.A., 2022. A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Engineering Applications of Artificial Intelligence, 110, p.104743.
Yang, S., He, R., Zhang, Z., Cao, Y., Gao, X. and Liu, X., 2020. CHAIN: cyber hierarchy and
interactional network enabling digital solution for battery full-lifespan management. Matter, 3(1),
pp.27-41.
Azizjon, M., Jumabek, A. and Kim, W., 2020, February. 1D CNN based network intrusion
detection with normalization on imbalanced data. In 2020 international conference on artificial
intelligence in information and communication (ICAIIC) (pp. 218-224). IEEE.
de Bello, F., Botta‐Dukát, Z., Lepš, J. and Fibich, P., 2021. Towards a more balanced
combination of multiple traits when computing functional differences between species. Methods in
Ecology and Evolution, 12(3), pp.443-448.
Wang, M., Zhang, Q., Lam, S., Cai, J. and Yang, R., 2020. A review on application of deep
learning algorithms in external beam radiotherapy automated treatment planning. Frontiers in
oncology, 10, p.580919.
Yang, L., Yao, H., Wang, J., Jiang, C., Benslimane, A. and Liu, Y., 2020. Multi-UAV-enabled
load-balance mobile-edge computing for IoT networks. IEEE Internet of Things Journal, 7(8),
pp.6898-6908.
Seyyedabbasi, A. and Kiani, F., 2023. Sand Cat swarm optimization: A nature-inspired
algorithm to solve global optimization problems. Engineering with Computers, 39(4), pp.2627-
Cai, Y., Luan, T., Gao, H., Wang, H., Chen, L., Li, Y., Sotelo, M.A. and Li, Z., 2021.
YOLOv4-5D: An effective and efficient object detector for autonomous driving. IEEE
Transactions on Instrumentation and Measurement, 70, pp.1-13.
Küstner, T., Fuin, N., Hammernik, K., Bustin, A., Qi, H., Hajhosseiny, R., Masci, P.G., Neji,
R., Rueckert, D., Botnar, R.M. and Prieto, C., 2020. CINENet: deep learning-based 3D cardiac
CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions.
Scientific reports, 10(1), p.13710.
Bratianu, C., 2020. Toward understanding the complexity of the COVID-19 crisis: A grounded
theory approach. Management & Marketing, 15.
Li, X., Wang, K., Lyu, Y., Pan, H., Zhang, J., Stambolian, D., Susztak, K., Reilly, M.P., Hu,
G. and Li, M., 2020. Deep learning enables accurate clustering with batch effect removal in singlecell RNA-seq analysis. Nature communications, 11(1), p.2338.
Zhang, J., Guo, H., Liu, J. and Zhang, Y., 2019. Task offloading in vehicular edge computing
networks: A load-balancing solution. IEEE Transactions on Vehicular Technology, 69(2),
pp.2092-2104.
Giatrakos, N., Alevizos, E., Artikis, A., Deligiannakis, A. and Garofalakis, M., 2020. Complex
event recognition in the big data era: a survey. The VLDB Journal, 29, pp.313-352.
Johnson, J.M. and Khoshgoftaar, T.M., 2019. Survey on deep learning with class imbalance.
Journal of Big Data, 6(1), pp.1-54.
Kasai, J., Pappas, N., Peng, H., Cross, J. and Smith, N.A., 2020. Deep encoder, shallow
decoder: Reevaluating non-autoregressive machine translation. arXiv preprint arXiv:2006.10369.
Lipare, A., Edla, D.R. and Kuppili, V., 2019. Energy efficient load balancing approach for
avoiding energy hole problem in WSN using Grey Wolf Optimizer with novel fitness function.
Applied Soft Computing, 84, p.105706.
Ayre, J. and McCaffery, K.J., 2022. Research Note: Thematic analysis in qualitative research.
J Physiother.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Ankur Tak, Sunil Chahal
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
