Challenges in AutoML and Declarative Studies Using Systematic Literature Review
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Machine Learning (ML) technologies have become essential tools, transforming industries and unlocking incredible potential in various fields. ML is now widely used for data-driven decision-making and predictive analytics across fields like healthcare, finance, transportation, and more. However, building and implementing ML models can be complex and time-consuming, often requiring programming proficiency and data science skills. Despite significant progress in ML, non-experts often struggle with selecting algorithms, optimizing models, and deploying ML solutions. This paper conducts a systematic literature review to explore challenges in the area of machine learning based on multiple categories involving features engineering and data extraction, learning model structure and activities, learning-based analysis and visualization, analysis algorithms in data-based systems, machine learning algorithms and systems development, and declarative ML-based prediction. Addressing these challenges underlines the importance of following AutoML and Declarative ML strategies in simplifying the ML process.
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[1] P. Kordjamshidi, D. Roth, and K. Kersting, "Systems AI: A Declarative Learning Based Programming Perspective," in IJCAI, 2018, pp. 5464-5471.
[2] P. Molino and C. Ré, "Declarative machine learning systems," Communications of the ACM, vol. 65, no. 1, pp. 42-49, 2021.
[3] A. Alexandrov et al., "The stratosphere platform for big data analytics," The VLDB Journal, vol. 23, pp. 939-964, 2014.
[4] M. Zaharia et al., "Resilient distributed datasets: A {Fault-Tolerant} abstraction for {In-Memory} cluster computing," in 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI 12), 2012, pp. 15-28.
[5] J. Dean and S. Ghemawat, "MapReduce: simplified data processing on large clusters," Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008.
[6] M. Boehm, A. V. Evfimievski, N. Pansare, and B. Reinwald, "Declarative machine learning-a classification of basic properties and types," arXiv preprint arXiv:1605.05826, 2016.
[7] X. Meng et al., "Mllib: Machine learning in apache spark," The journal of machine learning research, vol. 17, no. 1, pp. 1235-1241, 2016.
[8] R. Anil et al., "Apache mahout: Machine learning on distributed dataflow systems," The Journal of Machine Learning Research, vol. 21, no. 1, pp. 4999-5004, 2020.
[9] J. Hellerstein et al., "The MADlib analytics library or MAD skills, the SQL," arXiv preprint arXiv:1208.4165, 2012.
[10] J. Cohen, B. Dolan, M. Dunlap, J. M. Hellerstein, and C. Welton, "MAD skills: new analysis practices for big data," Proceedings of the VLDB Endowment, vol. 2, no. 2, pp. 1481-1492, 2009.
[11] P. Molino and C. Ré, "Declarative Machine Learning Systems: The future of machine learning will depend on it being in the hands of the rest of us," Queue, vol. 19, no. 3, pp. 46-76, 2021.
[12] D. J.-L. Lee and S. Macke, "A Human-in-the-loop Perspective on AutoML: Milestones and the Road Ahead," IEEE Data Engineering Bulletin, 2020.
[13] R. Elshawi and S. Sakr, "Automated machine learning: Techniques and frameworks," in Big Data Management and Analytics: 9th European Summer School, eBISS 2019, Berlin, Germany, June 30–July 5, 2019, Revised Selected Papers 9, 2020, pp. 40-69: Springer.
[14] C. Sohrabi et al., "PRISMA 2020 statement: What's new and the importance of reporting guidelines," vol. 88, ed: Elsevier, 2021, p. 105918.
[15] K. W. Khaw, A. Alnoor, H. Al-Abrrow, V. Tiberius, Y. Ganesan, and N. A. Atshan, "Reactions towards organizational change: a systematic literature review," Current Psychology, vol. 42, no. 22, pp. 19137-19160, 2023.
[16] M. Verbeke, P. Frasconi, K. De Grave, F. Costa, and L. De Raedt, "klognlp: Graph kernel–based relational learning of natural language," in Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 2014, pp. 85-90.
[17] S. Singh, T. Rocktäschel, L. Hewitt, J. Naradowsky, and S. Riedel, "WOLFE: an nlp-friendly declarative machine learning stack," in Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, 2015, pp. 61-65.
[18] C. Zhang, A. Kumar, and C. Ré, "Materialization optimizations for feature selection workloads," ACM Transactions on Database Systems (TODS), vol. 41, no. 1, pp. 1-32, 2016.
[19] I. Khan, X. Zhang, R. K. Ayyasamy, and R. Ali, "AutoFe-Sel: A Meta-learning based methodology for Recommending Feature Subset Selection Algorithms," KSII Transactions on Internet & Information Systems, vol. 17, no. 7, 2023.
[20] L. State, S. Ruggieri, and F. Turini, "Declarative Reasoning on Explanations Using Constraint Logic Programming," in European Conference on Logics in Artificial Intelligence, 2023, pp. 132-141: Springer.
[21] T. Li, J. Zhong, J. Liu, W. Wu, and C. Zhang, "Ease. ml: Towards multi-tenant resource sharing for machine learning workloads," Proceedings of the VLDB Endowment, vol. 11, no. 5, pp. 607-620, 2018.
[22] D. Xin, L. Ma, J. Liu, S. Macke, S. Song, and A. Parameswaran, "Helix: Accelerating human-in-the-loop machine learning," arXiv preprint arXiv:1808.01095, 2018.
[23] J. Wang, J. Wu, M. Li, J. Gu, A. Das, and C. Zaniolo, "Formal semantics and high performance in declarative machine learning using Datalog," The VLDB Journal, vol. 30, no. 5, pp. 859-881, 2021.
[24] H. Spieker, "Towards sequence-to-sequence reinforcement learning for constraint solving with constraint-based local search," in Proceedings of the AAAI Conference on Artificial Intelligence, 2019, vol. 33, no. 01, pp. 10037-10038.
[25] P. Kordjamshidi, D. Roth, and K. Kersting, "Declarative learning-based programming as an interface to AI systems," Frontiers in artificial intelligence, vol. 5, p. 755361, 2022.
[26] M. Boehm et al., "Hybrid parallelization strategies for large-scale machine learning in systemml," Proceedings of the VLDB Endowment, vol. 7, no. 7, pp. 553-564, 2014.
[27] J. K. Li and K.-L. Ma, "P6: A declarative language for integrating machine learning in visual analytics," IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, pp. 380-389, 2020.
[28] F. Lekschas, X. Zhou, W. Chen, N. Gehlenborg, B. Bach, and H. Pfister, "A generic framework and library for exploration of small multiples through interactive piling," IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, pp. 358-368, 2020.
[29] J. Leskovec, "Databases as Graphs: Predictive Queries for Declarative Machine Learning," in Proceedings of the 42nd ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, 2023, pp. 1-1.
[30] A. Ghoting et al., "SystemML: Declarative machine learning on MapReduce," in 2011 IEEE 27th International conference on data engineering, 2011, pp. 231-242: IEEE.
[31] H. Zhang, L. Zeng, W. Wu, and C. Zhang, "How good are machine learning clouds for binary classification with good features? extended abstract," in Proceedings of the 2017 Symposium on Cloud Computing, 2017, pp. 649-649.
[32] C. De Sa et al., "Deepdive: Declarative knowledge base construction," ACM SIGMOD Record, vol. 45, no. 1, pp. 60-67, 2016.
[33] J. P. Consuegra-Ayala, Y. Gutiérrez, Y. Almeida-Cruz, and M. Palomar, "Intelligent ensembling of auto-ML system outputs for solving classification problems," Information Sciences, vol. 609, pp. 766-780, 2022.
[34] D. Nunes de Oliveira and L. H. d. C. Merschmann, "An Auto-ML Approach Applied to Text Classification," in Proceedings of the Brazilian Symposium on Multimedia and the Web, 2022, pp. 108-116.
[35] S. Gurny, J. Falvo, and C. Varela, "Aircraft Weight Estimation During Take-off Using Declarative Machine Learning," in 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC), 2020, pp. 1-10: IEEE.
[36] D. Casacuberta, A. Guersenzvaig, and C. Moyano-Fernández, "Justificatory explanations in machine learning: for increased transparency through documenting how key concepts drive and underpin design and engineering decisions," Ai & Society, pp. 1-15, 2022.
[37] Z. J. Gao, S. Luo, L. L. Perez, and C. Jermaine, "The BUDS language for distributed bayesian machine learning," in Proceedings of the 2017 ACM International Conference on Management of Data, 2017, pp. 961-976.
[38] M. Musigmann et al., "Testing the applicability and performance of Auto ML for potential applications in diagnostic neuroradiology," Scientific reports, vol. 12, no. 1, p. 13648, 2022.
[39] R. J. Pais, F. Lopes, I. Parreira, M. Silva, M. Silva, and M. G. Moutinho, "Predicting Cancer Prognostics from Tumour Transcriptomics Using an Auto Machine Learning Approach," in Medical Sciences Forum, 2023, vol. 22, no. 1, p. 6: MDPI.
[40] C. Yang, S. An, B. Qiao, P. Guan, D. Huang, and W. Wu, "Exploring the influence of COVID-19 on the spread of hand, foot, and mouth disease with an automatic machine learning prediction model," Environmental Science and Pollution Research, vol. 30, no. 8, pp. 20369-20385, 2023.