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Model Selection Management Systems: The Next Frontier of Advanced Analytics

Authors:

Jeffrey Naughton,

Jignesh M. PatelAuthors Info & Claims

ACM SIGMOD Record, Volume 44, Issue 4

Pages 17 - 22

https://doi.org/10.1145/2935694.2935698

Published: 09 May 2016 Publication History

Abstract

John Boyd recognized in the 1960's the importance of situation awareness for military operations and introduced the notion of the OODA loop (Observe, Orient, Decide, and Act). Today we realize that many applications have to deal with situation awareness: Customer Relationship Management, Human Capital Management, Supply Chain Management, patient care, power grid management, and cloud services management, as well as any IoT (Internet of Things) related application; the list seems to be endless. Situation awareness requires applications to support the management of data, knowledge, processes, and other services such as social networking in an integrated way. These applications additionally require high personalization as well as rapid and continuous evolution. They must provide a wide variety of operational and functional requirements, including real time processing.

Handcrafting these applications is an almost impossible task requiring exhaustive resources for development and maintenance. Due to the resources and time involved in their development, these applications typically fall way short of the desired functionality, operational characteristics, and ease and speed of evolution. We - the authors - have developed a model enabling the development and maintenance of situation-aware applications in a declarative and therefore economical manner; we call this model KIDS - Knowledge Intensive Data-processing System.

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Cited By

Shi JWang YZhang CLuo ZChai CZhang M(2024)DMRNet: Effective Network for Accurate Discharge Medication Recommendation2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00262(3393-3406)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00262
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https://doi.org/10.1109/ICDE60146.2024.00172
Nagrecha KKumar A(2023)Saturn: An Optimized Data System for Multi-Large-Model Deep Learning WorkloadsProceedings of the VLDB Endowment10.14778/3636218.363622717:4(712-725)Online publication date: 1-Dec-2023
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Information & Contributors

Information

Published In

cover image ACM SIGMOD Record

ACM SIGMOD Record Volume 44, Issue 4

December 2015

59 pages

ISSN:0163-5808

DOI:10.1145/2935694

Editors:
Yanlei Diao
University of Massachusetts Amherst
,
Pablo Barceló
Universidad de Chile
,
Vanessa Braganholo
Universidade Federal Fluminense
,
Marco Brambilla
Politecnico di Milano
,
Chee Yong Chan
National University of Singapore
,
Rada Chirkova
North Carolina State University
,
Anastasios Kementsietsidis
Google Research
,
Olga Papaemmanoui
Brandeis Univesity
,
Aditya Parameswaran
University of Illinois
,
Anish Das Sarma
Google Research
,
Alkis Simitsis
HP Labs
,
Nesime Tatbul
ETH Zurich
,
Marianne Winslett
University of Illinois
,
Jun Yang
Duke University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 May 2016

Published in SIGMOD Volume 44, Issue 4

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https://doi.org/10.1109/ICDE60146.2024.00262
Yang KMeliou A(2024)Non-Invasive Fairness in Learning Through the Lens of Data Drift2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00172(2164-2178)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00172
Nagrecha KKumar A(2023)Saturn: An Optimized Data System for Multi-Large-Model Deep Learning WorkloadsProceedings of the VLDB Endowment10.14778/3636218.363622717:4(712-725)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.14778/3636218.3636227
Kale ANguyen THarris FLi CZhang JMa X(2023)Provenance documentation to enable explainable and trustworthy AI: A literature reviewData Intelligence10.1162/dint_a_001195:1(139-162)Online publication date: 8-Mar-2023
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Salles RPacitti EBezerra EMarques CPacheco COliveira CPorto FOgasawara E(2023)TSPredIT: Integrated Tuning of Data Preprocessing and Time Series Prediction ModelsTransactions on Large-Scale Data- and Knowledge-Centered Systems LIV10.1007/978-3-662-68014-8_2(41-55)Online publication date: 22-Sep-2023
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Xie JXu JWang GYao YLi ZCao CTong HKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)A deep learning dataloader with shared data preparationProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3601517(17146-17156)Online publication date: 28-Nov-2022
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Renggli CYao XKolar LRimanic LKlimovic AZhang C(2022)SHiFTProceedings of the VLDB Endowment10.14778/3565816.356583116:2(304-316)Online publication date: 1-Oct-2022
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Chai CWang JLuo YNiu ZLi G(2022)Data Management for Machine Learning: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3148237(1-1)Online publication date: 2022
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