research-article

Data is dead... without what-if models

Authors:

Paul P. Maglio,

Patricia G. Selinger,

Wang-Chiew TanAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 4, Issue 12

Pages 1486 - 1489

https://doi.org/10.14778/3402755.3402802

Published: 01 August 2011 Publication History

Abstract

Current database technology has raised the art of scalable descriptive analytics to a very high level. Unfortunately, what enterprises really need is prescriptive analytics to identify optimal business, policy, investment, and engineering decisions in the face of uncertainty. Such analytics, in turn, rest on deep predictive analytics that go beyond mere statistical forecasting and are imbued with an understanding of the fundamental mechanisms that govern a system's behavior, allowing what-if analyses. The database community needs to put what-if models and data on equal footing, developing systems that use both data and models to make sense of rich, real-world complexity and to support real-world decision-making. This model-and-data orientation requires significant extensions of many database technologies, such as data integration, query optimization and processing, and collaborative analytics. In this paper, we argue that data without what-if modeling may be the database community's past, but data with what-if modeling must be its future.

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Cited By

Liu ZKhojandi ALi XMohammed ADavis RKamaleswaran R(2022)A Machine Learning–Enabled Partially Observable Markov Decision Process Framework for Early Sepsis PredictionINFORMS Journal on Computing10.1287/ijoc.2022.117634:4(2039-2057)Online publication date: 1-Jul-2022
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Frazzetto DNielsen TPedersen TŠikšnys L(2022)Prescriptive analytics: a survey of emerging trends and technologiesThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-019-00539-y28:4(575-595)Online publication date: 10-Mar-2022
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Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 4, Issue 12

August 2011

303 pages

ISSN:2150-8097

Editor:
H. V. Jagadish

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VLDB Endowment

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Published: 01 August 2011

Published in PVLDB Volume 4, Issue 12

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Cited By

Liu ZKhojandi ALi XMohammed ADavis RKamaleswaran R(2022)A Machine Learning–Enabled Partially Observable Markov Decision Process Framework for Early Sepsis PredictionINFORMS Journal on Computing10.1287/ijoc.2022.117634:4(2039-2057)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1287/ijoc.2022.1176
Frazzetto DNielsen TPedersen TŠikšnys L(2022)Prescriptive analytics: a survey of emerging trends and technologiesThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-019-00539-y28:4(575-595)Online publication date: 10-Mar-2022
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Cavallo MDemiralp ÇMandryk RHancock MPerry MCox A(2018)A Visual Interaction Framework for Dimensionality Reduction Based Data ExplorationProceedings of the 2018 CHI Conference on Human Factors in Computing Systems10.1145/3173574.3174209(1-13)Online publication date: 21-Apr-2018
https://dl.acm.org/doi/10.1145/3173574.3174209
Kureshi ITheodoropoulos GMangina EO'Hare GRoche J(2015)Towards An Info-Symbiotic Decision Support System for Disaster Risk ManagementProceedings of the 19th International Symposium on Distributed Simulation and Real Time Applications10.1109/DS-RT.2015.26(85-91)Online publication date: 14-Oct-2015
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Gonçalves BPorto F(2014)γ-DBProceedings of the VLDB Endowment10.14778/2732967.27329717:11(959-962)Online publication date: 1-Jul-2014
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Haas PHull RGrohe M(2014)Model-data EcosystemsProceedings of the 33rd ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems10.1145/2594538.2594562(76-87)Online publication date: 18-Jun-2014
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