research-article

Informative Summarization of Numeric Data

Authors:

Michael Vollmer,

Divesh SrivastavaAuthors Info & Claims

SSDBM '19: Proceedings of the 31st International Conference on Scientific and Statistical Database Management

Pages 97 - 108

https://doi.org/10.1145/3335783.3335797

Published: 23 July 2019 Publication History

Abstract

We consider the following data summarization problem. We are given a dataset including ordinal or numeric explanatory attributes and an outcome attribute. We want to produce a summary of how the explanatory attributes affect the outcome attribute. The summary must be human-interpretable, concise, and informative in the sense that it can accurately approximate the distribution of the outcome attribute. We propose a solution that addresses the fundamental challenge of this problem--handling large numeric domains--and we experimentally show the effectiveness and efficiency of our approach on real datasets.

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Cited By

Karegar RMirsafian MGodfrey PGolab LKargar MSrivastava DSzlichta J(2024)Discovering approximate implicit domain orders through order dependenciesThe VLDB Journal10.1007/s00778-024-00847-y33:5(1257-1282)Online publication date: 21-May-2024
https://doi.org/10.1007/s00778-024-00847-y
Bianchi AKaregar RGodfrey PGolab LKargar MSrivastava DSzlichta J(2023)iORDER: Mining Implicit Domain Orders2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00283(3623-3626)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00283
Moustakas TKolomvatsos K(2023)Cluster based similarity extraction upon distributed datasetsCluster Computing10.1007/s10586-023-04116-527:3(2917-2929)Online publication date: 25-Aug-2023
https://doi.org/10.1007/s10586-023-04116-5
Show More Cited By

Index Terms

Informative Summarization of Numeric Data
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Information visualization
2. Information systems

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cover image ACM Other conferences

SSDBM '19: Proceedings of the 31st International Conference on Scientific and Statistical Database Management

July 2019

244 pages

ISBN:9781450362160

DOI:10.1145/3335783

Conference Chair:
Carlos Maltzahn,
Program Chair:
Tanu Malik

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 July 2019

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Deutsche Forschungsgemeinschaft

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SSDBM '19

SSDBM '19: 31st International Conference on Scientific and Statistical Database Management

July 23 - 25, 2019

CA, Santa Cruz, USA

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Overall Acceptance Rate 56 of 146 submissions, 38%

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Cited By

Karegar RMirsafian MGodfrey PGolab LKargar MSrivastava DSzlichta J(2024)Discovering approximate implicit domain orders through order dependenciesThe VLDB Journal10.1007/s00778-024-00847-y33:5(1257-1282)Online publication date: 21-May-2024
https://doi.org/10.1007/s00778-024-00847-y
Bianchi AKaregar RGodfrey PGolab LKargar MSrivastava DSzlichta J(2023)iORDER: Mining Implicit Domain Orders2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00283(3623-3626)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00283
Moustakas TKolomvatsos K(2023)Cluster based similarity extraction upon distributed datasetsCluster Computing10.1007/s10586-023-04116-527:3(2917-2929)Online publication date: 25-Aug-2023
https://doi.org/10.1007/s10586-023-04116-5
Youngmann BAmer-Yahia SPersonnaz A(2022)Guided exploration of data summariesProceedings of the VLDB Endowment10.14778/3538598.353860315:9(1798-1807)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.14778/3538598.3538603
Karegar RMirsafian MGodfrey PGolab LKargar MSrivastava DSzlichta J(2022)Discovering Domain Orders via Order Dependencies2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00087(1098-1110)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00087
Amer-Yahia SMilo TYoungmann BLi GLi ZIdreos SSrivastava D(2021)Exploring Ratings in Subjective DatabasesProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457259(62-75)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3457259
Algergawy AHamed HKönig-Ries B(2021)Towards Scientific Data Synthesis Using Deep Learning and Semantic WebThe Semantic Web: ESWC 2021 Satellite Events10.1007/978-3-030-80418-3_10(54-59)Online publication date: 21-Jul-2021
https://doi.org/10.1007/978-3-030-80418-3_10
Bichi ASamsudin RHassan RAlmekhlafi K(2021)A Review of Graph-Based Extractive Text Summarization ModelsInnovative Systems for Intelligent Health Informatics10.1007/978-3-030-70713-2_41(439-448)Online publication date: 6-May-2021
https://doi.org/10.1007/978-3-030-70713-2_41
Vollmer MEnglhardt ATrittenbach HBielski PKarrari SBöhm K(2019)Energy Time-Series Features for Emerging Applications on the Basis of Human-Readable Machine DescriptionsProceedings of the Tenth ACM International Conference on Future Energy Systems10.1145/3307772.3331022(474-481)Online publication date: 15-Jun-2019
https://dl.acm.org/doi/10.1145/3307772.3331022

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