research-article

Building blocks for exploratory data analysis tools

Authors:

Marti A. Hearst,

Archana Ganapathi,

Randy KatzAuthors Info & Claims

IDEA '13: Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics

Pages 9 - 17

https://doi.org/10.1145/2501511.2501515

Published: 11 August 2013 Publication History

Abstract

Data exploration is largely manual and labor intensive. Although there are various tools and statistical techniques that can be applied to data sets, there is little help to identify what questions to ask of a data set, let alone what domain knowledge is useful in answering the questions. In this paper, we study user queries against production data sets in Splunk. Specifically, we characterize the interplay between data sets and the operations used to analyze them using latent semantic analysis, and discuss how this characterization serves as a building block for a data analysis recommendation system. This is a work-in-progress paper.

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Cited By

Cersosimo MLara A(2022)Detecting Malicious Domains using the Splunk Machine Learning ToolkitNOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium10.1109/NOMS54207.2022.9789899(1-6)Online publication date: 25-Apr-2022
https://doi.org/10.1109/NOMS54207.2022.9789899
Au VThomas PJayasinghe G(2016)Query-Biased Summaries for Tabular DataProceedings of the 21st Australasian Document Computing Symposium10.1145/3015022.3015027(69-72)Online publication date: 5-Dec-2016
https://dl.acm.org/doi/10.1145/3015022.3015027
Alspaugh SChen BLin JGanapathi AHearst MKatz RVelasquez N(2014)Analyzing log analysisProceedings of the 28th USENIX conference on Large Installation System Administration10.5555/2717491.2717495(53-68)Online publication date: 9-Nov-2014
https://dl.acm.org/doi/10.5555/2717491.2717495

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cover image ACM Conferences

IDEA '13: Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics

August 2013

104 pages

ISBN:9781450323291

DOI:10.1145/2501511

Editors:
Duen Horng Chau
Georgia Tech
,
Jilles Vreeken
University of Antwerp
,
Matthijs van Leeuwen
KU Leuven
,
Christos Faloutsos
Carnegie Mellon University

Copyright © 2013 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 ACM 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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New York, NY, United States

Publication History

Published: 11 August 2013

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KDD' 13

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KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 11, 2013

Illinois, Chicago

Acceptance Rates

IDEA '13 Paper Acceptance Rate 11 of 25 submissions, 44%;

Overall Acceptance Rate 11 of 25 submissions, 44%

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KDD '25

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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

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Cited By

Cersosimo MLara A(2022)Detecting Malicious Domains using the Splunk Machine Learning ToolkitNOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium10.1109/NOMS54207.2022.9789899(1-6)Online publication date: 25-Apr-2022
https://doi.org/10.1109/NOMS54207.2022.9789899
Au VThomas PJayasinghe G(2016)Query-Biased Summaries for Tabular DataProceedings of the 21st Australasian Document Computing Symposium10.1145/3015022.3015027(69-72)Online publication date: 5-Dec-2016
https://dl.acm.org/doi/10.1145/3015022.3015027
Alspaugh SChen BLin JGanapathi AHearst MKatz RVelasquez N(2014)Analyzing log analysisProceedings of the 28th USENIX conference on Large Installation System Administration10.5555/2717491.2717495(53-68)Online publication date: 9-Nov-2014
https://dl.acm.org/doi/10.5555/2717491.2717495

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