research-article

Cohort Analysis with Ease

Authors:

Fei He,

Beng Chin OoiAuthors Info & Claims

SIGMOD '18: Proceedings of the 2018 International Conference on Management of Data

Pages 1737 - 1740

https://doi.org/10.1145/3183713.3193540

Published: 27 May 2018 Publication History

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Abstract

The tremendous volume of user behavior records generated in various domains provides data analysts new opportunities to mine valuable insights into user behavior. Cohort analysis, which aims to find user behavioral trends hidden in time series, is one of the most commonly used techniques. Since traditional database systems suffer from both operability and efficiency when processing cohort analysis queries, we proposed COHANA, a query processing system specialized for cohort analysis. In order to make COHANA easy-to-use, we present a comprehensive and powerful tool in this demo, covering the major use cases in cohort analysis with intuitive and accessible operations. Analysts can easily adapt COHANA to their own use with provided visualizations which can help verify their analysis assumptions and inconspicuous trends hidden in user behavior data.

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Qingchao Cai, Zhongle Xie, Meihui Zhang, Gang Chen, H.V. Jagadish, and Beng Chin Ooi. 2018. Efficient Temporal Dependence Discovery in Time Series Data. PVLDB.

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Norval D Glenn. 2005. Cohort analysis. Vol. Vol. 5. Sage.

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Dawei Jiang, Qingchao Cai, Gang Chen, H.V. Jagadish, Beng Chin Ooi, Kian-Lee Tan, and Anthony K.H. Tung. 2016. Cohort query processing. PVLDB Vol. 10, 1--12.

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Cited By

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Hossain M(2022)Behavioral Analytics of Consumer ComplaintsAI-Driven Intelligent Models for Business Excellence10.4018/978-1-6684-4246-3.ch003(42-67)Online publication date: 12-Aug-2022
https://doi.org/10.4018/978-1-6684-4246-3.ch003
Xie ZYing HYue CZhang MChen GOoi B(2020)Cool, a COhort OnLine analytical processing system2020 IEEE 36th International Conference on Data Engineering (ICDE)10.1109/ICDE48307.2020.00056(577-588)Online publication date: Apr-2020
https://doi.org/10.1109/ICDE48307.2020.00056
Cai QXie ZZhang MChen GJagadish HOoi B(2018)Effective temporal dependence discovery in time series dataProceedings of the VLDB Endowment10.14778/3204028.320403311:8(893-905)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.14778/3204028.3204033

Index Terms

Cohort Analysis with Ease
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Database query processing

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Published In

SIGMOD '18: Proceedings of the 2018 International Conference on Management of Data

May 2018

1874 pages

ISBN:9781450347037

DOI:10.1145/3183713

General Chairs:
Gautam Das
University of Texas at Arlington, USA
,
Christopher Jermaine
Rice University, USA
,
Philip Bernstein
Microsoft Research, USA

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

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Published: 27 May 2018

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the National Research Foundation Singapore under CREATE programme (E2S2-SP2 Project)
the National Research Foundation Prime Ministers Office Singapore

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SIGMOD/PODS '18

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SIGMOD

SIGMOD/PODS '18: International Conference on Management of Data

June 10 - 15, 2018

TX, Houston, USA

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SIGMOD '18 Paper Acceptance Rate 90 of 461 submissions, 20%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Hossain M(2022)Behavioral Analytics of Consumer ComplaintsAI-Driven Intelligent Models for Business Excellence10.4018/978-1-6684-4246-3.ch003(42-67)Online publication date: 12-Aug-2022
https://doi.org/10.4018/978-1-6684-4246-3.ch003
Xie ZYing HYue CZhang MChen GOoi B(2020)Cool, a COhort OnLine analytical processing system2020 IEEE 36th International Conference on Data Engineering (ICDE)10.1109/ICDE48307.2020.00056(577-588)Online publication date: Apr-2020
https://doi.org/10.1109/ICDE48307.2020.00056
Cai QXie ZZhang MChen GJagadish HOoi B(2018)Effective temporal dependence discovery in time series dataProceedings of the VLDB Endowment10.14778/3204028.320403311:8(893-905)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.14778/3204028.3204033

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Improving the Use, Analysis and Integration of Patient Health Data

A bootstrapping algorithm to improve cohort identification using structured data

Augmenting Markov Cohort Analysis to Compute (Co)Variances: Implications for Strength of Cost-Effectiveness

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Recommendations

Improving the Use, Analysis and Integration of Patient Health Data

A bootstrapping algorithm to improve cohort identification using structured data

Augmenting Markov Cohort Analysis to Compute (Co)Variances: Implications for Strength of Cost-Effectiveness

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