Prediction-based, prioritized market-share insight extraction

Renato Keshet; Alina Maor; George Kour

doi:10.1007/978-3-319-49586-6_6

Prediction-based, prioritized market-share insight extraction

Renato Keshet, Alina Maor, George Kour

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We present an approach for Business Intelligence (BI), where market share changes are tracked, evaluated, and prioritized dynamically and interactively. Out of all the hundreds or thousands of possible combinations of sub-markets and players, the system brings to the user those combinations where the most significant changes have happened, grouped into related insights. Time-series prediction and user interaction enable the system to learn what “significant” means to the user, and adapt the results accordingly. The proposed approach captures key insights that are missed by current top-down aggregative BI systems, and that are hard to be spotted by humans (e.g., Cisco’s US market disruption in 2010).

Original language	English
Title of host publication	Advanced Data Mining and Applications - 12th International Conference, ADMA 2016, Proceedings
Editors	Jianxin Li, Xue Li, Shuliang Wang, Jinyan Li, Quan Z. Sheng
Publisher	Springer Verlag
Pages	81-94
Number of pages	14
ISBN (Print)	9783319495859
DOIs	https://doi.org/10.1007/978-3-319-49586-6_6
State	Published - 2016
Externally published	Yes
Event	12th International Conference on Advanced Data Mining and Applications, ADMA 2016 - Gold Coast, Australia Duration: 12 Dec 2016 → 15 Dec 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10086 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Advanced Data Mining and Applications, ADMA 2016
Country/Territory	Australia
City	Gold Coast
Period	12/12/16 → 15/12/16

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2016.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-49586-6_6

Cite this

Keshet, R., Maor, A., & Kour, G. (2016). Prediction-based, prioritized market-share insight extraction. In J. Li, X. Li, S. Wang, J. Li, & Q. Z. Sheng (Eds.), Advanced Data Mining and Applications - 12th International Conference, ADMA 2016, Proceedings (pp. 81-94). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10086 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-49586-6_6

Keshet, Renato ; Maor, Alina ; Kour, George. / Prediction-based, prioritized market-share insight extraction. Advanced Data Mining and Applications - 12th International Conference, ADMA 2016, Proceedings. editor / Jianxin Li ; Xue Li ; Shuliang Wang ; Jinyan Li ; Quan Z. Sheng. Springer Verlag, 2016. pp. 81-94 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "We present an approach for Business Intelligence (BI), where market share changes are tracked, evaluated, and prioritized dynamically and interactively. Out of all the hundreds or thousands of possible combinations of sub-markets and players, the system brings to the user those combinations where the most significant changes have happened, grouped into related insights. Time-series prediction and user interaction enable the system to learn what “significant” means to the user, and adapt the results accordingly. The proposed approach captures key insights that are missed by current top-down aggregative BI systems, and that are hard to be spotted by humans (e.g., Cisco{\textquoteright}s US market disruption in 2010).",

author = "Renato Keshet and Alina Maor and George Kour",

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doi = "10.1007/978-3-319-49586-6_6",

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Keshet, R, Maor, A & Kour, G 2016, Prediction-based, prioritized market-share insight extraction. in J Li, X Li, S Wang, J Li & QZ Sheng (eds), Advanced Data Mining and Applications - 12th International Conference, ADMA 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10086 LNAI, Springer Verlag, pp. 81-94, 12th International Conference on Advanced Data Mining and Applications, ADMA 2016, Gold Coast, Australia, 12/12/16. https://doi.org/10.1007/978-3-319-49586-6_6

Prediction-based, prioritized market-share insight extraction. / Keshet, Renato; Maor, Alina; Kour, George.
Advanced Data Mining and Applications - 12th International Conference, ADMA 2016, Proceedings. ed. / Jianxin Li; Xue Li; Shuliang Wang; Jinyan Li; Quan Z. Sheng. Springer Verlag, 2016. p. 81-94 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10086 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Maor, Alina

AU - Kour, George

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

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Y1 - 2016

N2 - We present an approach for Business Intelligence (BI), where market share changes are tracked, evaluated, and prioritized dynamically and interactively. Out of all the hundreds or thousands of possible combinations of sub-markets and players, the system brings to the user those combinations where the most significant changes have happened, grouped into related insights. Time-series prediction and user interaction enable the system to learn what “significant” means to the user, and adapt the results accordingly. The proposed approach captures key insights that are missed by current top-down aggregative BI systems, and that are hard to be spotted by humans (e.g., Cisco’s US market disruption in 2010).

AB - We present an approach for Business Intelligence (BI), where market share changes are tracked, evaluated, and prioritized dynamically and interactively. Out of all the hundreds or thousands of possible combinations of sub-markets and players, the system brings to the user those combinations where the most significant changes have happened, grouped into related insights. Time-series prediction and user interaction enable the system to learn what “significant” means to the user, and adapt the results accordingly. The proposed approach captures key insights that are missed by current top-down aggregative BI systems, and that are hard to be spotted by humans (e.g., Cisco’s US market disruption in 2010).

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DO - 10.1007/978-3-319-49586-6_6

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SN - 9783319495859

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Advanced Data Mining and Applications - 12th International Conference, ADMA 2016, Proceedings

A2 - Li, Jianxin

A2 - Li, Xue

A2 - Wang, Shuliang

A2 - Li, Jinyan

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PB - Springer Verlag

T2 - 12th International Conference on Advanced Data Mining and Applications, ADMA 2016

Y2 - 12 December 2016 through 15 December 2016

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Keshet R, Maor A, Kour G. Prediction-based, prioritized market-share insight extraction. In Li J, Li X, Wang S, Li J, Sheng QZ, editors, Advanced Data Mining and Applications - 12th International Conference, ADMA 2016, Proceedings. Springer Verlag. 2016. p. 81-94. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-49586-6_6

Prediction-based, prioritized market-share insight extraction

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

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