More Web Proxy on the site http://driver.im/

research-article

Interesting event detection through hall of fame rankings

Authors:

Foteini Alvanaki,

Sebastian Michel,

Aleksandar StuparAuthors Info & Claims

DBSocial '13: Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks

Pages 7 - 12

https://doi.org/10.1145/2484702.2484704

Published: 22 June 2013 Publication History

Abstract

Everything is relative. Cars are compared by gas per mile, websites by page rank, students based on GPA, scientists by number of publications, and celebrities by beauty or wealth. In this paper, we study the characteristics of such entity rankings based on a set of rankings obtained from a popular Web portal. The obtained insights are integrated in our approach, coined Pantheon. Pantheon maintains sets of top-k rankings and reports identified changes in a way that appeals to users, using a novel combination of different characteristics like competitiveness, information entropy, and scale of change. Entity rankings are assembled by combining entity type attributes with data-driven categorical constraints and sorting criteria on numeric attributes. We report on the results of an experimental evaluation using real-world data obtained from a basketball statistics website.

References

[1]

R. Agrawal, T. Imielinski, and A. N. Swami. Database mining: A performance perspective. IEEE Trans. Knowl. Data Eng., 5(6), 1993.

Digital Library

[2]

F. Alvanaki, S. Michel, and A. Stupar. Building and maintaining halls of fame over a database. CoRR, abs/1208.1231, 2012.

[3]

E. Baikousi and P. Vassiliadis. Maintenance of top-k materialized views. Distributed and Parallel Databases, 27(2), 2010.

Digital Library

[4]

A. X. Chang and C. D. Manning. Sutime: A library for recognizing and normalizing time expressions. LREC, 2012.

[5]

databasebasketball.com - nba basketball statistics, draft, awards, and history. http://www.databasebasketball.com/.

[6]

M. A. Hasan, V. Chaoji, S. Salem, J. Besson, and M. J. Zaki. Origami: Mining representative orthogonal graph patterns. ICDM, 2007.

Digital Library

[7]

K. Järvelin and J. Kekäläinen. Cumulated gain-based evaluation of ir techniques. ACM Trans. Inf. Syst., 20(4), 2002.

Digital Library

[8]

R. Luce. Lexicographic tradeoff structures. Theory and Decision, 1978.

[9]

C. Manning and H. Schutze. Foundations of statistical natural language processing. MIT Press, MA, USA, 1999.

Digital Library

[10]

M. Miah, G. Das, V. Hristidis, and H. Mannila. Standing out in a crowd: Selecting attributes for maximum visibility. ICDE, 2008.

Digital Library

[11]

Microsoft SQL Server 2008 R2: Feature selection in data mining. http://msdn.microsoft.com/en-us/library/ms175382.aspx.

[12]

M. D. Smucker, J. Allan, and B. Carterette. A comparison of statistical significance tests for information retrieval evaluation. CIKM, 2007.

Digital Library

[13]

K. Toutanova, D. Klein, C. D. Manning, and Y. Singer. Feature-rich part-of-speech tagging with a cyclic dependency network. HLT-NAACL, 2003.

Digital Library

[14]

E. Unger, L. Harn, and V. Kumar. Entropy as a measure of database information. Sixth Annual Computer Security Applications Conference, 1990.

[15]

K. Yi, H. Yu, J. Yang, G. Xia, and Y. Chen. Efficient maintenance of materialized top-k views. ICDE, 2003.

[16]

M. A. Yosef, J. Hoffart, I. Bordino, M. Spaniol, and G. Weikum. Aida: An online tool for accurate disambiguation of named entities in text and tables. PVLDB, 4(12), 2011.

[17]

M. J. Zaki. Efficiently mining frequent trees in a forest: Algorithms and applications. IEEE Trans. Knowl. Data Eng., 17(8), 2005.

Digital Library

[18]

N. Zhong, C. Liu, Y. Yao, M. Ohshima, M. Huang, and J. Huang. Relational peculiarity oriented data mining. ICDM, 2004.

Digital Library

Cited By

Pal KReinartz FMichel S(2016)Mining Entity RankingsDatenbank-Spektrum10.1007/s13222-015-0205-216:1(27-38)Online publication date: 2-Feb-2016
https://doi.org/10.1007/s13222-015-0205-2
Sultana AHassan NLi CYang JYu C(2014)Incremental discovery of prominent situational facts2014 IEEE 30th International Conference on Data Engineering10.1109/ICDE.2014.6816644(112-123)Online publication date: Mar-2014
https://doi.org/10.1109/ICDE.2014.6816644
Ilieva EMichel SStupar AHe QIyengar ANejdl WPei JRastogi R(2013)The essence of knowledge (bases) through entity rankingsProceedings of the 22nd ACM international conference on Information & Knowledge Management10.1145/2505515.2507838(1537-1540)Online publication date: 27-Oct-2013
https://dl.acm.org/doi/10.1145/2505515.2507838

Index Terms

Interesting event detection through hall of fame rankings
1. Information systems
  1. Information systems applications
    1. Data mining

Recommendations

The essence of knowledge (bases) through entity rankings
CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

We consider the task of automatically phrasing and computing top-k rankings over the information contained in common knowledge bases (KBs), such as YAGO or DBPedia. We assemble the thematic focus and ranking criteria of rankings by inspecting the present ...
Measurement of the Weibo Hall of Fame Network
IMCCC '11: Proceedings of the 2011 First International Conference on Instrumentation, Measurement, Computer, Communication and Control

Weibo, a micro-blogging service provided by Sina at Sep. 2009, attracts over a hundred million users and is growing fast. Though Weibo user publishes any topic within the 140-character limit and follows others to receive their statuses as Twitter user ...
AI's Hall of Fame

In 2010, as the part of the celebration of its 25th anniversary, the IEEE Intelligent Systems editorial and advisory boards decided to launch the IEEE Intelligent Systems Hall of Fame to express appreciation and respect for the trailblazers who have ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

DBSocial '13: Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks

June 2013

114 pages

ISBN:9781450321914

DOI:10.1145/2484702

Conference Chairs:
Kristen LeFevre
Google Research
,
Ashwin Machanavajjhala
Duke University
,
Adam Silberstein
Trifacta

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]

Sponsors

SIGMOD: ACM Special Interest Group on Management of Data

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Deutsche Forschungsgemeinschaft

Conference

SIGMOD/PODS'13

Sponsor:

SIGMOD

SIGMOD/PODS'13: International Conference on Management of Data

June 22 - 27, 2013

New York, New York

Acceptance Rates

DBSocial '13 Paper Acceptance Rate 9 of 19 submissions, 47%;

Overall Acceptance Rate 9 of 19 submissions, 47%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
176
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 19 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Pal KReinartz FMichel S(2016)Mining Entity RankingsDatenbank-Spektrum10.1007/s13222-015-0205-216:1(27-38)Online publication date: 2-Feb-2016
https://doi.org/10.1007/s13222-015-0205-2
Sultana AHassan NLi CYang JYu C(2014)Incremental discovery of prominent situational facts2014 IEEE 30th International Conference on Data Engineering10.1109/ICDE.2014.6816644(112-123)Online publication date: Mar-2014
https://doi.org/10.1109/ICDE.2014.6816644
Ilieva EMichel SStupar AHe QIyengar ANejdl WPei JRastogi R(2013)The essence of knowledge (bases) through entity rankingsProceedings of the 22nd ACM international conference on Information & Knowledge Management10.1145/2505515.2507838(1537-1540)Online publication date: 27-Oct-2013
https://dl.acm.org/doi/10.1145/2505515.2507838

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents