Article

Bursty and hierarchical structure in streams

Author:

Jon KleinbergAuthors Info & Claims

KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 91 - 101

https://doi.org/10.1145/775047.775061

Published: 23 July 2002 Publication History

Abstract

A fundamental problem in text data mining is to extract meaningful structure from document streams that arrive continuously over time. E-mail and news articles are two natural examples of such streams, each characterized by topics that appear, grow in intensity for a period of time, and then fade away. The published literature in a particular research field can be seen to exhibit similar phenomena over a much longer time scale. Underlying much of the text mining work in this area is the following intuitive premise --- that the appearance of a topic in a document stream is signaled by a "burst of activity," with certain features rising sharply in frequency as the topic emerges.The goal of the present work is to develop a formal approach for modeling such "bursts," in such a way that they can be robustly and efficiently identified, and can provide an organizational framework for analyzing the underlying content. The approach is based on modeling the stream using an infinite-state automaton, in which bursts appear naturally as state transitions; in some ways, it can be viewed as drawing an analogy with models from queueing theory for bursty network traffic. The resulting algorithms are highly efficient, and yield a nested representation of the set of bursts that imposes a hierarchical structure on the overall stream. Experiments with e-mail and research paper archives suggest that the resulting structures have a natural meaning in terms of the content that gave rise to them.

References

[1]

R. Agrawal, R. Srikant, "Mining sequential patterns," Proc. Intl. Conf. on Data Engineering, 1995.

Digital Library

[2]

J. Allan, J.G. Carbonell, G. Doddington, J. Yamron, Y. Yang, 'Topic Detection and Tracking Pilot Study: Final Report," Proc. DARPA Broadcast News Transcription and Understanding Workshop, Feb. 1998.

[3]

J. Allan, R. Papka, V. Lavrenko, "On-line new event detection and tracking," Proc. SIGIR Intl. Conf. Information Retrieval, 1998.

Digital Library

[4]

K. Becker, M. Cardoso, "Mail-by-Example: A visual query interface for managing large volumes of electronic messages," Proc. 15th Brazilian Symposium on Databases, 2000.

Digital Library

[5]

D. Beeferman, A. Berger, J. Lafferty, "Statistical Models for Text Segmentation," Machine Learning 34(1999), pp. 177--210.

Digital Library

[6]

H. Berghel, "E-mail: The good, the bad, and the ugly," Communications of the ACM, 40:4(April 1997), pp. 11--15.

Digital Library

[7]

A. Birrell, S. Perl, M. Schroeder, T. Wobber, The Pachyderm E-mail System, 1997, at http://www.research.compaq.com/SRC/pachyderm/.

[8]

T. Blanton, Ed., White House E-mail, New Press, 1995.

[9]

G. Boone, "Concept features in Re:Agent, an intelligent e-mail agent," Proc. 2nd Intl. Conf. Autonomous Agents, 1998.

Digital Library

[10]

C. Chatfield, The Analysis of time Series: An Introduction, Chapman and Hall, 1996.

[11]

S. Chatman, Story and Discourse: Narrative Structure in Fiction and Film, Cornell Univ. Press, 1978.

[12]

D. Chudova, P. Smyth, "Unsupervised identification of sequential patterns under a Markov assumption," KDD Workshop on Temporal Data Mining, 2001.

[13]

W. Cohen. "Learning rules that classify e-mail." Proc. AAAI Spring Symp. Machine Learning and Information Access, 1996.

[14]

T. Cover, P. Hart, "Nearest neighbor pattern classification," IEEE Trans. Information Theory IT-13(1967), pp. 21--27.

[15]

W. Davison, L. Wall, S. Barber, trn, 1993 http://web.mit.edu/afs/sipb/project/trn/src/trn-3.6/.

[16]

R. Ehrich, J. Foith, "Representation of Random Waveforms by Relational 'Trees," IEEE Trans. Computers, C25:7(1976).

[17]

S. Fine, Y. Singer, N. Tishby, "The hierarchical hidden Markov model: Analysis and applications," Machine Learning 32(1998).

Digital Library

[18]

E.M. Forster, Aspects of the Novel, Harcourt, Brace, and World, Inc. 1927.

[19]

G. Gay, M. Stefanone, M. Grace-Martin, H. Hembrooke, "The effect of wireless computing in collaborative learning environments," Intl. J. Human-Computer Interaction, to appear.

[20]

G. Genette, Narrative Discourse: An Essay in Method, English translation (J.E. Lewin), Cornell Univ. Press, 1980.

[21]

G. Genette, Narrative Discourse Revisited, English translation (J.E. Lewin), Cornell Univ. Press, 1988.

[22]

B. Grosz, C. Sidner, "Attention, intentions, and the structure of discourse," Computational Linguistics 12(1986).

Digital Library

[23]

T. Gruber, Hypermail, Enterprise Integration Technologies.

[24]

V. Guralnik, J. Srivastava, "Event detection from time series data," Intl. Conf. Knowledge Discovery and Data Mining, 1999.

Digital Library

[25]

J. Han, W. Gong, Y. Yin, "Mining Segment-Wise Periodic Patterns in Time-Related Databases", Proc. Intl. Conf. Knowledge Discovery and Data Mining, 1998.

[26]

D. Hand, H. Mannila, P. Smyth, Principles of Data Mining, MIT Press, 2001.

Digital Library

[27]

S. Havre, B. Hetzler, L. Nowell, "ThemeRiver: Visualizing Theme Changes over Time," Proc. IEEE Symposium on Information Visualization, 2000.

Digital Library

[28]

D. Hawkins, "Point estimation of the parameters of piecewise regression models," Applied Statistics 25(1976)

[29]

B. Heckel, B. Hamann, "EmVis --- A Visual e-Mail Analysis Tool," Proc. Workshop on New Paradigms in Information Visualization and Manipulation, in conjunction with Conf. on Information and Knowledge Management, 1997.

Digital Library

[30]

J. Helfman, C. Isbell, "Ishmail: Immediate identification of important information," AT&T Labs Technical Report, 1995.

[31]

E. Horvitz, "Principles of Mixed-Initiative User Interfaces," Proc. ACM Conf. Human Factors in Computing Systems, 1999.

Digital Library

[32]

D. Hudson, "Fitting segmented curves whose join points have to be estimated," Journal of the American Statistical Association 61(1966) pp. 1097--1129.

[33]

F. P. Kelly, "Notes on effective bandwidths," in Stochastic Networks: Theory and Applications, (F.P. Kelly, S. Zachary, I. Ziedins, eds.) Oxford Univ. Press, 1996.

[34]

E. Keogh, P. Smyth, "A probabilistic approach to fast pattern matching in time series databases," Proc. Intl. Conf. Knowledge Discovery and Data Mining, 1997.

[35]

J.l. Klein et al., Plaintiffs' Memorandum in Support of Proposed Final Judgment, United States of America v. Microsoft Corporation and State of New York, ex rel. Attorney General Eliot Spitzer, et al., v. Microsoft Corporation, Civil Actions No. 98-1232 (TPJ) and 98-1233 (TPJ), April 2000.

[36]

M. Last, Y. Klein, A. Kandel, "Knowledge Discovery in Time Series Databases," IEEE Transactions on Systems, Man, and Cybernetics 31B(2001).

Digital Library

[37]

V. Lavrenko, M. Schmill, D. Lawrie, P. Ogilvie, D. Jensen, J. Allan, "Mining of Concurrent Text and Time-Series," KDD-2000 Workshop on Text Mining, 2000.

[38]

D.D. Lewis, K.A. Knowles, "Threading electronic mail: A preliminary study," Inf. Proc. Management 33(1997).

Digital Library

[39]

S.S. Lukesh, "E-mail and potential loss to future archives and scholarship, or, The dog that didn't bark," First Monday 4(9) (September 1999), at http://firstmonday.org

[40]

P. Maes, "Agents that reduce work and information overload," Communications of the ACM 37:7(1994), pp. 30--40.

Digital Library

[41]

H. Mannila, M. Salmenkivi, "Finding simple intensity descriptions from event sequence data," Proc. Intl. Conf. on Knowledge Discovery and Data Mining, 2001.

Digital Library

[42]

H. Mannila, H. Toivonen, A.I. Verkamo, "Discovering frequent episodes in sequences," Proc. Intl. Conf. on Knowledge Discovery and Data Mining, 1995.

[43]

R. Martin, V. Yohai, "Data mining for unusual movements in temporal data," KDD Wkshp. Temporal Data Mining, 2001.

[44]

M.L. Markus, "Finding a Happy Medium: Explaining the Negative Effects of Electronic Communication on Social Life at Work," ACM Trans. Info. Sys. 12(1994), pp. 119--149.

Digital Library

[45]

N. Miller, P. Wong, M. Brewster, H. Foote, "Topic Islands: A Wavelet-Based Text Visualization System," Proc. IEEE Visualization, 1998.

Digital Library

[46]

R. Moore, C. Baru, A. Rajasekar, B. Ludaescher, R. Marciano, M. Wan, W. Schroeder, A. Gupta, "Collection-Based Persistent Digital Archives -- Part 2," D-Lib Magazine, 6(2000).

Digital Library

[47]

K. Murphy, M. Paskin, "Linear time inference in hierarchical HMMs," Advances in Neural Information Processing Systems (NIPS) 14, 2001.

[48]

F. Olsen, "Facing Flood of E-Mail, Archives Seeks Help From Supercomputer Researchers," Chronicle of Higher Education, August 24, 1999.

[49]

T. Payne, P. Edwards, "Interface agents that learn: An investigation of learning issues in a mail agent interface," Applied Artificial Intelligence 11(1997), pp. 1--32.

[50]

S. Pollock, "A rule-based message filtering system," ACM Trans. Office Automation Systems 6(3):232--254, 1988.

Digital Library

[51]

L. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition," Proc. IEEE 77(1989).

[52]

M. Redmond, B. Adelson, "AlterEgo e-mail filtering agent," Proc. AAAI Workshop on Case-Based Reasoning, 1998.

[53]

J. Rennie, "ifile: An application of machine learning to e-mail filtering," Proc. KDD Workshop on Text Mining, 2000.

[54]

M. Sahami, S. Dumais, D. Heckerman, E. Horvitz. "A Bayesian approach to filtering junk email," Proc. AAAI Workshop on Learning for Text Categorization, 1998.

[55]

B. Schneier, Applied Cryptography Wiley, 1996.

[56]

R. Segal, J. Kephart. "MailCat: An intelligent assistant for organizing e-mail," Proc. Intl. Conf. Autonomous Agents, 1999.

Digital Library

[57]

R. Segal, J. Kephart. "Incremental Learning in SwiftFile," Proc. Intl. Conf. on Machine Learning, 2000.

Digital Library

[58]

S. Shaw, R. DeFigueiredo, "Structural Processing of Waveforms as Trees," IEEE Transactions on Acoustics, Speech, and Signal Processing 38:2(1990)

[59]

R. Swan, J. Allan, "Extracting significant time-varying features from text," Proc. 8th Intl. Conf. on Information Knowledge Management, 1999.

Digital Library

[60]

R. Swan, J. Allan, "Automatic generation of overview timelines," Proc. SIGIR Intl. Conf. Information Retrieval, 2000.

Digital Library

[61]

R. Swan, D. Jensen, "TimeMines: Constructing Timelines with Statistical Models of Word Usage," KDD-2000 Workshop on Text Mining, 2000.

[62]

S. Whittaker, C. Sidner, "E-mail overload: Exploring personal information management of e-mail," Proc. ACM SIGCHI Conf. on Human Factors in Computing Systems, 1996.

Digital Library

[63]

P. Wong, W. Cowley, H. Foote, E. Jurrus, J. Thomas, "Visualizing sequential patterns for text mining," Proc. IEEE Information Visualization, 2000

Digital Library

[64]

Y. Yang, T. Ault, T. Pierce, C.W. Lattimer, "Improving text categorization methods for event tracking," Proc. SIGIR Intl. Conf. Information Retrieval, 2000.

Digital Library

[65]

Y. Yang, T. Pierce, J.G. Carbonell, "A Study on Retrospective and On-line Event Detection," Proc. SIGIR Intl. Conf. Information Retrieval, 1998.

Digital Library

Cited By

Özmen EBarış AÇirci EYüksel SBeytemür O(2024)Periprosthetic Fracture Literature Since 1990: A Bibliometric AnalysisBagcilar Medical Bulletin10.4274/BMB.galenos.2024.2024-04-035(213-220)Online publication date: 27-Sep-2024
https://doi.org/10.4274/BMB.galenos.2024.2024-04-035
Facciorusso SSpina SPicelli ABaricich AFrancisco GMolteni FWissel JSantamato A(2024)The Role of Botulinum Toxin Type-A in Spasticity: Research Trends from a Bibliometric AnalysisToxins10.3390/toxins1604018416:4(184)Online publication date: 9-Apr-2024
https://doi.org/10.3390/toxins16040184
Aguzzi JCuadros JDartnell LCosta CViolino SCanfora LDanovaro RRobinson NGiovannelli DFlögel SStefanni SChatzievangelou DMarini SPicardi GFoing B(2024)Marine Science Can Contribute to the Search for Extra-Terrestrial LifeLife10.3390/life1406067614:6(676)Online publication date: 24-May-2024
https://doi.org/10.3390/life14060676
Show More Cited By

Recommendations

Bursty and Hierarchical Structure in Streams

A fundamental problem in text data mining is to extract meaningful structure from document streams that arrive continuously over time. E-mail and news articles are two natural examples of such streams, each characterized by topics that appear, grow in ...
Mining correlated bursty topic patterns from coordinated text streams
KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining

Previous work on text mining has almost exclusively focused on a single stream. However, we often have available multiple text streams indexed by the same set of time points (called coordinated text streams), which offer new opportunities for text ...
Backpressure in shared-memory-based ATM switches under multiplexed bursty sources
INFOCOM'96: Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 2

In this paper, we study a shared-memory center-stage switch with input multiplexers and output demultiplexers, where backpressure is applied from the demultiplexers to the center stage, and from the center stage to the multiplexers. We consider three ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining

July 2002

719 pages

ISBN:158113567X

DOI:10.1145/775047

Conference Chair:
Osmar R. Zaïane
University of Alberta, Canada
,
General Chair:
Randy Goebel
University of Alberta, Canada
,
Program Chairs:
David Hand
Imperial College, UK
,
Daniel Keim
AT&T
,
Raymond Ng
University of British Columbia, Canada

Copyright © 2002 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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 July 2002

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Article

Conference

KDD02

Sponsor:

KDD02: The Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

July 23 - 26, 2002

Alberta, Edmonton, Canada

Acceptance Rates

KDD '02 Paper Acceptance Rate 44 of 307 submissions, 14%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

719
Total Citations
View Citations
3,017
Total Downloads

Downloads (Last 12 months)145
Downloads (Last 6 weeks)23

Reflects downloads up to 06 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Özmen EBarış AÇirci EYüksel SBeytemür O(2024)Periprosthetic Fracture Literature Since 1990: A Bibliometric AnalysisBagcilar Medical Bulletin10.4274/BMB.galenos.2024.2024-04-035(213-220)Online publication date: 27-Sep-2024
https://doi.org/10.4274/BMB.galenos.2024.2024-04-035
Facciorusso SSpina SPicelli ABaricich AFrancisco GMolteni FWissel JSantamato A(2024)The Role of Botulinum Toxin Type-A in Spasticity: Research Trends from a Bibliometric AnalysisToxins10.3390/toxins1604018416:4(184)Online publication date: 9-Apr-2024
https://doi.org/10.3390/toxins16040184
Aguzzi JCuadros JDartnell LCosta CViolino SCanfora LDanovaro RRobinson NGiovannelli DFlögel SStefanni SChatzievangelou DMarini SPicardi GFoing B(2024)Marine Science Can Contribute to the Search for Extra-Terrestrial LifeLife10.3390/life1406067614:6(676)Online publication date: 24-May-2024
https://doi.org/10.3390/life14060676
Hu RBao ZJia JLv K(2024)Identification of Emerging Technological Hotspots from a Multi-Source Information Perspective: Case Study on Blockchain Financial TechnologyInformation10.3390/info1509058115:9(581)Online publication date: 19-Sep-2024
https://doi.org/10.3390/info15090581
He HAbdul-Rashid SRaja Ghazilla R(2024)Research Trends and Hot Spots in Telemedicine for the Elderly: A Scientometric AnalysisHealthcare10.3390/healthcare1218185312:18(1853)Online publication date: 14-Sep-2024
https://doi.org/10.3390/healthcare12181853
Nitu MDascalu M(2024)Beyond Lexical Boundaries: LLM-Generated Text Detection for Romanian Digital LibrariesFuture Internet10.3390/fi1602004116:2(41)Online publication date: 25-Jan-2024
https://doi.org/10.3390/fi16020041
Wang CChe YXia MLin CChen YLi XChen HLuo JFan G(2024)The Evolution and Future Directions of Green Buildings Research: A Scientometric AnalysisBuildings10.3390/buildings1402034514:2(345)Online publication date: 26-Jan-2024
https://doi.org/10.3390/buildings14020345
Lu CZhu LXie YXu WZhao YCao Y(2024)Analysis of Hot Topics and Evolution of Research in World-class Agricultural Universities Based on BERTopicApplied Mathematics and Nonlinear Sciences10.2478/amns-2024-03279:1Online publication date: 26-Feb-2024
https://doi.org/10.2478/amns-2024-0327
De Silva CBosio AGervasoni A(2024)ESG and venture capital research: A bibliometric literature review at the intersection of two fieldsCorporate Ownership and Control10.22495/cocv21i1art621:1(55-71)Online publication date: 14-Feb-2024
https://doi.org/10.22495/cocv21i1art6
Eisenberg TShein-Idelson M(2024)ReptiLearn: An automated home cage system for behavioral experiments in reptiles without human interventionPLOS Biology10.1371/journal.pbio.300241122:2(e3002411)Online publication date: 29-Feb-2024
https://doi.org/10.1371/journal.pbio.3002411
Show More Cited By

View Options

Get Access

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