A Survey of Temporal Knowledge Discovery Paradigms and Methods

With the increase in the size of data sets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry. At the same time, interest in temporal databases has been increasing and a growing number of both prototype and implemented systems are using an enhanced temporal understanding to explain aspects of behavior associated with the implicit time-varying nature of the universe. This paper investigates the confluence of these two areas, surveys the work to date, and explores the issues involved and the outstanding problems in temporal data mining.

[1]  Rajeev Motwani,et al.  Beyond market baskets: generalizing association rules to correlations , 1997, SIGMOD '97.

[2]  Maurice Mulvenna,et al.  Navigation Pattern Discovery from Internet Data , 1999 .

[3]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[4]  John F. Roddick,et al.  An updated bibliography of temporal , 2001 .

[5]  Christos Faloutsos,et al.  Efficient retrieval of similar time sequences under time warping , 1998, Proceedings 14th International Conference on Data Engineering.

[6]  John F. Roddick,et al.  An Updated Bibliography of Temporal, Spatial, and Spatio-temporal Data Mining Research , 2000, TSDM.

[7]  Drew McDermott,et al.  A Temporal Logic for Reasoning About Processes and Plans , 1982, Cogn. Sci..

[8]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[9]  Philip S. Yu,et al.  Adaptive query processing for time-series data , 1999, KDD '99.

[10]  David Wai-Lok Cheung,et al.  Maintenance of Discovered Knowledge: A Case in Multi-Level Association Rules , 1996, KDD.

[11]  John F. Roddick,et al.  Incremental Meta-Mining from Large Temporal Data Sets , 1998, ER Workshops.

[12]  Bharat Bhargava,et al.  Advanced Database Systems , 1993, Lecture Notes in Computer Science.

[13]  Kyuseok Shim,et al.  Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases , 1995, VLDB.

[14]  John F. Roddick,et al.  Higher Order Mining: Modelling And Mining TheResults Of Knowledge Discovery , 2000 .

[15]  L. Edwin McKenzie,et al.  Bibliography: Temporal Databases , 1986, SIGMOD Rec..

[16]  Babis Theodoulidis,et al.  Knowledge Discovery in Temporal Databases: The Initial Step , 1995, KDOOD/TDOOD.

[17]  Alberto O. Mendelzon,et al.  Similarity-based queries , 1995, PODS '95.

[18]  X.S. Wang,et al.  Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences , 1998, IEEE Trans. Knowl. Data Eng..

[19]  Jiawei Han,et al.  Data-Driven Discovery of Quantitative Rules in Relational Databases , 1993, IEEE Trans. Knowl. Data Eng..

[20]  Willi Klösgen,et al.  A Support System for Interpreting Statistical Data , 1991, Knowledge Discovery in Databases.

[21]  Laxmi Parida Pattern Discovery in Biomolecular Data: Tools, Techniques and Applications , 1999 .

[22]  Willi Klösgen,et al.  Efficient discovery of interesting statements in databases , 2004, Journal of Intelligent Information Systems.

[23]  Ramez Elmasri,et al.  The Consensus Glossary of Temporal Database Concepts - February 1998 Version , 1997, Temporal Databases, Dagstuhl.

[24]  Sushil Jajodia,et al.  Discovering Temporal Patterns in Multiple Granularities , 2000, TSDM.

[25]  Arie Segev,et al.  A consensus glossary of temporal database concepts , 1994, SIGMOD 1994.

[26]  Heikki Mannila,et al.  Rule Discovery from Time Series , 1998, KDD.

[27]  Padhraic Smyth,et al.  An Information Theoretic Approach to Rule Induction from Databases , 1992, IEEE Trans. Knowl. Data Eng..

[28]  Eamonn J. Keogh,et al.  An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback , 1998, KDD.

[29]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.

[30]  Sunita Sarawagi,et al.  Mining Surprising Patterns Using Temporal Description Length , 1998, VLDB.

[31]  Ted D. Wade,et al.  Finding temporal patterns - a set-based approach , 1994, Artif. Intell. Medicine.

[32]  Marlon Dumas,et al.  Analyse de données géographiques : application des Bases de Données Temporelles. , 1998 .

[33]  Eamonn J. Keogh,et al.  Scaling up Dynamic Time Warping to Massive Dataset , 1999, PKDD.

[34]  Haym Hirsh,et al.  Learning to Predict Rare Events in Event Sequences , 1998, KDD.

[35]  Sholom M. Weiss,et al.  Data Mining and Forecasting in Large-Scale Telecommunication Networks , 1996, IEEE Expert.

[36]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[37]  John F. Roddick Data Warehousing and Data Mining: Are We Working on the Right Things? , 1998, ER Workshops.

[38]  David Wai-Lok Cheung,et al.  A General Incremental Technique for Maintaining Discovered Association Rules , 1997, DASFAA.

[39]  Mukesh K. Mohania,et al.  Incremental Maintenance of Materialized Views , 1997, DEXA.

[40]  J. Hong,et al.  Incremental Discovery of Rules and Structure by Hierarchical and Parallel Clustering , 1991, Knowledge Discovery in Databases.

[41]  Ramakrishnan Srikant,et al.  Mining Sequential Patterns: Generalizations and Performance Improvements , 1996, EDBT.

[42]  Peter C. Cheeseman,et al.  Bayesian Classification (AutoClass): Theory and Results , 1996, Advances in Knowledge Discovery and Data Mining.

[43]  Vasant Dhar,et al.  Knowledge Discovery from Databases: the Nyu Project , 1995 .

[44]  Fei Chen,et al.  Discovering Technical Traders in the T-bond Futures Market , 1998, KDD.

[45]  Christos Faloutsos,et al.  Fast subsequence matching in time-series databases , 1994, SIGMOD '94.

[46]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[47]  Heikki Mannila,et al.  Methods and Problems in Data Mining , 1997, ICDT.

[48]  Abraham Silberschatz,et al.  What Makes Patterns Interesting in Knowledge Discovery Systems , 1996, IEEE Trans. Knowl. Data Eng..

[49]  Myra Spiliopoulou,et al.  WUM - A Tool for WWW Ulitization Analysis , 1998, WebDB.

[50]  Richard T. Snodgrass,et al.  A Bibliography on Temporal Databases , 1988 .

[51]  John F. Roddick,et al.  Temporal semantics in information systems - a survey , 1992, Inf. Syst..

[52]  Sourav S. Bhowmick,et al.  Research Issues in Web Data Mining , 1999, DaWaK.

[53]  John F. Roddick,et al.  Temporal, Spatial, and Spatio-Temporal Data Mining: First International Workshop TSDM 2000 Lyon, France, September 12, 2000 Revised Papers , 2001 .

[54]  Drew McDermott,et al.  Temporal Data Base Management , 1987, Artif. Intell..

[55]  Donald J. Berndt,et al.  Finding Patterns in Time Series: A Dynamic Programming Approach , 1996, Advances in Knowledge Discovery and Data Mining.

[56]  Eamonn J. Keogh,et al.  Relevance feedback retrieval of time series data , 1999, SIGIR '99.

[57]  Alexander Tuzhilin,et al.  Discovering Unexpected Patterns in Temporal Data Using Temporal Logic , 1997, Temporal Databases, Dagstuhl.

[58]  Benjamin W. Wah,et al.  Editorial: Two Named to Editorial Board of IEEE Transactions on Knowledge and Data Engineering , 1996 .

[59]  Jiawei Han,et al.  An attribute-oriented approach for learning classification rules from relational databases , 1990, [1990] Proceedings. Sixth International Conference on Data Engineering.

[60]  Marc B. Vilain,et al.  A System for Reasoning About Time , 1982, AAAI.

[61]  Tomasz Imielinski,et al.  An Interval Classifier for Database Mining Applications , 1992, VLDB.

[62]  Thomas G. Dietterich,et al.  Discovering Patterns in Sequences of Events , 1985, Artif. Intell..

[63]  Christos Faloutsos,et al.  Efficient Similarity Search In Sequence Databases , 1993, FODO.

[64]  Michal Pechoucek,et al.  Maintenance of Discovered Knowledge , 1999, PKDD.

[65]  Christian Freksa,et al.  Temporal Reasoning Based on Semi-Intervals , 1992, Artif. Intell..

[66]  Balaji Padmanabhan,et al.  Pattern Discovery in Temporal Databases: A Temporal Logic Approach , 1996, KDD.

[67]  Dina Q. Goldin,et al.  On Similarity Queries for Time-Series Data: Constraint Specification and Implementation , 1995, CP.

[68]  llsoo Ahn,et al.  Temporal Databases , 1986, Computer.

[69]  Rakesh Agrawal,et al.  Parallel Algorithms for High-dimensional Similarity Joins for Data Mining Applications , 1997, Very Large Data Bases Conference.

[70]  Eamonn J. Keogh,et al.  A Probabilistic Approach to Fast Pattern Matching in Time Series Databases , 1997, KDD.

[71]  Jiawei Han,et al.  Maintenance of discovered association rules in large databases: an incremental updating technique , 1996, Proceedings of the Twelfth International Conference on Data Engineering.

[72]  Mohammed J. Zaki,et al.  PlanMine: Sequence Mining for Plan Failures , 1998, KDD.

[73]  John F. Roddick,et al.  A bibliography of temporal, spatial and spatio-temporal data mining research , 1999, SKDD.

[74]  Sushil Jajodia,et al.  Temporal Database Bibliography Update , 1997, Temporal Databases, Dagstuhl.

[75]  Nick Kline,et al.  An update of the temporal database bibliography , 1993, SGMD.

[76]  Mohammed J. Zaki,et al.  Mining features for sequence classification , 1999, KDD '99.

[77]  R L Blum,et al.  Discovery, confirmation, and incorporation of causal relationships from a large time-oriented clinical data base: the RX project. , 1982, Computers and biomedical research, an international journal.

[78]  Alberto O. Mendelzon,et al.  Similarity-based queries for time series data , 1997, SIGMOD '97.

[79]  Roberto J. Bayardo,et al.  Mining the most interesting rules , 1999, KDD '99.

[80]  James F. Allen Maintaining knowledge about temporal intervals , 1983, CACM.

[81]  John F. Roddick,et al.  A bibliography of temporal , 1999 .

[82]  Dimitrios Gunopulos,et al.  Mining Process Models from Workflow Logs , 1998, EDBT.

[83]  Marie-Christine Fauvet,et al.  Handling temporal grouping and pattern-matching queries in a temporal object model , 1998, CIKM '98.

[84]  Philip K. Chan,et al.  Systems for Knowledge Discovery in Databases , 1993, IEEE Trans. Knowl. Data Eng..

[85]  John F. Roddick,et al.  Database Issues in Knowledge Discovery and Data Mining , 1999, Australas. J. Inf. Syst..

[86]  Balaji Padmanabhan,et al.  A Belief-Driven Method for Discovering Unexpected Patterns , 1998, KDD.

[87]  James F. Allen An Interval-Based Representation of Temporal Knowledge , 1981, IJCAI.

[88]  Necip Fazil Ayan,et al.  An efficient algorithm to update large itemsets with early pruning , 1999, KDD '99.

[89]  Sushil Jajodia,et al.  Temporal Databases: Theory, Design, and Implementation , 1993 .

[90]  Michael D. Soo,et al.  Bibliography on temporal databases , 1991, SGMD.

[91]  Tim Oates,et al.  Identifying distinctive subsequences in multivariate time series by clustering , 1999, KDD '99.

[92]  Willi Klösgen Deviation and Association Patterns for Subgroup Mining in Temporal, Spatial, and Textual Data Bases , 1998, Rough Sets and Current Trends in Computing.

[93]  Raymond T. Ng,et al.  Very large data bases , 1994 .

[94]  Jiawei Han,et al.  Mining Segment-Wise Periodic Patterns in Time-Related Databases , 1998, KDD.

[95]  John F. Roddick,et al.  Handling Discovered Structure in Database Systems , 1996, IEEE Trans. Knowl. Data Eng..

[96]  Tom Fawcett,et al.  Activity monitoring: noticing interesting changes in behavior , 1999, KDD '99.

[97]  Kyuseok Shim,et al.  High-Dimensional Similarity Joins , 2002, IEEE Trans. Knowl. Data Eng..

[98]  Robert L. Blum,et al.  Discovery and Representation of Causal Relationships from a Large Time-Oriented Clinical Database: The RX Project , 1982, Lecture Notes in Medical Informatics.

[99]  James R. Slagle,et al.  Automating the Discovery of Causal Relationships in a Medical Records Database: The POSCH AI Project , 1991, Knowledge Discovery in Databases.

[100]  Changzhou Wang,et al.  Supporting fast search in time series for movement patterns in multiple scales , 1998, CIKM '98.

[101]  Mohammed J. Zaki Efficient enumeration of frequent sequences , 1998, CIKM '98.

[102]  原田 秀逸 私の computer 環境 , 1998 .

[103]  Xiaodong Chen,et al.  Discovering Temporal Association Rules in Temporal Databases , 1998, IADT.

[104]  Heikki Mannila,et al.  Finding interesting rules from large sets of discovered association rules , 1994, CIKM '94.

[105]  Giuseppe Psaila,et al.  Querying Shapes of Histories , 1995, VLDB.

[106]  Xiaodong Chen,et al.  Mining Temporal Features in Association Rules , 1999, PKDD.

[107]  Kaizhong Zhang,et al.  Approximate tree pattern matching , 1997 .

[108]  Mohamad Saraee,et al.  Knowledge discovery in temporal databases , 1995 .

[109]  Babis Theodoulidis,et al.  The ORES temporal database management system , 1994, SIGMOD '94.

[110]  Christos Faloutsos,et al.  Efficiently supporting ad hoc queries in large datasets of time sequences , 1997, SIGMOD '97.