Temporal interval pattern languages to characterize time flow

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Volumn 4, Issue 3, 2014, Pages 196-212

  Höppner, Frank ^a   Peter, Sebastian ^b  

^a OSTFALIA UNIVERSITY OF APPLIED SCIENCES   (Germany)

^b UNIVERSITY OF KONSTANZ   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

KNOWLEDGE REPRESENTATION;

DOMAIN KNOWLEDGE; HUMAN PERCEPTION; PATTERN DIVERSITY; PATTERN LANGUAGES; PATTERN REPRESENTATION; STATIC REPRESENTATION; TEMPORAL INTERVALS; TEMPORAL PATTERN;

DECISION TREES;

EID: 84899505444     PISSN: 19424787     EISSN: 19424795     Source Type: Journal    
DOI: 10.1002/widm.1122     Document Type: Review

References (70)

1. Miller GA. The magical number seven plus or minus two: Some limits on our capacity for processing information. Psychol Rev 1956, 63:579-588.
2. Shahar Y. A framework for knowledge-based temporal abstraction. Artif Intell 1997, 90:79-133.
3. Lin J, Keogh E, Wei L, Lonardi S. Experiencing SAX: a novel symbolic representation of time series. Data Min Knowl Discov 2007, 15:107-144.
4. Sacchi L, Larizza C, Combi C, Bellazzi R. Data mining with temporal abstractions: learning rules from time series. Data Min Knowl Discov 2007, 15:217-247.
5. Mörchen F, Ultsch A. Optimizing time series discretization for knowledge discovery. In: Knowledge Discovery and Data Mining (KDD), Chicago, IL, 2005, 660.
6. Lindeberg T. Effective scale: a natural unit for measuring scale-space lifetime. IEEE Trans Pattern Anal Mach Intell 1993, 15:1068-1074.
7. Witkin AP. Scale space filtering. In: Artificial Intelligence (IJCAI), Karlsruhe, Germany, 1983, 1019-1022.
8. Das G, Lin K-I, Mannila H, Renganathan G, Smyth P. Rule discovery from time series. In: Knowledge Discovery in Databases (KDD), New York, NY: AAAI Press; 1998, 16-22.
9. Keogh E, Lin J. Clustering of time-series subsequences is meaningless: implications for previous and future research. Knowl Inf Syst 2004, 8:154-177.
10. Ye L, Keogh E. Time series shapelets: a new primitive for data mining. In: ACM SIGKDD Knowledge Discovery and Data Mining, Paris, France, 2009, 947-956.
11. Wang Q, Megalooikonomou V, Faloutsos C. Time series analysis with multiple resolutions. Inf Syst 2010, 35:56-74.
12. Castro N, Azevedo P. Multiresolution motif discovery in time series. In: SDM, Columbus, OH, 2010.
13. Allen JF. Maintaining knowledge about temporal intervals. Commun ACM 1983, 26:832-843.
14. Allen JF, Kautz HA, Pelavin RN, Tenenberg JD. Reasoning About Plans. San Mateo, CA: Morgan Kaufman; 1991.
15. Freksa C. Temporal reasoning based on semi-intervals. Artif Intell 1992, 54:199-227.
16. Roddick J, Mooney C. Linear temporal sequences and their interpretation using midpoint relationships. IEEE Trans Knowl Data Eng 2005, 17:133-135.
17. de Amo S, Junior WP, Giacometti A. MILPRIT: a constraint-based algorithm for mining temporal relational patterns. Int J Data Warehousing Min 2008, 4:42-61.
18. Wu S-Y, Chen Y-L. Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events. Data Knowl Eng 2009, 68:1309-1330.
19. Ma J, Hayes P. Primitive intervals versus point-based intervals: rivals or allies? Comput J 2005, 49:32-41.
20. Dechter R, Meiri I, Pearl J. Temporal constraint networks. Artif Intell 1991, 49:61-95.
21. Meiri I. Combining qualitative and quantitative constraints in temporal reasoning. Artif Intell 1996, 87:1-51.
22. Laxman S, Sastry PS, Unnikrishnan KP. Discovering frequent generalized episodes when events persist for different durations. IEEE Trans Fuzzy Syst 2007, 19:1188-1201.
23. Concaro S, Sacchi L, Cerra C, Fratino P, Bellazzi R. Mining health care administrative data with temporal association rules on hybrid events. Methods Inf Med 2011, 50:166-79.
24. Fan SX, Yeh J-S, and Lin Y-L. Hybrid temporal pattern mining with time grain on stock index. In: International Conference on Genetic and Evolutionary Computing, Kitakyushu, Japan, 2011, 212-215.
25. R. Villafane, K. A. Hua, D. Tran, B. Maulik. Mining interval time series. In: Data Warehousing and Knowledge Discovery (DAWAK), Florence, Italy, volume 15, 1999, 318-330.
26. Kam P-S, Fu AW-C. Discovering temporal patterns for interval-based events. In: Proceedings of the International Conference on Data Warehousing and Knowledge Discovery, London, volume 1874, 2000, 317-326.
27. Cohen PR. Fluent learning: elucidating the structure of episodes. In: Proceedings of the International Symposium on Intelligent Data Analysis, number 2189 in LNCS, Lisbon, Portugal 2001, 268-277.
28. Patel D, Hsu W, and Lee ML. Mining relationships among interval-based events for classification. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, ACM, New York, NY, USA, 2008, 393-404.
29. Höppner F. Discovery of temporal patterns-learning rules about the qualitative behaviour of time series. In: Principles and Practice of Knowledge Discovery in Databases (PKDD), volume 2168 of LNAI, Freiburg, Germany, 2001, 192-203.
30. Batal I, Fradkin D, Harrison J, Mörchen F, and Hauskrecht M. Mining recent temporal patterns for event detection in multivariate time series data. In: ACM SIGKDD International Conference on Knowledge Discovery and Datamining, Beijing, China, 2012, 280.
31. Moskovitch R, Shahar Y. Medical temporal-knowledge discovery via temporal abstraction. In: Proceedings of the American Medical Informatics Association (AMIA), San Francisco, CA, January 2009, 452-456.
32. Winarko E, Roddick JF. ARMADA: an algorithm for discovering richer relative temporal association rules from interval-based data. Data Knowl Eng 2007, 63:76-90.
33. Wu S-Y, Chen Y-L. Mining Nonambiguous Temporal Patterns for Interval-Based Events. IEEE Trans Knowl Data Eng 2007, 19:742-758.
34. Höppner F, Topp A. Classification based on the trace of variables over time. In: Proceedinds of the International Conference on Intelligent Data Engineering and Automated Learning (IDEAL), Birmingham, UK, volume 4881, 2007, 739-749.
35. Papapetrou P, Kollios G, Sclaroff S, Gunopulos D. Mining frequent arrangements of temporal intervals. Knowl Inf Syst 2009, 21:133-171.
36. Mörchen F, Ultsch A. Efficient mining of understandable patterns from multivariate interval time series. Data Min Knowl Discov 2007, 15:181-215.
37. Mörchen F, Fradkin D. Robust mining of time intervals with semi-interval partial order patterns. In: Proceedings of the SIAM International Conference on Data Mining, Columbus, OH, 2010, 315-326.
38. Chen Y-C, Jiang J-C, Peng W-C, Lee S-Y. An efficient algorithm for mining time interval-based patterns in large databases. In: Information and Knowledge Management; Toronto, Canada. (CIKM), 2010, 49-58.
39. Peter S, Höppner F, Berthold MR. Pattern Graphs: a knowledge-based tool for multivariate temporal pattern retrieval. In: Proceedings of the IEEE International Conference on Intelligent Systems, Sofia, Bulgaria, 2012, 195-206.
40. Peter S, Höppner F, Berthold MR. Learning pattern graphs for multivariate temporal pattern retrieval. In: Proceedings on the International Symposium on Intelligent Data Analysis, Helsinki, Finland, volume 7619 of LNCS, Springer, 2012, 264-275.
41. Höppner F, Peter S, Berthold MR. Enriching multivariate temporal patterns with context information to support classification. In: Computational Intelligence in Intelligent Data Analysis, volume 445 of Studies in Computational Intelligence, Springer, 2013, 195-206.
42. Álvarez MR, Félix P, Cariñena P. Discovering metric temporal constraint networks on temporal databases. Artif Intell Med 2013, 58:139-154.
43. Alvarez MR, Felix P, Cariñena P, Otero A. A data mining algorithm for inducing constraint networks. In: Proceedings of the International Conference on Information Processing and Management of Uncertainty, Dortmund, Germany, 2010, 300-309.
44. Dousson C, Duong TV. Discovering chronicles with numerical time constraints from alarm logs for monitoring dynamic systems. In: IJCAI, Stockholm, Sweden, 1999, 620-626.
45. Dao M-S. and Babaguchi N. Sports event detection using temporal patterns mining and web-casting text. In: ACM Workshop on Analysis and Retrieval of Events/Actions and Workflows in Video Streams (AREA), Vancouver, Canada, 2008, 33-40.
46. Aiello M, Monz C, Todoran L, Worring M. Document understanding for a broad class of documents. Int J Doc AnalRecognit 2002, 5:1-16.
47. Fleischman M, Decamp P, Roy D. Mining temporal patterns of movement for video content classification. In: ACM Workshop on Multimedia Information Retrieval (MIR), Santa Barbara, CA, 2006, 183-192.
48. Papapetrou P, Bense G, Kollios G. Discovering frequent poly-regions in DNA sequences. In: Proc. ICDM Data Mining Workshops on Bioinformatics, Hong Kong, 2006.
49. Höppner F, Klawonn F. Finding informative rules in interval sequences. Intell Data Anal 2002, 6:237-256.
50. Uma V, Aghila G. Temporal pattern mining and reasoning using reference event based temporal relations 'RETR'. Int J Comput Appl 2011, 36:45-49.
51. Guyet T, Quiniou R. Mining temporal patterns with quantitative intervals. In: IEEE International Conference on Data Mining Workshops, number IEEE ICDM, IEEE, Pisa, Italy, 2008, 218-227.
52. Höppner F. Discovery of core episodes from sequences-using generalization for defragmentation of rule sets. In: Pattern Detection and Discovery, number 2447 in LNCS, September 2002, 199-213.
53. Mörchen F. Unsupervised pattern mining from symbolic temporal data. ACM SIGKDD Explor Newsl 2007, 9:41-55.
54. Badaloni S, Giacomin M. A fuzzy extension of Allen's interval algebra. In: Lamma E, Mello P, eds. Proceedings of the Italian Association for Artificial Intelligence. Bologna: Pitagora Editrice; 1999, 228-237.
55. Cãlin M, Gâlea D. A fuzzy relation for comparing intervals. In: Reusch B, ed. Proceedings of the 7th Fuzzy Days, vol. 2206. Germany: Dortmund; 2001, 904-916.
56. Nagypál G, Motik B. A fuzzy model for representing uncertain, subjective, and vague temporal knowledge in ontologies. In: On The Move to Meaningful Internet Systems 2003, Sicily, Italy, 2003, 906-923.
57. Ohlbach H. Relations between fuzzy time intervals. In: Symposium on Temporal Representation and Reasoning, Tatihou, Normandie, France, 2004, 44-51.
58. Peter S, Höppner F, Berthold MR. Pattern graphs: combining multivariate time series and labelled interval sequences for classification. In: Research and Development in Intelligent Systems, Cambridge, England, 2013.
59. Pei J, Han J, Mortazavi-Asl B, Pinto H, Chen Q, Dayal U, Hsu M-C. PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. In: Proceedings 17th International Conference on Data Engineering, IEEE Computer Society, Heidelberg, Germany, 2001, 215-224.
60. Hilderman RJ, Hamilton HJ. Knowledge Discovery and Measures of Interest. Norwell, MA: Kluwer; 2001.
61. Castro NC, Azevedo PJ. Significant motifs in time series. Stat Anal Data Min 2012, 5:35-53.
62. Lam HT, Mörchen F, Fradkin D, Calders T. Mining compressing sequential patterns. Stat Anal Data Min 2014, 7(1):34-52.
63. Batal I, Sacchi L, Bellazzi R, Hauskrecht M. Multivariate time series classification with temporal abstractions. In: FLAIRS Conference, Sanibel Island, Florida, 2009.
64. Batal I, Valizadegan H, Cooper GF, Hauskrecht M. A pattern mining approach for classifying multivariate temporal data. In: Proceedings of the IEEE International Conference on Bioinformatics BioMed, Wuhan, China, 2011, 358-365.
65. Agrawal R, Srikant R. Mining sequential patterns. In: Proceedings of the International Conference on Data Engineering, Taipei, Taiwan, March 1995, 3-14.
66. Mannila H, Toivonen H, Verkamo AI. Discovery of frequent episodes in event sequences. Data Min Knowl Discov 1997, 1:259-289.
67. Wang J, Han J. BIDE: efficient mining of frequent closed sequences. In: International Conference on Data Engineering, IEEE Computer Society, Boston, MA, 2004, 79-90.
68. Chen Y-C, Peng W-C, Lee S-Y. CEMiner-an efficient algorithm for mining closed patterns from time interval-based data. In: IEEE International Conference on Data Mining (ICDM), Vancouver, Canada, December 2011, 121-130.
69. Casas-Garriga G. Summarizing sequential data with closed partial orders. SIAM Int Conf Data Min, Newport Beach, CA, 2005.
70. Rácz B, Bodon F, Schmidt-Thieme L. On benchmarking frequent itemset mining algorithms. In: Proceedings of the 1st International Workshop on Open Source Data Mining Frequent Pattern Mining Implementations-OSDM '05, Chicago, IL, 2005, 36-45.