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Temporal interval pattern languages to characterize time flow
Author(s) -
Höppner Frank,
Peter Sebastian
Publication year - 2014
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1122
Subject(s) - computer science , representation (politics) , temporal database , interval (graph theory) , knowledge extraction , domain knowledge , knowledge representation and reasoning , data mining , artificial intelligence , natural language processing , mathematics , combinatorics , politics , political science , law
Knowledge discovery from temporal data (e.g., time series) is among the most challenging problems in data mining. Compared to static representations like rules or decision trees, the temporal component greatly increases the pattern diversity. It is important to keep the human perception of time flow in mind when representing temporal patterns, otherwise we open the floodgates to misinterpretation and misconception. This article gives an overview of temporal interval patterns, which are considered as being a well‐suited mechanism of knowledge representation, and focusses on the various pattern representation languages. Four typical phenomena in temporal data, and how the pattern languages can cope with them, are discussed. Given the domain knowledge, this provides the reader some guidance on which pattern language may be best‐suited for a given application. WIREs Data Mining Knowl Discov 2014, 4:196–212. doi: 10.1002/widm.1122 This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Fundamental Concepts of Data and Knowledge > Knowledge Representation