Home
This Title All WIREs
WIREs RSS Feed
How to cite this WIREs title:
WIREs Data Mining Knowl Discov
Impact Factor: 2.111

Temporal interval pattern languages to characterize time flow

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

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

Conflict of interest: The authors have declared no conflicts of interest for this article.

Pictorial example of starting‐up a car: time on horizontal axis and vertical axis displays the observed attributes.
[ Normal View | Magnified View ]
Building blocks of most approaches to temporal pattern mining.
[ Normal View | Magnified View ]
Three state sequences (a)–(c) where slight variations in the end‐points lead to different interval relationships.
[ Normal View | Magnified View ]
Group‐wise time warping distorts selected interval relationships.
[ Normal View | Magnified View ]
Representing the time constraint of Figure by a pattern graph.
[ Normal View | Magnified View ]
‘A peak in x while y is increasing’, where the peak is expressed by a pair of ‘x increasing’ and ‘’x decreasing' implicitly requires that ‘y increasing’ holds without interruption—otherwise the (invalid) example on the right matches the pattern, too.
[ Normal View | Magnified View ]
Three problematic cases for A1 (first row), TSKR (second row), and semi‐interval sequential patterns (SISP; third row).
[ Normal View | Magnified View ]
An example pattern (P) expressed by (1) containment pattern, (2) A1/A2/Fluent, (3) relationship matrix (RM), (4) TSKR, (5) SIPO, (6) coincidence representation (CR), and finally (7) pattern graphs (PG), (8) TCSP/STP.
[ Normal View | Magnified View ]
All 13 possible qualitative relationships of interval A (gray) with respect to interval B (white) as defined by Allen.
[ Normal View | Magnified View ]
Poor thresholds and inappropriate smoothing (left) leading to fragmented intervals.
[ Normal View | Magnified View ]

Browse by Topic

Fundamental Concepts of Data and Knowledge > Knowledge Representation
Algorithmic Development > Spatial and Temporal Data Mining

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts