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Visual discovery and model-driven explanation of time series patterns

Accepted version
Peer-reviewed

Type

Conference Object

Change log

Authors

Sarkar, A 
Spott, M 
Blackwell, AF 

Abstract

Gatherminer is an interactive visual tool for analysing time series data with two key strengths. First, it facilitates bottom-up analysis, i.e., the detection of trends and patterns whose shapes are not known beforehand. Second, it integrates data mining algorithms to explain such patterns in terms of the time series’ metadata attributes – an extremely difficult task if the space of attribute-value combinations is large. To accomplish these aims, Gatherminer automatically rearranges the data to visually expose patterns and clusters, whereupon users can select those groups they deem ‘interesting.’ To explain the selected patterns, the visualisation is tightly coupled with automated classification techniques, such as decision tree learning. We present a brief evaluation with telecommunications experts comparing our tool against their current commercial solution, and conclude that Gatherminer significantly improves both the completeness of analyses as well as analysts’ confidence therein.

Description

Keywords

46 Information and Computing Sciences, 4607 Graphics, Augmented Reality and Games

Journal Title

Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC

Conference Name

2016 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)

Journal ISSN

1943-6092
1943-6106

Volume Title

Publisher

IEEE
Sponsorship
Advait is supported by an EPSRC+BT iCASE award and a Cambridge Computer Laboratory Robert Sansom scholarship.