Visual Analysis of Time‐Series Similarities for Anomaly Detection in Sensor Networks. (12th July 2014)

• Record Type: Journal Article
• Authors: Steiger, Martin; Bernard, Jürgen; Mittelstädt, Sebastian; Lücke‐Tieke, Hendrik; Keim, Daniel; May, Thorsten; Kohlhammer, Jörn
• Is Part Of: Computer graphics forum. Volume 33:Number 3(2014)
• Page Start: 401
• Page End: 410
• Publication Date: 2014-07-12
• Subjects: Categories and Subject Descriptors (according to ACM CCS): -- I.3.6 [Computer Graphics]: Methodology and Techniques—Interaction techniques -- H.5.2 [Information Interfaces and Presentation]: User Interfaces—User‐centered design -- C.2.3 [Computer‐Communication Networks]: Network Operations—Network monitoring; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.12396
• Languages: English
• ISSNs: 0167-7055

Abstract

Abstract: We present a system to analyze time‐series data in sensor networks. Our approach supports exploratory tasks for the comparison of univariate, geo‐referenced sensor data, in particular for anomaly detection. We split the recordings into fixed‐length patterns and show them in order to compare them over time and space using two linked views. Apart from geo‐based comparison across sensors we also support different temporal patterns to discover seasonal effects, anomalies and periodicities. The methods we use are best practices in the information visualization domain. They cover the daily, the weekly and seasonal and patterns of the data. Daily patterns can be analyzed in a clustering‐based view, weekly patterns in a calendar‐based view and seasonal patters in a projection‐based view. The connectivity of the sensors can be analyzed through a dedicated topological network view. We assist the domain expert with interaction techniques to make the results understandable. As a result, the user can identify and analyze erroneous and suspicious measurements in the network. A case study with a domain expert verified the usefulness of our approach.