Detection and Exploration of Outlier Regions in Sensor Data Streams

Franke, Conny; Gertz, Michael

doi:10.1109/icdmw.2008.21

Cited by 21 publications

(7 citation statements)

References 10 publications

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“…Flow anomaly discovery can be considered as detecting discontinuities or inconsistencies of a non-spatiotemporal attribute within a neighborhood defined by the flow between nodes, and such discontinuities are persistent over a period of time. A time-scalable technique called SWEET (Smart Window Enumeration and Evaluation of persistent-Thresholds) was proposed [57,108,109] that utilizes several algebraic properties in the flow anomaly problem to discover these patterns efficiently. To account for flow anomalies across multiple locations, recent work [58] defines a teleconnected flow anomaly pattern and proposes a RAD (Relationship Analysis of Dynamic-neighborhoods) technique to efficiently identify this pattern.…”

Section: Common Approachesmentioning

confidence: 99%

Spatiotemporal Data Mining: A Computational Perspective

Shekhar

Jiang

Ali

et al. 2015

IJGI

174

113

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Explosive growth in geospatial and temporal data as well as the emergence of new technologies emphasize the need for automated discovery of spatiotemporal knowledge. Spatiotemporal data mining studies the process of discovering interesting and previously unknown, but potentially useful patterns from large spatiotemporal databases. It has broad application domains including ecology and environmental management, public safety, transportation, earth science, epidemiology, and climatology. The complexity of spatiotemporal data and intrinsic relationships limits the usefulness of conventional data science techniques for extracting spatiotemporal patterns. In this survey, we review recent computational techniques and tools in spatiotemporal data mining, focusing on several major pattern families: spatiotemporal outlier, spatiotemporal coupling and tele-coupling, spatiotemporal prediction, spatiotemporal partitioning and summarization, spatiotemporal hotspots, and change detection. Compared with other surveys in the literature, this paper emphasizes the statistical foundations of spatiotemporal data mining and provides comprehensive coverage of computational approaches for various pattern families.ISPRS Int. J. Geo-Inf. 2015, 4 2307We also list popular software tools for spatiotemporal data analysis. The survey concludes with a look at future research needs.

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Section: Common Approachesmentioning

confidence: 99%

Spatiotemporal Data Mining: A Computational Perspective

Shekhar

Jiang

Ali

et al. 2015

IJGI

174

113

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“…Further, the spatial interpolation surface can reflect the locally maximum spatial anomaly degree, i.e. provide a visual representation of local heterogeneity (Franke and Gertz ). As shown in Figure a, the spatial interpolation surface is obtained from the spatial anomaly degree calculated for the simulated data, where the entities in AHEPs have been removed.…”

Section: The Saprd Algorithmmentioning

confidence: 99%

A Spatial Anomaly Points and Regions Detection Method Using Multi‐Constrained Graphs and Local Density

Shi

Deng

Yang

et al. 2016

Transactions in GIS

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Spatial anomalies may be single points or small regions whose non‐spatial attribute values are significantly inconsistent with those of their spatial neighborhoods. In this article, a Spatial Anomaly Points and Regions Detection method using multi‐constrained graphs and local density (SAPRD for short) is proposed. The SAPRD algorithm first models spatial proximity relationships between spatial entities by constructing a Delaunay triangulation, the edges of which provide certain statistical characteristics. By considering the difference in non‐spatial attributes of adjacent spatial entities, two levels of non‐spatial attribute distance constraints are imposed to improve the proximity graph. This produces a series of sub‐graphs, and those with very few entities are identified as candidate spatial anomalies. Moreover, the spatial anomaly degree of each entity is calculated based on the local density. A spatial interpolation surface of the spatial anomaly degree is generated using the inverse distance weight, and this is utilized to reveal potential spatial anomalies and reflect their whole areal distribution. Experiments on both simulated and real‐life spatial databases demonstrate the effectiveness and practicability of the SAPRD algorithm.

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“…Preliminary work introduced a time-scalable technique called SWEET (Smart Window Enumeration and Evaluation of persistent-Thresholds) that utilizes several algebraic properties in the flow anomaly problem to discover these patterns efficiently. [120][121][122] However, further research is needed to discover other types of patterns within this environment. In the context of transportation networks, researchers proposed similar ST outlier patterns for identifying traffic accidents known as anomalous window discovery.…”

Section: Spatio-temporal Data Miningmentioning

confidence: 99%

“…Spatial outlier detection techniques do not consider the flow (i.e., TT) between spatial locations and cannot detect any type of FAs. Preliminary work introduced a time‐scalable technique called SWEET (Smart Window Enumeration and Evaluation of persistent‐Thresholds) that utilizes several algebraic properties in the flow anomaly problem to discover these patterns efficiently 120–122. However, further research is needed to discover other types of patterns within this environment.…”

Section: Future Directions and Research Needsmentioning

confidence: 99%

Identifying patterns in spatial information: A survey of methods

Shekhar

Evans

Kang

et al. 2011

WIREs Data Min & Knowl

141

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Explosive growth in geospatial data and the emergence of new spatial technologies emphasize the need for automated discovery of spatial knowledge. Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial databases. The complexity of spatial data and implicit spatial relationships limits the usefulness of conventional data mining techniques for extracting spatial patterns. In this paper, we explore the emerging field of spatial data mining, focusing on different methods to extract patterns from spatial information. We conclude with a look at future research needs.

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Detection and Exploration of Outlier Regions in Sensor Data Streams

Cited by 21 publications

References 10 publications

Spatiotemporal Data Mining: A Computational Perspective

Spatiotemporal Data Mining: A Computational Perspective

A Spatial Anomaly Points and Regions Detection Method Using Multi‐Constrained Graphs and Local Density

Identifying patterns in spatial information: A survey of methods

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