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  1. Proceedings of the 4th ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS '13)
  2. Mining robust neighborhoods for quality control of sensor data
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Towards window stream queries over continuous phenomena
Spatiotemporal transformation of social media geostreams: a case study of Twitter for flu risk analysis
Enhanced database support for location-based services
A novel real-time framework for extracting patterns from trajectory data streams
Automatic identification of points of interest in global navigation satellite system data: a spatial temporal approach
EHSTC: an enhanced method for semantic trajectory compression
ADTOS: arrival departure tradeoff optimization system
Geo-clouds: visualizing news over geographical maps
Clustering spatial data streams for targeted alerting in disaster response
Continuum: a spatiotemporal data model to represent and qualify filiation relationships
Mining robust neighborhoods for quality control of sensor data

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Conference Proceedings

Mining robust neighborhoods for quality control of sensor data

Content Provider ACM Digital Library
Author Angryk, Rafal A. Galarus, Douglas E.
Abstract Neighborhoods, as used for spatial and spatial-temporal data mining, define areas of similarity in data. Unless defined to account for outliers, missing data and spatial-temporal variation, the robustness of methods utilizing neighborhoods will suffer. The focus of this paper is to demonstrate that neighborhoods can be defined and used in a robust manner that is resistant to such challenges. Our approach employs robust methods in both neighborhood construction and neighborhood application to estimate observations. These methods were tested with a large weather sensor data set from the National Weather Service that includes quality control indicators. Results were compared to a popular method used in the weather community, evaluated by root-mean-squared error and grouped by quality control indicator. Our first time published results show that our methods are robust in the presence of outliers, missing data and spatial-temporal variation, yielding results consistent with quality control labels assigned to the data by the provider by way of an extensive rule-based system, indicating that our approaches show promise for use in quality control assessment.
Starting Page 86
Ending Page 95
Page Count 10
File Format PDF
ISBN 9781450325325
DOI 10.1145/2534303.2534309
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-11-05
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Outliers Robust neighborhoods Quality control Sensor networks Spatial-temporal data mining Spatial-temporal neighborhoods
Content Type Text
Resource Type Article
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