{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T18:21:51Z","timestamp":1767205311544,"version":"build-2238731810"},"reference-count":26,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2012,12,12]],"date-time":"2012-12-12T00:00:00Z","timestamp":1355270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Sensors"],"abstract":"<jats:p>Environmental monitoring applications are designed for supplying derived and often integrated information by tracking and analyzing phenomena. To determine the condition of a target place, they employ a geosensor network to get the heterogeneous sensor data. To effectively handle a large volume of sensor data, applications need a data abstraction model, which supports the summarized data representation by encapsulating raw data. For faster data processing to answer a user\u2019s queries with representative attributes of an abstracted model, we propose such a data abstraction model, the Layered Slopes in Grid for Sensor Data Abstraction (LSGSA), which is based on the SGSA. In a single grid-based layer for each sensor type, collected data is represented by slope directional vectors in two layered slopes, such as height and surface. To answer a user query in a central monitoring server, LSGSA is used to reduce the time needed to extract event features from raw sensor data as a preprocessing step for interpreting the observed data. The extracted features are used to understand the current data trends and the progress of a detected phenomenon without accessing raw sensor data.<\/jats:p>","DOI":"10.3390\/s121217074","type":"journal-article","created":{"date-parts":[[2012,12,12]],"date-time":"2012-12-12T11:02:13Z","timestamp":1355310133000},"page":"17074-17093","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Geosensor Data Representation Using Layered Slope Grids"],"prefix":"10.3390","volume":"12","author":[{"given":"Yongmi","family":"Lee","sequence":"first","affiliation":[{"name":"Database\/Bioinformatics Lab, Chungbuk National University, Cheongju 361-763, Korea"}]},{"given":"Young","family":"Jung","sequence":"additional","affiliation":[{"name":"Korea Institute of Science Technology and Information, 245 Daehangno, Yuseong, Daejeon 305-806, Korea"}]},{"given":"Kwang","family":"Nam","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Engineering, Kunsan National University, Kunsan 573-701, Korea"}]},{"given":"Silvia","family":"Nittel","sequence":"additional","affiliation":[{"name":"School of Computing and Information Science, University of Maine, Orono, 5711 Boardman Hall, Rm. 344, Orono, ME 04467, USA"}]},{"given":"Kate","family":"Beard","sequence":"additional","affiliation":[{"name":"School of Computing and Information Science, University of Maine, Orono, 5711 Boardman Hall, Rm. 344, Orono, ME 04467, USA"}]},{"given":"Keun","family":"Ryu","sequence":"additional","affiliation":[{"name":"Database\/Bioinformatics Lab, Chungbuk National University, Cheongju 361-763, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2012,12,12]]},"reference":[{"key":"ref_1","unstructured":"Elson, J., and Estrin, D. 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