The Experts below are selected from a list of 56433 Experts worldwide ranked by ideXlab platform
David Truffet - One of the best experts on this subject based on the ideXlab platform.
-
Data Partitioning for Parallel Spatial Join Processing
Geoinformatica, 1998Co-Authors: Xiaofang Zhou, David J. Abel, David TruffetAbstract:The cost of Spatial join processing can be very high because of the large sizes of Spatial objects and the computation-intensive Spatial Operations. While parallel processing seems a natural solution to this problem, it is not clear how Spatial data can be partitioned for this purpose. Various Spatial data partitioning methods are examined in this paper. A framework combining the data-partitioning techniques used by most parallel join algorithms in relational databases and the filter-and-refine strategy for Spatial Operation processing is proposed for parallel Spatial join processing. Object duplication caused by multi-assignment in Spatial data partitioning can result in extra CPU cost as well as extra communication cost. We find that the key to overcome this problem is to preserve Spatial locality in task decomposition. In this paper we show that a near-optimal speedup can be achieved for parallel Spatial join processing using our new algorithms.
-
SSD - Data Partitioning for Parallel Spatial Join Processing
Advances in Spatial Databases, 1997Co-Authors: Xiaofang Zhou, David J. Abel, David TruffetAbstract:The cost of Spatial join processing can be very high because of the large sizes of Spatial objects and the computation-intensive Spatial Operations. While parallel processing seems a natural solution to this problem, it is not clear how Spatial data can be partitioned for this purpose. Various Spatial data partitioning methods are examined in this paper. A framework combining the data-partitioning techniques used by most parallel join algorithms in relational databases and the filter-and-refine strategy for Spatial Operation processing is proposed for parallel Spatial join processing. Object duplication caused by multi-assignment in Spatial data partitioning can result in extra CPU cost as well as extra communication cost. We find that the key to overcome this problem is to preserve Spatial locality in task decomposition. We show in this paper that a near-optimal speedup can be achieved for parallel Spatial join processing using our new algorithms.
Monica Wachowicz - One of the best experts on this subject based on the ideXlab platform.
-
Modelling Offset Regions around Static and Mobile Locations on a Discrete Global Grid System: An IoT Case Study
ISPRS International Journal of Geo-Information, 2020Co-Authors: David Bowater, Monica WachowiczAbstract:With the huge volume of location-based point data being generated by Internet of Things (IoT) devices and subsequent rising interest from the Digital Earth community, a need has emerged for Spatial Operations that are compatible with Digital Earth frameworks, the foundation of which are Discrete Global Grid Systems (DGGSs). Offsetting is a fundamental Spatial Operation that allows us to determine the region within a given distance of an IoT device location, which is important for visualizing or querying nearby location-based data. Thus, in this paper, we present methods of modelling an offset region around the point location of an IoT device (both static and mobile) that is quantized into a cell of a DGGS. Notably, these methods illustrate how the underlying indexing structure of a DGGS can be utilized to determine the cells in an offset region at different Spatial resolutions. For a static IoT device location, we describe a single resolution approach as well as a multiresolution approach that allows us to efficiently determine the cells in an offset region at finer (or coarser) resolutions. For mobile IoT device locations, we describe methods to efficiently determine the cells in successive offset regions at fine and coarse resolutions. Lastly, we present a variety of results that demonstrate the effectiveness of the proposed methods.
Ok Hyun Paek - One of the best experts on this subject based on the ideXlab platform.
-
temporal moving pattern mining for location based service
Journal of Systems and Software, 2004Co-Authors: Ok Hyun PaekAbstract:The primary objective of location-based service (LBS) which is generally described as a mobile information service is to provide useful location aware information, at a minimum cost and resources, to its users. This functionality can be implemented through data mining techniques. However, since the conventional studies on data mining do not consider Spatial and temporal aspects of data simultaneously, these techniques have limited application in studying the moving objects of LBS with respect to the Spatial attributes that is changing over time. Defining individual users of LBS as moving objects, this paper proposes a new data mining technique and algorithms for identifying temporal patterns from series of locations of moving objects that have temporal and Spatial dimensions. For this purpose, we use the Spatial Operation to generalize a location of moving point, applying time constraints between locations of moving objects to make valid moving sequences. Through the experiments, we show that our technique generates temporal patterns found in frequent moving sequences in efficient. Finally, the spatio-temporal technique proposed in this work is an innovative approach in providing knowledge applicable to improving the quality of LBS.
Xiaofang Zhou - One of the best experts on this subject based on the ideXlab platform.
-
Data Partitioning for Parallel Spatial Join Processing
Geoinformatica, 1998Co-Authors: Xiaofang Zhou, David J. Abel, David TruffetAbstract:The cost of Spatial join processing can be very high because of the large sizes of Spatial objects and the computation-intensive Spatial Operations. While parallel processing seems a natural solution to this problem, it is not clear how Spatial data can be partitioned for this purpose. Various Spatial data partitioning methods are examined in this paper. A framework combining the data-partitioning techniques used by most parallel join algorithms in relational databases and the filter-and-refine strategy for Spatial Operation processing is proposed for parallel Spatial join processing. Object duplication caused by multi-assignment in Spatial data partitioning can result in extra CPU cost as well as extra communication cost. We find that the key to overcome this problem is to preserve Spatial locality in task decomposition. In this paper we show that a near-optimal speedup can be achieved for parallel Spatial join processing using our new algorithms.
-
SSD - Data Partitioning for Parallel Spatial Join Processing
Advances in Spatial Databases, 1997Co-Authors: Xiaofang Zhou, David J. Abel, David TruffetAbstract:The cost of Spatial join processing can be very high because of the large sizes of Spatial objects and the computation-intensive Spatial Operations. While parallel processing seems a natural solution to this problem, it is not clear how Spatial data can be partitioned for this purpose. Various Spatial data partitioning methods are examined in this paper. A framework combining the data-partitioning techniques used by most parallel join algorithms in relational databases and the filter-and-refine strategy for Spatial Operation processing is proposed for parallel Spatial join processing. Object duplication caused by multi-assignment in Spatial data partitioning can result in extra CPU cost as well as extra communication cost. We find that the key to overcome this problem is to preserve Spatial locality in task decomposition. We show in this paper that a near-optimal speedup can be achieved for parallel Spatial join processing using our new algorithms.
David Bowater - One of the best experts on this subject based on the ideXlab platform.
-
Modelling Offset Regions around Static and Mobile Locations on a Discrete Global Grid System: An IoT Case Study
ISPRS International Journal of Geo-Information, 2020Co-Authors: David Bowater, Monica WachowiczAbstract:With the huge volume of location-based point data being generated by Internet of Things (IoT) devices and subsequent rising interest from the Digital Earth community, a need has emerged for Spatial Operations that are compatible with Digital Earth frameworks, the foundation of which are Discrete Global Grid Systems (DGGSs). Offsetting is a fundamental Spatial Operation that allows us to determine the region within a given distance of an IoT device location, which is important for visualizing or querying nearby location-based data. Thus, in this paper, we present methods of modelling an offset region around the point location of an IoT device (both static and mobile) that is quantized into a cell of a DGGS. Notably, these methods illustrate how the underlying indexing structure of a DGGS can be utilized to determine the cells in an offset region at different Spatial resolutions. For a static IoT device location, we describe a single resolution approach as well as a multiresolution approach that allows us to efficiently determine the cells in an offset region at finer (or coarser) resolutions. For mobile IoT device locations, we describe methods to efficiently determine the cells in successive offset regions at fine and coarse resolutions. Lastly, we present a variety of results that demonstrate the effectiveness of the proposed methods.