range aggregate processing in spatial databases
Range Aggregate Processing in Spatial Databases Yufei Tao Department of Computer Science City University of Hong Kong Tat Chee Avenue, Hong Kong Dimitris Papadias Department of Computer Science Hong Kong University of Science and Technology Clear Water Bay, Hong Kong AbstractRange Aggregate Processing in Spatial Databases Yufei Tao Traditional research in spatial databases often aims at the range query, which retrieves the data objects lying inside (or(PDF) Range aggregate processing in spatial databases
A range aggregate query returns summarized information about the points falling in a hyperrectangle (eg, the total number of these points instead of their concrete ids) This paper studies spatial indexes that solve such queries efficiently and proposes the aggregate Pointtree (aPtree), which achieves logarithmic cost to the data set cardinality (independently of the query size) for twoCiteSeerX Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract—A range aggregate query returns summarized information about the points falling in a hyperrectangle (eg, the total number of these points instead of their concrete ids) This paper studies spatial indexes that solve such queries efficiently and proposes the aggregate Pointtree (aPtree), which achievesCiteSeerX — Range Aggregate Processing in Spatial Databases
Range Aggregate Processing in Spatial Databases Yufei Tao and Dimitris Papadias Abstract—A range aggregate query returns summarized information about the points falling in a hyperrectangle (eg, the total number of these points instead of their concrete ids) This paper studies spatial indexes that solve such queries efficiently andWe first review the range aggregate processing methods in spatial databases The range aggregate (RA) query was proposed for the scenario where users are interested in summarized information about objects in a given range rather than individual objects Thus, a RA query returns an aggregation value over objects qualified for a given range InA Scalable Algorithm for Maximizing Range Sum in Spatial
Range aggregate processing in spatial databases By null Yufei Tao and D Papadias Cite BibTex; Full citation; Publisher: Institute of Electrical and Electronics Engineers (IEEE) Year: 2004 DOI identifier: 101109/tkde200493 OAI identifier: Provided by: MUCC (Crossrefof two different approaches to distributed spatial aggregate processing Categories and Subject Descriptors H28 [Database Management]: Database applications— Spatial Databases and GIS;H24[DatabaseManagement]: Systems—Query processing, Distributed databases ∗This research is based upon work supported inpart bySupporting Spatial Aggregation in Sensor Network Databases
Range aggregate processing in spatial databases By null Yufei Tao and D Papadias Cite BibTex; Full citation; Publisher: Institute of Electrical and Electronics Engineers (IEEE) Year: 2004 DOI identifier: 101109/tkde200493 OAI identifier: Provided by: MUCC (CrossrefPredicted Range Aggregate Processing in Spatiotemporal Databases Wei Liao, Guifen Tang, Ning Jing, Zhinong Zhong School of Electronic Science and Engineering, National University of Defense Technology Changsha, China Abstract Predicted range aggregate (PRA) query is an important researching issue in spatiotemporalPredicted range aggregate processing in spatiotemporal
spatial aggregates is devoted to mechanisms to support range queries, or box queries Aggregate range queries perform some aggregate operation over spatial or spatiotemporal data that fall into a user speci ed area (the range or box), possibly over some speci ed time window [17, 10, 13] Such aggregation mechanisms seem to stem from theApr 01, 2007· The Rtree is known to be one of the most popular index structures to efficiently process window queries in spatial databases Intuitively, the aggregate Rtree (aRtree) , improves the Rtree’s performance in range sum queries by storing, in each intermediate entry, preaggregated sums of the objects in the subtree Fig 1 shows an example of an aRtreeIndexing range sum queries in spatiotemporal databases
of two different approaches to distributed spatial aggregate processing Categories and Subject Descriptors H28 [Database Management]: Database applications— Spatial Databases and GIS;H24[DatabaseManagement]: Systems—Query processing, Distributed databases ∗This research is based upon work supported inpart byprocessing either kANN queries or aggregate range queries on remote spatial databases In other words, a new strategy for efficiently processing these queries is required This paper applies Regular Polygon based Search Algorithm (RPSA)toefficiently searching approximate aggregate rangeEfficient Maximum Range Search on Remote Spatial
Jul 20, 2012· Spatial range query is one of the most common queries in spatial databases, where a user invokes a query to find all the surrounding interest objects Most studies in range search consider Euclidean distances to retrieve the result in low cost, but with poor accuracy (ie, Euclidean distance less than or equal network distance) Thus, researchers show that range search in network distanceApr 02, 2009· A probabilistic threshold range aggregate (PTRA) query retrieves summarized information about the uncertain objects satisfying a range query, with respect to a given probability threshold This paper is the first one to address this important type of queryProbabilistic Threshold Range Aggregate Query Processing
tance In spatial data management, records in the database have a spatial extent, and users can pose expressive queries such as a spatial join between two relations (join all objects that overlap or are within certain distance of each other) or a range query (report all objects in a selected range, or return an aggregate over the selected objects)Spatial Databases: Accomplishments and Research Needs, S Shekhar , S "Range Aggregate Processing in Spatial Databases," IEEE Transactions on Knowledge and Data Engineering, vol 16, no 12, pp 15551570, December, 2004 Haibo Hu, Dik Lun Lee "Range NearestNeighbor Query," IEEE Transactions on Knowledge and Data EngineeringList of Papers and Books Hui Xiong
Aggregation of Data by Using Top K Spatial Query Preferences Hanaa Mohsin Ali AlAbboodi College of Engineering , University of Babylon,Iraq *:hanaamohsin77@Gmail Abstract: A spatial database is a database that is optimized to store and query data that represents objects defined in a geometric spaceTao, Y, Papadias, D Range Aggregate Processing in Spatial Databases IEEE Transactions on Knowledge and Data Engineering (TKDE), 16(12), 15551570, 2004 D Range Queries Involving Spatial Relations: A Performance Analysis Proceedings of the 2 nd European Conference on Spatial Information Theory (COSIT), Semmering, AustriaPublications of Dimitris Papadias
Spatial Databases: A Tour Prentice Hall Google Scholar {13} SHERWANI, N 1998 Algorithms for VLSI Physical Design Automation Kluwer Academic Google Scholar Digital Library {14} TAO, Y AND PAPADIAS, D 2004 Range aggregate processing in spatial databases IEEE Transactions on Knowledge and Data Engineering 16, 12, 15551570Spatial Databases 11 Introduction 111 Spatial Database Spatial database management systems [43, 58, 120, 119, 97, 74] aim at the effective and efficient management spatial query processing including point, regional, range, and nearest neighbor queries; and spatial data methods using a variety of indexes such as set of aggregateSpatial Databases
Apr 01, 2007· The Rtree is known to be one of the most popular index structures to efficiently process window queries in spatial databases Intuitively, the aggregate Rtree (aRtree) , improves the Rtree’s performance in range sum queries by storing, in each intermediate entry, preaggregated sums of the objects in the subtree Fig 1 shows an example of an aRtreeprocessing either kANN queries or aggregate range queries on remote spatial databases In other words, a new strategy for efficiently processing these queries is required This paper applies Regular Polygon based Search Algorithm (RPSA)toefficiently searching approximate aggregate rangeEfficient Maximum Range Search on Remote Spatial
be solved efficiently [19] In online analytical processing (OLAP), spatial databases such as geographic information systems (GIS), and several other applications, rangeaggregate queries (eg, rangeCOUNT, rangeSUM, etc) play an extremely important role, and a large number of algorithms and indexing structuresTao, Y, Papadias, D Range Aggregate Processing in Spatial Databases IEEE Transactions on Knowledge and Data Engineering (TKDE), 16(12), 15551570, 2004 D Range Queries Involving Spatial Relations: A Performance Analysis Proceedings of the 2 nd European Conference on Spatial Information Theory (COSIT), Semmering, AustriaPublications of Dimitris Papadias
We consider variations of the standard orthogonal range searching motivated by applications in database querying and VLSI layout processing In a generic instance of such a problem, called a rangeAggregation of Data by Using Top K Spatial Query Preferences Hanaa Mohsin Ali AlAbboodi College of Engineering , University of Babylon,Iraq *:hanaamohsin77@Gmail Abstract: A spatial database is a database that is optimized to store and query data that represents objects defined in a geometric spaceAggregation of Data by Using Top K Spatial Query
Despite the existence of obstacles in many database applications, traditional spatial query processing assumes that points in space are directly reachable and utilizes the Euclidean distance metric In this paper, we study spatial queries in the presence of obstacles, where the obstructed distance between two points is defined as the length ofGupta P (2006) Rangeaggregate query problems involving geometric aggregation operations, Nordic Journal of Computing, 13:4, (294308), Mamoulis N and Tao Y Query processing in spatial network databases Proceedings of the 29th international conference on Very large data bases Spatial databases with application to GIS | Guide books
Efficient processing of topK queries is a crucial requirement in many interactive applications that deals with huge amount of data In particular efficient topK processing has shown a great impact on performance in domains such as the web, text and data integration, business analytics, distributed aggregation of network logs and sensor data, data mining and so ondatabase with respect to the quality of their locations, quantified by aggregating nonspatial characteristics of other features (eg, restaurants, super market, hospital, railway station, etc) in the spatial neighborhood of the flat (defined by a spatial range around it) Quality may be subjective and queryparametricAn Efficient Algorithm for processing Topk Spatial
Distributed Processing of Range Queries with NonSpatial Selections DongEun Kim, HaRim Jung, GiWoong Nam, Hee Yong Youn and UngMo Kim School of Information and Communication Engineering, Sungkyunkwan University 27309, 2066, SeobuRo, JanganGu, Suwon, Gyeong gido, Korea dongsilver1@gmail, harim3826@gmail, , ,tance In spatial data management, records in the database have a spatial extent, and users can pose expressive queries such as a spatial join between two relations (join all objects that overlap or are within certain distance of each other) or a range query (report all objects in a selected range, or return an aggregate over the selected objects)Approximation Techniques for Spatial Data
Range aggregate processing in spatial databases ResearchGate In this paper, we consider range count queries on multidimensional data points, where the result is the size of R (eg, the number of hotels in an areaAggregate processing of multidimensional objects has also been studied theoretically, leading to several interesting resultssolutions for the topk spatial preference query based on the influence score Figure 1: (a) Range score,(b) Influence score, c=02 km LITERATURE REVIEW Object ranking is a popular retrieval task in various applications In relational databases, we rank tuples using an aggregate score function on their attribute values [3] ForAN EFFICIENT AND EFFECTIVE RANKING ON SPATIAL DATA
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