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range aggregate processing in spatial databases

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 [email protected] Dimitris Papadias Department of Computer Science Hong Kong University of Science and Technology Clear Water Bay, Hong Kong [email protected] Abstract

(PDF) Range aggregate processing in spatial databases

Range 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

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA

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 hyper-rectangle (e.g., the total number of these points instead of their concrete ids).

CiteSeerX — Range Aggregate Processing in Spatial Databases

CiteSeerX Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract—A range aggregate query returns summarized information about the points falling in a hyper-rectangle (e.g., 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 Point-tree (aP-tree), which achieves

A Scalable Algorithm for Maximizing Range Sum in Spatial

We 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 sum-marized information about objects in a given range rather than individual objects. Thus, a RA query returns an ag-gregation value over objects qualified for a given range. In

Algorithms for Fundamental Spatial Aggregate Operations

Aggregate range queries perform some aggregate operation over spatial or spatiotemporal data that fall into a user speci ed area (the range or box), pos- sibly over some speci ed time window [17, 10, 13].

Indexing range sum queries in spatio-temporal databases

Apr 01, 2007 Although range sum queries can be processed simply using operational databases, their computation is expensive due to the direct access of potentially huge amounts of data, rendering online processing inapplicable. To efficiently process these queries

Supporting Spatial Aggregation in Sensor Network Databases

of two different approaches to distributed spatial aggregate processing. Categories and Subject Descriptors H.2.8 [Database Management]: Database applications— Spatial Databases and GIS;H.2.4[DatabaseManagement]: Systems—Query processing, Distributed databases ∗This research is based upon work supported inpart by

Probabilistic Threshold Range Aggregate Query Processing

Apr 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 query.

(PDF) Query Processing in Spatial Databases Containing

Query Processing in Spatial Databases Containing Obstacles The aggregate travel distance can be measured in terms of the total, the maximum or the minimum travel distance of all group members

Predicted range aggregate processing in spatio-temporal

Predicted Range Aggregate Processing in Spatio-temporal Databases Wei Liao, Guifen Tang, Ning Jing, Zhinong Zhong School of Electronic Science and Engineering, National University of Defense Technology Changsha, China [email protected] Abstract Predicted range aggregate (PRA) query is an important researching issue in spatio-temporal

Algorithms for Fundamental Spatial Aggregate Operations

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), pos-sibly over some speci ed time window [17, 10, 13]. Such aggregation mechanisms seem to stem from the

Indexing range sum queries in spatio-temporal databases

Apr 01, 2007 The R-tree is known to be one of the most popular index structures to efficiently process window queries in spatial databases. Intuitively, the aggregate R-tree (aR-tree),improves the R-tree’s performance in range sum queries by storing, in each intermediate entry, pre-aggregated sums of the objects in the subtree. Fig. 1 shows an example of an aR-tree.

Efficient Maximum Range Search on Remote Spatial Databases

Jan 01, 2013 Supporting aggregate range queries on remote spatial databases suffers from 1) huge and/or large numbers of databases, and 2) limited type of access interfaces. This paper applies the Regular Polygon based Search Algorithm (RPSA) to effectively addressing these problems.

(PDF) Data Structures for Range-Aggregate Extent Queries

to the upper (resp. lower) hull of the convex hull of [15] S. Shekhar and S. Chawla. Spatial Databases: A Tour. Prentice Hall, 2002. Si . Let ωiL (resp. ωiR ) be the minimum distance be- [16] Y. Tao and D. Papadias. Range aggregate processing in spatial databases.

range aggregate processing in spatial da ases

CiteSeerX — Range Aggregate Processing in Spatial DatabasesCiteSeerX Range aggregate queries [7] apply an aggregation SQL operator, eg, SUM, over a set of selected contiguous ranges in the domains of the dimensional attribut Usually, such queries are resource intensive as they have high computational overheads in terms of temporal and spatial needs.Range Aggregate Processing in Spatial

Probabilistic Threshold Range Aggregate Query Processing

Apr 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 query.

Efficient Maximum Range Search on Remote Spatial

processing either k-ANN 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 range

Supporting spatial aggregation in sensor network databases

Our spatial aggregate operators are compatible as the primary keys. with the aggregate processing of TAG and easily portable A set of different aggregation queries are now formally to TinyDB. definable on the the realized conceptual model of the sensor Zhao et al. in

Spatial databases with application to GIS Guide books

Gupta P (2006) Range-aggregate query problems involving geometric aggregation operations, Nordic Journal of Computing, 13:4, (294-308), Mamoulis N and Tao Y Query processing in spatial network databases Proceedings of the 29th international conference on Very large data bases

Approximately processing aggregate range queries on remote

Jan 01, 2013 Processing aggregate range queries on remote spatial databases suffers from accessing huge and/or large number of databases that operate autonomously and simple and/or restrictive web API interfaces. To overcome these difficulties, this paper applies a revised version of regular polygon-based search algorithm (RPSA) to approximately search aggregate range query results over remote spatial

Predicted Range Aggregate Processing in Spatio-temporal

Predicted Range Aggregate Processing in Spatio-temporal Databases . By Wei Liao, Guifen Tang, Ning Jing and Zhinong Zhong. Abstract. Predicted range aggregate (PRA) query is an important researching issue in spatio-temporal databases. Recent studies have developed two major classes of PRA query methods: (1) accurate approaches, which search the

Finding top- k relevant groups of spatial web objects

Jun 16, 2015 The web is increasingly being accessed from geo-positioned devices such as smartphones, and rapidly increasing volumes of web content are geo-tagged. In addition, studies show that a substantial fraction of all web queries has local intent. This development motivates the study of advanced spatial keyword-based querying of web content. Previous research has primarily focused

Article: Approximately processing aggregate range queries

Title: Approximately processing aggregate range queries on remote spatial databases. Authors: Hideki Sato; Ryoichi Narita. Addresses: School of Informatics, Daido University, 10-3 Takiharu-cho, Minami-ku, Nagoya, 457-8530, Japan ' Aichi Toho University, 3-11 Heiwagaoka, Meito-ku, Nagoya, 465-8515, Japan

Predicted range aggregate processing in spatio-temporal

Predicted Range Aggregate Processing in Spatio-temporal Databases Wei Liao, Guifen Tang, Ning Jing, Zhinong Zhong School of Electronic Science and Engineering, National University of Defense Technology Changsha, China [email protected] Abstract Predicted range aggregate (PRA) query is an important researching issue in spatio-temporal

Algorithms for Fundamental Spatial Aggregate Operations

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), pos-sibly over some speci ed time window [17, 10, 13]. Such aggregation mechanisms seem to stem from the

Indexing range sum queries in spatio-temporal databases

Apr 01, 2007 The R-tree is known to be one of the most popular index structures to efficiently process window queries in spatial databases. Intuitively, the aggregate R-tree (aR-tree),improves the R-tree’s performance in range sum queries by storing, in each intermediate entry, pre-aggregated sums of the objects in the subtree. Fig. 1 shows an example of an aR-tree.

Article: Approximately processing aggregate range queries

Title: Approximately processing aggregate range queries on remote spatial databases. Authors: Hideki Sato; Ryoichi Narita. Addresses: School of Informatics, Daido University, 10-3 Takiharu-cho, Minami-ku, Nagoya, 457-8530, Japan ' Aichi Toho University, 3-11 Heiwagaoka, Meito-ku, Nagoya, 465-8515, Japan

Predicted Range Aggregate Processing in Spatio-temporal

Predicted Range Aggregate Processing in Spatio-temporal Databases . By Wei Liao, Guifen Tang, Ning Jing and Zhinong Zhong. Abstract. Predicted range aggregate (PRA) query is an important researching issue in spatio-temporal databases. Recent studies have developed two major classes of PRA query methods: (1) accurate approaches, which search the

Moving range query processing in spatial databases

Moving range query processing in spatial databases Recent developments in mobile communications have brought dramatic and fundamental changes to the modern world. These developments have resulted in a great demand for applications that integrate geographic locations and services to

Publications of Dimitris Papadias

Tao, Y., Papadias, D. Range Aggregate Processing in Spatial Databases. IEEE Transactions on Knowledge and Data Engineering (TKDE), 16(12), 1555-1570, 2004.. D. Range Queries Involving Spatial Relations: A Performance Analysis. Proceedings of the 2 nd European Conference on Spatial Information Theory (COSIT), Semmering, Austria.

Spatial Analytics with Oracle Database 19c

moving complex spatial logic into the database. The processing power and bandwidth of Oracle Exadata Database Machine is exploited, realizing extreme performance capabilities that are orders of magnitude over what was previously possible. This white paper provides an overview of the spatial features in Oracle Database.

Spatial databases with application to GIS Guide books

Hockenberry M and Selker T A sense of spatial semantics CHI '06 Extended Abstracts on Human Factors in Computing Systems, (851-856) Gupta P (2006) Range-aggregate query problems involving geometric aggregation operations, Nordic Journal of Computing, 13:4, (294-308), Online publication date: 1-Dec-2006. Gertz M, Hart Q, Rueda C, Singhal S and

Spatial database Wikipedia

A spatial database is a database that is optimized for storing and querying data that represents objects defined in a geometric space. Most spatial databases allow the representation of simple geometric objects such as points, lines and polygons. Some spatial databases handle more complex structures such as 3D objects, topological coverages, linear networks, and TINs.

Range-aggregate query problems involving geometric

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, 1555-1570.

An Efficient Algorithm for processing Top-k Spatial

database with respect to the quality of their locations, quantified by aggregating non-spatial characteristics of other features (e.g., 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 query-parametric.

Yufei Tao's Publications CUHK CSE

Range Aggregate Processing in Spatial Databases. IEEE Transactions on Knowledge and Data Engineering (TKDE), 16(12): 1555-1570, 2004. 2003 . Dimitris Papadias, Yufei Tao, Greg Fu, and Bernhard Seeger. An Optimal and Progressive Algorithm for Skyline Queries. Proceedings of ACM Conference on Management of Data (SIGMOD), pages 467-478, 2003. Long

Query processing in spatial databases containing obstacles

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 of