predicted range aggregate processing in spatio

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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

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CiteSeerX — Predicted Range Aggregate Processing in

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 common moving objects indexes to obtain an accurate result; and (2) estimate methods, which utilize approximate techniques to estimate the result with an acceptable error.

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Predicted Range Aggregate Processing in Spatio-temporal

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 common moving objects indexes to obtain an accurate result; and (2) estimate methods, which utilize approximate techniques to estimate the result with an acceptable error.

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Predicted range aggregate processing in spatio-temporal

Microsoft Word Predicted range aggregate processing in spatio-temporal database0622.doc Author: wliao Created Date: 7/7/2006 8:15:58 PM

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[PDF] Range aggregate processing in spatial databases

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 logarithmic cost to the data set cardinality (independently of the query size) for two-dimensional data.

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Range Aggregate Processing in Spatial Databases.

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 logarithmic cost to the data set cardinality (independently of the query size) for two

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Range aggregate processing in spatial databases HKUST

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 logarithmic cost to the data set cardinality ( independently of the query size) for two

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CiteSeerX — Range Aggregate Processing in Spatial

We also present models that accurately predict the space consumption and query cost of the aP-tree and are therefore suitable for query optimization. Extensive experiments confirm that the proposed methods are efficient and practical. Index Terms—Database, spatial database, range queries, aggregation

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Meaningful spatial prediction and aggregation

01-01-2014· Meaningful aggregation. Spatio-temporal aggregation procedures describe how to generate lattice data from point pattern or geostatistical data. They consist of two steps: First, groups are composed according to spatio-temporal predicates, usually referred to as group composition. Then, an aggregate value is computed for each group of objects.

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Indexing range sum queries in spatio-temporal databases

01-04-2007· 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.

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range aggregate processing in spatial databases

CiteSeerX — Range Aggregate Processing in Spatial . 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

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range aggregate processing spatial databases

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).

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CiteSeerX — Range Aggregate Processing in Spatial

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

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Range Aggregate Processing in Spatial Databases IEEE

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). No. 12 Range Aggregate Processing in Spatial Databases

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Range aggregate processing in spatial databases CORE

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: 10.1109/tkde.2004.93. OAI identifier: Provided by: MUCC (Crossref

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Predictive Spatio-Temporal Queries: A Comprehensive Survey

to predict the coming path of each of the underlying vide an approximate answer for aggregate spatio-temporal queries for the future, in addition to the [12, 18, 63] is considered for processing continuous range queries. Additional work considers query selectivity which plays a com-plementary role in the area of predictive spatio

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Indexing range sum queries in spatio-temporal

01-04-2007· 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.

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Scalable Spatial Predictive Query Processing for Moving

predictive spatial queries include predictive range query, e.g., “find all hotels that will be located within two miles of a user’s anticipated location after 30 minutes“, predictive KNN query, e.g., “find the three taxis that most likely to pass by my

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Spatial Prediction an overview ScienceDirect Topics

07-08-2003· As shown in Fig. 12, now the motion vector mv in the current block is predicted from its three nearest previously processed spatial neighbors mv1, mv2, and mv3 similar to the approach in [8]. The spatial prediction scheme can predict the motion vector

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Exploring spatial autocorrelation in R Gwen Antell

24-07-2019· An essential but feared topic in spatial analysis is spatial autocorrelation (SAC). If this term is new to you, check out my primer on autocorrelation in ecology.If you work with spatial data in R (or plan to do so) and want to calculate the degree to which SAC could affect your investigation, read on!

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