Download Advances in Spatial and Temporal Databases: 10th by Leong Hou U, Nikos Mamoulis, Man Lung Yiu (auth.), Dimitris PDF

By Leong Hou U, Nikos Mamoulis, Man Lung Yiu (auth.), Dimitris Papadias, Donghui Zhang, George Kollios (eds.)

For researchers and execs within the box of databases, this can be a must-read textual content that offers them correct up to date info on their field.

Its 479 pages signify the refereed lawsuits of the tenth overseas Symposium on Spatial and Temporal Databases, held in Boston, united states in July 2007.

The 26 revised complete papers have been completely reviewed and chosen from a complete of seventy six submissions.

This makes them, basically, the distilled knowledge of diverse top researchers and teachers within the field.

The papers are categorized in several different types, every one comparable to a convention consultation. This makes the e-book a good dependent and simple to reference volume.

Topics coated comprise non-stop tracking; indexing and question processing; and mining.

There are papers too that take care of aggregation and interpolation; semantics and modeling; and privacy.

Also lined are the most important subject matters corresponding to uncertainty and approximation; streaming facts; disbursed structures; and spatial networks.

This paintings comes with on-line documents in addition to updates.

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Read or Download Advances in Spatial and Temporal Databases: 10th International Symposium, SSTD 2007, Boston, MA, USA, July 16-18, 2007. Proceedings PDF

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Extra info for Advances in Spatial and Temporal Databases: 10th International Symposium, SSTD 2007, Boston, MA, USA, July 16-18, 2007. Proceedings

Example text

Yu et al. [YPK05], Xiong et al. [XMA05] and Mouratidis et al. [MHP05] describe algorithms for exact k-NN queries; all three methods index the data with a regular grid and maintain the k-NN results by considering only object movements that may influence some query. The aforementioned techniques aim at low processing time. There exist, however, methods designed for network cost minimization [MPBT05, HXL05] by exploitation of the objects’ computational resources; their rationale is similar to that of the safe regions explained in Figure 3.

I) a1 ≤ X + − MAXD 2 − Y−2 (iii) a2 ≥ 2 X + + MAXD 2 − Y−2 2 (ii) b1 ≤ Y+ − MAXD 2 − X 2− (iv) b2 ≥ 2 Y+ + MAXD 2 − X 2− 2 where X+=xo2+xd, X−=|xo2-xd|, Y+=yo2+yd, and Y−=|yo2-yd|. Proof: As described, we do not update the MBR if LB = MDmin/V > Tt2(Pt1) − ΔT. It implies that for every side S = Sleft, Stop, Sright, Sbottom, MD(o2,d,S) ≥ MDmin > V* (Tt2(Pt1)−ΔT) = MAXD. Solving the inequality with one side will get one of the four conditions. Without loss of generality, we use Sbottom: y=b1 to show the derivation.

Definition 1. Given a road network, the travel time network is a directed weighted graph G = {V, E, W}, where the vertices in V are the end points or the intersections of the road segments, each edge e, denoted by , in E is the road segments with start point ni and end point nj, and each weight in W is the travel time of the corresponding road segment. In the road network, we use a point p represented by to indicate a location in where d is the travel time from ni to p.

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