Ranking Spatial Data by Ranking Spatial Data by

Quality PreferencesQuality Preferences

-Saurav(2sd10is044)

-Raja kr. Singh(2sd10is033)

-Veena mahajanshettar(2sd10is060)

Contents:Contents: Abstract Introduction Existing system Proposed system Block diagram Conclusion references

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Abstract:Abstract:• A spatial preference query ranks objects based on the qualities of features in their spatial neighborhood. • For example , using a real estate agency database of flats for lease, a customer may want to rank the flats with respect to the appropriateness of their location, defined after aggregating the qualities of other features within their spatial neighborhood. • Such a neighborhood concept can be specified by the user via different functions. • It can be an explicit circular region within a given distance from the flat. • Here, we formally define spatial preference queries and propose appropriate indexing techniques and search algorithms for them. • Extensively evaluation of our methods on both real and synthetic data reveal that an optimized branch-and-bound solution is efficient and robust with respect to different parameters.

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Introduction:Introduction:

• Spatial database systems manage large collections of geographic entities, which apart from spatial attributes contain non-spatial information (e.g., name, size etc.).• Here, we study an interesting type of preference queries, which select the best spatial location with respect to the quality of facilities in its spatial neighborhood. •Given a set D of interesting objects (e.g., candidate locations), a top-k spatial preference query retrieves the k objects in D with the highest scores.• The score of an object is defined by the quality of features (e.g., facilities or services) in its spatial neighborhood. • As a motivating example, consider a real estate agency office that holds a database with available flats for lease.

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Existing system:Existing system:•  To our knowledge, there is no existing efficient solution for processing the

top-k spatial preference query.

• Object ranking is a popular retrieval task in various applications.

• For example, a real estate agency maintains a database that contains information of flats available for rent. A potential customer wishes to view the top-10 flats with the largest sizes and lowest prices. In this case, the score of each flat is expressed by the sum of two qualities: size and price,(e.g., 1 means the largest size and the lowest price).

• In spatial databases, ranking is often associated to nearest neighbor (NN) retrieval. Given a query location, we are interested in retrieving the set of nearest objects to it that qualify a condition (e.g., restaurants).

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Proposed system:Proposed system:We Propose

• Spatial ranking, which orders the objects according to their distance from a reference point, and

•Non-spatial ranking, which orders the objects by an aggregate function on their non-spatial values. Our top- k spatial preference query integrates these two types of ranking in an intuitive way. As indicated by our examples, this new query has a wide range of applications in service recommendation and decision support systems.

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Block Diagram: Block Diagram:

Fig. 1. Examples of top-k spatial preference queries. (a) Range score, ¼ 0:2 km. (b) Influence score, _ ¼ 0:2 km

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Conclusion:Conclusion:

The top-k spatial preference queries provide a novel type of ranking for spatial objects based on qualities of features in their neighbourhood. The neighbourhood of an object p is captured by the scoring function:

The range score restricts the neighbourhood to a crisp region centered at p, The influence score relaxes the neighbourhood to the whole space and assigns higher weights to locations closer to p.

The algorithm performs a multiway join on feature trees to obtain qualified combinations of feature points and then search for their relevant objects in the object tree.

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References References

[1] Man Lung Yiu, Hua Lu, Member, Ieee, Nikos Mamoulis, And Michail Vaitis”Ranking Spatial Data By Quality Preferences” Ieee Transactions On Knowledge And Data Engineering, Vol. 23, No. 3, March 2011

 [2]. M.L. Yiu, X. Dai, N. Mamoulis, and M. Vaitis, “Top-k Spatial PreferenceQueries,” Proc. IEEE Int‟l Conf. Data Eng. (ICDE),2007.

[3]. N. Bruno, L. Gravano, and A. Marian, “Evaluating Top-k Queries over Web-AccessibleDatabases,” Proc. IEEE Int‟l Conf. Data Eng. (ICDE), 2002.

[4]. Guttman, “R-Trees: A Dynamic Index Structure for Spatial Searching,” Proc. ACMSIGMOD, 1984.

[5]G.R. Hjaltason and H. Samet, “Distance Browsing in Spatial Databases,” ACMTrans. Database Systems, vol. 24, no. 2, pp. 265-318, 1999.

[6]. R. Weber, H.-J. Schek, and S. Blott, “A Quantitative Analysis and Performance Studyfor Similarity-Search Methods in High- Dimensional Spaces,” Proc. Int‟l Conf. VeryLarge Data Bases (VLDB), 1998.

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