Ranking spatial data by quality preferences ppt

atial Data by
Ranking Sp
Preferences
Quality

-Saurav(2sd10is044)
)
-Raja kr. Singh(2sd10is033
2sd10is060)
-Veena mahajanshettar(

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

SDMCET (Information Science & Engineering)

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2

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:
• 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:
•

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:
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 nonspatial 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:

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:
 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
[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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Ranking spatial data by quality preferences ppt

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