Top-k Selection Queries over Spatial Data: Mapping Strategies and

Performance Evaluation

Supervisor Presented By

Mrs. E.Baby Anitha M.E.,(Ph.D), N.Subhashini,

Assistant Professor. II M.E.(CSE).

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Introduction

Spatial Mining

Spatial Database

Spatial Object

Spatial Preference Queries

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Objective

To minimize the number of accesses to the input rankings , the object with the top-k aggregate scores have been identified and also reduces the I/O cost .

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Literature ReviewYiu,M.L. Dai,X. Mamoulis,N. and Vaitis,M. (2007) ‘Top-k Spatial Preference Queries’, Proc. IEEE Int’l Conf. Data Eng. (ICDE

Object Ranking. Brance and Bound and Feature join algorithm.

Yiu,M.L. Dai,X. Mamoulis,N. and Vaitis,M. (2007) ‘Top-k Spatial Preference Queries’, Proc. IEEE Int’l Conf. Data Eng. (ICDE

Dynamic index structure. Searching and Updating.

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Yiu,M.L. Dai,X. Mamoulis,N. and Vaitis,M. (2007) ‘Top-k Spatial Preference Queries’, Proc. IEEE Int’l Conf. Data Eng. (ICDE

Extract Geographic knowledge from the web. Query processing. Combination of text and spatial data processing .

Joao B Rocha-Junior;Akrivi Vlachou and Christos Doulkeridis,”Efficient Processing of Top-k Spatial Preference Queries: Proc VLDB Endowment.

Skyline Feature Algorithm. To improve the query processing. To reduce cost of maintenance..

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Existing System

There is no efficient method for processing spatial

preference query.

Object ranking .

To support users in their exploratory search the

search engines are offering semantic search

suggestions.

Brute force approach.

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Drawbacks

Ranking is not accurate.

Anyone can easily cheat this ranking system. 

It does not consider the Search engine optimization

factors even to determine the ranking.

Brute force method is expensive for large input

data sets.

In spatial database ranking is often associated with

the nearest neighbor retrieval.

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Proposed System Mapping of pairs of data and feature objects to

the distance score

space.

Minimal subset of pairs is sufficient to answer all

top-k spatial preference queries.

Top k-spatial preference queries, to find the

shortest path and shortest path distance.

Distance between two points defined by their

shortest path distance rather than Euclidean distance.

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Modules

User interface design Preprocessing Classification Ranking and scoring Clustering results

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Module DescriptionUser interface design:

The goal of user interface design is to make the user's

interaction as simple and efficient as possible, in terms of

accomplishing user goals.

Preprocessing:

Transform the data into the format that will be more easily

and effectively processed for the purpose of the user. 

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Ranking and score:

Spatial Ranking

Non-Spatial Ranking

Three types of scoring methods are used,

Range score

Influence score

Upper Bound Score

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Range score and Influence score

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Cont’d Feature points are labeled by quality values that can be obtained from the rating provider. The location of object dataset D and two feature datasets F1 and F2. The score r(p) is defined in terms of:

1)Maximum quality for each feature.

2)Aggregation of those quality. To find range score and influence score.

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R-Tree

In order to handle spatial data effectively database

system needs the index mechanism that will help to

retrieve data item quickly.

R-Tree provides dynamic index structure

The object is represented by a Minimum Bounding

rectangle(MBR).

MBR is the smallest rectangle which completely

contains objects.14

R-Tree Example

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Upper Bound Score

It provides maximum quality of entries. Two reasons for computing the score

1) Low I/O cost

2) Bounds are reasonably high in order to facilitate the effective pruning. Level 1 entries (non-leaf nodes) are used. Because

i)Fewer level1 entries than leaf entries

ii) High level entries cannot provide tight bounds

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Clustering Results

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.

The 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.

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Diagram

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System RequirementsHardware SpecificationPROCESSOR : Intel Pentium Core 2 Duo 1.8GHzHARD DISK : 160 GBRAM : 2 GB DDR IIMONITOR : 15” Color Monitor

Software Specification OPERATING SYSTEM : Windows XP BACK END : MySql LANGUAGE USED : Java WEB-SERVER : Apache Tomcat 5.0

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ConclusionA top-k spatial preference queries, provide a novel type

of ranking for spatial objects.

The neighborhood of an object p is captured by scoring

function range score and influence score.

Branch and Bound derives upper bound scores for non-

leaf entries in the object tree.

It is scalable to large data sets.

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Register New Place DetailsRegister New Place Details

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Query SearchQuery Search

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Search ResultSearch Result

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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

Preference Queries,” Proc. IEEE Int’l Conf. Data Eng. (ICDE), 2007.

[3] N. Bruno, L. Gravano, and A. Marian, “Evaluating Top-k Queries

over Web-Accessible Databases,” Proc. IEEE Int’l Conf. Data Eng.

(ICDE), 2002.2007.

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Cont’dCont’d

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

[5] Yen Yu Chen, Suel T., and Markowetz A., “Efficient Query

Processing in Geographic Web Search Engines,” Proc. ACM

SIGMOD, 2006.

[6] Joao B Rocha-Junior;Akrivi Vlachou and Christos

Doulkeridis,”Efficient Processing of Top-k Spatial Preference

Queries: Proc VLDB Endowment.

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THANK YOUTHANK YOU

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