Ranking Spatial Data by Quality Preferences

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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 (e.g., restaurants, cafes, hospital, market, etc.) 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. Another intuitive definition is to assign higher weights to the features based on their proximity to the flat. In this paper, we formally define spatial preference queries and propose appropriate indexing techniques and search algorithms for them. Extensive evaluation of our methods on both real and synthetic data reveals that an optimized branch-and-bound solution is efficient and robust with respect to different parameters.

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. In relational databases, we rank tuples using an aggregate score function on their attribute values.

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, after normalization to the domain (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). Assuming that the set of interesting objects is indexed by an R-tree, we can apply distance bounds and traverse the index in a branch-and-bound fashion to obtain the answer.

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Proposed System:

We Propose

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

(ii) 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. To our knowledge, there is no existing efficient solution for processing the top-k spatial preference query. A brute-force approach for evaluating it is to compute the scores of all objects in D and select the top-k ones. This method, however, is expected to be very expensive for large input datasets.

Module Description:

1.     Spatial Ranking

2.     Non-Spatial ranking

3.     Neighbor (NN) Retrieval

4.     Spatial Query Evaluation on R-trees

Spatial Ranking

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

Non-Spatial Ranking:

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. To our knowledge, there is no existing efficient solution for processing the top-k spatial preference query.

Neighbor (NN) Retrieval:

Object ranking is a popular retrieval task in various applications. In relational databases, we rank tuples using an aggregate score function on their attribute values. 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, after normalization to the domain [0,1] (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). Assuming that the set of interesting objects is indexed by an R-tree [3], we can apply distance bounds and traverse the index in a branch-and-bound fashion to obtain the answer.

Nevertheless, it is not always possible to use multidimensional indexes for top-k retrieval. First, such indexes break-down in high dimensional spaces. Second, top-k queries may involve an arbitrary set of user-specified attributes (e.g., size and price) from possible ones (e.g., size, price, distance to the beach, number of bedrooms, floor, etc.) and indexes may not be available for all possible attribute combinations (i.e., they are too expensive to create and maintain). Third, information for different rankings to be combined (i.e., for different attributes) could appear in different databases (in a distributed database scenario) and unified indexes may not exist for them. Solutions for top-k queries focus on the efficient merging of object rankings that may arrive from different (distributed) sources. Their motivation is to minimize the number of accesses to the input rankings until the objects with the top-k aggregate scores have been identified. To achieve this, upper and lower bounds for the objects seen so far are maintained while scanning the sorted lists.

Spatial Query Evaluation:

The most popular spatial access method is the R-tree, which indexes minimum bounding rectangles (MBRs) of objects. Figure 2 shows a set D = fp1;: : : ; p8g of spatial objects (e.g., points) and an R-tree that indexes them. R-trees can efficiently process main spatial query types, including spatial range queries, nearest neighbor queries, and spatial joins. Given a spatial region W, a spatial range query retrieves from D the objects that intersect W. For instance, consider a range query that asks for all objects within the shaded area in Figure 2. Starting from the root of the tree, the query is processed by recursively following entries, having MBRs that intersect the query region. For instance, e1 does not intersect the query region, thus the sub-tree pointed by e1 cannot contain any query result. In contrast, e2 is followed by the algorithm and the points in the corresponding node are examined recursively to find the query result.

System Configuration:-

H/W System Configuration:-

ü Processor             -Pentium –III

ü Speed                             –    1.1 Ghz

ü RAM                    –    256 MB(min)

ü Hard Disk            –   20 GB

ü Floppy Drive       –    1.44 MB

ü Key Board            –    Standard Windows Keyboard

ü Mouse                  –    Two or Three Button Mouse

ü Monitor                –    SVGA

S/W System Configuration:-

v   Operating System          : Windows95/98/2000/XP

v   Application  Server       : Tomcat5.0/6.X

v   Front End                      : HTML, Java, JSP

v    Script                            : JavaScript.

v   Server side Script          : Java Server Pages.

v   Database                        : Ms-Access

v   Database Connectivity   : JDBC.

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