04-05-2012, 02:13 PM
Top-k Preferences for Ranking Objects in Spatial Neighborhood
Abstract
Using aggregate nearest neighbors (ANN) or appropriate indexing techniques and search algorithms spatial preference queries were developed. The recent top-k spatial preference queries ranks objects based on the qualities of features in their spatial neighborhood. For example, a customer using a real estate agency database may want to rank the flats with respect to their spatial neighborhood, defined after aggregating the qualities of other entities (e.g., transportation, restaurants, hospital, market, etc) in the region. An intuition is to retrieve results by assigning higher weights to the features based on their proximity to the flat in a preferential search and vice versa in a normal search. Such a neighborhood concept can be specified by the user via different functions. Experimental evaluations suggest that an optimized branch-and-bound solution is efficient and robust with respect to different parameters.
Existing System
1. Uses top-k spatial preference queries to rank objects based on the qualities of other features (custom preferences) in their spatial neighborhood.
2. Five algorithms for processing top-k spatial preference queries are
• The baseline algorithm SP computes the scores of every object by querying on feature data sets.
• The algorithm GP is a variant of SP that reduces I/O cost by computing scores of objects in the same leaf node concurrently.
• The algorithm BB derives upper bound scores for nonleaf entries in the object tree, and prunes those that cannot lead to better results.
• The algorithm BB* is a variant of BB that utilizes an optimized method for computing the scores of objects (and upper bound scores of nonleaf entries).
• The algorithm FJ 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.
3. The domain of choices presented to the end user has increased.
4. Top-k spatial preference queries for a road network are not initiated.
Proposed System
1. Proposes to implement top-k spatial preference query on a road network, in which the distance between two points is defined by Shortest path distance.
2. End user choice domain has increased.
Hardware Required:
System : Pentium IV 1.8 GHz(recommended)
Hard Disk : 40 GB
Monitor : Standard Color Monitor
I/O devices : Standard Keyboard and Mouse
RAM : 256 MB(recommended)
Software Required:
Operating System : Windows
Technology : Knowledge and data Engineering
Web Technologies : Will be Quoted after Design Phase
Web Server : Will be Quoted after Design Phase
Database : Will be Quoted after Design Phase
Software Tools : Will be Quoted after Design Phase
1. Jdk 1.7 or 1.6 or Asp.Net, C#
2. Windows for development
3. Eclipse 3.6/Net Beans 7.0/ Visual Studio 2008