Paper Presentation & Seminar Topics: Probabilistic Group nearest Neighbor Queries in Uncertain Databases

Probabilistic Group nearest Neighbor Queries in Uncertain Databases


The importance of query processing over uncertain data has recently arisen due to its wide usage in many real-world applications. In the context of uncertain databases, previous works have studied many query types such as nearest neighbor query, range query, top-k query, skyline query, and similarity join. In this paper, we focus on another important query, namely, probabilistic group nearest neighbor (PGNN) query, in the uncertain database, which also has many applications. Specifically, given a set, Q, of
query points, a PGNN query retrieves data objects that minimize the aggregate distance (e.g., sum, min, and max) to query set Q. Due to the inherent uncertainty of data objects, previous techniques to answer group nearest neighbor (GNN) query cannot be directly applied to our PGNN problem. Motivated by this, we propose effective pruning methods, namely, spatial pruning and probabilistic pruning, to reduce the PGNN search space, which can be seamlessly integrated into our PGNN query procedure. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approach, in terms of the wall clock time and the speed-up ratio against linear scan.

Existing System:-

• While many proposed techniques for answering queries (e.g., nearest neighbor (NN) query and range query) assume that data objects are precise, they cannot be directly applied to handle uncertain data (otherwise, inaccuracy or even errors may be introduced.

Proposed System:-

• Here we are using GNN Query and two pruning techniques are used like
• Spatial Pruning.
• Probabilistic Pruning.

To query the data in uncertain database.

Hardware Interface:-
• Hard disk : 40 GB
• RAM : 512 MB
• Processor Speed : 2.20GHz
• Processor : Pentium IV Processor

Software Interface:-
• JDK 1.5
• Swing Builder