Efficient Computation of Range Aggregates against uncertain location based queries
Abstract
We propose a novel multi-dimensional search approach that allows users to perform fuzzy searches for structure and metadata conditions in addition to keyword conditions. Our techniques individually score each dimension and integrate the three dimension scores into a meaningful unified score. We also design indexes and algorithms to efficiently identify the most relevant files that match multi-dimensional queries. We perform a thorough experimental evaluation of our approach and show that our relaxation and scoring framework for fuzzy query conditions in non content dimensions can significantly improve ranking accuracy. We also show that our query processing strategies perform and scale well, making our fuzzy search approach practical for every day usage. Efficient Computation of Range Aggregates against uncertain location based queries
HARDWARE & SOFTWARE REQUIREMENTS:
HARDWARE REQUIREMENTS:
- System : Pentium IV 2.4 GHz
- Hard Disk : 40 GB
- Floppy Drive : 44 MB
- Monitor : 15 VGA color
- Mouse : Logitech
- Keyboard : 110 keys enhanced
- RAM : 256 MB
SOFTWARE REQUIREMENTS:
- O/S : Windows XP.
- Language : C# .Net 2005
- Data Base : Sql Server 2005.
EXISTING SYSTEM:
- Existing method of semi-structured data users access and store in personal information management systems, there is a critical need for powerful search tools to retrieve often very heterogeneous data in a simple and efficient way. Existing tools typically support some IR-style ranking on the textual part of the query, but only consider structure (e.g., file directory) and metadata (e.g., date, file type) as filtering conditions.
- That is, there is no tolerance of minor typos and format inconsistencies which, on the other hand, are typical user searching behavior and happen very frequently. This significant drawback makes existing techniques unsuitable in data engineering as it greatly affects system usability, rendering user searching experiences very frustrating and system efficacy very low.
PROPOSED SYSTEM:
- We propose a novel multi-dimensional search approach that allows users to perform fuzzy searches for structure and metadata conditions in addition to keyword conditions.
- We also design indexes and algorithms to efficiently identify the most relevant files that match multi-dimensional queries. We perform a thorough experimental evaluation of our approach and show that our relaxation and scoring framework for fuzzy query conditions in non content dimensions can significantly improve ranking accuracy.
- We propose a novel approach that allows users to efficiently perform fuzzy searches across three different dimensions: content, metadata, and structure. We describe individual IDF-based scoring approaches for each dimension and present a unified scoring framework for multi-dimensional queries over personal information file systems. We also present new data structures and index construction optimizations to make finding and scoring fuzzy matches efficient.
- We propose the following techniques and algorithms to address the above challenges. We incrementally build the query dependent DAG structures at query time, only materializing those DAG nodes necessary to answer a query. To improve sorted access efficiency, we propose techniques to skip the scoring of unneeded DAG nodes by taking advantage of the containment property of the DAG.