QUERY AWARE DETERMINIZATION OF UNCERTAIN OBJECTS

QUERY AWARE DETERMINIZATION OF UNCERTAIN OBJECTS
Abstract:
This paper considers the problem of determinizing probabilistic data to enable such data to be
stored in legacy systems that accept only deterministic input. Probabilistic data may be generated by
automated data analysis/enrichment techniques such as entity resolution, information extraction, and
speech processing. The legacy system may correspond to pre-existing web applications such as Flickr,
Picasa, etc. The goal is to generate a deterministic representation of probabilistic data that optimizes the
quality of the end-application built on deterministic data. We explore such a determinization problem in
the context of two different data processing tasks—triggers and selection queries. We show that
approaches such as thresholding or top-1 selection traditionally used for determinization lead to
suboptimal performance for such applications. Instead, we develop a query-aware strategy and show its
advantages over existing solutions through a comprehensive empirical evaluation over real and synthetic
datasets.
ALGOROTHMS:
 Uploading & Downloading Algorithms
 Searching Algorithms.
EXISTING SYSTEM
Determinizing Probabilistic Data. While we are not aware of any prior work that directly
addresses the issue of determinizing probabilistic data as studied in this paper, the works that are much
related to ours is this project. They explore how to determinized answers to a query over a probabilistic
database. In contrast, we are interested in best deterministic representation of data (and not that of an
answer to a query) so as to continue to use existing end-applications that take only deterministic input.
The differences in the two problem settings lead to different challenges. Authors in address a problem
that chooses the set of uncertain objects to be cleaned, in order to achieve the best improvement in the
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quality of query answers. However, their goal is to improve quality of single query, while ours is to
optimize quality of overall query workload.
PROPOSED SYSTEM:
A variety of advanced probabilistic data models have been proposed in the past. Our focus
however was determinizing probabilistic objects, such as image tags and speech output, for which the
probabilistic attribute model suffices. We note that determining probabilistic data stored in more
advanced probabilistic models such as and/or tree might also be interesting. Extending our work to deal
with data of such complexity remains an interesting future direction of work. There are several related
research efforts that deal with the problem of selecting terms to index document for document retrieval.
A term-centric pruning method described in retains top postings for each term according to the
individual score impact that each posting would have if the term appeared in an adhoc search query.
Authors in propose a scalable term selection for text categorization, which is based on coverage of the
terms. The focus of these research efforts is on relevance – that is, getting the right set of terms that are
most relevant to document. In our problem, a set of possibly relevant terms and their relevance to the
document are already given by other data processing techniques. Thus, our goal is not to explore the
relevance of terms to documents, but to select keywords from the given set of terms to represent the
document, such that the quality of answers to triggers/queries is optimized.
MODULE
 User
 Admin
User:
1. New user can register then only Login to Application.
Off: 0416-2247353 / 6066663 Mo: +91 9500218218 /8870603602,

2. Register completed, then admin approved user only to login.
3. Admin verification mail will be sending your mail ID.
4. Then only user can login to home page.
5. User first we search the image file.
6. We use any of queries that processed to get output in Grid view.
7. Grid view shows related search items. User selects that particular image then
Download it.
8. Also user can change our Password and Update their Details.
9. Home Page has about the project Details.
Admin:
1. Admin verify the register details and approve the Users.
2. Upload the Images to Cloud and give different titles for the images.
3. Admin views User Details and can lock the particular User.
4. Admin views the Upload files Details and can delete the unwanted files to Cloud.
5. Chart view shows the user performance in this application.
6. Chart shows the File searching and execution time.
7. Chart shows the number of the searching images.
8. Chart shows the number of images user downloaded to Cloud.
Off: 0416-2247353 / 6066663 Mo: +91 9500218218 /8870603602,

9. Chart shows the efficient and more accessing images details.
10. All over user actions and performance view an admin.
SYSTEM SPECIFICATION
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 14’ Colour Monitor.
• Mouse : Optical Mouse.
• Ram : 512 Mb.
Software Requirements:
• Operating system : Windows 7 Ultimate.
• Coding Language : ASP.Net with C#
• Front-End : Visual Studio 2010 Professional.
• Data Base : SQL Server 2008.
Off: 0416-2247353 / 6066663 Mo: +91 9500218218 /8870603602,

QUERY AWARE DETERMINIZATION OF UNCERTAIN OBJECTS

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QUERY AWARE DETERMINIZATION OF UNCERTAIN OBJECTS