Crowdsourcing Enumeration Queries Estimators and Interfaces
Abstract:
Hybrid human computer database systems promise to greatly expand the usefulness of query
processing by incorporating the crowd for data gathering and other tasks. Such systems raise
many implementation questions. Perhaps the most fundamental issue is that the closed world
assumption underlying relational query semantics does not hold in such systems. As a
consequence the meaning of even simple queries can be called into question. Furthermore, query
progress monitoring becomes difficult due to non-uniformities in the arrival of crowdsourced
data and peculiarities of how people work in crowdsourcing systems. To address these issues,
we develop statistical tools that enable users and systems developers to reason about query
completeness. These tools can also help drive query execution and crowdsourcing strategies. We
evaluate our techniques using experiments on a popular crowdsourcing platform.
Existing system:
Perhaps the most fundamental issue is that the closed world assumption underlying relational
query semantics does not hold in such systems. As a consequence the meaning of even simple
queries can be called into question. Furthermore, query progress monitoring becomes difficult
due to non-uniformities in the arrival of crowdsourced data and peculiarities of how people work
in crowdsourcing systems.
Proposed system:
crowdsourced system like CrowdDB, once the records in the stored table are exhausted, jobs can
be sent to the crowd asking for additional records. The question then becomes: when is the query
result set complete? Crowdsourced queries can be inherently fuzzy or have unbounded result
sets, with tuples scattered over the web or only in human minds. For example, consider a query
for a list of graduating Ph.D. students currently on the job market, or companies in California
interested in green technology. These types of queries are the main use cases for crowd-enabled
database systems, as each is labor-intensive for the user issuing the query to perform, but not
executed frequently enough to justify the use of a complex machine learning solution.
Further Details Contact: A Vinay 9030333433, 08772261612
Email: info@takeoffprojects.com | www.takeoffprojects.com
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
System
:
Pentium IV 2.4 GHz.
Hard Disk
:
40 GB.
Floppy Drive
:
Monitor
Mouse
Ram
1.44 Mb.
:
:
15 VGA Colour.
Logitech.
:
512 Mb.
Further Details Contact: A Vinay 9030333433, 08772261612
Email: info@takeoffprojects.com | www.takeoffprojects.com
SOFTWARE REQUIREMENTS:
Operating system
:
Windows XP/7.
Coding Language
:
JAVA/J2EE
IDE
:
Netbeans 7.4
Database
:
MYSQL
Further Details Contact: A Vinay 9030333433, 08772261612
Email: info@takeoffprojects.com | www.takeoffprojects.com