CrowdOp: Query Optimization For Declarative Crowd sourcing Systems

ABSTRACT

We study the query optimization problem in declarative crowdsourcing systems. Declarative crowd sourcing is designed to hide the complexities and relieve the user the burden of dealing with the crowd. The user is only required to submit an SQL-like query and the system takes the responsibility of compiling the query, generating the execution plan and evaluating in the crowd sourcing marketplace. A given query can have many alternative execution plans and the difference in crowd sourcing cost between the best and the worst plans may be several orders of magnitude. Therefore, as in relational database systems, query optimization is important to crowdsourcing systems that provide declarative query interfaces. In this paper, we propose CROWDOP, a cost-based query optimization approach for declarative crowdsourcing systems. CROWDOP considers both cost and latency in the query optimization objectives and generates query plans that provide a good balance between the cost and latency. We develop efficient algorithms in the CROWDOP for optimizing three types of queries: selection queries,join queries and complex selection-join queries. We validate our approach via extensive experiments by simulation as well as with the real crowd on Amazon Mechanical Turk.

AIM

The main aim of this paper is CROWDOP considers both cost and latency in the query optimization objectives and generates query plans that provide a good balance between the cost and latency

SCOPE

The scope of this paper is develop efficient algorithms in the CROWDOP for optimizing three types of queries: selection queries,    join queries and complex selection-join queries.

EXISTING SYSTEM

Recent crowdsourcing systems, such as CrowdDB, Qurk and Deco, provide an SQL-like query language as a declarative interface to the crowd. An SQL like declarative interface is designed to encapsulate the complexities of dealing with the crowd and provide the crowdsourcing system an interface that is familiar to most database users. Consequently, for a given query, a declarative system must first compile the query, generate the execution plan, post the human intelligence tasks (HITs) to the crowd according to the plan, collect the answers, handle errors and resolve the inconsistencies in the results. While declarative querying improves the usability of the system, it requires the system to have the capability to optimize and provide a “near optimal” query execution plan for each query. Since a declarative crowdsourcing query can be evaluated in many ways, the choice of execution plan has a significant impact on overall performance, which includes the number of questions being asked, the types/difficulties of the questions and the monetary cost incurred.

DISADVANTAGES

·      Our optimization objectives that consider both monetary cost and latency.

·      To efficiently select the best query plan with respect to the defined optimization objectives

PROPOSED SYSTEM

In this paper, propose CROWDOP, a cost-based query optimization approach for declarative crowdsourcing systems. CROWDOP considers both cost and latency in the query optimization objectives and generates query plans that provide a good balance between the cost and latency. We develop efficient algorithms in the CROWDOP for optimizing three types of queries: selection queries, join queries and complex selection-join queries. We validate our approach via extensive experiments by simulation as well as with the real crowd on Amazon Mechanical Turk.

ADVANTAGES      

1. The effective optimization algorithms for select, join and complex queries.
2. The  effectiveness of our query optimizer and validates our cost model and latency model.

 System Architecture

System Configuration

Hardware Requirements

• Speed                  -    1.1 Ghz
• Processor              -    Pentium IV
• RAM                    -    512 MB (min)
• Hard Disk            -    40 GB
• Key Board                    -    Standard Windows Keyboard
• Mouse                  -    Two or Three Button Mouse
• Monitor                -     LCD/LED

 Software requirements

• Operating System              : Windows 7             
•  Front End                           : ASP.Net and C#
• Database                             : MSSQL
• Tool                                    : Microsoft Visual studio

References                              

Meihui Zhang,Kok, S.,Meiyu Lu, Crowd Op: Query Optimization for Declarative Crowd sourcing Systems” IEEE Transactions on Knowledge and Data Engineering, Volume 27   Issue 8  March 2015.