We think about the query optimization issue in Generic crowdsourcing system. Generic crowdsourcing is intended to conceal the complexities and calm the client the weight of managing the group. The client is just needed to present a SQL-like question and the framework assumes the liability of arranging the inquiry, creating the execution plan and assessing in the crowdsourcing commercial center. A given query can have numerous options execution arranges and the distinction in crowdsourcing expense between the best and the most exceedingly worst arranges may be a few requests of extent. In this manner, as in social database frameworks, query optimization is imperative to crowdsourcing frameworks that give revelatory question interfaces. In this paper, we propose CROWDOP, an expense based query advancement approach for explanatory crowdsourcing frameworks. CROWDOP considers both cost and latency in query advancement destinations and produces question arranges that give a decent harmony between the cost and latency. We create proficient calculations in the CROWDOP for upgrading three sorts of inquiries: selection queries join queries, and complex selection-join queries. Deco is a far reaching framework for noting decisive questions postured over put away social information together with information got on demand from the group. In this paper we assume Deco's cost based query streamlining agent, expanding on Deco's information model, query dialect, and query execution motor exhibited befor
