ECADS: An Efficient Approach for Accessing Data and Query Workload

In current scenario a huge amount of data is introduced over the web, because data introduced by the various sources, that data contains heterogeneity in na-ture. Data extraction is one of the major tasks in data mining. In various techniques for data extraction have been proposed from the past, which provides functionali-ty to extract data like Collaborative Adaptive Data Shar-ing (CADS), pay-as-you-go etc. The drawbacks associat-ed with these techniques is that, it is not able to provide global solution for the user. Through these techniques to get accurate search result user need to know all the de-tails whatever he want to search. In this paper we have proposed a new searching technique "Enhanced Collaborative Adaptive Data Sharing Platform (ECADS)" in which predefined queries are provided to the user to search data. In this technique some key words are provided to user related with the domain, for efficient data extraction task. These keywords are useful to user to write proper queries to search data in efficient way. In this way it provides an accurate, time efficient and a global search technique to search data. A comparison analysis for the existing and proposed technique is pre-sented in result and analysis section. That shows, pro-posed technique provide better than the existing tech-nique.