. CONCLUSIONS AND FUTUE WORK
In this paper, document streams and the process of top-k queries and continuous monitoring of such system are explored. It is found that making top-k queries on continuous document streams is very challenging. In the literature different approaches are found to deal with document streams. However, an adaptive approach that continuously monitors document steams with queries is proposed in this paper. We studied RIO and MRIO methods presented in 22. Inspired by the work, we proposed an algorithm known as AIO that follows an adaptive approach. We built a prototype application and implemented the algorithm as part of a three-tier web application. Web interface is used to make experiments. However, the algorithm lies in server and the streaming takes place in server. End users can make queries from the web based interface in order to observe the response time in presence of number of queries and different length in the queries involved. The experimental results revealed the significance of AIO and its performance improvement over other state-of-the-art algorithms. In future we intent to improve the AIO method with new programming paradigm such as MapReduce with distributed programming frameworks like Hadoop