Skyline computation for frequent queries in update intensive environment

The skyline queries produce the tuples which are 'promising' on the dimensions of the user's interest. The popular datasets often get queried by the users where dimensions of the user queries often overlap. For such frequent, overlapping skyline queries repeating computations on large datasets result in unacceptable response time. In the scenarios where, there exists a little deviation in the query dimensions than those of the popular dimensions or when the dataset gets updated, the re-use of the previous results can help in either avoiding or reducing further computational costs.In this paper we focus exactly on this problem and aim at optimizing the response time of frequent or near to frequent skyline queries raised against the static and the update intensive dataset. We propose two novel, simple yet efficient algorithms namely the QPSkyline and the QPUpdateSkyline algorithm which make use of the proposed data structure called as 'Query Profiler' which aims at preserving the metadata of the skyline queries. The QPSkyline algorithm works in static environment and the QPUpdateSkyline algorithm is applicable for the datasets which experience frequent updates. The experiments performed on the real life dataset demonstrate the effectiveness and scalability of the proposed algorithms.