A systematic literature review of skyline query processing over data stream

Recently, skyline query processing over data stream has gained a lot of attention especially from the database community owing to its own unique challenges. Skyline queries aims at pruning a search space of a potential large multi-dimensional set of objects by keeping only those objects that are not worse than any other. Although an abundance of skyline query processing techniques have been proposed, there is a lack of a Systematic Literature Review (SLR) on current research works pertinent to skyline query processing over data stream. In regard to this, this paper provides a comparative study on the state-of-the-art approaches over the period between 2000 and 2022 with the main aim to help readers understand the key issues which are essential to consider in relation to processing skyline queries over streaming data. Seven digital databases were reviewed in accordance with the Preferred Reporting Items for Systematic Reviews (PRISMA) procedures. After applying both the inclusion and exclusion criteria, 23 primary papers were further examined. The results show that the identified skyline approaches are driven by the need to expedite the skyline query processing mainly due to the fact that data streams are time varying (time sensitive), continuous, real time, volatile, and unrepeatable. Although, these skyline approaches are tailored made for data stream with a common aim, their solutions vary to suit with the various aspects being considered, which include the type of skyline query, type of streaming data, type of sliding window, query processing technique, indexing technique as well as the data stream environment employed. In this paper, a comprehensive taxonomy is developed along with the key aspects of each reported approach, while several open issues and challenges related to the topic being reviewed are highlighted as recommendation for future research direction

Computing Subspace Skylines without Dominance Tests Using Set Interaction Approaches

Now a day’s preference answering plays major role in all crucial applications. If user wants to find top k–objects from a set of high dimensional data based on any monotonic function requires huge computation. One of the promising methods to compute preference set is Skyline Technology. Sky line computation returns the set objects that are not overruled by any other objects in n a multi dimensional space. If data is high dimensional, different users requests sky line set based on different dimensions. It requires subspace skyline computation. If objects are d-dimensional we need to compute skyline sets in 2d different subspaces, called as SKYLINE CUBE computation, which incurs lot of computation cost. In this paper we address the problem of finding subspace skyline computation with minimum effort by using simple set interaction methods. By that we can decrease the number of subspace skylines need to be searched to find full sky cube. In this paper we developed one algorithm which uses Boolean algebra rules, skyline lattice to reduce dominance test for preparing sub space skylines