الفهرس 
Computing continuous skyline queries in distributed environment
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Abstract
Although most of the proposed algorithms of computing skyline queries focused basically on dealing with static databases and querying static points; with the expanding number of sensors, wireless communications and mobile applications, the demand for continuous skyline queries has increased. Skyline query computations are essential for multi-criteria decision making applications such as product or hotel recommendations, check-ins recommendation, information services, applications that focus on moving objects in road networks, and navigation services. Unlike traditional skyline queries which only consider static attributes, continuous skyline queries include dynamic attributes, as well as the static ones. In general, the main idea of computing skyline queries computation is to check the domination of skyline points over all dimensions, thus, considering both the static and dynamic attributes without separation is required. In addition to the rapid growth in information and the extremely fast increase in the data volume have driven the need to adapt new processing environments that are suitable for storing, processing, and maintaining huge amounts of data. Hence, MapReduce framework is used as a solution for the big data problems In this thesis, we present efficient algorithms for computing continuous skyline queries without discriminating between static and dynamic attributes while guarantying the accuracy of computing the continuous skyline query. The proposed algorithms present an efficient way for early exclusion of the points which will not be in the initial skyline result; this pruning phase reduces the overall required number of comparisons. Then, the association between the spatial positions of data points is examined; this phase gives an idea of where changes in the result might occur and consequently enables us to efficiently update the skyline result (continuous update) rather than computing the skyline from scratch