Browsing by Author "MO Mokhtar, Hoda"

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    Directional Skyline Queries
    (SPRINGER, 2012) El-Dawy, Eman; MO Mokhtar, Hoda; El-Bastawissy, Ali
    Continuous monitoring of queries over moving objects has become an important topic as it supports a wide range of useful mobile applications. A continuous skyline query involves both static and dynamic dimensions. In the dynamic dimension, the data object not only has a distance from the query object, but it also has a direction with respect to the query object motion. In this paper, we propose a direction-oriented continuous skyline query algorithm to compute the skyline objects with respect to the current position of the user. The goal of the proposed algorithm is to help the user to retrieve the best objects that satisfy his/her constraints and fall either in any direction around the query object, or is aligned along the object’s direction of motion. We also create a pre-computed skyline data set that facilitates skyline update, and enhances query running time and performance. Finally, we present experimental results to demonstrate the performance and efficiency of our proposed algorithms
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    Multi-level continuous skyline queries (MCSQ)
    (IEEE, 2011) El-Dawy, Eman; MO Mokhtar, Hoda; El-Bastawissy, Ali
    Most of the current work on skyline queries mainly dealt with querying static query points over static data sets. With the advances in wireless communication, mobile computing, and positioning technologies, it has become possible to obtain and manage (model, index, query, etc.) the trajectories of moving objects in real life, and consequently the need for continuous skyline query processing has become more and more pressing. In this paper, we address the problem of efficiently maintaining continuous skyline queries which contain both static and dynamic attributes. We present a Multi-level Continuous Skyline Query (MCSQ) algorithm, which basically creates a pre-computed skyline data set, facilitates skyline update, and enhances query running time and performance. Our algorithm in brief proceeds as follows: First, we distinguish the data points that are permanently in the skyline and use them to derive a search bound. Second, we establish a pre-computed data set for dynamic skyline that depends on the number of skyline levels (M) which is later used to update the first (initial) skyline points. Finally, every time the skyline needs to be updated we use the pre-computed data sets of skyline to update the previous skyline set and consequently updating first skyline. Finally, we present experimental results to demonstrate the performance and efficiency of our algorithm.