• International Journal of Contents
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• v.6 no.1
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• pp.47-52
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• 2010

Skyline queries are an important new search capability for multi-dimensional databases. Most of the previous works have focused on processing skyline queries over static data set. However, most of the real applications deal with the dynamic data set. Since dynamic data set constantly changes as time passes, the continuous skyline computation over dynamic data set becomes ever more complicated. In this paper, we propose a multiple layer grids method for continuous skyline computation (MLGCS) that maintains multiple layer grids to manage the dynamic data set. The proposed method divides the work space into multiple layer grids and creates the skyline influence region in the grid of each layer. In the continuous environment, the continuous skyline queries are only handled when the updating data points are in the skyline influence region of each layer grid. Experiments based on various data distributions show that our proposed method outperforms the existing methods.

• Journal of Korea Multimedia Society
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• v.14 no.12
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• pp.1591-1600
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• 2011

The skyline of a multidimensional data set is the maximal subset whose elements are not dominated by other elements of the set. Skyline computation is considered to be very useful for a decision making system that deals with multidimensional data analyses. Recently, a great deal of interests has been shown to improve the performance of skyline computation algorithms. In order to speedup, the number of comparisons between data elements should be reduced. In this paper, we propose a filter lining scheme to accomplish such objectives. The scheme divides the multidimensional data space into angle-based partitions, and places a filter for each partition, and then connects them together in order to establish the final filter line. The filter line can be used to eliminate data, that are not part of the skyline, from the original data set in the preprocessing stage. The filter line is adaptively improved during the data scanning stage. In addition, skylines are computed for each remaining data partition, and are then merged to form the final skyline. Our scheme is an improvement of the previously reported simple preprocessing scheme using simple filters. The performance of the scheme is shown by experiments.

• The KIPS Transactions:PartD
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• v.18D no.5
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• pp.313-316
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• 2011

The skyline of a multi-dimensional data set is a subset that consists of the data that are not dominated by other members of the set. Skyline computation can be very useful for decision making for multi-dimensional data set. However, in case that the skyline is very large, it may not be much useful for decision making. In this paper, we propose an algorithm for computing a part of the skyline considering location restrictions that the user provides, such as origin movement, degree ranges and/or distances from the origin. The algorithm eliminates noncandidate data rapidly, and returns in order the skyline points that satisfy the user's requests. We show that the algorithm is efficient by experiments.

• Journal of Computing Science and Engineering
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• v.6 no.3
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• pp.207-218
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• 2012

In recent years, the skyline query paradigm has been established as a reliable method for database query personalization. While early efficiency problems have been solved by sophisticated algorithms and advanced indexing, new challenges in skyline retrieval effectiveness continuously arise. In particular, the rise of the Semantic Web and linked open data leads to personalization issues where skyline queries cannot be applied easily. We addressed the special challenges presented by linked open data in previous work; and now further extend this work, with a heuristic workflow to boost efficiency. This is necessary; because the new view on linked open data dominance has serious implications for the efficiency of the actual skyline computation, since transitivity of the dominance relationships is no longer granted. Therefore, our contributions in this paper can be summarized as: we present an intuitive skyline query paradigm to deal with linked open data; we provide an effective dominance definition, and establish its theoretical properties; we develop innovative skyline algorithms to deal with the resulting challenges; and we design efficient heuristics for the case of predicate equivalences that may often happen in linked open data. We extensively evaluate our new algorithms with respect to performance, and the enriched skyline semantics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.1931-1953
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• 2017

The reverse skyline query plays an important role in information searching applications. This paper deals with continuous reverse skyline queries in sensor networks, which retrieves reverse skylines as well as the set of nodes that reported them for continuous sampling epochs. Designing an energy-efficient approach to answer continuous reverse skyline queries is non-trivial because the reverse skyline query is not decomposable and a huge number of unqualified nodes need to report their sensor readings. In this paper, we develop a new algorithm that avoids transmission of updates from nodes that cannot influence the reverse skyline. We propose a data mapping scheme to estimate sensor readings and determine their dominance relationships without having to know the true values. We also theoretically analyze the properties for reverse skyline computation, and propose efficient pruning techniques while guaranteeing the correctness of the answer. An extensive experimental evaluation demonstrates the efficiency of our approach.

• ETRI Journal
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• v.38 no.6
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• pp.1197-1206
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• 2016

Performing continuous skyline queries of dynamic data sets is now more challenging as the sizes of data sets increase and as they become more volatile due to the increase in dynamic updates. Although previous work proposed support for such queries, their efficiency was restricted to small data sets or uniformly distributed data sets. In a production database with many concurrent queries, the execution of continuous skyline queries impacts query performance due to update requirements to acquire exclusive locks, possibly blocking other query threads. Thus, the computational costs increase. In order to minimize computational requirements, we propose a method based on a multi-layer grid structure. First, relational data object, elements of an initial data set, are processed to obtain the corresponding multi-layer grid structure and the skyline influence regions over the data. Then, the dynamic data are processed only when they are identified within the skyline influence regions. Therefore, a large amount of computation can be pruned by adopting the proposed multi-layer grid structure. Using a variety of datasets, the performance evaluation confirms the efficiency of the proposed method.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.1
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• pp.1-8
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• 2017

Given a multi-dimensional dataset of tuples, a skyline query returns a subset of tuples which are not 'dominated' by any other tuples. Skyline query is very useful in Big data analysis since it filters out uninteresting items. Much interest was devoted to the MapReduce-based parallel processing of skyline queries in large-scale distributed environment. There are three requirements to improve parallelism in MapReduced-based algorithms: (1) workload should be well balanced (2) avoid redundant computations (3) Optimize network communication cost. In this paper, we introduce MR-SEAP (MapReduce sample Skyline object Equality Angular Partitioning), an efficient angular space partitioning based skyline query processing using sampling-based pruning, which satisfies requirements above. We conduct an extensive experiment to evaluate MR-SEAP.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.1
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• pp.17-24
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• 2020

The traveling salesman problem is an algorithmic problem tasked with finding the shortest route that a salesman visits, visiting each city and returning to the started city. Due to the exponential time complexity of TSP, it's hard to implement on cases like amusement park or delivery. Also, TSP is hard to meet user's demand that is associated with multi-dimensional attributes like travel time, interests, waiting time because it uses only one attribute - distance between nodes. This paper proposed Top-n Skyline-Multi Dimension TSP to resolve formerly adverted problems. The proposed algorithm finds the shortest route faster than the existing method by decreasing the number of operations, selecting multi-dimensional nodes according to the dominance of skyline. In the simulation, we compared computation time of dynamic programming algorithm to the proposed a TS-MDT algorithm, and it showed that TS-MDT was faster than dynamic programming algorithm.

• Proceedings of the Korea Contents Association Conference
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• 2014.06a
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• pp.495-496
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• 2014

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.1
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• pp.370-381
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• 2012

Multi-criteria result extraction is crucial in many scientific applications that support real-time stream processing, such as habitat research and disaster monitoring. Skyline evaluation is computational intensive especially over continuous time-interval data streams where each object has its own customized expiration time. In this work, we propose TI-Sky - a continuous skyline evaluation framework. To ensure correctness, the result space needs to be continuously maintained as new objects arrive and older objects expire. TI-Sky strikes a perfect balance between the costs of continuously maintaining the result space and the costs of computing the final skyline result from this space whenever a pull-based user query is received. Our key principle is to incrementally maintain a partially precomputed skyline result space - however doing so efficiently by working at a higher level of abstraction. TI-Sky's algorithms for insertion, deletion, purging and result retrieval exploit both layers of granularity. Our experimental study demonstrates the superiority of TI-Sky over existing techniques to handle a wide variety of data sets.