An alternative solution of skyline operation to reduce computational complexity
01 Sep 2016-Vol. 2016, pp 308-312
TL;DR: This research work focuses on the reduction of computational complexity by selecting the most important dimension of the database and transfers the other entire dimension in that form and finally ranks the points accordingly.
Abstract: Single-criteria decision making queries can be answered using simple SQL queries, however a multi-criteria decision making problems are often not answered by normal SQL queries. In order to solve these types of queries we may need to use co-operative query languages etc. However using additional query based system incurs extra cost. Moreover, if the criteria in a query are complementary to each other simple SQL queries are not capable of addressing this issue. A query in which multi-criteria decision making is required, often more than a single attribute of the relation is analyzed to fetch the desired result. In this context dominance analysis is performed to obtain a set of points (tuples) those are at least equally good in all the dimensions in compare to other points in the dataset. Skyline points are computed to find points which are not dominated (dominance analysis) by any other point in the system. A point is called “skyline point” if and only if it is not dominated by any other points in the system. Computation of skyline requires comparison of each point to all the other points in the system which in turn increases complexity. The complexity may increase at exponential rate when the numbers of dimensions increase. This research work focuses on the reduction of computational complexity. It is incorporated here by selecting the most important dimension of the database and transfers the other entire dimension in that form. And finally ranks the points accordingly.
Citations
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TL;DR: In this paper, the capability of skyline is extended to work with multiple dimensions and to search the multiple interesting points from the given search space. But, they restrict the computational complexity within a fixed upper bound.
Abstract: Skyline is a technique in database management system for multi-criterion decision making based on dominance analysis. Skyline overcomes the limitation of relational databases by handling the criteria that are inversely proportional to each other. Traditional skyline operation is conceptualized over two dimensions only, and it finds out single interesting point. In this paper we extend the capability of skyline to work with multiple dimensions and to search the multiple interesting points from the given search space. The work furthermore ranks skyline points with respect to the multiple interesting points. However, we restrict the computational complexity within a fixed upper bound. Skyline is commonly applied on tourism industries, and we consider two different case studies from this domain and execute the proposed methodology over the real-life data. Comparative study is given based on different parameters, and statistical analysis is also performed to illustrate the efficacy of the proposed method over the existing methods.
5 citations
01 Oct 2018
TL;DR: This research article focuses upon multiple visiting points for the travelers in an optimized way and uses Taxicab geometry for distance calculation, which is a simple Non-Euclidian geometry with minimum time complexity.
Abstract: This article describes how multi-criteria decision making problems are difficult to handle in normal SQL query processing. Skyline computation is generally used to solve these types of requirements by using dominance analysis and finding shortest distance with respect to a prime interesting point. However, in real life scenarios shortest distance may not be applicable in most of the cases due to different obstacles or barriers exist between the point of interests or places. In order to consider the presence of obstacles for geographically dispersed data, this research work uses Taxicab geometry for distance calculation, which is a simple Non-Euclidian geometry with minimum time complexity. Another limitation of previous skyline based works are that they only focus upon a single interesting point and can't be apply for multiple interesting points. This research article focuses upon multiple visiting points for the travelers in an optimized way. In addition to this, the article also selects areas for setting up of new business properties considering the constraints.
2 citations
01 Jul 2017
TL;DR: This research work uses Taxicab distance calculation to consider the presence of obstacles and apply it to compute skyline of geographically dispersed data.
Abstract: Skyline computation is relevant in multi-criteria decision making where the criteria are inversely proportional to each other. Skyline is generally computed using dominance analysis and applicable in a situation where shortest distance is computed with respect to a point of importance. In real life scenarios different cost parameters are obviously high for the points which are designated as "important" where as users search for the points which are generally of low cost. These types of inverse conditions are managed in skyline computation. Existing research works majorly apply shortest distance calculation for searching the points of importance and it is assumed that the points are connected without any obstructions. However in practical cases this assumption is often wrong as different obstacles or barriers exist between the points or places. In this research work we use Taxicab distance calculation to consider the presence of obstacles and apply it to compute skyline of geographically dispersed data.
1 citations
Cites background or methods from "An alternative solution of skyline ..."
...In order to compute skyline, the system first obtain “skyline point” by applying dominance analysis [1][10][11]....
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...In fact most of the previous work [1][10][11] depict there example through a tour plan, where the traveler are eager to select a hotel which is cheapest in its own class but closest to the visiting point (Point of Interest)....
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01 Jan 2019
TL;DR: An alternative approach is proposed to use the features of skyline but avoiding the constraints of skyline, which categorizes the attributes in three classes and calculates the cost associated with each dimension in a sequence to reduce complexity.
Abstract: Multi-criteria decision making problem when applied on relational model multiple attributes of tables are analyzed. In this context, the system has to be capable of identifying non-dominated tuples (points). One of the common solutions is runtime computation of skyline. Points those are not dominated by any other point in the system are called Skyline point. Hence computation is required to search non-dominated tuples (points) of the system. Traditional skyline computations require observations of each point and compare to all the other points in the system which incurs high time complexity. Moreover skyline demands the attributes to have complementary relationship. In this paper an alternative approach is proposed to use the features of skyline but avoiding the constraints of skyline. The proposed methodology categorizes the attributes in three classes and calculates the cost associated with each dimension in a sequence to reduce complexity.
References
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TL;DR: This work shows how SSL can be extended to pose Skyline queries, present and evaluate alternative algorithms to implement the Skyline operation, and shows how this operation can be combined with other database operations, e.g., join.
Abstract: We propose to extend database systems by a Skyline operation. This operation filters out a set of interesting points from a potentially large set of data points. A point is interesting if it is not dominated by any other point. For example, a hotel might be interesting for somebody traveling to Nassau if no other hotel is both cheaper and closer to the beach. We show how SSL can be extended to pose Skyline queries, present and evaluate alternative algorithms to implement the Skyline operation, and show how this operation can be combined with other database operations, e.g., join.
2,509 citations
"An alternative solution of skyline ..." refers background or methods in this paper
...One of the major limitations of Skyline Operator is that it uses dominance analysis through checking all the points and parameters that increase time complexity....
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...If we consider a set of n-dimensional points, a point dominates another point if it is at least equally good in all the n-dimensions and better in at least one dimension [1]....
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...Two main algorithm was proposed in [1]....
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...In the concept of Skyline Operator [1][9], the dominance analysis is performed through checking all the skyline points and parameters....
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...The concept of Skyline Operator [1] [2] [9] has been introduced to support multi-criteria decision making problems, where criteria are complementary to each other....
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01 Mar 2005
TL;DR: In this paper, a branch-and-bound skyline (BBS) algorithm based on nearest-neighbor search is proposed, which is I/O optimal and performs a single access only to those nodes that may contain skyline points.
Abstract: The skyline of a d-dimensional dataset contains the points that are not dominated by any other point on all dimensions. Skyline computation has recently received considerable attention in the database community, especially for progressive methods that can quickly return the initial results without reading the entire database. All the existing algorithms, however, have some serious shortcomings which limit their applicability in practice. In this article we develop branch-and-bound skyline (BBS), an algorithm based on nearest-neighbor search, which is I/O optimal, that is, it performs a single access only to those nodes that may contain skyline points. BBS is simple to implement and supports all types of progressive processing (e.g., user preferences, arbitrary dimensionality, etc). Furthermore, we propose several interesting variations of skyline computation, and show how BBS can be applied for their efficient processing.
905 citations
09 Jun 2003
TL;DR: BBS is a progressive algorithm also based on nearest neighbor search, which is IO optimal, i.e., it performs a single access only to those R-tree nodes that may contain skyline points and its space overhead is significantly smaller than that of NN.
Abstract: The skyline of a set of d-dimensional points contains the points that are not dominated by any other point on all dimensions. Skyline computation has recently received considerable attention in the database community, especially for progressive (or online) algorithms that can quickly return the first skyline points without having to read the entire data file. Currently, the most efficient algorithm is NN (nearest neighbors), which applies the divide -and-conquer framework on datasets indexed by R-trees. Although NN has some desirable features (such as high speed for returning the initial skyline points, applicability to arbitrary data distributions and dimensions), it also presents several inherent disadvantages (need for duplicate elimination if d>2, multiple accesses of the same node, large space overhead). In this paper we develop BBS (branch-and-bound skyline), a progressive algorithm also based on nearest neighbor search, which is IO optimal, i.e., it performs a single access only to those R-tree nodes that may contain skyline points. Furthermore, it does not retrieve duplicates and its space overhead is significantly smaller than that of NN. Finally, BBS is simple to implement and can be efficiently applied to a variety of alternative skyline queries. An analytical and experimental comparison shows that BBS outperforms NN (usually by orders of magnitude) under all problem instances.
853 citations
05 Mar 2003
TL;DR: A skyline algorithm, SFS, based on presorting that is general, for use with any skyline query, efficient, and well behaved in a relational setting is proposed.
Abstract: The skyline, or Pareto, operator selects those tuples that are not dominated by any others. Extending relational systems with the skyline operator would offer a basis for handling preference queries. Good algorithms are needed for skyline, however, to make this efficient in a relational setting. We propose a skyline algorithm, SFS, based on presorting that is general, for use with any skyline query, efficient, and well behaved in a relational setting.
788 citations
"An alternative solution of skyline ..." refers methods in this paper
...Step 1: Start Step 2: Loop for i=1 to numbers info[i][m+1] = (info[i][2] × u) + info[i][3] + ....
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...SFS is progressive but still need to scan the whole database and in worst case the time complexity remains the same....
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...SFS [3] O(nlog2 n + kn) O(nlog2 n + kn) O(kn2)...
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...The SFS (Sort-First-Skyline) [3] is an improved version of BNL that use a pre-sorting on tuples using a monotonic function....
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Proceedings Article•
11 Sep 2001
TL;DR: This paper presents two novel algorithms, Bitmap and Index, to compute the skyline of a set of points, and shows that the proposed algorithms provide quick initial response time with Index being superior in most cases.
Abstract: In this paper, we focus on the retrieval of a set of interesting answers called the skyline from a database. Given a set of points, the skyline comprises the points that are not dominated by other points. A point dominates another point if it is as good or better in all dimensions and better in at least one dimension. We present two novel algorithms, Bitmap and Index, to compute the skyline of a set of points. Unlike most existing algorithms that require at least one pass over the dataset to return the rst interesting point, our algorithms progressively return interesting points as they are identi ed. Our performance study further shows that the proposed algorithms provide quick initial response time with Index being superior in most cases.
781 citations
"An alternative solution of skyline ..." refers background or methods in this paper
...Further, [5][6] introduced another approach to avoid scanning the whole database....
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...The bitmap method [5][6] represents each point by binary bit string, which contains the information of the relative position of each dimension of the point....
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