Taxicab Geometry Based Analysis on Skyline for Business Intelligence
01 Oct 2018-Vol. 6, Iss: 4, pp 86-102
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.
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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 Jan 2022
TL;DR: In this article, a modified Prophet forecasting model is proposed for online retailing, which emphasizes more upon customers' feedback to increase the effect of bullwhip effect on future business growth.
Abstract: Bullwhip effect is a distribution channel phenomenon caused by the unmeasured inflated ordering details that move upstream at every level of supply chain. If Bullwhip effect oppressed supply chain management a lot, then it results business loss. Nowadays, due to incorporating new business plans in online retailing, uncertain and fluctuating business growths are noticed very frequently. Fluctuating business growth should address quickly, otherwise there is a chances for generating erroneous ordering that in turn could increase the effect of bullwhip. Another reason for increasing bullwhip effect is poor forecasting of sale. Future business progress depends not only upon present sale but also upon customers’ feedback about that product. Specially, in case of online retailing, customers’ feedback is one of the important factors for future business growth. In this research, we have proposed a modified Prophet forecasting model for emphasizing more upon customers’ feedback. Fluctuating business growths are handled through the concept of virtual data warehouse. Experimental result shows the efficiency of proposed model.
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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
20 Aug 2002
TL;DR: In this paper, a new online algorithm that computes the Skyline is presented, which returns the first results immediately, produces more and more results continuously, and allows the user to give preferences during the running time of the algorithm so that the user can control what kind of results are produced next (e.g., rather cheap or rather near restaurants).
Abstract: Skyline queries ask for a set of interesting points from a potentially large set of data points. If we are traveling, for instance, a restaurant might be interesting if there is no other restaurant which is nearer, cheaper, and has better food. Skyline queries retrieve all such interesting restaurants so that the user can choose the most promising one. In this paper, we present a new online algorithm that computes the Skyline. Unlike most existing algorithms that compute the Skyline in a batch, this algorithm returns the first results immediately, produces more and more results continuously, and allows the user to give preferences during the running time of the algorithm so that the user can control what kind of results are produced next (e.g., rather cheap or rather near restaurants).
893 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
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