In a multi-criteria decision making system it is required to analyze more than one attribute of the table. In this context no one would be interested on a point (tuples) which is dominated by any other point in the system, they are only interested on skyline points. A point is called a skyline point if and only if it is not dominated by any other point in the system. Therefore for the multi-criteria decision making system, it needs to compute the points those are not dominated by any other point in the system. And then find the best suited value using some mathematical operations, and finally display the list of that in the sorted order, starting from the best value. This paper propose a effective way of computing skyline and ranking points by finding the Best Skyline Point (BSP) among the dataset. © 2015 IEEE.

SOUMYA SEN

GENERAL

P GHOSH

University of Calcutta (informally known as Calcutta University or CU) is a collegiate public state university located in Kolkata, West Bengal, India.&nbsp;Within India it is recognized as a &quot;Five-Star University&quot; and accredited &quot;A&quot; Grade by National Assessment and Accreditation Council.

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The University of Calcutta has secured the Fifth position among the Universities of India in the prestigious &quot;Indian University Ranking 2019&quot; list, released by the NIRF of the Ministry of Human Resource Development, Government of India.

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It was established on 24 January 1857, and was one of the first institutions in Asia to be established as a multidisciplinary and Western-style university. Its alumni and faculty include several heads of state, heads of government, social reformers, prominent artists, only academy award winner and Dirac medal winner in India, many Fellows of the Royal Society and five Nobel laureates as of 2019 which is highest in South Asia.

University Of Calcutta

Ranking skyline points by computing nearest neighbor of Best Skyline Point

12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015

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. © Springer Nature Singapore Pte Ltd. 2019.

Advances in Intelligent Systems and Computing

Reducing computational complexity of skyline by the use of logical and physical bucket

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. © 2016 IEEE.

Journal	Data powered by Typeset12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015
Publisher	Data powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open Access	No