An Indexed Non-probability Skyline Query Processing Framework for Uncertain Data

Lawal, Ma’aruf Mohammed; Ibrahim, Hamidah; Sani, Nor Fazlida Mohd; Yaakob, Razali

doi:10.1007/978-981-15-3383-9_26

Cited by 4 publications

(4 citation statements)

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“…Data uncertainty is defined as the degree to which data are inaccurate, imprecise, untrusted, unknown or incomplete. The works by [23], [27], [29], [39], [40], [41], and [51] for instance strived to solve issues of skyline analysis on uncertain data. Pei et al (2007) has introduced the notion of probabilistic skyline in the context of uncertain data in discrete domains where each object is associated with probability distributions over a set of possible values called instances.…”

Section: Related Workmentioning

confidence: 99%

“…This pioneering work has then inspired several other works in the same context like [29], [40], and [51]. Meanwhile, the works by [23], [27], and [41], dealt with uncertainty in continuous domains where the uncertain data are represented as continuous range of values, in which the precise values are not known during the skyline computation. Besides, several attempts have been made to tackle the issues related to the incompleteness of data in a database.…”

Section: Related Workmentioning

confidence: 99%

“…With the rapid growth of decision support systems and the increasing size of multidimensional data have witnessed an abundance of skyline algorithms being proposed for data processing in order to retrieve useful insights. These variants of skyline algorithms are introduced to deal with different characteristics of data, such as uncertain data [23], [27], [29], [39], [40], [41], [51], incomplete data [3], [6], [13], [14], [18], [19], [20], [24], [28], [33], [49], [50], encrypted data [8], [31], and streaming data [1], [15]; while others are based on the platform being considered like distributed database [2], cloud computing [22], [32], road networks [16], and others.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Skyline Computation Over an Incomplete Database With Changing States and Structures

et al. 2021

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Skyline query has been studied extensively and a significant number of skyline algorithms have been proposed, mostly attempt to resolve the optimisation problem that is mainly associated with reduction in the processing time of skyline computations. While databases change their states and/or structures throughout their lifetime to reflect the current and latest information of the applications, the skyline set derived before changes are made towards the initial state of a database is no longer valid in the new state/structure of the database. The domination relationships between objects identified in the initial state might no longer hold in the new state. Nonetheless, computing the skylines over the entire new state/structure of the database is inefficient, as not all pairwise comparisons between the objects are necessary to be performed. In tackling the above issue, this paper proposes a solution, named ∆Skyline, which aims at avoiding unnecessary skyline computations when a database changes its state and structure due to a data definition operation(s) (add or remove a dimension(s)). This is achieved by identifying and retaining the prominent dominance relationships when pairwise comparisons are performed; which are then utilised in the process of computing a new skyline set. ∆Skyline consists of two optimisation components, namely: ∆ ! Skyline which derives a new skyline set when a new dimension(s) is added to a database and ∆ " Skyline which derives a new skyline set when an existing dimension(s) is removed from a database. To make our solution more useful, it is applied on a database with incomplete data. Extensive experiments have been conducted to evaluate the performance and prove the efficiency of our proposed solution.INDEX TERMS Multi-criteria decision making; skyline queries; incomplete database; dynamic database; pairwise comparisons.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient Skyline Computation Over an Incomplete Database With Changing States and Structures

et al. 2021

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“…These algorithms attempt not only to resolve the optimisation problem but also issues related to the uncertainty of data which is defined as the degree to which data are inaccurate, imprecise, untrusted, unknown or incomplete. These include among others ISkyline [14], sorting-based bucket skyline [18], Incoskyline [2], Jincoskyline [1], and OIS [12] that handle the issues of incompleteness of data; probabilistic skyline model [25], τ-Skyline [29], SkyQUD [20,21,22,23] and SkyQuiD [17] focus on the challenges in computing skyline queries for uncertain database; the works by [4] and [10] attempt to solve the issues related to uncertain data in a data stream; while the work by [3] focuses on dynamic database. Nonetheless, these algorithms are specifically designed to cater only a single user query.…”

Section: Related Workmentioning

confidence: 99%

A Fragmentation Region-based Skyline Computation Framework for a Group of Users

Dehaki¹,

Ibrahim²,

Udzir³

et al. 2021

AI, Machine Learning and Applications

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Skyline processing, an established preference evaluation technique, aims at discovering the best, most preferred objects, i.e. those that are not dominated by other objects, in satisfying the user’s preferences. In today’s society, due to the advancement of technology, ad-hoc meetings or impromptu gathering are becoming more and more common. Deciding on a suitable meeting point (object)for a group of people (users) to meet is not a straightforward task especially when these users are located at different places with distinct preferences. A place which is close by to the users might not provide the facilities/services that meet all the users’ preferences; while a place having the facilities/services that meet most of the users’ preferences might be too distant from these users. Although the skyline operator can be utilised to filter the dominated objects among the objects that fall in the region of interest of these users, computing the skylines for various groups of users in similar region would mean rescanning the objects of the region and repeating the process of pair wise comparisons among the objects which are undoubtedly unwise. On this account, this study presents a region-based skyline computation framework which attempts to resolve the above issues by fragmenting the search region of a group of users and utilising the past computed skyline results of the fragments. The skylines, which are the objects recommended to be visited by a group of users, are derived by analysing both the locations of the users, i.e. spatial attributes, as well as the spatial and non-spatial attributes of the objects. Several experiments have been conducted and the results show that our proposed framework outperforms the previous works with respect to CPU time.

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