Multi-dimensional aggregation: a viable solution for interval data
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ORIGINAL RESEARCH
Multi-dimensional aggregation: a viable solution for interval data Shailender Kumar1
Received: 22 November 2019 / Accepted: 21 April 2020 Ó Bharati Vidyapeeth’s Institute of Computer Applications and Management 2020
Abstract Now a day multi-dimensional data modeling and aggregate query processing which are key assets of business intelligence solutions are being frequently realized to the unorthodox data. For interval values which are recorded when the data is on hold, multidimensional aggregation is the only viable solution and the author emphasizes over this aspect in this paper. Actually, such intervals reflect the state of reality of either current data or such data which were part of the present database. Every possible challenge which interval data throws upon is resolved in this paper through introduction of aggregation operator. Although the intervals are unknown at first but they eventually depend on the actual data and it turns out to be quiet handy while associating them with the resulting tuples. Only those result groups are selected for this purpose, which are specified partially. The interval data signifies that data holds either for each interim in the interval or entire interval and in both of these two cases it faces contention with the operators. In this paper, the author presents the empirical analysis of the aggregation operator after its implementation over the huge industrial data sets and claims that it holds an edge over the other temporal aggregation algorithms. Keywords Aggregates Databases Query processing Static Stable
& Shailender Kumar [email protected] 1
Department of Computer Science and Engineering, Delhi Technological University, Delhi, India
1 Introduction In most of the present reality database employments, e.g., in the fiscal, medicinal, and systematic spheres, handle time-oriented data, which is raw facts with linked timephases that holds certain time-varying feature of the facts, classically at the point when the certainties were or is right in the displayed veracity or when the realities was or is section of the contemporary condition of the database. In distinction to this, contemporary database administration delivers valuable tiny integral query language support for time-varying facts administration [1]. In phase through the growing dispersion of professional aptitude, collective calculation turn out to be significant. An aggregate operator transfigures a reflection temporal arrangement into a prompt upshot arrangement [2]. Ordinarily, this is accomplished by principal part the contention transient connection into bunches of lines with undistinguishable esteems for more than one components, then appertain an aggregate procedure, e.g., max or min, to every cluster in one go. For time-traverse esteemed time-arranged databases, total is exceptionally testing on the grounds that the time-traverse esteems can likewise be utilized for portraying the bunches of contention columns [3]. In this paper, the author proposes a novel time-oriented
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