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.

中文翻译：

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多维聚合：区间数据的可行解决方案

如今，对于非传统数据，多维数据建模和聚合查询处理已成为商业智能解决方案的关键资产，如今已被频繁实现。对于在数据保留时记录的间隔值，多维聚合是唯一可行的解​​决方案，因此笔者在本文中强调了这一方面。实际上，这样的间隔反映了当前数据或作为当前数据库一部分的这些数据的现实状态。本文通过引入聚合运算符解决了区间数据引发的每个可能的挑战。尽管间隔一开始是未知的，但是它们最终取决于实际数据，并且在将它们与结果元组关联时显得非常方便。为此仅选择那些结果组，部分指定。间隔数据表示该间隔中的每个临时数据或整个间隔中的数据均保持不变，并且在这两种情况下，它都面临与运算符的竞争。在本文中，作者提出了对聚合算子在庞大的工业数据集上实施后的经验分析，并声称它比其他时间聚合算法更具优势。