Oct 17, 2008 · 'OLAP-Sequential Mining: Summarizing Trends from Historical' published in 'New Trends in Data Warehousing and Data Analysis'

Oct 25, 2019 · OLAP-Sequential Mining: Summarizing Trends from Historical Multidimensional Data using Closed Multidimensional Sequential Patterns. Annals.

Moreover, the data are stored through time, and we thus argue that sequential patterns are one of the best ways to summarize the trends from such databases. Se- ...

As data are historized, we argue that sequentialpatterns are well-suitedto this task. Sequential patterns have been studied for morethan ten years [1], with a ...

OLAP-Sequential Mining: Summarizing Trends from Historical. In Stanislaw Kozielski, Robert Wrembel, editors, New Trends in Data Warehousing and Data Analysis.

Feb 1, 2023 · OLAP is used to support business intelligence and decision-making processes. Grouping of data in a multidimensional matrix is called data cubes.

A time-series database consists of sequences of values or events obtained over repeated measurements of time. Suppose that you are given time-series data ...

New Trends in Data Warehousing and Data Analysis brings together the most recent research and practical achievements in the DW and OLAP technologies.

ABSTRACT. We study the problem of succinctly summarizing a database of event sequences in terms of generalized sequential patterns. That.

Jun 23, 2024 · Sequential patterns in data mining refer to the discovery of regularities in the behavior of individuals over time.