Formal model of time point-based sequential data for OLAP-like analysis

Bulletin of the Polish Academy of Sciences. Technical Sciences

R. WrembelInstitute of Computing Science, Poznan University of Technology, 3a Piotrowo St., 90-965 Pozna?, PolandOther articles by this author:De Gruyter OnlineGoogle Scholar¹  B. B?belInstitute of Computing Science, Poznan University of Technology, 3a Piotrowo St., 90-965 Pozna?, PolandEmailOther articles by this author:De Gruyter OnlineGoogle Scholar¹  T. MorzyInstitute of Computing Science, Poznan University of Technology, 3a Piotrowo St., 90-965 Pozna?, PolandOther articles by this author:De Gruyter OnlineGoogle Scholar¹  Z. KrólikowskiInstitute of Computing Science, Poznan University of Technology, 3a Piotrowo St., 90-965 Pozna?, PolandOther articles by this author:De Gruyter OnlineGoogle Scholar¹ 

[1] Institute of Computing Science, Poznan University of Technology, 3a Piotrowo St., 90-965 Pozna?, Poland

关键词: Keywords: OLAP;    sequential data;    sequential OLAP;    formal model;   

DOI  :  10.2478/bpasts-2014-0032

学科分类：工程和技术（综合）

来源: Polska Akademia Nauk * Centrum Upowszechniania Nauki / Polish Academy of Sciences, Center for the Advancement of Science

【 摘 要 】

Numerous nowadays applications generate huge sets of data, whose natural feature is order, e.g,. sensor installations, RFID devices, workflow systems, Website monitors, health care applications. By analyzing the data and their order dependencies one can acquire new knowledge. However, nowadays commercial BI technologies and research prototypes allow to analyze mostly set oriented data, neglecting their order (sequential) dependencies. Few approaches to analyzing data of sequential nature have been proposed so far and all of them lack a comprehensive data model being able to represent and analyze sequential dependencies. In this paper, we propose a formal model for time point-based sequential data. The main elements of this model include an event and a sequence of events. Measures are associated with events and sequences. Measures are analyzed in the context set up by dimensions in an OLAP-like manner by means of the set of operations. The operations in our model are categorized as: operations on sequences, on dimensions, general operations, and analytical functions.

【 授权许可】

Unknown