Rizzi
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Introduction
Operational databases are focused on recording transactions, thus they are prevalently
characterized by an OLTP (online transaction processing) workload. Conversely,
data warehouses (DWs) allow complex analysis of data aimed at decision support;
the workload they support has completely different characteristics, and is widely
known as OLAP (online analytical processing). Traditionally, OLAP applications
are based on multidimensional modeling that intuitively represents data under the
metaphor of a cube whose cells correspond to events that occurred in the business
domain (Figure 1). Each event is quantified by a set of measures; each edge of the
cube corresponds to a relevant dimension for analysis, typically associated to a
hierarchy of attributes that further describe it. The multidimensional model has a
twofold benefit. On the one hand, it is close to the way of thinking of data analyzers,
who are used to the spreadsheet metaphor; therefore it helps users understand
data. On the other hand, it supports performance improvement as its simple structure
allows designers to predict the user intentions.
Multidimensional modeling and OLAP workloads require specialized design techniques.
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