On-Line Analytical Processing (OLAP) generally refers to a technique of providing fast analysis of multi-dimensional data. OLAP provides a multi-dimensional conceptual framework for data that may include support for hierarchies. This conceptual framework is advantageous since it often provides the most logical way to organize data relating to businesses or other types of organizations.
OLAP typically involves analyzing data stored in a multi-dimensional database. A multi-dimensional database may organize data in multiple dimensions and multiple fields along a given dimension. For example, a business may employ a five-dimensional database storing six months of weekly data relating to sales figures for fifty products that are sold in ten regions by five outlets. A user may be interested in identifying patterns associated with the sales figures in order to guide a decision-making process for the business. For instance, the user may be interested in identifying trends or unusual values associated with the sales figures. Even for this relatively simple five-dimensional database, 2500 separate time series may need to be analyzed. If additional fields or dimensions are included, the number of time series to be analyzed can quickly multiply.
Previous attempts for identifying patterns in a multi-dimensional database often involved a manual process, which can be tedious, time-consuming, and prone to errors or inconsistencies. While automated methods have been proposed, such methods are generally limited to exception reporting. Exception reporting typically requires a standard in order to identify exceptions in data. However, this standard was not always well defined, thus limiting the significance that can be attached to an identified exception. In addition, exception reporting is limited to finding exceptions in data and, accordingly, may fail to identify or distinguish other kinds of patterns that may be of interest to a user.
It is against this background that a need arose to develop the apparatus and method described herein.
USPTO Backgrounds
USPTO Backgrounds