"According to current business practices, unified information sets and results drawn from such information sets can be released to third parties according to so-called 'releasability' rules. Theses rules might apply to any and all of the data from which the unified information sets are drawn, the dimensions (or points or ranges along the dimensions), the third party (or members or sub-organizations of the third party), and so on. Given this, there can be a complex interaction between the data, the dimensions, the third party, the releasability rules, the levels along the dimensions at which aggregations are performed, the information that is drawn from the unified information sets, and so on. In practice, configuring a system to apply the releasability rules is an error-prone process that requires extensive manual set up and results in a brittle mechanism that cannot adapt to on-the-fly changes in data, dimensions, third parties, rules, aggregations, projections, user queries, and so on.
"Various projection methodologies are known in the art. Still other projection methodologies are subjects of the present invention. In any case, different projection methodologies provide outputs that have different statistical qualities. Analysts are interested in specifying the statistical qualities of the outputs at query-time. In practice, however, the universe of data and the projection methodologies that are applied to it are what drive the statistical qualities. Existing methods allow an analyst to choose a projection methodology and thereby affect the statistical qualities of the output, but this does not satisfy the analyst's desire to directly dictate the statistical qualities.
"Information systems are a significant bottle neck for market analysis activities. The architecture of information systems is often not designed to provide on-demand flexible access, integration at a very granular level, or many other critical capabilities necessary to support growth. Thus, information systems are counter-productive to growth. Hundreds of market and consumer databases make it very difficult to manage or integrate data. For example, there may be a separate database for each data source, hierarchy, and other data characteristics relevant to market analysis. Different market views and product hierarchies proliferate among manufacturers and retailers. Restatements of data hierarchies waste precious time and are very expensive. Navigation from among views of data, such as from global views to regional to neighborhood to store views is virtually impossible, because there are different hierarchies used to store data from global to region to neighborhood to store-level data. Analyses and insights often take weeks or months, or they are never produced. Insights are often sub-optimal because of silo-driven, narrowly defined, ad hoc analysis projects. Reflecting the ad hoc nature of these analytic projects are the analytic tools and infrastructure developed to support them. Currently, market analysis, business intelligence, and the like often use rigid data cubes that may include hundreds of databases that are impossible to integrate. These systems may include hundreds of views, hierarchies, clusters, and so forth, each of which is associated with its own rigid data cube. This may make it almost impossible to navigate from global uses that are used, for example, to develop overall company strategy, down to specific program implementation or customer-driven uses. These ad hoc analytic tools and infrastructure are fragmented and disconnected.
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