Multidimensional Dataset Query Processing for Individual Contribution Analysis
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Solution Overview
Problem
Current query processing systems for multidimensional datasets, particularly in OLAP applications, lack the ability to efficiently identify and present the relative contributions of individuals associated with specific trends or values across multiple dimensions, making it difficult to analyze and respond to complex business queries effectively.
Innovation Solution
A method and system that process multidimensional datasets to identify individuals associated with certain values or trends by scanning cells in the dataset, calculating their relative contributions, and presenting this information in a graphical format, allowing users to analyze and respond to queries related to business metrics such as sales, profits, or consumer behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional query processing systems are used for multidimensional datasets, then basic data retrieval is possible, but the ability to efficiently identify and present relative contributions of individuals associated with specific trends or values is lacking
Solution Approach 1:
The system segments the multidimensional dataset by associating specific cells with individual persons through data tables, allowing the relative contribution of each person to be independently calculated and presented. This segmentation enables precise identification of individual contributions while maintaining efficient query processing through structured data organization.
2Adaptability or versatility
If multidimensional datasets are organized with multiple dimensions and cells, then comprehensive data analysis is enabled, but the complexity of identifying individual contributions increases
Solution Approach 1:
Data tables serve as intermediaries between the multidimensional dataset cells and persons. These tables associate persons with specific cells, providing a structured mapping that simplifies the identification of individual contributions. The intermediary structure reduces query processing complexity by pre-establishing relationships rather than requiring complex real-time analysis.
3Measurement precision
If the system calculates relative contribution trends for each person, then detailed performance analysis is achieved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary calculations of relative contribution trends by maintaining pre-computed associations between persons and dataset cells. When queries are executed, the system can quickly retrieve and present pre-calculated contribution data rather than performing complex real-time computations, significantly reducing processing time while maintaining detailed performance analysis capability.
Data Source
AI summary
A method of processing a query. The method comprises providing at least one multidimensional dataset having at least three dimensions formed according to a plurality of data tables associating between a plurality of persons and a plurality of roles and comprising a plurality of cells, receiving a query defining at least one factual value coordinated by at least one of the plurality of cells, the at least one factual value representing a measure defined in at least one of the plurality of data tables, deriving at least one person from the plurality of persons, the at least one person being associated with the with multidimensional dataset with the at least one cell, and outputting an indication of the at least one person in response to the query.