Exception Rule Generator for OLAP Data Granularity
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Solution Overview
Problem
Non-specialized users in business domains lack the ability to define powerful exception rules for OLAP data without expert knowledge of database structures or programming, limiting their ability to identify exceptional data cells across varying data granularities.
Innovation Solution
A computerized method and system for automatically defining and applying exception rules to reports from multi-dimensional databases, allowing users to set global exception parameters, locate the most relevant time dimension, detect exceptional cells, and calculate exception qualities, independent of data granularity, enabling non-experts to identify and visualize exceptional data cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If exception rules are manually defined by non-specialized users, then ease of operation improves, but measurement precision deteriorates because users lack expert knowledge of database structures
Solution Approach 1:
The patent introduces an intermediary system (exception rule generator) that translates user-friendly parameters into precise database queries. This mediator handles the complexity of database structure interpretation while users only need to provide high-level exception criteria, resolving the contradiction between ease of use and detection precision.
Solution Approach 2:
The system enables self-service by allowing non-specialized users to define exception rules without expert knowledge. The automated generation of exception rules from user parameters empowers users to perform sophisticated exception detection independently, improving ease of operation while maintaining precision through the system's intelligent processing.
2Adaptability or versatility
If exception rules are made independent of data granularity, then adaptability improves, but device complexity increases due to automated dimension location and quality calculation
Solution Approach 1:
The system dynamically adapts to different data granularities by automatically locating the most relevant time dimension based on the specific data context. This dynamic adjustment allows the exception rules to remain independent of fixed granularities while the system internally handles the complexity of dimension selection and quality calculation.
Data Source
AI summary
A method and system for automatically defining and applying an exception rule to reports created from at least one multi-dimensional database, comprising: defining global exception parameters, displaying at least one report created from a multi-dimensional database, receiving a user command to detect exceptional cells in the displayed report, locating the most relevant time dimension in the semantics of the source database of the report, detecting exceptional cells in the displayed report according to the exception parameters and the most relevant time dimension, calculating an exception quality for each detected exceptional cell, and indicating on the displayed report the exceptional cells, the indication comprising differentiating between different exception qualities, wherein the exception rule is independent of data granularity in the time dimension in the report.