Data Abstraction Layer for Dynamic Aggregate Querying
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Solution Overview
Problem
Current data processing systems face challenges in supporting changes to underlying database schemas and evolving data representations, particularly due to their reliance on Structured Query Language (SQL), which limits flexibility and requires significant maintenance and rewriting of applications to accommodate different data models and representations.
Innovation Solution
A data abstraction layer with logical fields and aggregate access methods allows users to compose queries independently of the underlying data representation, enabling dynamic aggregation and filtering of data without the need for static views, and supports multiple data sources through an abstract data layer that includes aggregate data calculation and event-based querying.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SQL is used for data access, then data retrieval capability is improved, but adaptability to schema changes and alternative data representations deteriorates
Solution Approach 1:
The patent introduces an abstract data layer with logical fields that acts as an intermediary between the application and the underlying physical data sources. This abstraction layer translates high-level logical field references into physical data access operations, allowing the application to remain unchanged when the underlying schema or data representation changes. The logical fields map to physical fields through a configurable mapping mechanism, enabling flexible adaptation to different data models while maintaining stable application logic.
2Ease of operation
If static views are used for data access, then query simplicity is improved, but flexibility for dynamic aggregation and filtering deteriorates
Solution Approach 1:
The patent implements dynamic aggregation and filtering capabilities by allowing aggregation operations to be defined on logical fields without requiring pre-defined static views. The system can dynamically compute aggregate values (such as averages, sums, counts) based on runtime conditions and groupings specified in the query. This dynamic approach enables flexible data analysis while maintaining query simplicity through the logical field abstraction.
3Quantity of substance
If multiple data sources are accessed, then data comprehensiveness is improved, but system complexity deteriorates
Solution Approach 1:
The patent creates a universal data access layer that can access multiple different data sources (relational databases, XML files, flat files, etc.) through a common interface. The logical fields and access methods provide a unified mechanism for querying diverse data sources, eliminating the need for separate access code for each data source type. This multi-functional approach enables comprehensive data access while reducing system complexity through standardization.
Data Source
AI summary
A method and apparatus for defining and processing aggregate data is disclosed. Using database abstraction techniques, a set of logical fields may be used to compose queries of a set of underlying physical data sources. In one embodiment, a logical field may represent an aggregate data value calculated from the elements appearing in a column of a relational table in an underlying physical data source. The elements may be divided into to subsets to calculate multiple aggregate values. An abstract derived entity is a data object present in a database abstraction model that may be accessed as though it were a relational table contained in an underlying physical data source. In one embodiment, columns of the table defined by the abstract derived entity may be populated with aggregate data values joined to other data as specified by a composition rule included in the definition of the abstract derived entity.