Flexible Energy Information Aggregation via Dynamic Script Generation
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Solution Overview
Problem
Current methods for aggregating energy-related data are resource-intensive and prone to inaccuracies due to the need for manual code writing and inflexible querying systems, leading to delays and inefficiencies in data extraction and analysis.
Innovation Solution
A graphical user interface is used to generate scripts dynamically for aggregating energy information, allowing for flexible selection and combination of energy dimensions and data sources, eliminating the need for manual code development and reducing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If professional programmers manually write custom code for data aggregation, then the data can be retrieved according to specific tasks, but the process is resource-intensive and time-consuming
Solution Approach 1:
The system enables non-programmer users to perform data aggregation themselves through a visual interface. Users can select data sources, specify aggregation criteria, and generate queries without needing programming knowledge. The system automatically translates user selections into executable queries, eliminating the need for manual code writing while maintaining accurate data retrieval.
Solution Approach 2:
A visual query builder interface acts as an intermediary between the user and the database system. This intermediary translates user-friendly selections into complex SQL queries automatically, bridging the gap between simple user interactions and sophisticated data retrieval operations without requiring users to write code.
2Reliability
If professional programmers manually write custom code for data aggregation, then the data can be retrieved according to specific tasks, but the process is expensive
Solution Approach 1:
The system empowers end-users to perform their own data aggregation tasks through an intuitive visual interface, eliminating the need to hire or pay professional programmers for routine query development. This self-service capability significantly reduces development costs while maintaining data accuracy through the system's automated query generation.
3Productivity
If inflexible querying methods are used, then the system can search and return data records, but it requires knowledge of data structures and produces inaccurate results
Solution Approach 1:
Instead of requiring users to write complex queries to get simple results, the system inverts the approach: users make simple selections through a visual interface, and the system automatically generates the complex queries needed to retrieve accurate results. This reversal eliminates the need for users to understand data structures while maintaining fast data retrieval.
4Adaptability or versatility
If manual code writing and testing is performed, then custom data aggregation can be achieved, but the process takes days or weeks to complete
Solution Approach 1:
The system enables users to immediately perform custom data aggregation by selecting from available data sources and specifying criteria through a visual interface. The automated query generation and execution eliminates the multi-day or multi-week manual development and testing process, delivering results in minutes or seconds while maintaining full custom aggregation capability.
Solution Approach 2:
The system performs preliminary work by pre-defining data sources, available aggregation operations, and result formats. This preparation allows users to simply select from pre-configured options rather than building queries from scratch, dramatically accelerating the aggregation process while maintaining versatility.
Data Source
AI summary
Systems, methods, and other embodiments associated with flexible aggregation of energy information are described. In one embodiment, a method includes receiving a request via a graphical user interface that comprises a plurality of selectable inputs that define the request. A plurality of energy dimensions are selected from a set of available energy dimensions, a plurality of combinations of the plurality of energy dimensions are determined, an energy bucket is generated for each combination of the plurality of energy dimensions, and the script is generated based upon the energy buckets. A target database is accessed on a remote target server, and the script is executed on the target database to initiate extraction of data that matches each energy bucket into a dynamic table in the target database, and run an energy option on the data extracted into the dynamic table.