Unbounded Analytic Framework in Data Store Systems
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Solution Overview
Problem
Current Time Series database solutions face inefficiencies when handling large, unbounded 1D and 2D arrays, leading to data storage challenges and inefficient algorithm execution due to the need for in-database types that store all data, which is not scalable for massive IoT data collections.
Innovation Solution
Implementing a data store system with processing units that identify and execute analytic functions on specific columns of a data store table, using identifier and index columns to order and process data, allowing for efficient handling of unbounded arrays by separating data abstraction from storage, enabling parallel processing and scalable data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If in-database types are created to store all data associated with 1D and 2D arrays, then data storage capability is improved, but data storage efficiency deteriorates due to large amounts of data being stored in a single location
Solution Approach 1:
The patent segments the storage of array data by distributing elements across multiple database rows rather than storing all data in a single location. Each row can store a portion of the array elements, allowing parallel access and improved storage efficiency while maintaining the ability to handle large quantities of data
Solution Approach 2:
The patent introduces an additional dimension to data storage by using multiple rows and columns to represent array elements. Instead of a single-location storage model, the system uses a two-dimensional table structure where rows and columns work together to store array data, enabling efficient retrieval and processing
2Adaptability or versatility
If in-database types are used to hold array data, then data type support is improved, but algorithm execution efficiency deteriorates due to inefficient non-parallel algorithms
Solution Approach 1:
The patent segments array data across multiple database rows, enabling parallel processing of different portions of the array. This segmentation allows multiple algorithms to execute simultaneously on different rows, dramatically improving algorithm execution efficiency while maintaining support for various data types including 1D and 2D arrays
Solution Approach 2:
The patent enables the database system to automatically perform parallel processing operations on array data stored in tabular format. The system leverages its existing parallel query execution capabilities to efficiently process array elements distributed across multiple rows without requiring specialized non-parallel algorithms
3Device complexity
If traditional storage methods are used for arrays, then data storage simplicity is improved, but scalability deteriorates when handling massive IoT data collections
Solution Approach 1:
The patent makes the database table structure universal by enabling it to store not only traditional relational data but also 1D and 2D array data. The same table structure can accommodate different data types and sizes, providing a scalable solution that grows with IoT data collections while maintaining storage simplicity through familiar tabular formats
4Ease of operation
If data is stored in a single location for array types, then data access simplicity is improved, but processing parallelism deteriorates
Solution Approach 1:
The patent segments array data across multiple database rows while maintaining simple access patterns through SQL queries. The segmentation enables processing parallelism by allowing simultaneous access to different rows, while the use of standard SQL operations preserves data access simplicity for users
Data Source
AI summary
A data store system may include a storage device configured to store a plurality of data store tables. The data store may further include a plurality of processing units. At least one processing unit from the plurality of processing units may receive an analytic function call. The at least one processing unit may further identify, in the analytic function call, at least one column of a data store table on which to execute an analytic function in the analytic function call and may further identify, in the analytic function call, an identifier column of the data store table. Each row of the at least one column may be associated with a common row value of the identifier column. The at least one processing unit may further identify, in the analytic function call, at least one index column of the data store table. Each value in each at the least one index column may identify an index value on which to index each value of the at least one column with respect to each value of the identifier column. The at least one processing unit may further order values of the at least one column in accordance with the identifier column and the at least one index column, execute the analytic function on the ordered values to generate a result set, and order the result set in accordance with the identifier column and the at least one index column. A computer-readable medium and method are also disclosed.