Database Management System for Efficient Device Status Change Analysis
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Solution Overview
Problem
Conventional relational database management systems face challenges in efficiently analyzing vast amounts of data from networked devices due to limited built-in support for querying historical data, leading to high resource consumption and performance issues, especially when dealing with large numbers of devices and conditions.
Innovation Solution
A database management system that includes a first data storage structure for unordered timestamped data elements and a second data storage structure for mapping device identifiers, condition identifiers, and timestamps, allowing for efficient identification of status changes within specified time periods using minimal updates and open database standards like SQL.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional relational database management systems are used to store and analyze device data, then vast amounts of data can be stored, but resource consumption increases significantly and system performance reduces
Solution Approach 1:
The patent segments the database into two distinct storage structures: a first storage structure for raw device data and a second storage structure for pre-computed status change information. This segmentation allows queries to operate on the smaller, pre-processed second structure rather than scanning all raw data, thereby maintaining high query performance while preserving full data storage capacity.
Solution Approach 2:
The system performs preliminary action by automatically detecting and storing status changes in the second data storage structure as data is ingested. This pre-computation of status changes eliminates the need for resource-intensive queries during analysis, as the system has already identified relevant changes before queries are executed.
2Adaptability or versatility
If separate analytical structures are used to analyze data stored in relational databases, then data analysis capability is improved, but device complexity increases
Solution Approach 1:
The patent merges the storage and analytical functions into a single database management system. The second data storage structure serves dual purposes: it stores pre-computed status change information for efficient querying and simultaneously provides the analytical results. This integration eliminates the need for separate analytical structures while maintaining enhanced data analysis capability.
3Adaptability or versatility
If vendor-specific and complex queries are used to analyze enterprise printing system data, then data analysis is achieved, but computational resources are consumed to a degree that significantly reduces system performance
Solution Approach 1:
The system performs preliminary action by pre-computing and storing status change information in the second data storage structure. This eliminates the need for complex, resource-intensive queries during analysis, as the system has already identified relevant status changes before queries are executed.
Solution Approach 2:
The patent creates a simplified copy of the essential information needed for analysis in the second data storage structure. Instead of querying the complex raw data, the system queries this simplified copy containing only status change information, dramatically reducing computational resource consumption while maintaining analysis capability.
Data Source
AI summary
A database management system comprises a first data storage structure, a second data storage structure, and data access means. The first data storage structure is for storing one or more datasets for each of a plurality of devices, each dataset comprising unordered timestamped data elements that indicate statuses of a particular device related condition at different points in time. The second data storage structure is for storing and mapping between: device identifiers that identify the devices, condition identifiers that identify the device related conditions, and timestamps that identify when two temporally consecutive data elements of a given dataset indicate different statuses. The data storage management system is operable to update the second data storage structure whenever a new data element of one of the datasets indicating a status different from the status indicated by a temporally preceding data element of said one of the datasets is stored in the first data storage structure. The data access means is for, in response to receiving a request for data in respect of a specified time period and at least one device related condition of at least one device, using the second data storage structure to identify when one or more status changes occurred within the specified time period for said at least one device related condition.