Data Exporter Widget Logic Local Visualization Recreation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In modern computing systems, especially in cloud environments, downloading visualization data from remote servers can be challenging due to large data sets overwhelming local systems, leading to long wait times or system failures, making it difficult for users to access and recreate data visualizations without continuous connection to remote servers.
Innovation Solution
A data exporter component within the visualization application generates a remote data request to retrieve the necessary data from remote servers, using widget logic and export managers to capture and recreate data visualizations locally, even when pages are changed or data visualizations are eliminated, through a job execution engine that handles data retrieval, conversion, and persistence tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored remotely in cloud environments, then data accessibility and storage scalability are improved, but local system access to underlying data and ease of data export are worsened
Solution Approach 1:
A data exporter component is introduced as an intermediary between the remote data storage system and the local visualization application. This exporter receives export requests, retrieves data from remote sources, and delivers it to local storage, thereby mediating the data access process and resolving the contradiction between remote storage benefits and local data accessibility
Solution Approach 2:
The data export process is divided into separate functional components: a data exporter module that handles remote data retrieval, a job execution engine that manages export tasks, and a local storage system that receives exported data. This segmentation allows each component to specialize in specific tasks, improving overall system efficiency and data accessibility
2Quantity of substance
If large data sets are downloaded to local systems, then data visualization capability is improved, but system performance and stability are worsened due to memory spikes and processing overload
Solution Approach 1:
The large data set is divided into smaller manageable chunks or partitions that are processed and transferred in segments rather than as a single large block. This segmentation reduces the memory burden on local systems, preventing memory spikes and system crashes while still enabling complete data visualization through incremental processing
Solution Approach 2:
Data preprocessing and filtering operations are performed on the remote server before data transfer to the local system. This preliminary action reduces the volume of data that needs to be transferred and processed locally, thereby maintaining system stability while still providing sufficient data for effective visualization
3Adaptability or versatility
If data export processes are performed on local systems, then data recreation capability is improved, but processing time and wait times are worsened due to system overload
Solution Approach 1:
The data exporter acts as an asynchronous intermediary that initiates data export operations and continues with other tasks while the data is being transferred. This mediator architecture enables the system to start multiple export operations without waiting for each to complete, significantly reducing perceived wait time while maintaining full data recreation capability
Solution Approach 2:
The system implements periodic status updates and progress reporting during data export operations, allowing users to monitor export progress without blocking the export process. This periodic feedback mechanism reduces perceived wait time while the actual data export continues in the background
Data Source
AI summary
In one embodiment, the present disclosure pertains to downloading visualization data. Data visualizations in a user interface display may be created from remote data. Portions of the remote data may be downloaded to a local computer to recreate the data visualizations without the need for remote data requests. In another embodiment, a data export architecture includes jobs comprising a plurality of tasks configured in series. In one example embodiment, the jobs may correspond to particular data visualizations to be downloaded.