Data Export Job Engine for Visualization Download
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Solution Overview
Problem
In modern computer systems, especially in cloud environments, downloading data for data visualization is challenging due to remote data storage, leading to long wait times, memory spikes, or system failures when large data sets are involved, as users lack easy access to the underlying data necessary to recreate visualizations without connecting 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, allowing users to download and store visualization data locally, using widget logic and export managers to manage queries and filters, ensuring data integrity even when visualizations are changed or eliminated, and employing dynamic paging to optimize data transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If data is stored remotely in cloud environments, then data accessibility and storage scalability are improved, but data download time and system resource consumption increase
Solution Approach 1:
The patent segments large data sets into smaller chunks or pages that can be downloaded incrementally. The system divides the data export process into multiple manageable units, allowing users to download only the specific portions needed rather than entire data sets, thereby reducing download time and resource consumption while maintaining access to remote storage capabilities.
Solution Approach 2:
The patent extracts and downloads only the specific data subsets required for visualization rather than downloading complete data sets. The system identifies and extracts only the relevant data portions needed for creating visualizations, eliminating unnecessary data transfer and reducing both download time and system resource usage.
2Ease of operation
If large data sets are downloaded to local systems, then data availability for offline use is improved, but memory consumption and system stability deteriorate
Solution Approach 1:
The patent implements segmentation by dividing large data sets into smaller, manageable chunks that fit within local system memory constraints. Data is downloaded and processed in incremental portions rather than as a single large block, preventing memory spikes and system failures while maintaining offline data accessibility.
Solution Approach 2:
The patent applies partial action by downloading and storing only the specific subsets of data needed for visualization purposes rather than complete data sets. This selective data extraction ensures that local systems handle only necessary data volumes, reducing memory consumption while preserving data accessibility for intended uses.
3Adaptability or versatility
If data export functionality is added to local systems, then data independence is improved, but system complexity and resource requirements increase
Solution Approach 1:
The patent introduces an intermediary data export service or server that handles complex data processing and export operations remotely. Local systems interact with this intermediary service through simplified interfaces, gaining data independence capabilities without directly implementing complex export functionality locally. The intermediary manages the complexity of data extraction, transformation, and export while local systems maintain ease of operation.
Data Source
AI summary
In one embodiment, the present disclosure pertains to a data export job engine. A data export architecture may include jobs comprising a plurality of tasks configured in series. In one example embodiment, the jobs may correspond to particular data visualizations to be downloaded. In another embodiment, 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.