Dynamic Data Analysis Interface with Iterative Query Processing
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Solution Overview
Problem
Data analysts face significant challenges in efficiently exploring large volumes of data due to the time and resource-intensive nature of data analysis, often encountering 'needle-in-a-haystack' scenarios with high volumes of innocuous or noisy data, which requires numerous iterations and consumes substantial computational resources.
Innovation Solution
A dynamic and interactive data analysis system with user interfaces that provide highly responsive visualization tools, allowing data analysts to explore multiple data paths while maintaining detailed session histories, enabling quick navigation and retraceability through breadcrumb and tree view interfaces, and optimizing queries through caching and optimization techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data analysis methods are used to explore large volumes of data, then data analysts can perform comprehensive analysis, but the process becomes time and resource intensive with high consumption of computer memory and processing power
Solution Approach 1:
The patent segments the data analysis process into multiple iterations where each iteration processes a subset of data. The system divides the large data volume into manageable chunks that can be processed sequentially, reducing memory consumption while maintaining analysis comprehensiveness. This is achieved through iterative query execution that processes data in phases rather than loading everything into memory at once.
Solution Approach 2:
The patent implements preliminary actions by caching query results and intermediate data structures between iterations. The system performs preliminary processing of data subsets and stores the results for later retrieval, avoiding redundant computation. This caching mechanism reduces processing time and resource consumption in subsequent iterations by reusing previously computed results.
2Loss of information
If data analysts perform multiple iterations to explore data and locate discrete pieces of information, then they can find valuable information within noisy data, but each iteration incurs additional time and resource costs
Solution Approach 1:
The patent applies preliminary action by pre-processing and indexing data before the main analysis iterations. The system performs initial data validation, filtering, and structure creation that prepares the data for faster subsequent queries. This preliminary preparation reduces the time required for each iteration while ensuring that no valuable information is lost during the exploration process.
Solution Approach 2:
The patent uses copying by creating and maintaining multiple versions of data structures and query results across iterations. The system copies relevant data subsets and intermediate results between iterations, allowing analysts to efficiently navigate and compare different data views without reprocessing the entire dataset each time. This copying mechanism preserves information completeness while reducing redundant processing time.
3Measurement precision
If data analysts attempt to locate discrete pieces of information from large data sources, then they can find valuable information, but the data sources primarily consist of innocuous or noisy data which provides little probative value
Solution Approach 1:
The patent applies local quality by implementing different processing strategies for different portions of the data based on their characteristics. The system identifies and applies specialized filtering and analysis techniques to specific data subsets that are more likely to contain valuable information, while using simpler processing for known innocuous data. This localized approach improves detection accuracy for valuable information without unnecessarily complicating the processing of all data.
Solution Approach 2:
The patent implements partial action by focusing computational resources on processing only the most promising data subsets rather than analyzing every piece of data with equal depth. The system uses heuristics and preliminary filtering to identify data regions that warrant detailed analysis, applying more sophisticated processing only where needed. This approach improves information detection accuracy while managing processing complexity by avoiding excessive analysis of obviously uninteresting data.
Data Source
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AI summary
The systems and methods described herein provide highly dynamic and interactive data analysis user interfaces which enable data analysts to quickly and efficiently explore large volume data sources. The data analysis system enables the user to investigate large volumes of data over many different paths of analysis while maintaining detailed and retraceable steps taken by the user over the course of an investigation, as captured via the user's queries and user interaction with the user interfaces provided by the data analysis system. Data analysis paths may involve exploration of high volume data sets, such as Internet proxy data, which may include trillions of rows of data. The data analyst may pursue a data analysis path that involves, among other things, applying filters, joining to other tables in a database, viewing interactive data visualizations, and so on. The data analysis system enables a user to refresh or insert a node in a workflow. The data analysis system further provides data editing, reporting, enrichment, expression language, and charting functionality.