International business machines corporation (20240111777). Scalable Visual Analytics Pipeline for Large Datasets simplified abstract

Scalable Visual Analytics Pipeline for Large Datasets

Organization Name

international business machines corporation

Inventor(s)

Andrea Giovannini of Zurich (CH)

Joy Tzung-yu Wu of San Jose CA (US)

Tanveer Syeda-mahmood of Cupertino CA (US)

Ashutosh Jadhav of San Jose CA (US)

Scalable Visual Analytics Pipeline for Large Datasets - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240111777 titled 'Scalable Visual Analytics Pipeline for Large Datasets

Simplified Explanation

The patent application describes mechanisms for implementing a visual analytics pipeline that generates a chronology-aware graph data structure from an input database of records based on specified features in an ontology data structure. This graph data structure represents events and their chronological relationships, allowing for the execution of queries and pattern discovery operations to identify and visualize patterns of event paths.

• Mechanisms for generating a chronology-aware graph data structure from a database of records
• Execution of chronology-aware graph queries to filter and analyze vertices and features
• Pattern discovery operation to identify frequent patterns of event paths
• Visualization of identified patterns in a graphical representation

Potential Applications

The technology can be applied in various fields such as finance, healthcare, and cybersecurity for analyzing and visualizing complex data relationships and patterns.

Problems Solved

- Efficient analysis of chronological data relationships - Identification of frequent patterns in event paths - Visualization of complex data structures

Benefits

- Improved understanding of data relationships - Enhanced decision-making based on identified patterns - Streamlined data analysis processes

Potential Commercial Applications

"Visual Analytics Pipeline for Pattern Discovery in Chronological Data" can be utilized in industries such as marketing, fraud detection, and supply chain management for optimizing operations and identifying trends.

Possible Prior Art

One possible prior art could be the use of graph databases and visualization tools for analyzing and visualizing data relationships. Additionally, there may be existing patents related to pattern discovery algorithms in data analysis.

How does this technology compare to existing data visualization tools in terms of performance and scalability?

The article does not provide a direct comparison with existing data visualization tools in terms of performance and scalability. It would be beneficial to understand how this technology stands out in these aspects compared to other tools on the market.

What are the potential limitations or challenges in implementing this visual analytics pipeline in real-world scenarios?

The article does not address potential limitations or challenges in implementing this visual analytics pipeline in real-world scenarios. It would be important to explore any obstacles that organizations may face when adopting this technology and how they can be overcome.

Original Abstract Submitted

mechanisms are provided to implement a visual analytics pipeline. the mechanisms generate, from an input database of records, a chronology-aware graph data structure of a plurality of records based features specified in an ontology data structure. the chronology-aware graph data structure has vertices representing one or more of events or records based features corresponding to events, and edges representing chronological relationships between events. the mechanisms execute a chronology-aware graph query on the chronology-aware graph data structure to generate a filtered set of vertices and corresponding features corresponding to criteria of the chronology-aware graph query. the mechanisms execute a pattern discovery operation on the filtered set of vertices and corresponding features to identify a subset of vertices and corresponding features that correspond to a relatively higher frequency set of patterns of event paths, and generate a visual analytics graphical representation for the subset of vertices and corresponding features.