18540368. Scalable Visual Analytics Pipeline for Large Datasets simplified abstract (International Business Machines Corporation)

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 18540368 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. The mechanisms then execute queries and pattern discovery operations on the graph data structure to identify and visualize patterns of event paths.

• The mechanisms generate a chronology-aware graph data structure from an input database of records.
• The graph data structure has vertices representing events or features corresponding to events, and edges representing chronological relationships between events.
• The mechanisms execute queries on the graph data structure to filter and identify a subset of vertices and features based on specified criteria.
• Pattern discovery operations are then performed on the filtered set of vertices and features to identify patterns of event paths.
• A visual analytics graphical representation is generated for the subset of vertices and corresponding features.

Potential Applications

This technology could be applied in various fields such as fraud detection, network analysis, and historical data analysis.

Problems Solved

This technology helps in identifying patterns and relationships in large datasets, making it easier to extract valuable insights and make informed decisions.

Benefits

The technology allows for efficient visualization and analysis of complex data structures, enabling users to uncover hidden patterns and trends.

Potential Commercial Applications

One potential commercial application of this technology could be in the financial sector for detecting fraudulent activities or in the healthcare industry for analyzing patient data.

Possible Prior Art

One possible prior art for this technology could be existing graph database systems or visual analytics tools that perform similar functions.

How does this technology handle real-time data processing?

This article does not address how the mechanisms in the visual analytics pipeline handle real-time data processing and if they are capable of analyzing data streams as they come in.

What are the scalability limitations of this technology?

The article does not mention the scalability limitations of the visual analytics pipeline and whether it can efficiently handle large volumes of data.

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.