17529899. AUTOMATIC DATA DOMAIN IDENTIFICATION simplified abstract (International Business Machines Corporation)

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AUTOMATIC DATA DOMAIN IDENTIFICATION

Organization Name

International Business Machines Corporation

Inventor(s)

Malolan Chetlur of Jakkur (IN)

Arvind Agarwal of New Delhi (IN)

Subhendu Dey of Kolkata (IN)

Sameep Mehta of Bangalore (IN)

Sandipan Sarkar of Kolkata (IN)

AUTOMATIC DATA DOMAIN IDENTIFICATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 17529899 titled 'AUTOMATIC DATA DOMAIN IDENTIFICATION

Simplified Explanation

The patent application describes an apparatus that includes a processing device with a processor and memory. When executing program code, the processing device is configured to perform various tasks such as extracting entities from data artifacts, code artifacts, and user interface artifacts. It then generates dependency graphs based on the relationships among these entities and performs lexical and semantic analysis on these graphs to identify the data domain of the datasets.

  • The apparatus includes a processing device with a processor and memory.
  • The processing device extracts entities from data artifacts, code artifacts, and user interface artifacts.
  • It generates dependency graphs based on the relationships among these entities.
  • The processing device performs lexical and semantic analysis on the dependency graphs.
  • The analysis helps identify the data domain of the datasets.

Potential applications of this technology:

  • Software development: The apparatus can be used to analyze code artifacts and identify dependencies between different components, helping developers understand the structure and relationships within a software system.
  • Data analysis: By extracting entities from data artifacts and performing analysis on the dependency graphs, the apparatus can help identify patterns and relationships within datasets, enabling more effective data analysis and decision-making.
  • User interface design: The apparatus can analyze user interface artifacts and identify dependencies between different elements, helping designers optimize the user experience and improve the overall usability of software applications.

Problems solved by this technology:

  • Complex data analysis: The apparatus simplifies the process of analyzing large datasets by automatically extracting entities and identifying relationships, reducing the manual effort required for data analysis.
  • Understanding software systems: By generating dependency graphs and performing analysis, the apparatus helps developers gain a better understanding of the structure and dependencies within a software system, facilitating maintenance and troubleshooting tasks.
  • Improving user interface design: The analysis of user interface artifacts helps designers identify dependencies and optimize the layout and functionality of software applications, leading to improved user experience.

Benefits of this technology:

  • Efficiency: The apparatus automates the extraction of entities and analysis of dependency graphs, saving time and effort in data analysis and software development tasks.
  • Accuracy: By leveraging lexical and semantic analysis, the apparatus can provide more accurate insights into the data domain and relationships within datasets.
  • Optimization: The analysis of dependency graphs helps identify areas for optimization in software systems and user interfaces, leading to improved performance and usability.


Original Abstract Submitted

An apparatus is disclosed which includes at least one processing device comprising a processor coupled to a memory. The at least one processing device, when executing program code, is configured to: extract one or more entities identified in a plurality of data artifacts based at least in part on one or more datasets, extract one or more entities identified in a plurality of code artifacts based at least in part on the one or more datasets, extract one or more entities identified in a plurality of user interface artifacts based at least in part on the one or more datasets, generate a set of dependency graphs each based at least in part on one or more relationships among the respective extracted one or more entities, and perform one or more of a lexical analysis and a semantic analysis on the set of dependency graphs to identify a data domain of the one or more datasets.

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