Abstract: A system and method of creating an entity relationship map includes receiving a stream of lexical matter associated with one or more categories (302) and identifying one or more tokens from the received lexical matter based on the one or more categories (304). A frequency of one or more of unique lexical token and recurring lexical token are determined (306) and one or more outliers based on a standard deviation range associated with the at least one category is eliminated (308). Sentences with the one or more recurring lexical tokens are selected (310) to find one or more lexical neighbors and the entity relationship map is created based on an association between the unique lexical tokens and the at least one lexical neighbor (312).

Abstract: A method and system for authenticating software licenses of a software includes a request for a software authentication received from one or more software subscribers and one or more electronic licenses distributed between one or more software vendors and the one or more software subscribers. Further, one or more tokens are validated through an authentication engine at a delivery packet delivered to the software subscriber. A license key associated with each validated token is generated and distributed through a licensing engine. The software is initiated to be enabled through the license key.

Abstract: A method and system can implement error and event log correlation in an apparatus and include extracting one or more log information associated with a storage location and creating a flexible structure of the one or more log information. The one or more log information is translated to a database store based on a user input. A match level is determined between an event and error data through the one or more log information extracted. When the match level exceeds a predetermined value, a relationship between the event and error data is created through an algorithm and a shareable entry is created for the relationship in a format usable by another apparatus.

Abstract: A method generating a platform-agnostic abstract syntax tree (AST) comprises receiving data in a predefined format, through an input unit; subsequently parsing the data to extract model information corresponding to the predefined format of the data; and transforming, by a processing server, the model information to an abstract syntax tree (AST) structure. The above steps aid in generating, by the processing server, a platform-agnostic AST by combining predefined metadata and the abstract syntax tree (AST) structure.

Abstract: A method and system of a data join includes capture of metadata information associated with one of semi-structured data and unstructured data. A flattened structure for one of the semi-structured data and the unstructured data is defined, and an entity is extracted from the unstructured data. Further, one of the semi-structured data and an entity extracted unstructured data are flattened based on the flattened structure, and flattened semi-structured data and flattened entity extracted unstructured data with relational data are joined.

Abstract: A method and system automates training named entity recognition in natural language processing to build configurable entity definitions includes receiving input documents or entities through an administration module and defining a domain for each entity. Further, one or more entities corresponding to the domain specific entity in the received documents are determined and a training file to one of pick a right parser, extract content and label the entity ambiguity is generated. One or more user actions are collected and maintained at a repository through a knowledge engine. Still further, one or more labelled ambiguous words are predicted and the knowledge engine is updated. Data may be fetched, through a training pipeline execution engine and each entity may be associated with one or more documents based on the fetched data from the document store to build configurable entity definitions.

Abstract: A system and method of creating an entity relationship map includes receiving a stream of lexical matter associated with one or more categories (302) and identifying one or more tokens from the received lexical matter based on the one or more categories (304). A frequency of one or more of unique lexical token and recurring lexical token are determined (306) and one or more outliers based on a standard deviation range associated with the at least one category is eliminated (308). Sentences with the one or more recurring lexical tokens are selected (310) to find one or more lexical neighbors and the entity relationship map is created based on an association between the unique lexical tokens and the at least one lexical neighbor (312).

Abstract: A method and system can implement error and event log correlation in an apparatus and include extracting one or more log information associated with a storage location and creating a flexible structure of the one or more log information. The one or more log information is translated to a database store based on a user input. A match level is determined between an event and error data through the one or more log information extracted. When the match level exceeds a predetermined value, a relationship between the event and error data is created through an algorithm and a shareable entry is created for the relationship in a format usable by another apparatus.

Abstract: A method and system for authenticating software licenses of a software includes a request for a software authentication received from one or more software subscribers and one or more electronic licenses distributed between one or more software vendors and the one or more software subscribers. Further, one or more tokens are validated through an authentication engine at a delivery packet delivered to the software subscriber. A license key associated with each validated token is generated and distributed through a licensing engine. The software is initiated to be enabled through the license key.

Abstract: A method and system automates training named entity recognition in natural language processing to build configurable entity definitions includes receiving input documents or entities through an administration module and defining a domain for each entity. Further, one or more entities corresponding to the domain specific entity in the received documents are determined and a training file to one of pick a right parser, extract content and label the entity ambiguity is generated. One or more user actions are collected and maintained at a repository through a knowledge engine. Still further, one or more labelled ambiguous words are predicted and the knowledge engine is updated. Data may be fetched, through a training pipeline execution engine and each entity may be associated with one or more documents based on the fetched data from the document store to build configurable entity definitions.

Abstract: A method generating a platform-agnostic abstract syntax tree (AST) comprises receiving data in a predefined format, through an input unit; subsequently parsing the data to extract model information corresponding to the predefined format of the data; and transforming, by a processing server, the model information to an abstract syntax tree (AST) structure. The above steps aid in generating, by the processing server, a platform-agnostic AST by combining predefined metadata and the abstract syntax tree (AST) structure.

Abstract: A method and system of a data join includes capture of metadata information associated with one of semi-structured data and unstructured data. A flattened structure for one of the semi-structured data and the unstructured data is defined, and an entity is extracted from the unstructured data. Further, one of the semi-structured data and an entity extracted unstructured data are flattened based on the flattened structure, and flattened semi-structured data and flattened entity extracted unstructured data with relational data are joined.