Abstract: System and method for automatic entity relationship (ER) model generation for services as software is disclosed. ER model generation by automated knowledge acquisition is disclosed, and automation of knowledge generation process is disclosed. Information extraction process is automated and multilevel validation of information extraction process is provided. System comprises training module to train information extraction model, and knowledge generation module for population of ER model. Standard Operators are generated based on the ER model so generated (populated). Context aware entity extraction is implemented for the ER model generation. System and method leverages existing ER model to make the system self-learning and intelligent, and provides common platform for knowledge generation from different data sources comprising documents, database, website, web corpus, and blog.

Abstract: Disclosed is method and system for converting text used to perform an operation to reusable actionable knowledge form. Procedural knowledge available in run books is converted into reusable actionable knowledge form. The method comprises structuring the text by performing merging, grouping, editing, removing statements and marking statements present in structured text into action segments, predicates, and comments, by parsing technique and rule based reasoning. Predicate comprise conditions, action segment comprise actionable statements executed upon fulfilling conditions. Actionable statement is used to perform a task of the operation.

Abstract: A system and method for creating an instance model is provided. The system provides an information extraction and modeling framework from wide spectrum of document types such as PDF, Text, HTML, LOG, CSV, images, audio/video files and DOCX. In this framework information is extracted and mapped on a domain conceptual model like ER model and the instance model is created. Initially a template model is created using the existing ER model and the plurality of data sources. The template model, the existing ER model and the information extracted from the plurality of data sources are then provided as input to generate the instance model. The system or method is not limited to extract information from log files. This can be useful for different types of files type if the structures and formats of data are different. The system can also be used with unstructured type of data sources.

Abstract: A system and method for creating an instance model is provided. The system provides an information extraction and modeling framework from wide spectrum of document types such as PDF, Text, HTML, LOG, CSV, images, audio/video files and DOCX. In this framework information is extracted and mapped on a domain conceptual model like ER model and the instance model is created. Initially a template model is created using the existing ER model and the plurality of data sources. The template model, the existing ER model and the information extracted from the plurality of data sources are then provided as input to generate the instance model. The system or method is not limited to extract information from log files. This can be useful for different types of files type if the structures and formats of data are different. The system can also be used with unstructured type of data sources.

Abstract: The present subject matter relates to providing automated units for infrastructure support. In an example, an operation log having information pertaining to actions performed to resolve a ticket, may be filtered based on filtering attributes. The filtering attributes may aid in selection of content relevant for identifying an automatable unit from the operation log. The automatable unit may be one of a standard operator unit and a control flow unit. The content may be further analyzed to generate the automatable unit. The content may be analyzed using one of a sequence mining technique and a graph mining technique. Further, the automatable unit may be provided in a support service knowledgebase accessible by the users for dynamically resolving tickets similar to the ticket.

Abstract: System and method for automatic entity relationship (ER) model generation for services as software is disclosed. ER model generation by automated knowledge acquisition is disclosed, and automation of knowledge generation process is disclosed. Information extraction process is automated and multilevel validation of information extraction process is provided. System comprises training module to train information extraction model, and knowledge generation module for population of ER model. Standard Operators are generated based on the ER model so generated (populated). Context aware entity extraction is implemented for the ER model generation. System and method leverages existing ER model to make the system self-learning and intelligent, and provides common platform for knowledge generation from different data sources comprising documents, database, website, web corpus, and blog.

Abstract: Disclosed is method and system for converting text used to perform an operation to reusable actionable knowledge form. Procedural knowledge available in run books is converted into reusable actionable knowledge form. The method comprises structuring the text by performing merging, grouping, editing, removing statements and marking statements present in structured text into action segments, predicates, and comments, by parsing technique and rule based reasoning. Predicate comprise conditions, action segment comprise actionable statements executed upon fulfilling conditions. Actionable statement is used to perform a task of the operation.

Abstract: The present subject matter relates to providing automated units for infrastructure support. In an example, an operation log having information pertaining to actions performed to resolve a ticket, may be filtered based on filtering attributes. The filtering attributes may aid in selection of content relevant for identifying an automatable unit from the operation log. The automatable unit may be one of a standard operator unit and a control flow unit. The content may be further analyzed to generate the automatable unit. The content may be analyzed using one of a sequence mining technique and a graph mining technique. Further, the automatable unit may be provided in a support service knowledgebase accessible by the users for dynamically resolving tickets similar to the ticket.