Abstract: Pre-trained models for Named Entity Recognition (NER) come with static NE classes, limited in number, and remain same irrespective of domain of the input text. Thus, domain specific training is required. Embodiments of the present disclosure provide a method and system for enhanced NER using a custom-built REGEX matcher and a heuristic entity ruler. The invention helps in discovering the NE's of the given text with pipeline-based approach with combination of models of NLP transformer, custom-built REGEX, and heuristic entity rules. The method automatically handles class resolution based on the heuristic entity ruler. The method enables a user to customize or add any new heuristic rules for entity ruler or custom regex as a knowledgebase to train the model with automatic relearning and unlearning. The extracted NEs are provided for further processing or masking in a structured format.

Abstract: In the DevOps process, testing teams use automated testcases to test a product in a regression testing running daily, which generate thousands of log files per run, in various distributed environment with different formats. Solutions to errors depend on SMEs and the impact of solution leads to rework of defect/errors and high degree of heterogeneity. This leads to huge bottleneck in automation of DevOps process leading to loss of productivity and agility. Present disclosure addresses the challenges by providing systems and methods that auto capture the log files of different formats efficiently in a scalable, extendible, and plug-able way. The system then mines and parses the log files based on given identifiers to standardize and de-duplicate to create unique error records with detail description including cause, position, module, timestamp, etc. The system predicts the solutions leveraging the database containing solutions and errors by using a modified Smooth Inverse Frequency technique.

Abstract: Existing systems provide data selection for one differential technique considering an analytical problem or synthetic data but not an arrangement for selection of one or more techniques together. The embodiments herein provide a method and system for differential privacy enabled service with hybrid rule management and similarity metrics to select data. The system generates a query table called universal data from the associates tables and databases. The system further based on query on the universal table of single columns or selected columns with different parameters using different privacy rules generates differential private data stored in temp tables/views. The system retrieves queried data of different techniques and parameters interactively viewing the privacy and similarity of each data types of retrieved data with universal data using different metrics like bar charts, Histograms, average, std.

Abstract: This disclosure relates generally to data analytics, and more particularly to a system and method for data analytics. The method includes accessing and processing sample data associated with a task to obtain a processed sample data set comprising additional columns than columns in the source data. During processing, metadata corresponding to the additional columns of the processed sample data set is dynamically captured. A plurality of data mining models are iteratively executed using the processed sample data set and the metadata. Said data mining models are obtained by varying variables and versions of a set of data mining models stored in a model repository to obtain a metrics corresponding to the plurality of data mining models. The metrics are indicative of performance of the data mining models. A data mining model is selected from amongst the plurality of data mining models based on the metrics to perform data analytics.

Abstract: This disclosure relates generally to data analytics, and more particularly to a system and method for data analytics. The method includes accessing and processing sample data associated with a task to obtain a processed sample data set comprising additional columns than columns in the source data. During processing, metadata corresponding to the additional columns of the processed sample data set is dynamically captured. A plurality of data mining models are iteratively executed using the processed sample data set and the metadata. Said data mining models are obtained by varying variables and versions of a set of data mining models stored in a model repository to obtain a metrics corresponding to the plurality of data mining models. The metrics are indicative of performance of the data mining models. A data mining model is selected from amongst the plurality of data mining models based on the metrics to perform data analytics.