Abstract: Data cataloging has become a necessity for empowering organizations with analytical ability. Conventional cataloging systems may fail to provide proper visualization of data to the different stakeholders of an organization. The present disclosure provides a hierarchical dynamic cataloging system so that visualization of data at different levels would be possible for different stake holders. In the present disclosure, a hierarchical structure of algorithms and multiple stake holders along with relevant metadata is generated. Further, a catalog is generated by performing a mapping across components comprised in the hierarchical structure and identifying relationship across the components based on mapping. The catalog gets dynamically updated and provides a dynamic view of the algorithms and associated metadata to the multiple stakeholders of an organization.

Abstract: Machine Learning (ML) models are deployed in digital platforms for data analytics. However, it is realized that there is growing trends of recognition that machine learning models expose new vulnerabilities in software systems, for instance training data poisoning, adversarial responses, model extraction, and the like. Embodiments of the present disclosure provide systems and methods for safeguarding training dataset by exploiting immutability feature and generating immutable machine learning models for data analytics. More specifically, immutable records of events are governed by smart contracts within highly secure permissioned distributed ledger. This dataset is used for training multiple machine learning models which are immutable in nature and further utilized for triggering actions for incoming request(s) from IoT platforms.

Abstract: Machine Learning (ML) models are deployed in digital platforms for data analytics. However, it is realized that there is growing trends of recognition that machine learning models expose new vulnerabilities in software systems, for instance training data poisoning, adversarial responses, model extraction, and the like. Embodiments of the present disclosure provide systems and methods for safeguarding training dataset by exploiting immutability feature and generating immutable machine learning models for data analytics. More specifically, immutable records of events are governed by smart contracts within highly secure permissioned distributed ledger. This dataset is used for training multiple machine learning models which are immutable in nature and further utilized for triggering actions for incoming request(s) from IoT platforms.

Abstract: Data cataloging has become a necessity for empowering organizations with analytical ability. Conventional cataloging systems may fail to provide proper visualization of data to the different stakeholders of an organization. The present disclosure provides a hierarchical dynamic cataloging system so that visualization of data at different levels would be possible for different stake holders. In the present disclosure, a hierarchical structure of algorithms and multiple stake holders along with relevant metadata is generated. Further, a catalog is generated by performing a mapping across components comprised in the hierarchical structure and identifying relationship across the components based on mapping. The catalog gets dynamically updated and provides a dynamic view of the algorithms and associated metadata to the multiple stakeholders of an organization.

Abstract: Advanced analytics refers to theories, technologies, tools, and processes that enable an in-depth understanding and discovery of actionable insights in big data, wherein conventional systems and methods may be prone to errors leading to inaccuracies.