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Getting started with Cognos Framework Manager Framework Manager is a metadata modeling tool that drives query generation for IBM Cognos software. A model is a collection of metadata that includes physical information and business information for one or more data sources. Whenever we start creating a model, we need to understand the requirement first and then we can choose whether to go for star schema or snowflake schema or just a relational schema. While creating a framework manager metadata model we will come across the below terms: Project: A project contains a model, namespaces, packages, data sources, and related information for maintaining and sharing model information. The project will be saved as a .cpf file and an XML file which defines the project. Different terms associated with Framework Manager: Models: Model contains your dimension tables, fact tables, and the related join information. A framework manager model is a metadata layer between your database and the reporting applications. Using Model, we can create dimension tables, fact tables and even create filters. Namespace: A namespace uniquely identifies query items, dimensions, query subjects, and other objects. You import different databases into separate namespaces to avoid duplicate names. Packages: A package is a subset of the dimensions, query subjects, and other objects defined in the project. A package is what is actually published to the IBM Cognos BI server, and it is used to create reports, analyses, and ad hoc queries. Dimensions: A dimension is a broad grouping of data about a major aspect of a business, such as products, dates, or markets. Query Subjects: A query subject is a set of query items that have an inherent relationship. In most cases, query subjects behave like tables. Query subjects produce the same set of rows regardless of which columns were queried. There are different types of query subjects which we can learn going further. Query Items: A query it

Mesenchymal stem cells are a subset of pluripotent stem cells which can be isolated from the mesoderm. With their self-replication renewal and multi-direction differentiation characteristics, they possess a high potential for various therapies in medicine. Mesenchymal stem cells have a unique immune phenotype and immune regulation ability. Therefore, mesenchymal stem cells are already widely used in stem cell transplantations, tissue engineering and organ transplantation. And Beyond these applications, they are used as an ideal tool in tissue engineering as seeder cells in a series of basic and clinical research experiments.

Deep learning is a subset of machine learning that is concerned with algorithms inspired by the structure and function of the brain. Deep learning frameworks are software libraries that allow developers to more easily create and train deep learning models. There are many different deep learning frameworks available, but some of the most popular ones are TensorFlow, Keras, and PyTorch.

Deep learning is a subset of machine learning that is concerned with algorithms inspired by the structure and function of the brain. Neural networks, which are a key component of deep learning, are artificial intelligence systems that can learn and complex tasks by automatically extracting patterns from data.

ML is a subset of AI that helps to increase the software prediction rate without designing for the software. In Data analytics courses in India, you will learn primary to advanced level concepts of the ML methods. And understand the classic Machine learning algorithm, that is the base of all learnings.

Introduction Keeping legacy systems up-to-date is the critical challenge businesses face today. Businesses are finding a way to support innovation while maintaining the stability and reliability of the older systems. Many businesses choose not to modernize their legacy systems because they require significant investment, which can impede progress. Generative AI is being seen as the solution and its expanding capabilities to bridge the gap between legacy infrastructure and modern business demands. This blog explores how gen AI modernizes legacy system can deliver its capabilities to transform and modernize legacy systems-making them more flexible and adaptable to the needs of today's world without completely replacing them. Generative artificial intelligence (AI) is becoming an important tool in accelerating modernize legacy systems, from automating code modernization to improving data interoperability so the system becomes more robust. Understanding Legacy Systems To better understand how generative AI can modernize legacy systems, let's first explore the definition of a legacy system. Legacy systems refer to the software and hardware within an organization that houses the necessary information. Businesses run their core processes through legacy systems. Outdated technology: built using the platform and the technology that are old, now obsolete, or difficult to maintain. High maintenance costs: As the system and the technology are old-school, the cost of maintaining them is high as the experts who can get their hands on the system are less. Incompatibility with modern solutions: The integration of Legacy systems with other modern platforms is nearly impossible due to their lack of flexibility. Security risks: Older systems can't withstand modern security attacks, and they are prone to simple hacking. The data can't be lost, and at the same time, the system has to adapt itself to modern measures. Businesses are increasingly seeking solutions to modernize