Examples of using Data models in English and their translations into Indonesian
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Implementing analytical data models, such as OLAP cubes.
Data models for different systems are arbitrarily different.
Using these, the users can observe the changes in the data models.
Data models define not only data elements, but also their structure and relationships.
The growth of the smart home industry has added new technologies andlearnings, such as data models and service discovery design.
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Data models are progressive- it can be be performed during various types of projects and in multiple phases of projects.
The reason for these problemsis a lack of standards that will ensure that data models will both meet business needs and be consistent.
Data models are progressive; there is no such thing as the final data model for a business or application.
If Excel Services is configured to support Data Models, you can view and interact with PowerPivot and Power View content in a browser window.
Data models support data and computer systems by providing the definition and format of data. .
The dominant database language is the standard SQL for the Relational model, which has influenced database languages also for other data models.
Physical data models for each implementation would differ significantly, not least due to underlying operating-system requirements that may sit underneath them.
The reduced run-time flexibility compared to full SQL systems is compensated by marked gains in scalability andperformance for certain data models.
From traditional artist and repertoire, also known as A&R,to predictive and proactive data models, fueled by innovations in machine learning and the way in which our industry defines a hit record.
If data models are developed on a system by system basis, then not only is the same analysis repeated in overlapping areas, but further analysis must be performed to create the interfaces between them.
The Project intends on addressing the time-to-market challenge by bringing together and building upon the best aspects of market-proven technologies such as Google'sOpenWeave as well as other protocols and data models from partner organizations.
If data models are developed on a system by system basis, then not only is the same analysis repeated in overlapping areas, but further analysis must be performed to create the interfaces between them.
Will acquire the skills in various areas of artificial intelligence, including machine learning,as well as approaches to building different data models, which will allow for creating complex solutions based on the multi-level analysis;
Conventional data models, on the other hand, have a fixed and limited domain scope, because the instantiation(usage) of such a model only allows expressions of kinds of facts that are predefined in the model. .
These types of databases are optimized specifically for applications that require large data volume, low latency,and flexible data models, which are achieved by relaxing some of the data consistency restrictions of other databases.
OutSystems covers the widest range of capabilities for full-stack development and full lifecycle management including business process management(BPM), integration workflows, UIs,business logic, data models, web services, and APIs.
High-performance: NoSQL database are optimized for specific data models(such as document, key-value, and graph) and access patterns that enable higher performance than trying to accomplish similar functionality with relational databases.
The algorithm has a capability that is known as auto-tuning capability, with the help of which it derives or extracts insights or values from raw data such as age or gender, and after that,it can create predictive data models.
This tutorial explains the basics of DBMS such as its architecture, data models, data schemas,data independence, E-R model, relation model, relational database design, and storage and file structure and much more.
Some SCM applications are based on open data models that support the sharing of data both inside and outside the enterprise, called the extended enterprise, and includes key suppliers, manufacturers, and end customers of a specific company.
Learn to implement multidimensional and tabular data models, deliver reports with Microsoft SQL Server Reporting Services, create dashboards with Microsoft SharePoint Server PerformancePoint Services, and discover business insights by using data mining.
Having the ability to design and create databases, develop logical data models, create data services by using Transact-SQL, manage and maintain databases, configure and manage database security, monitor and optimize databases, install and configure Microsoft SQL Server.