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A single record in this table is referred to as an analytical record or analytic record (AR), and represents the subject of the prediction (e.g. a customer) and stores all data (variables) describing this subject. [2] If for example the subject is a customer then the record may be referred to as a customer analytic record or "CAR". [3] [4] [5]
Analytical Performance Modeling is a method to model the behaviour of a system in a spreadsheet. It is used in Software performance testing . It allows evaluation of design options and system sizing based on actual or anticipated business usage.
Supported data models (conceptual, logical, physical) Supported notations Forward engineering Reverse engineering Model/database comparison and synchronization Teamwork/repository Database Workbench: Conceptual, logical, physical IE (Crow’s foot) Yes Yes Update database and/or update model No Enterprise Architect
Data analysis focuses on the process of examining past data through business understanding, data understanding, data preparation, modeling and evaluation, and deployment. [8] It is a subset of data analytics, which takes multiple data analysis processes to focus on why an event happened and what may happen in the future based on the previous data.
The dimensional model is a specialized adaptation of the relational model used to represent data in data warehouses in a way that data can be easily summarized using online analytical processing, or OLAP queries. In the dimensional model, a database schema consists of a single large table of facts that are described using dimensions and measures.
Overview of a data-modeling context: Data model is based on Data, Data relationship, Data semantic and Data constraint. A data model provides the details of information to be stored, and is of primary use when the final product is the generation of computer software code for an application or the preparation of a functional specification to aid a computer software make-or-buy decision.
reports, dashboards and scorecards based on that data structure (reporting led automation), what-if analysis and scenario-modeling (predictive or analytic led automation). In most cases, these three levels are discrete functions, loosely banded together as a single product, and there is little automation of the process from end to end.
While the functional model retains the key features of the spreadsheet, it also overcomes its main limitations. With the functional model, data is arranged in a grid of cells, but cells are identified by business concept instead of just row or column. Rather than worksheets, the objects of the functional model are dimensions and cubes.