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JasperReports reports are defined in an XML file format, called JRXML, [2] which can be hand-coded, generated, or designed using a tool. The file format is defined by a document type definition or XML schema for newer versions, providing limited interoperability. [19] [2] JRXML files have the filename extension .jrxml.
SQL Server Data Tools for Business Intelligence (SSDT BI) reduces the RDL (Report Definition Language) component to graphic icons in a GUI (Graphical User Interface). In this way, instead of writing code, the user can drag-and-drop graphic icons into an SSRS report format for most aspects of the SSRS report.
Business intelligence (BI) consists of strategies, methodologies, and technologies used by enterprises for data analysis and management of business information. [1] Common functions of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text ...
OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing and data mining. [3] Typical applications of OLAP include business reporting for sales, marketing , management reporting, business process management (BPM), [ 4 ] budgeting and forecasting , financial reporting and similar ...
Supports organized business with meaning and useful data; Applies human visual perception to visual presentation of information [16] It can be accessed easily by its intended audience [17] A research-based framework for Business Intelligence dashboard design suggests that "cross-visual interactivity" is the most impactful of all features. [18]
In other words, business intelligence focusses on description, while business analytics focusses on prediction and prescription. [1] Business analytics makes extensive use of analytical modeling and numerical analysis, including explanatory and predictive modeling, [2] and fact-based management to drive decision making.
A common data warehouse example involves sales as the measure, with customer and product as dimensions. In each sale a customer buys a product. The data can be sliced by removing all customers except for a group under study, and then diced by grouping by product. A dimensional data element is similar to a categorical variable in statistics.
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
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