5 Minute Overview of Pentaho Business Analytics. Mondrian - Interactive Statistical Data Visualization in JAVA. MESI. Many Eyes. Try out the newest version of IBM Many Eyes!
New site design and layout Find visualization by category and industry New visualization expertise and thought leadership section Expertise on the Expert Eyes blog Learn best practices to create beautiful, effective visualizations New, innovative visualizations from the visualizations experts of IBM Research New visualization options. Data warehouse. Data Warehouse Overview In computing, a data warehouse (DW, DWH), or an enterprise data warehouse (EDW), is a database used for reporting and data analysis.
Integrating data from one or more disparate sources creates a central repository of data, a data warehouse (DW). Data warehouses store current and historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons. Entity–attribute–value model. Entity–attribute–value model (EAV) is a data model to describe entities where the number of attributes (properties, parameters) that can be used to describe them is potentially vast, but the number that will actually apply to a given entity is relatively modest.
In mathematics, this model is known as a sparse matrix. EAV is also known as object–attribute–value model, vertical database model and open schema. OpenReports. Jasperreports : JasperForge. JasperReports. It can be used in Java-enabled applications, including Java EE or web applications, to generate dynamic content.
It reads its instructions from an XML or .jasper file. JasperReports is part of the Lisog open source stack initiative. Public Data Explorer. Indicadores sobre Desarrollo Humano Informe sobre Desarrollo Humano 2013, Programa de las Naciones Unidas para el Desarrollo Los datos empleados para calcular el Índice de Desarrollo Humano (IDH), y los otros indicadores compuestos que se publican en el Informe Sobre Desarrollo ...
Desempleo en Europa (mensual) DSPL Tutorial - DSPL: Dataset Publishing Language - Google Code. DSPL stands for Dataset Publishing Language.
Datasets described in DSPL can be imported into the Google Public Data Explorer, a tool that allows for rich, visual exploration of the data. Note: To upload data to Google Public Data using the Public Data upload tool, you must have a Google Account. This tutorial provides a step-by-step example of how to prepare a basic DSPL dataset. Hans Rosling shows the best stats you've ever seen. Business analytics and business intelligence leaders - Pentaho. 03. Hello World Example. Although this will be a simple example, it will introduce you to some of the fundamentals of PDI: Working with the Spoon tool Transformations Steps and Hops Predefined variables Previewing and Executing from Spoon Executing Transformations from a terminal window with the Pan tool.
Overview Let's suppose that you have a CSV file containing a list of people, and want to create an XML file containing greetings for each of them. If this were the content of your CSV file: Loop over fields in a MySQL table to generate csv files. Dynamic SQL Queries in PDI a.k.a. Kettle | Adventures with Open Source BI. Email When doing ETL work every now and then the exact SQL query you want to execute depends on some input parameters determined at runtime.
This requirement comes up most frequently when SELECTing data. This article shows the techniques you can employ with the “Table Input” step in PDI to make it execute dynamic or parametrized queries. The samples you can get in the downloads section are self-contained and they use an in-memory database, so they work out of the box. Just download and run the samples. Slowly changing dimension. For example, you may have a dimension in your database that tracks the sales records of your company's salespeople.
Creating sales reports seems simple enough, until a salesperson is transferred from one regional office to another. How do you record such a change in your sales dimension? Power Your Decisions With SAP Crystal Solutions. OpenMRS: ETL/Data Warehouse/Reporting. ETL Process. The ETL (Extract, Transform, Load) process is comprised of several steps and its architecture depends on the specific data warehouse system.
In this post, an outline of the process will be given along with choices that are/could be used for OpenMRS. Data sources, staging area and data targets Data sources: The only data source for the moment is the OpenMRS database.Staging area: This refers to an intermediate area between the source database and the DW database. This is where the extracted data from the source systems are stored and manipulated through transformations.
At this time, there is no need for a sophisticated staging area, other than a few independent tables (called orphans), which are stored in the DW database.Data Targets: The DW database. Another approach for reporting: A Data Warehouse System. Why would we want to build a data warehouse system? We might consider doing this for some of the following reasons: An overview of the data warehouse How can the above requirements be met? What are the main components of such a system? DW Data Model. This post is going to describe the data model for the OpenMRS data warehouse. It will be edited frequently to add documentation for the model and to modify it. Star Schemas. Building Reports (Step By Step Guide) - Documentation - OpenMRS Wiki.
You can create three different types of reports: a Period Indicator Report, a Row-Per-Patient Report, or a Custom Report (Advanced). All reports contain a Report Definition which is linked to one or more DataSet Definitions. In the first two options, the link between the Report Definition and the appropriate DataSet Definition is set automatically.
Openmrs-reporting-etl-olap - A data warehouse system for OpenMRS, based on other open source projects. Pentaho and OpenMRS Integration. We have a great opportunity to explore how Pentaho can provide ETL, analytics, and reporting benefits to OpenMRS, an open source medical records platform and community interested in global health care. Check out the first projects underway, and decide if you have time to participate: Pentaho ETL and Designs for Dimensional Modeling Cohort Queries as Pentaho Reporting DatasourceThis project still needs a lead developer; we'd like to have these projects run in tandem. To get involved, feel free to email me directly, or contact any of the OpenMRS mentors listed in the projects. Pentaho ETL and Designs for Dimensional Modeling (Design Page, R&D) - Projects - OpenMRS Wiki.
Abstract OpenMRS has few tools in place allowing for easier analysis of concept, patient, location, encounter or visit data in an aggregated, dimensional manner. OLAP (Online Analytical Processing) is one technology encompassed under the umbrella of business intelligence that facilitates rapid answers to multi-dimensional querying of data. Click on the image at right for a simple sample of what dimensional modeling looks like at a high level. This functionality extends beyond traditional reporting in several ways: Cohort Queries as a Pentaho Reporting Data Source - Projects - OpenMRS Wiki. Welcome to the Pentaho Community. Concept Dictionary Creation and Maintenance Under Resource Constraints: Lessons from the AMPATH Medical Record System. Welcome to Apelon DTS. OpenMRS. Advanced Concept Management at OpenMRS. OpenMRS Database Schema. Main Page - MaternalConceptLab.