5 Minute Overview of Pentaho Business Analytics. Mondrian - Interactive Statistical Data Visualization in JAVA. MESI. Many Eyes. 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. The data stored in the warehouse is uploaded from the operational systems (such as marketing, sales, etc., shown in the figure to the right). The data may pass through an operational data store for additional operations before it is used in the DW for reporting. A data warehouse constructed from integrated data source systems does not require ETL, staging databases, or operational data store databases. A data mart is a small data warehouse focused on a specific area of interest. This definition of the data warehouse focuses on data storage. History 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. There are certain cases where an EAV schematic is an optimal approach to data modelling for a problem domain. However, in many cases where data can be modelled in statically relational terms an EAV based approach is an anti-pattern which can lead to longer development times, poor use of database resources and more complex queries when compared to a relationally-modelled data schema.
Structure of an EAV table This data representation is analogous to space-efficient methods of storing a sparse matrix, where only non-empty values are stored. Data is recorded as three columns:  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. Features JasperReports is an open source reporting library that can be embedded into any Java application. Features include: Scriptlets may accompany the report definition, which the report definition can invoke at any point to perform additional processing. For users with more sophisticated report management requirements, reports designed for JasperReports can be easily imported into the JasperServer - the interactive report server.. Jaspersoft Teodor Danciu began work on JasperReports in June 2001, the sf.net project was registered in September 2001 and JasperReports 0.1.5 was released on November 3, 2001. JasperReports Version 1.0 was released on July 21, 2005. JRXML Third-party tools IDE Integration Further reading Public Data Explorer.
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. A DSPL dataset is a bundle that contains an XML file and a set of CSV files. The CSV files are simple tables containing the data of the dataset. The only prerequisite for understanding this tutorial is a good level of understanding of XML. Contents Overview Before starting to create our dataset, here is a high-level overview of what a DSPL dataset contains: General information: About the dataset Concepts: Definitions of "things" that appear in the dataset (e.g., countries, unemployment rate, gender, etc.)
This example dataset defines the following concepts: country gender population state unemployment rate year ... 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: last_name, name Suarez,Maria Guimaraes,Joao Rush,Jennifer Ortiz,Camila Rodriguez,Carmen da Silva,Zoe This would be the output in your XML file: - <Rows> - <row><msg>Hello, Maria! The creation of the file with greetings from the flat file will be the goal for your first Transformation. A Transformation is made of Steps linked by Hops. Preparing the environment Before starting a Transformation, create a Tutorial folder in the installation folder or some other convenient place. Transformation walkthrough Creating the Transformation Pan or. Loop over fields in a MySQL table to generate csv files.
Dynamic SQL Queries in PDI a.k.a. Kettle. 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.
Binding Field Values to the SQL Query The first approach to executing dynamic queries will be familiar to many readers who are used to executing SQL statements from code: you start by writing a skeleton of your query that contains placeholders. As an example, let’s have a look at a table that contains data about US presidents. To use this approach in PDI, you start by producing a row that has fields for each placeholder in the order they appear in the query. Limitations Downloads Cheers Slawo. 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? You could calculate the sum or average of each salesperson's sales, but if you use that to compare the performance of salespeople, that might give misleading information. If the salesperson was transferred and used to work in a hot market where sales were easy, and now works in a market where sales are infrequent, his/her totals will look much stronger than the other salespeople in their new region. Or you could create a second salesperson record and treat the transferred person as a new sales person, but that creates problems.
Dealing with these issues involves SCD management methodologies referred to as Type 0 through 6. Type 0 The Type 0 method is passive. Type 1 Example of a supplier table: 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. Extraction The extraction of the data is done using SQL queries on the OpenMRS db. Cleaning and conforming Delivering Dimension Tables At this step the dimension tables are loaded with the new and changed data. SchedulingTo do. 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? The approach chosen in this project is to build a data warehouse. "A data warehouse is a system that extracts, cleans, conforms and delivers source data into a dimensional data store and then supports and implements querying and analysis for the purpose of decision making.
" The data warehouse makes a copy of transaction and other source data, in a separate database that is designed using dimensional models (or star schemas). From a user`s point of view, the data warehouse, it is used to watch the performance of an organization and its activities through time. Some of the general use cases that the data warehouse is good at: Summaries/aggregations of data, categorized and grouped in every way possible (slicing and dicing). 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 Conventions used: wk - (warehouse key) used for the surrogate (primary) keys of the dimension tablesnk - (natural key) used for the natural keys of the dimension tablesdd - (degenerate dimension) used for the degenerate dimensions in fact tablesbridge - used for tables that serve as bridge between dimension tables that have a many-to-many relationshipObservations fact table and associated dimensions Orders fact table with associated dimensions Encounter fact table and associated dimensions Drug order fact tables and associated dimensions Patient dimension and its subdimensions Concept dimension with bridge tables and subdimensions Experimental: Monthly cohort snapshot, using a sample cohort minidimension.
Concepts and definitions References. 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. However, to create a Custom Report (Advanced), you must manually link the Report Definition and DataSet Definition. For more information, see Types of Reports. The two tutorials that follow demonstrate how to build a Period Indicator Report and a Simple Row-Per-Patient Report. Building Row-Per-Domain Reports Building a Simple Row-Per-Patient Step-By-Step This step-by-step tutorial will guide the user in the creation of a Simple Row-Per-Patient Report (See Row-Per-Domain Object Report Definition for details).
This Simple Row-Per-Patient Report will output a list of patients from Boston and output their birthdate and gender. Step 1. Step 2. 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. kindest regards and in His grace,Gretchen. 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: The community edition of the Pentaho Business Intelligence suite includes Pentaho Analysis, an OLAP engine (specifically ROLAP) project named Mondrian.
The project will include ongoing development of a set of prototype ETL transformations and models in order to flesh out detailed requirements and validate design decisions. Project Champions Andrew Kanter Burke Mamlin Objectives There will be two sets of parallel objectives defined. MVP Project Objectives Overall Project Objectives. Cohort Queries as a Pentaho Reporting Data Source - Projects - OpenMRS Wiki. Skip to end of metadataGo to start of metadata Abstract Pentaho Reporting Community Edition (CE) includes the Pentaho Report Designer, Pentaho Reporting Engine, Pentaho Reporting SDK and the common reporting libraries shared with the entire Pentaho BI Platform.
This suite of open-source reporting tools allows you to create relational and analytical reports from a wide range of data-sources and output types including: PDF, Excel, HTML, Text, Rich-Text-File and XML and CSV outputs of your data. The OpenFormula/Excel-formula expressions help you to create more dynamic reports exactly the way you want them. The open architecture and powerful API and extension points makes Pentaho Reporting a prime candidate for integration with OpenMRS. Cohorts in OpenMRS are the building blocks of an Indicator Report.
The first development effort of interest is to develop a native data source provider for OpenMRS cohort queries inside of Pentaho Report Designer. Project Champions Darius Jazayeri Objectives. 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.