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On demand data in Python, Part 3: Coroutines and asyncio
Much of the data in modern big data applications comes from the web or databases. You need to write code to process this at scale, but you don't want everything to grind to a halt in the process. Python 3 introduced a system for cooperative multitasking, which alleviates this problem, using asynchronous coroutines. Asynchronous coroutines build on similar concepts to generators. They are objects created from special functions which can be suspended and resumed. They make it possible to break down complex and inefficient processing into simple tasks that cooperate to maximize trade-offs between CPU and input/output. Learn these core techniques following a simple sequence of examples.
Also available in: Chinese  
Optimize queries in Cloudant
This article compiles insights from multiple people in regard to methods and experiences related to optimizing queries when operating Cloudant NoSQL DB. The knowledge contained here will help you have a more in-depth understanding of the most suitable queries for each applicable scenario, to provide the most efficient data query service for your application.
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An introduction to Kafka
An introduction to one of the most popular platforms for distributed messaging or streaming data.
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Metaprogramming in Python
This article explains how you can use metaprogramming in Python and how it can simplify certain tasks.
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How data becomes knowledge, Part 2: Data lakes and data swamps
Get an understanding of data lakes and data swamps in this easy to follow, yet insightful article.
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Unleash the value of Guardium data by using the Guardium Big Data Intelligence solution
Organizations that use IBM Security Guardium activity monitoring for data security and compliance struggle with the quantity of collected audit data, especially if they have 10 or more Guardium collectors. IBM Security Guardium Big Data Intelligence provides the power of a big data platform that is purpose-built for data security requirements. It helps augment existing Guardium deployments with the ability to quickly create an optimized security data lake that can retain large quantities of historical data over long time horizons.
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Code pattern: Mine insights from software development artifacts
There is a lot of unstructured text content that is generated in any domain – software development lifecycle, finance, healthcare, social media, etc. Valuable insights can be generated by analyzing unstructured text content and correlating the information across various document sources. This pattern uses Watson Natural Language Understanding, Python Natural Language Toolkit, OrientDB, Node-RED, and IBM Data Science Experience to build a complete analytics solution that generates insights for informed decision-making.
Analyze crime data with Apache Spark and Hive ETL, Part 2: Explore the analyses
In this second part of the "Analyze crime data with Apache Spark and Hive ETL" tutorial series, you will learn how to integrate data from different sources. You will also see the computation of normalized statistics for crime rates enabling easy comparison of crime rates across different geographic areas.
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Analyze crime data with Apache Spark and Hive ETL, Part 1: Learn about Extract, Transform, and Load (ETL)
In this tutorial, you learn to analyze U.K. crime data from inception to final results, covering data download, data transformation and loading into a distributed data warehouse, Apache Hive, then subsequent analysis using Apache Spark. Part 1 describes the Extract, Transform and Load (ETL) activities.
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Social power, influence, and performance in the NBA, Part 2: Exploring the individual NBA players
In this tutorial series, learn how to analyze how social media affects the NBA using Python, pandas, Jupyter Notebooks, and a touch of R. Part 2 explores individual athletes in the NBA: endorsement data, true on-the-court performance, and social power with Twitter and Wikipedia.
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Social power, influence, and performance in the NBA, Part 1: Explore valuation and attendance using data science and machine learning
In this tutorial series, learn how to analyze how social media affects the NBA using Python, pandas, Jupyter Notebooks, and a touch of R. Here in Part 1, learn the basics of data science and machine learning around the teams in the NBA.
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Detect complex events in a real-time data stream
Get, run, and extend a Bluemix starter app that uses the Streaming Analytics service and an IBM Streams application to detect complex events from a real-time data stream.
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IBM Cognos Proven Practices: Guidelines to General Configuration and Automatic Journals for IBM Cognos Controller
How to set up both general configuration and automatic journals.
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IBM Business Analytics Proven Practices: Business Analytics Proven Practices: Pagination for RAVE visualizations used in IBM Cognos Report Studio
The steps to provide the pagination feature in a Cognos BI 10.2.2 report.
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IBM Cognos Proven Practices: Guidelines to Using Historical Rates in IBM Cognos Controller
Guidelines to setting up and using historical rates in IBM Cognos Controller.
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Analyze data faster using Spark and IBM Cloud Object Storage
Using Stocator: an open-source storage connector that leverages object store semantics
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IBM Business Analytics Proven Practices: Display a "No Data Available" message on empty RAVE visualization pages
Learn how to embed a visualization object in a list in Cognos Report Studio.
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Predictive Cloud Computing for professional golf and tennis, Part 8: Forecasting
In professional golf and tennis tournaments, athletes welcome any competitive advantage. Looking beyond the current time horizon into the future allows players to position their rackets before the ball arrives and to select the appropriate club based on future weather conditions. The Predictive Cloud Computing project is no different. Time series algorithms project web origin traffic trends into the future so that cloud services can be provisioned or de-provisioned to anticipate sporting content demand. Sinusoidal patterns with a trend, cycle, and level that have been learned from historical data at rest are ensembled together with real-time demand patterns. The combination of InfoSphere Stream, InfoSphere BigInsights, RabbitMQ, WebSphere Java Application, Apache math commons, and custom algorithms have produced forecasts with an 18.44% mean absolute percentage error (MAPE). As a result, provisioning cloud services for future demand loads has saved thousands of compute minutes.
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Start your data science education with the Data Science Fundamentals Learning Path
This article describes a short, straightforward learning path to begin building your data science skills. The recently launched Data Science Fundamentals Learning Path at Big Data University guides you through no-charge online courses that prepare you to earn your IBM Data Science Foundations Level 1 and Level 2 badges.
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Analyze the analysts with IBM i2 Analyze
When you are dealing with data that might be sensitive, companies often find it necessary to keep an audit trail of who is accessing the data, and what they are doing with it. When companies and senior staff can be held legally accountable for any fraudulent activity by their employees, the need for an audit trail is becoming more urgent than ever. The latest release of IBM i2 Analyze features an auditing mechanism for user interactions with the IBM i2 Analyze Information Store. This tutorial shows how you can use this mechanism to provide an analysis-ready view of audit information.
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Choose IBM Open Platform for your Hadoop and Spark projects
Learn about the IBM Open Platform (IOP), which includes a set of open-source components that are supported by IBM's deep Hadoop, Spark, and big data expertise. IOP includes what we believe is the best combination of components to deliver a comprehensive range of capabilities across the most common big data use cases. Find out about the components and their value.
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Cleanse and visualize location data using Spark and Bluemix
Improve indoor-location data accuracy by cleansing and visualizing data using Spark and the Spark on Bluemix notebook. Follow the short tutorial to download and create your own notebook to practice handling sample location data.
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Configuring i2 Analyze for LDAP authentication
Deploying i2 Analyze in a production environment requires integration of i2 Analyze with a centralized authentication system, such as LDAP. Knowing how i2 Analyze handles both authentication and authorization of users and groups in an LDAP repository will affect the configuration of the i2 Analyze security schema.
Use balancing to produce more relevant models and data results
Download example streams and datasets to become familiar with how to use SPSS Modeler to balance data. Learn about weighting, balancing, boosting, reducing, balance nodes, and dynamic nodes; and learn when to use each one based on your business objectives.
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Predictive Cloud Computing for professional golf and tennis, Part 6: Maven, unit and integration testing, and static code analysis
This article describes the various build tools and techniques that were used in the Predictive Cloud Computing (PCC) project. For example, Apache Maven managed the project builds including dependent builds, unit testing, and integration tests to validate code function. Static code analysis was used to ensure future code maintainability. Overall, through the use of these resources, the team achieved an increase in successful changes and a more maintainable code base when contrasted to previous ad hoc methods of build, test, and code review.
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Java Streams, Part 4: From concurrent to parallel
This fourth installment of the Java Streams series identifies and explains factors that determine the effectiveness of parallel processing, putting them into historical and technical context. An understanding of these factors provides a foundation for making optimal use of the Streams library for parallel execution. (The next installment applies the principles outlined here directly to Streams.)
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Java Streams, Part 5: Parallel stream performance
This fifth and final installment of the Java Streams series continues the previous installment's discussion of factors that influence the effectiveness of parallel processing, and applies them to the Streams library. Find out why some stream pipelines parallelize better than others, and see how to analyze your own streams code for parallel performance.
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Java Streams, Part 1: An introduction to the java.util.stream library
Explore the Java Streams library, introduced in Java SE 8, in this series by Java Language Architect Brian Goetz. By taking advantage of the power of lambda expressions, the java.util.stream package makes it easy to run functional-style queries on collections, arrays, and other data sets.
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Java Streams, Part 3: Streams under the hood
Explore the Java Streams library, introduced in Java SE 8, in this series by Java Language Architect Brian Goetz. By taking advantage of the power of lambda expressions, the java.util.stream package makes it easy to run functional-style queries on collections, arrays, and other data sets. In this installment, learn how to fine-tune your queries for maximum efficiency.
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Build an enterprise reporting solution
Use the Embeddable Reporting service on IBM Bluemix to build an end-to-end reporting solution for analyzing open data sets.
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Analyze weather data within your browser using Spark on IBM Cloud
Apache Spark is a next generation distributed data processing engine that for the first time is making available entirely new capabilities to data scientists, business analysts, and application developers. Analytics for Apache Spark works with commonly used tools available in IBM Cloud so that you can quickly start tapping into the full power of Apache Spark. This tutorial shows you how to use iPython Notebook, which uses the Spark API, to analyze raw weather data from the real world. You can easily use this example as a basis for more analytics on IBM Cloud.
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Predictive Cloud Computing for professional golf and tennis, Part 5: Continuous integration and deployment
Techniques such as continuous deployment and integration, used throughout Predictive Cloud Computing (PCC), have enabled the development team to provide rapid and safe iterative improvements during professional golf and tennis tournaments. The results of the investment into continuous integration and deployment have enabled the team to focus on analytics and code, increasing team productivity.
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Accelerating SAP CO-PA with DB2 for Linux, UNIX, and Windows
SAP Profitability Analysis (CO-PA) reports, which are primarily used for drill-down analysis, fetch a significant number of records and aggregate them to a small result set. Using intrapartition parallelism, a feature available in DB2, can speed up the query processing by eight times or more, depending on hardware resources and data distribution. In this tutorial, we’ll demonstrate the benefits of intrapartition parallelism and discuss how to use it effectively for reporting queries.
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How IBM leads in building big data analytics solutions in the cloud
Creating solutions and architectures that harness the value and power of big data and cloud can give your company a competitive advantage, spark new innovations, and increase revenues. The Cloud Standards Customer Council's Customer Cloud Architecture for Big Data and Analytics describes a well-tested and popular reference architecture for traditional analytics production environments. In this article, see how IBM supports this architecture in a secure, scalable, and flexible manner in dedicated and on-premises public could, hybrid cloud, and private cloud environments.
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Predictive Cloud Computing for professional golf and tennis, Part 3: Big data in motion
Major golf and Grand Slam tennis tournaments provide real-time and historical sporting information to immerse their fans in the action. Each tournament provides content that includes streaming video, game statistics, scores, images, schedule of play, and text. Previous tutorials in this series explain how Predictive Cloud Computing (PCC) forecasts and predicts tournament popularity to automatically allocate shared computing resources and describe an overview of a BigEngine application that has been deployed within a high availability environment. In this tutorial, we provide an overview of the streaming computing architecture deployed for IBM's presentation of professional golf and tennis tournaments. We discuss the use of IBM InfoSphere Streams and our Streams Processing Language (SPL) customizations supported by the Java language. Also, we show the implementation of a UIMA PEAR file within Streams' processing element and how it provides annotations for sporting social leadership boards. Throughout the tutorial, several examples and concrete code listings serve as examples.
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Predictive Cloud Computing for professional golf and tennis, Part 1: Introduction
Major golf and Grand Slam tennis tournaments provide real-time and historical sporting information to immerse their fans in the action. Each tournament provides content that includes streaming video, game statistics, scores, images, schedule of play, and text. This tutorial describes how Predictive Cloud Computing (PCC) forecasts and predicts tournament popularity to automatically allocate shared computing resources as needed. PCC thus saves sporting customers many hours per day while displaying key statistics about the impact of sporting big data on the infrastructure.
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Integrating WebSphere Commerce and Digital Data Exchange (DDX)
Integrating multiple web analytics systems with WebSphere Commerce can be a cumbersome task. Learn in this article, by adopting IBM Digital Data Exchange (DDX) component in the overall solution, how it simplifies the overall task of onboarding multiple web analytics systems in WebSphere Commerce solution. This article walks you through the implementation of web analytics solutions including IBM Digital Analytics and Google Analytics in WebSphere Commerce using DDX.
Optimizing the single-page application experience
This article explains how IBM® Tealeaf Document Object Model (DOM) Capture and Replay works to provide high fidelity visualizations that Tealeaf practitioners and their management can use to understand, manage, and optimize their customers' experience with single-page applications (SPAs). The article explains the rise of SPAs and the benefits they provide, as well as the technical implications and challenges that SPAs present to capture and replay solution providers. For Tealeaf practitioners, the paper provides a list of best practices, including the steps for configuring and implementing a Tealeaf DOM Capture and Replay solution for SPAs.
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Top cognitive computing tutorials (November 2015)
Three of our most popular articles on cognitive computing are highlighted in this article for your convenience.
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Top Internet of Things tutorials (November 2015)
Three of our most popular articles on Internet of Things are highlighted in this article for your convenience.
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Top Big Data and Analytics tutorials
So many articles were published in 2015, but I had the task of choosing only a few. It was very hard to narrow down the list, so if I didn't choose your favorite article, add it to the comment section to share your favorites of the year.
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Gain keen insights from big data with Datameer on IBM SoftLayer
We systematically collect and analyze large amounts of multi-structured data to extract actionable insights such as patterns, risks, associations, and opportunities. The tools that enable the smooth transition from data to pragmatic knowledge are maturing and stabilizing. Synchronized platforms and infrastructures simplify the complicated processes of data virtualization, processing, mining, analysis, and visualization. Datameer is an end-to-end platform for data integration, analytics, and insights. In this article, learn how to migrate and configure Datameer to run in the IBM SoftLayer cloud to deliver big data analytics as a service to your worldwide users.
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Explore the advanced analytics platform, Part 8: The Information Governance Model
Big data lakes are ingesting large volumes of internal and external data. Without proper governance, these data lakes risk becoming data marshes. These marshes might contain disorganized, low quality data that can corrupt the entire data repository, leading to confusion and chaos. This tutorial proposes a set of processes, and a tool set, to structure the data and implement information governance in the big data lake. The result is improved trustworthiness and organization of the information that is contained in the data lake. This underlying information governance architecture is embedded in the Advanced Analytics Platform.
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Women in technology: Making an impact
In this video series from IBM developerWorks, some of IBM's prominent female technical leaders share their experiences and insight on various technical and career topics.
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Explore the advanced analytics platform, Part 7: The customer profile hub
Customer profile patterns help you to gain a deep understanding of your customers. Traditional master data management either federated or consolidated customer profile data. With big data, two new types of data sources are emerging, conversations and usage. These data sources add more dimensions to customer views and provide a richer insight to customer behavior, preferences, and usage patterns. This tutorial explains how to create a rich customer profile by using the Advanced Analytics Platform to get a deep understanding of your customer.
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Autoscale your database with ScaleBase for zero downtime of cloud applications
Clouds are becoming the core IT infrastructure for hosting and delivering web scale, enterprise-grade, highly elastic applications in a "use and pay" fashion. Start-ups as well as established business behemoths face tedious challenges with highly scalable, mission-critical applications. Back-end databases are the essential module for every type of transactional, interactive, and collaborative application. Data storage and management systems must be cognitively adaptive to fulfill emerging needs. However, traditional databases are insufficient for cloud-scale applications. ScaleBase is an innovative, highly scalable, zero downtime, cloud-scale database solution. This article describes how to prepare and migrate ScaleBase to IBM SoftLayer. A sample application shows how ScaleBase functions in an online, off premise, on-demand cloud environment.
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Take control of your requirements projects with Configuration Management
Get hands-on experience with IBM Rational DOORS Next Generation and the configuration management capabilities it supports. Access Rational DOORS Next Generation online through a sandbox on Jazz.net to work through a set of exercises.
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Meet the demands of big data analytics with the in-memory speed of Aerospike
Today, people want cloud environments to host high performance, enterprise class, and real-time workloads to meet varied expectations of individual users and businesses. Aerospike is an open source, real-time NoSQL database and key-value store that provides in-memory performance for big data and context-driven applications that must sense and respond "right now" at a fraction of previous costs. Aerospike operates on a global scale with enterprise-grade reliability. Clouds are destined to be both commercially viable and capable of delivering process and data-intensive applications. This article showcases how the Aerospike platform on IBM SoftLayer cloud will be a game changer for big data analytics and other demanding applications.
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Building Scala applications that access IBM Data Servers
Build applications in Scala that access IBM Data Servers like IBM DB2 for Linux, Unix and Windows, IBM DB2 for z/OS, IBM Big SQL, IBM dashDB, and IBM Informix. This article includes sample code and instructions for creating Scala applications that access IBM data servers using IBM Data Server Driver for JDBC and SQLJ. I discuss multiple implementation options as samples of different approaches that you can take to create such applications.
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Analyze notes with the AlchemyAPI service on IBM Bluemix
Learn how to use AlchemyAPI and IBM DevOps Services for Bluemix to quickly and easily develop, build, and deploy a natural language processing program.
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Do real-time analytics with the VoltDB database in IBM SoftLayer
Data is a strategic asset that enterprises use to precisely plan and proceed with those plans with confidence and clarity. Data-driven enterprises can overcome all kinds of unexpected business challenges and changes. How? By gathering all of their data, systematically gleaned from different and distributed sources, and applying IT-enabled deeper analytics processes to extract actionable insights. Data is now captured in unprecedented volumes, and traditional data analytics platforms and infrastructures are facing constraints. We need robust and resilient algorithms and end-to-end solutions for big and fast data. Several product vendors offer big data analytics systems that facilitate a smooth transition from captured and consolidated data to valuable information. In this article, learn how VoltDB enables high performance and real-time big data analytics in the SoftLayer cloud.
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Looking at the evolution of service composition, from SOA to cognitive services
Today, developers can create a wide variety of service compositions with a new breed of cognitive computing services. There are endless possibilities for creating service compositions, but that wasn't always the case back in the early days of service-oriented architecture (SOA) services. In this article, I review the history of service composition development and show how much it has evolved.
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Use IBM SPSS Statistics for business intelligence
IBM SPSS Statistics is an effective tool for Business Intelligence. This series of articles demonstrates the use of SPSS Statistics to extract information from raw data, identify techniques and strategies to enhance business performance, and forecast effects of dynamic market conditions. This first article in the series introduces sample raw data and generates frequency tables, graphs, crosstabs, and statistics. Business-level conclusions from statistical analysis are also explored in this article.
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IBM Business Analytics Proven Practices: IBM Cognos Active Report 10.2 Cookbook
A description of the various features available within IBM Cognos Active Report and how they can be used to create and distribute interactive reporting applications.
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Perform sentiment analysis in a big data environment
Public opinion views about government policies are scattered across the Internet, in Twitter and News Feeds. People can express their views quickly and easily from mobile devices, which are ubiquitous. Sentiment analysis can be performed against the data that is gathered from these disparate sources (tweets, RSS feeds, and mobile apps). This data can be aggregated, transformed, or reformatted and then stored in a Hadoop Distributed File System (HDFS). This data can be processed and analyzed as data at rest (data lake) or as data in motion (data stream).
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Use the IBM z13 SIMD unit and the IBM z/OS XL C/C++ compiler to add parallelism to your C/C++ programs
The IBM z13 hardware provides a new SIMD unit. This article describes how to use the IBM z/OS XL C/C++ language to take advantage of the new processor and exploit the enhanced parallelism it offers. This article also provides an overview of the new data types, the operations that can be done on those data types, and the built-in functions to make vector programming easier.
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Develop a predictive analytics model for a complex data set
In this tutorial, we discuss IBM Db2 Warehouse, which is a data warehousing service in the cloud that provides built-in in-database analytics. We show you how to use IBM Cloud to create an instance of Db2 Warehouse on the cloud, use its R analytics capabilities to develop a predictive model for your data set, and then leverage R's Shiny application to generate interactive and robust visualizations and predictions. We use the Kaggle data science competitions as our sample data.
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Installing IBM SPSS C&DS Server and other product adapters in a single installation session
The IBM SPSS Collaboration and Deployment Services product (IBM SPSS C&DS) is an application that other IBM SPSS products (Modeler, Statistics, Analytical Decision Management, and so on) use to provide enhanced capabilities. The integration between these IBM SPSS products is achieved by installing product adapters on the base C&DS Server. You can save time and effort by installing the C&DS Server and the adapters together in a single installation session by using IBM Installation Manager.
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Develop a public transportation system simulator with Node.js and the dashDB service
This article shows you how to develop a simulator of public transportation system using cloud services such as IBM Bluemix DevOps Services, IBM Bluemix, and dashDB (formerly known as Analytics Warehouse and BLU Acceleration). Developers with intermediate knowledge of JavaScript and those who want to get started with cloud services can learn how to develop a simple but useful application for data analysis and streams.
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Build a data mining app using Java, Weka, and the dashDB service
The dashDB (formerly known as Analytics Warehouse and BLU Acceleration) service provides data warehousing and analytics as a service on IBM Bluemix. Developers can develop and deploy a heavy-duty analytic application using blazing-fast IBM BLU database technology offered in the cloud. Learn how to develop a data mining application using the Weka statistical analysis tool and leveraging the IBM BLU columnar database.
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Create a business intelligence and analytics service in Ruby with the dashDB service
The dashDB Service available in IBM Bluemix provides a powerful, easy-to-use, and agile platform for business intelligence and analytics. It is an enterprise-class managed service that is powered by the in-memory optimized, column-organized BLU Acceleration data warehouse technology. This article demonstrates how easy it is to incorporate the dashDB service into your application so that you can focus on your application.
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Perform text analytics with Bluemix analytics services
This tutorial explains how to perform text analytics using the Analytics for Hadoop service and dashDB service (formerly known as the Analytics Warehouse service) on IBM Bluemix. Most of the processes are performed on a client machine with Eclipse IDE and the BigInsights plugin installed. After extracting the desired text, we use R in dashDB to plot charts with the results.
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Working with wearables and Bluemix
Wearable technology devices are becoming very popular these days. They can enhance the lives of human beings and, from a technical perspective, are big drivers of big data. This article provides a survey of several wearable devices available in the market today and how you can use Bluemix services to interact with them.
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Move a toy car with your mind
Learn how to extract brain wave data from the Muse wearable device to perform an action in the real world: in this case, move a toy car. The article explains how to work with the Arduino and the toy car remote control from the hardware side; and from the software side, how to work with Bluemix to create a Node.js web application to see real-time brain wave activity.
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Analytics for Hadoop on Bluemix, Part 1: Analytics for Hadoop on Bluemix: Sign in and access the service
Sign up for a free IBM Bluemix account, log in to Bluemix, and start the Hadoop for Analytics service.
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Collect and analyze social data without writing a single line of code
Do you think about how much you might learn from analyzing social data, but don't act because you don't have enough time or resource to build what you need? In this tutorial, we show you how easy it is to use the Node-RED workflow editor in IBM Bluemix to capture a social data feed (a Twitter feed) and then build a Hadoop Distributed File System (HDFS) file from that data. We also show you how to use the IBM Analytics for Hadoop service to analyze the data and produce summary charts. You’ll be amazed at how easy it is to turn an unknown data set into information you can use.
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IBM Business Analytics Proven Practices: Configure Microsoft Internet Information Services 7.x for IBM Cognos 10
How to set up IBM Cognos 10 with Internet Information Services (IIS) 7.x. This document applies to IIS 7.x installed on a Windows 2008 Server (GA or R2) and all versions of IBM Cognos 10 Business Intelligence and/or Enterprise Planning. The only gateway implementations covered in this document are ISAPI and CGI as those are the only ones supported by IIS.
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Integrate Hadoop with an existing RDBMS
Learn how to integrate Apache Hadoop with a relational database management system (RDBMS). In this era of rapid generation of semi-structured and unstructured data, organizations are implementing this integrated design approach. This approach is especially useful when organizations have invested heavily in RDBMSs, but they still want to harness the potential of capturing and analyzing unstructured data from sources like social media in Hadoop systems.
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IBM Business Analytics Proven Practices: Auto Cascading Multi and Single Value Prompts without Page Refresh in IBM Cognos 10 Report Studio
This document provides a technique to auto-cascade single or multi select value prompts without refreshing the page, or re-running the prompt queries.
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IBM Business Analytics Proven Practices: IBM Cognos TM1 Server Monitor Plug-in for Apache JMeter
The IBM Cognos TM1 Server Monitor Plug-in for Apache JMeter is a plugin for the Apache JMeter performance testing tool. It allows its user to monitor TM1 server activity in real-time while running an Apache JMeter performance test or to view and analyze existing TM1Top log files.
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IBM Business Analytics Proven Practices: How to Implement a Corporate-Wide Reporting Style Using IBM Cognos BI
Many companies would like to implement their own corporate style guide across all of their reports and dashboards, truly engaging their users. This can be a huge undertaking, especially when you have many existing reports and/or intend to create many more new reports. What is the best way to approach this task? The traditional method is to modify each report one by one via the Properties pane in Report Studio. This document will describe multiple alternatives, providing advice on how to approach this problem more efficiently and effectively.
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Build your own wearable with IBM Watson IoT Platform and IBM Bluemix
Build a hybrid mobile app that connects to a wearable device and sends sensor data from the device to the cloud. You'll use IBM Bluemix, the IBM Watson IoT Platform, Apache Cordova, and the WICED Sense Development Kit for this tutorial's nifty do-it-yourself project.
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IBM Business Analytics Proven Practices: Implementing an Interactive Map Widget in IBM Cognos Workspace
This document provides the steps required to implement an Interactive Map Widget for use with IBM Cognos Workspace 10.2.1 and 10.2.2.
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IBM Business Analytics Proven Practices: A Framework For Text Classification Using IBM SPSS Modeler
This article explores building SVM-based classification framework for text classification.
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Set up and use federation in InfoSphere BigInsights Big SQL V3.0
Big SQL V3.0 supports federation to many data sources, including IBM DB2 for Linux, UNIX, and Windows, IBM PureData System for Analytics, IBM PureData System for Operational Analytics, Teradata, and Oracle. Federation enables users to send distributed requests to multiple data sources within a single SQL statement. Learn how to use the federation capabilities in Big SQL V3.0.
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Leverage IBM Cognos on IBM Bluemix using the Embeddable Reporting service
Discover how easy it is to leverage Cognos on Bluemix by using the Embeddable Reporting service. In this tutorial, get step-by-step instructions on how to create, develop, and deploy an app with the Embeddable Reporting service on Bluemix from scratch.
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IBM Business Analytics Proven Practices: Access Reporter for Series 7
Access Reporter is a utility that will audit the access permissions on all objects within the Upfront datastore as well as provide all user memberships within the Series 7 namespace.
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Centralize logs for IBM Bluemix apps using the ELK Stack
IBM Bluemix, IBM's next-generation cloud platform, enables developers to quickly build, deploy, and manage cloud applications. Due to its cloud nature, Bluemix does not automatically persist application log files. However, it does let developers drain their logs to external log management services. This feature, coupled with the components of the ELK Stack (Elasticsearch, Logstash, and Kibana), not only provide developers a centralized location for persistent logs, but also enables them to run analytics on and visualize the log data. This article is targeted for intermediate developers who are familiar with Bluemix and have a basic familiarity with Linux. It provides a brief background on the ELK Stack and then walks you through the process of installing and configuring the components.
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IBM Business Analytics Proven Practices: Working with Blob Files and IBM Cognos TM1
This document outlines the blob files in Cognos TM1 which uses Architect, Performance Modeler, TM1 Web Cube Viewer and TM1 Application Server to create, manage and store blobs.
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IBM Business Analytics Proven Practices: Resetting All Persisted Views for an IBM Cognos TM1 Application
This document will provide tips on re-oriented cube views and charting options across various IBM Cognos TM1 applications.
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IBM Business Analytics Proven Practices: IBM Cognos 10 Audit Extension
A IBM Cognos 10 SDK application that provides additional auditing, including Role Auditing, for IBM Cognos 10 BI. The version of the application is 1.1.03 and it will work with IBM Cognos 10 BI versions 10.1 and up.
Also available in: Portuguese  
Discover and use real-world terminology with IBM Watson Content Analytics
Use linguistic analysis in IBM Watson Content Analytics (WCA) to explore domain-specific terminology, and build domain dictionaries that reflect users' "real-life" vocabulary preferences. Use these dictionaries in WCA Studio to build concept annotators.
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IBM Cognos Proven Practices - System Management Methodology for IBM Cognos 10: Setting Thresholds
This is part of a group of documents and examples commonly referred to as the System Management Methodology (SMM). The SMM provides information and techniques as examples of how an administrator uses the standard features of IBM Cognos Administration along with IBM Cognos Business Intelligence functionality in order to increase their own productivity and pro-actively manage IBM Cognos BI applications, users, and servers.
Also available in: Portuguese  
IBM Cognos Proven Practices - System Management Methodology for IBM Cognos 10: Installation
This is part of a group of documents and examples commonly referred to as the System Management Methodology (SMM). The SMM provides information and techniques as examples of how an administrator uses the standard features of IBM Cognos Administration along with IBM Cognos Business Intelligence functionality in order to increase their own productivity and pro-actively manage IBM Cognos BI applications, users, and servers.
Also available in: Portuguese  
IBM Cognos Proven Practices - System Management Methodology for IBM Cognos 10: System Metrics
This is part of a group of documents and examples commonly referred to as the System Management Methodology (SMM). The SMM provides information and techniques as examples of how an administrator uses the standard features of IBM Cognos Administration along with IBM Cognos Business Intelligence functionality in order to increase their own productivity and pro-actively manage IBM Cognos BI applications, users, and servers.
IBM Cognos Proven Practices - System Management Methodology for IBM Cognos 10: Overview
This is part of a group of documents and examples commonly referred to as the System Management Methodology (SMM). The SMM provides information and techniques as examples of how an administrator uses the standard features of IBM Cognos Administration along with IBM Cognos Business Intelligence functionality in order to increase their own productivity and pro-actively manage IBM Cognos BI applications, users, and servers.
Integrate IBM Predictive Maintenance and Quality (PMQ) with ILS deviceWISE to onboard high-value asset data
High-value asset-based industries need to be aware of the performance of their components in a real-time mode. Handle future breakdowns or failures, and control the impact to your organization's financial performance. Feed data from level 2 systems and historians in real time to build this awareness and control. Learn to integrate IBM Predictive Maintenance and Quality with ILS deviceWISE to receive data from remote sources.
Also available in: Russian  
IBM Business Analytics Proven Practices: Cognos BI - Dynamic Cube Security Case Studies
This document will expand on Chapter 7 - Dimensional security of the IBM Cognos Dynamic Cube Redbook and show some examples of how the various combinations of dimensional security filters can affect the view of data your users can see.
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Explore the advanced analytics platform, Part 5: Deep dive into discovery and visualization
In this article, explore a pattern for data lake exploration and learn the steps for data integration across the end-to-end data flow. Through a series of exploration use cases, the tutorial defines the characteristics of the exploration and their execution by using the pattern.
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Deploy Open Web Analytics on your website
One of the leading open source web analytics offerings is Open Web Analytics (OWA). This article provides step-by-step instructions on deploying OWA on an IBM platform as a service (PaaS) of IBM BlueMix. Web applications hosted on Bluemix, as well as those hosted outside Bluemix, can create customizable reports containing statistics on website traffic, updates to shopping carts, page views translating into purchases, and several other useful metrics.
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IBM Business Analytics Proven Practices: IBM SPSS Modeler - ODBC Configuration Best Practices and Troubleshooting
An overview of Open Database Connectivity (ODBC) configuration, best practices when performing these tasks, and troubleshooting techniques to assist in the resolution of common problems in this area.
Also available in: Russian  
Analytics for Hadoop on Bluemix, Part 2: Analytics for Hadoop on Bluemix: Navigate the BigInsights web console
Use the IBM InfoSphere BigInsights web console to check the status of services, view the health of your system, and monitor the status of your big data environment.
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Editors' picks: Top 13 Bluemix tutorials
If you've been following developerWorks over the last few months, you've noticed how excited we are about Bluemix, IBM's open-standards cloud platform. Using Bluemix, along with IBM, third-party, and open source services, you can build, deploy, run, and manage almost any kind of application you can dream of. We've published so much content about Bluemix and its many services and runtimes that we wanted to step back and give you a look at what we think are some of the very best tutorials we've published on this exciting topic. So here it is, the Bluemix Top 13 as chosen by the developerWorks editorial team!
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Build a Shiny application to analyze #Bluemix sentiment using the Bluemix R custom buildpack
Create a Shiny application that analyzes the popularity of entities on Twitter and performs a sentiment analysis of the tweets using R. Learn how to run R on IBM Bluemix using a custom buildpack. Customize your application to perform any kind of complex analysis on your data stored in the cloud.
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Explore the advanced analytics platform, Part 6: Dive into orchestration with a combination of SPSS, Operational Decision Management (ODM), and Streams using care and fraud management case studies
An essential component of any analytics platform is the ability to analyze large volumes of data at high velocity that results in real-time actionable steps. Often, intelligent processes require attention and focus on a few "out of range" observations midst a much larger field of "normal" observations. The real-time action requires selecting, focusing, and investigating these observations while dealing with data-in-motion. At the same time, we must understand historical trends and adapt to the changing definition of "out of range". This article describes the D4 (Discover, Detect, Decide, Drive) pattern, which permits high-speed analytics with rapid execution while under extreme data velocity and volume requirements. We then introduce two example use cases, identify architecture components, and discuss some integration design considerations common with the D4 pattern.
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Feed InfoSphere Streams applications with live data from databases
This tutorial shows you how to connect IBM InfoSphere Data Replication Change Data Capture (CDC) as a near real-time data source for InfoSphere Streams. Walk through several integration options for a ready-to-use and custom user exit, then explore the pros and cons of each. Downloadable example sources for the user exit are provided and can be customized to fit your requirements.
Also available in: Russian   Portuguese  
IBM Cognos Proven Practices: IBM Cognos TM1 FEEDERS
One of the more advanced concepts in the development of IBM Cognos TM1 cubes is the proper implementation of FEEDERS within TM1 rules. This document describes FEEDERS and how to use them effectively for improved performance when building IBM Cognos TM1 cubes.
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IBM Business Analytics Proven Practices: How to implement element or cube based security for IBM Cognos TM1
Implementing element or cube based security for IBM Cognos TM1.
Also available in: Russian   Portuguese  
Integrate the BIRT Viewer with Java and Java EE web applications
Learn to integrate the Business Intelligence and Reporting Tools (BIRT) Report viewer with a custom Java and Java Platform, Enterprise Edition (Java EE) web application. Also, use the features of the BIRT report viewer application in custom web applications.
Also available in: Chinese   Japanese  
IBM Business Analytics Proven Practices: IBM Cognos BI Authentication and Single Sign-On
Implement seamless authentication between security systems and IBM Cognos BI, using these guidelines. This document contains important information about the technical concepts and backgrounds involved and the design of authentication and single sign-on (SSO) functionality in IBM Cognos BI. It also discusses setting expectations regarding supported vs unsupported environments and information that must be gathered for troubleshooting the IBM Cognos BI side of things. This document is intended for security architects and administrators designing authentication for a system includes Cognos BI.
Also available in: Chinese  
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