Here is the link to another place that stores the PJCs/Beans article without adds.
Moved the content into a page – Dimensional Modeling
The IBM Watson Personality Insights service uses linguistic analysis to extract cognitive and social characteristics from input text such as email, text messages, tweets, forum posts, and more. By deriving cognitive and social preferences, the service helps users to understand, connect to, and communicate with other people on a more personalized level.
1. Clone the GitHub repo as shown below.
pas@192-168-1-4:~/bluemix-apps/watson$ git clone https://github.com/watson-developer-cloud/personality-insights-nodejs.git
At Devoxx 2014, a large amount of presentations involved Google’s Material Design and Polymer. Before we dive into Polymer, it might be useful to have a look at some of the design guidelines proposed by Google. These guidelines are bundled into a style guide, named Material Design.
There comes the point in any sufficiently complex or difficult problem diagnosis that the log files in OBIEE alone are not sufficient for building up a complete picture of what’s going on. Even with the debug/trace data that Presentation Services and other components can be configured precisely to write you’re sometimes just left having to guess what is going on inside the black box of each of the OBIEE system components.
The IBM Watson Tradeoff Analytics service helps you make better choices under multiple conflicting goals. The service combines smart visualization and recommendations for tradeoff exploration.
The following demo application shows how to use the IBM Watson Tradeoff Analytics Service from IBM Bluemix. This is the demo application for this service.
1. Clone the GitHub project as shown below.
pas@pass-mbp:~/bluemix-apps/watson$ git clone https://github.com/watson-developer-cloud/tradeoff-analytics-nodejs.git
Many organisations using Oracle’s business intelligence and data warehousing tools are now looking to extend their capabilities using “big data” technologies. Customers running their data warehouses on Oracle Databases are now looking to use Hadoop to extend their storage capacity whilst offloading initial data loading and ETL to this complementary platform; other customers are using Hadoop and Oracle’s Big Data Appliance to add new capabilities around unstructured and sensor data analysis, all at considerably lower-cost than traditional database storage.