HP is taking legal action against Oracle for allegedly breaching its 2010 partnership agreement of porting HP’s core software products with the latest versions of Itanium. In March, Oracle announced it would not be developing any new versions for products designed for the Itanium processor. Itanium has the ability to power the so-called Business Critical Systems hardware for extremely demanding enterprise applications. However, Oracle said the chip line is about to die.

The companies’ lawyer met in the Santa Clara County Superior Court with Judge James Kleinberg presiding to discuss their side of the event. Others in the courtroom included Ann Livermore, HP board member and former enterprise business chief, and Oracle’s co-President Safra Catz. Catz and Livermore were the two key negotiators for the agreement. Livermore was to testify later in the day. Kleinberg is set to rule if the companies had a legally binding contract.

Jeff Thomas, HP’s lawyer, focused on the so-called Hurd Agreement wording, where the companies reiterated their partnership after Oracle hired Mark Hurd, former CEO for HP. HP also sued Hurd for breaking the confidentiality agreement.

Thomas and the lead lawyer for Oracle focused on one paragraph of the agreement, which read Oracle would continue to provide its product suite on the HP platform in a way that’s consistent with the existing partnership before Hurd’s hiring.

Let’s face it, fad or not, companies are starting to ask themselves how they could possibly use machine learning and AI technologies in their organization. Many are being lured by the promise of profits by discovering winning patterns with algorithms that will enable solid predictions… The reality is that most technology and business professionals do not have sufficient understanding of how machine learning works and where it can be applied.  For a lot of firms, the focus still tends to be on small-scale changes instead of focusing on what really matters…tackling their approach to machine learning.

In the recent Wall Street Journal article, Machine Learning at Scale Remains Elusive for Many Firms, Steven Norton captures interesting comments from the industry’s data science experts. In the article, he quotes panelists from the MIT Digital Economy Conference in NYC, on businesses current practices with AI and machine learning. All agree on the fact that, for all the talk of Machine Learning and AI’s potential in the enterprise, many firms aren’t yet equipped to take advantage of it fully.

Panelist,  Michael Chui, partner at McKinsey Global Institute states that “If a company just mechanically says OK, I’ll automate this little activity here and this little activity there, rather than re-thinking the entire process and how it can be enabled by technology, they usually get very little value out of it. “Few companies have deployed these technologies in a core business process or at scale.”

Panelist, Hilary Mason, general manager at Cloudera Inc., had this to say, “With very few exceptions, every company we work with wants to start with a cost-savings application of automation.” “Most organizations are not set up to do this well.”

As part of our Java Tutorials program, we will list a number of interview questions to aid in a better understaing of Java and J2EE and, hopefully, provide a greater likelihood of getting a job.  Let us begin with the basics:

1. What is meant by J2EE?

J2EE is an abreviation for Java 2 Platform Enterprise Edition

2.  What is the purpose of J2EE?

The purpose of J2EE is to provide a component based platform in a multitier application model with transaction management, web services and reusable component support.

3.  What is the tier structure of the typical J2EE application?

A typical J2EE application consists of the following tiers/machines:  the client machine (browser/non-browser application), the J2EE server (an application server such as Oracle, JBoss, GlassFish, Tomcat) and a database.

Data has always been important to business. While it wasn't long ago that businesses kept minimal information on people who bought their products, nowadays companies keep vast amounts of data. In the late 20th century, marketers began to take demographics seriously. It was hard to keep track of so much information without the help of computers.

Only large companies in the '60s and '70s could afford the research necessary to deliver real marketing insight. The marketers of yesteryear relied upon focus groups and expensive experiments to gauge consumer behavior in a controlled environment. Today even the smallest of companies can have access to a rich array of real-world data about their consumers' behavior and their consumers. The amount of data that is stored today dwarfs the data of only a few years ago by several orders of magnitude.

So what kind of information are businesses storing for marketing purposes? Some examples include:

- Demographic information — age, gender, ethnicity, education, occupation and various other individual characteristics.