Weblogic Administration Training Classes in Tracy, California
Learn Weblogic Administration in Tracy, California and surrounding areas via our hands-on, expert led courses. All of our classes either are offered on an onsite, online or public instructor led basis. Here is a list of our current Weblogic Administration related training offerings in Tracy, California: Weblogic Administration Training
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Blog Entries publications that: entertain, make you think, offer insight
Although reports made in May 2010 indicate that Android had outsold Apple iPhones, more recent and current reports of the 2nd quarter of 2011 made by National Purchase Diary (NPD) on Mobile Phone Track service, which listed the top five selling smartphones in the United States for the months of April-June of 2011, indicate that Apple's iPhone 4 and iPhone 3GS outsold other Android phones on the market in the U. S. for the third calendar quarter of 2011. This was true for the previous quarter of the same year; The iPhone 4 held the top spot. The fact that the iPhone 4 claimed top spot does not come as a surprise to the analysts; rather, it is a testament to them of how well the iPhone is revered among consumers. The iPhone 3GS, which came out in 2009 outsold newer Android phones with higher screen resolutions and more processing power. The list of the five top selling smartphones is depicted below:
- Apple iPhone 4
- Apple iPhone 3GS
- HTC EVO 4G
- Motorola Droid 3
- Samsung Intensity II[1]
Apple’s iPhone also outsold Android devices7.8:1 at AT&T’s corporate retail stores in December. A source inside the Apple company told The Mac Observer that those stores sold some 981,000 iPhones between December 1st and December 27th 2011, and that the Apple device accounted for some 66% of all device sales during that period (see the pie figure below) . Android devices, on the other hand, accounted for just 8.5% of sales during the same period.
According to the report, AT&T sold approximately 981,000 iPhones through AT&T corporate stores in the first 27 days of December, 2011 while 126,000 Android devices were sold during the same period. Even the basic flip and slider phones did better than Android, with 128,000 units sold.[2] However, it is important to understand that this is a report for one particular environment at a particular period in time. As the first iPhone carrier in the world, AT&T has been the dominant iPhone carrier in the U.S. since day one, and AT&T has consistently claimed that the iPhone is its best selling device.
Chart courtesy of Mac Observer: http://www.macobserver.com/tmo/article/iphone_crushes_android_at_att_corporate_stores_in_december/
A more recent report posted in ismashphone.com, dated January 25 2012, indicated that Apple sold 37 million iPhones in Q4 2011. It appears that the iPhone 4S really helped take Apple’s handset past competing Android phones. According to research firm Kantar Worldpanel ComTech, Apple’s U.S. smartphone marketshare has doubled to 44.9 percent.[3] Meanwhile, Android marketshare in the U.S. dropped slightly to 44.8 percent. This report means that the iPhone has edged just a little bit past Android in U.S. marketshare. This is occurred after Apple’s Q1 2012 conference call, which saw themselling 37 million handsets. Meanwhile, it’s reported that marketers of Android devices, such as Motorola Mobility, HTC and Sony Ericsson saw drops this quarter.
A business rule is the basic unit of rule processing in a Business Rule Management System (BRMS) and, as such, requires a fundamental understanding. Rules consist of a set of actions and a set of conditions whereby actions are the consequences of each condition statement being satisfied or true. With rare exception, conditions test the property values of objects taken from an object model which itself is gleaned from a Data Dictionary and UML diagrams. See my article on Data Dictionaries for a better understanding on this subject matter.
A simple rule takes the form:
if condition(s)
then actions.
An alternative form includes an else statement where alternate actions are executed in the event that the conditions in the if statement are not satisfied:
if condition(s)
then actions
else alternate_actions
It is not considered a best prectice to write rules via nested if-then-else statements as they tend to be difficult to understand, hard to maintain and even harder to extend as the depth of these statements increases; in other words, adding if statements within a then clause makes it especially hard to determine which if statement was executed when looking at a bucket of rules. Moreoever, how can we determine whether the if or the else statement was satisfied without having to read the rule itself. Rules such as these are often organized into simple rule statements and provided with a name so that when reviewing rule execution logs one can determine which rule fired and not worry about whether the if or else statement was satisfied. Another limitation of this type of rule processing is that it does not take full advantage of rule inferencing and may have a negative performance impact on the Rete engine execution. Take a class with HSG and find out why.
Rule Conditions
Machine learning systems are equipped with artificial intelligence engines that provide these systems with the capability of learning by themselves without having to write programs to do so. They adjust and change programs as a result of being exposed to big data sets. The process of doing so is similar to the data mining concept where the data set is searched for patterns. The difference is in how those patterns are used. Data mining's purpose is to enhance human comprehension and understanding. Machine learning's algorithms purpose is to adjust some program's action without human supervision, learning from past searches and also continuously forward as it's exposed to new data.
The News Feed service in Facebook is an example, automatically personalizing a user's feed from his interaction with his or her friend's posts. The "machine" uses statistical and predictive analysis that identify interaction patterns (skipped, like, read, comment) and uses the results to adjust the News Feed output continuously without human intervention.
Impact on Existing and Emerging Markets
The NBA is using machine analytics created by a California-based startup to create predictive models that allow coaches to better discern a player's ability. Fed with many seasons of data, the machine can make predictions of a player's abilities. Players can have good days and bad days, get sick or lose motivation, but over time a good player will be good and a bad player can be spotted. By examining big data sets of individual performance over many seasons, the machine develops predictive models that feed into the coach’s decision-making process when faced with certain teams or particular situations.
General Electric, who has been around for 119 years is spending millions of dollars in artificial intelligence learning systems. Its many years of data from oil exploration and jet engine research is being fed to an IBM-developed system to reduce maintenance costs, optimize performance and anticipate breakdowns.
Over a dozen banks in Europe replaced their human-based statistical modeling processes with machines. The new engines create recommendations for low-profit customers such as retail clients, small and medium-sized companies. The lower-cost, faster results approach allows the bank to create micro-target models for forecasting service cancellations and loan defaults and then how to act under those potential situations. As a result of these new models and inputs into decision making some banks have experienced new product sales increases of 10 percent, lower capital expenses and increased collections by 20 percent.
Emerging markets and industries
By now we have seen how cell phones and emerging and developing economies go together. This relationship has generated big data sets that hold information about behaviors and mobility patterns. Machine learning examines and analyzes the data to extract information in usage patterns for these new and little understood emergent economies. Both private and public policymakers can use this information to assess technology-based programs proposed by public officials and technology companies can use it to focus on developing personalized services and investment decisions.
Machine learning service providers targeting emerging economies in this example focus on evaluating demographic and socio-economic indicators and its impact on the way people use mobile technologies. The socioeconomic status of an individual or a population can be used to understand its access and expectations on education, housing, health and vital utilities such as water and electricity. Predictive models can then be created around customer's purchasing power and marketing campaigns created to offer new products. Instead of relying exclusively on phone interviews, focus groups or other kinds of person-to-person interactions, auto-learning algorithms can also be applied to the huge amounts of data collected by other entities such as Google and Facebook.
A warning
Traditional industries trying to profit from emerging markets will see a slowdown unless they adapt to new competitive forces unleashed in part by new technologies such as artificial intelligence that offer unprecedented capabilities at a lower entry and support cost than before. But small high-tech based companies are introducing new flexible, adaptable business models more suitable to new high-risk markets. Digital platforms rely on algorithms to host at a low cost and with quality services thousands of small and mid-size enterprises in countries such as China, India, Central America and Asia. These collaborations based on new technologies and tools gives the emerging market enterprises the reach and resources needed to challenge traditional business model companies.
Python and Ruby, each with roots going back into the 1990s, are two of the most popular interpreted programming languages today. Ruby is most widely known as the language in which the ubiquitous Ruby on Rails web application framework is written, but it also has legions of fans that use it for things that have nothing to do with the web. Python is a big hit in the numerical and scientific computing communities at the present time, rapidly displacing such longtime stalwarts as R when it comes to these applications. It too, however, is also put to a myriad of other uses, and the two languages probably vie for the title when it comes to how flexible their users find them.
A Matter of Personality...
That isn't to say that there aren't some major, immediately noticeable, differences between the two programming tongues. Ruby is famous for its flexibility and eagerness to please; it is seen by many as a cleaned-up continuation of Perl's "Do What I Mean" philosophy, whereby the interpreter does its best to figure out the meaning of evening non-canonical syntactic constructs. In fact, the language's creator, Yukihiro Matsumoto, chose his brainchild's name in homage to that earlier language's gemstone-inspired moniker.
Python, on the other hand, takes a very different tact. In a famous Python Enhancement Proposal called "The Zen of Python," longtime Pythonista Tim Peters declared it to be preferable that there should only be a single obvious way to do anything. Python enthusiasts and programmers, then, generally prize unanimity of style over syntactic flexibility compared to those who choose Ruby, and this shows in the code they create. Even Python's whitespace-sensitive parsing has a feel of lending clarity through syntactical enforcement that is very much at odds with the much fuzzier style of typical Ruby code.
For example, Python's much-admired list comprehension feature serves as the most obvious way to build up certain kinds of lists according to initial conditions:
a = [x**3 for x in range(10,20)]
b = [y for y in a if y % 2 == 0]
first builds up a list of the cubes of all of the numbers between 10 and 19 (yes, 19), assigning the result to 'a'. A second list of those elements in 'a' which are even is then stored in 'b'. One natural way to do this in Ruby is probably:
a = (10..19).map {|x| x ** 3}
b = a.select {|y| y.even?}
but there are a number of obvious alternatives, such as:
a = (10..19).collect do |x|
x ** 3
end
b = a.find_all do |y|
y % 2 == 0
end
It tends to be a little easier to come up with equally viable, but syntactically distinct, solutions in Ruby compared to Python, even for relatively simple tasks like the above. That is not to say that Ruby is a messy language, either; it is merely that it is somewhat freer and more forgiving than Python is, and many consider Python's relative purity in this regard a real advantage when it comes to writing clear, easily understandable code.
And Somewhat One of Performance
Tech Life in California
| Company Name | City | Industry | Secondary Industry |
|---|---|---|---|
| Mattel, Inc. | El Segundo | Retail | Sporting Goods, Hobby, Book, and Music Stores |
| Spectrum Group International, Inc. | Irvine | Retail | Retail Other |
| Chevron Corp | San Ramon | Energy and Utilities | Gasoline and Oil Refineries |
| Jacobs Engineering Group, Inc. | Pasadena | Real Estate and Construction | Construction and Remodeling |
| eBay Inc. | San Jose | Software and Internet | E-commerce and Internet Businesses |
| Broadcom Corporation | Irvine | Computers and Electronics | Semiconductor and Microchip Manufacturing |
| Franklin Templeton Investments | San Mateo | Financial Services | Investment Banking and Venture Capital |
| Pacific Life Insurance Company | Newport Beach | Financial Services | Insurance and Risk Management |
| Tutor Perini Corporation | Sylmar | Real Estate and Construction | Construction and Remodeling |
| SYNNEX Corporation | Fremont | Software and Internet | Data Analytics, Management and Storage |
| Core-Mark International Inc | South San Francisco | Manufacturing | Food and Dairy Product Manufacturing and Packaging |
| Occidental Petroleum Corporation | Los Angeles | Manufacturing | Chemicals and Petrochemicals |
| Yahoo!, Inc. | Sunnyvale | Software and Internet | Software and Internet Other |
| Edison International | Rosemead | Energy and Utilities | Gas and Electric Utilities |
| Ingram Micro, Inc. | Santa Ana | Computers and Electronics | Consumer Electronics, Parts and Repair |
| Safeway, Inc. | Pleasanton | Retail | Grocery and Specialty Food Stores |
| Gilead Sciences, Inc. | San Mateo | Healthcare, Pharmaceuticals and Biotech | Pharmaceuticals |
| AECOM Technology Corporation | Los Angeles | Real Estate and Construction | Architecture,Engineering and Design |
| Reliance Steel and Aluminum | Los Angeles | Manufacturing | Metals Manufacturing |
| Live Nation, Inc. | Beverly Hills | Media and Entertainment | Performing Arts |
| Advanced Micro Devices, Inc. | Sunnyvale | Computers and Electronics | Semiconductor and Microchip Manufacturing |
| Pacific Gas and Electric Corp | San Francisco | Energy and Utilities | Gas and Electric Utilities |
| Electronic Arts Inc. | Redwood City | Software and Internet | Games and Gaming |
| Oracle Corporation | Redwood City | Software and Internet | Software and Internet Other |
| Symantec Corporation | Mountain View | Software and Internet | Data Analytics, Management and Storage |
| Dole Food Company, Inc. | Thousand Oaks | Manufacturing | Food and Dairy Product Manufacturing and Packaging |
| CBRE Group, Inc. | Los Angeles | Real Estate and Construction | Real Estate Investment and Development |
| First American Financial Corporation | Santa Ana | Financial Services | Financial Services Other |
| The Gap, Inc. | San Francisco | Retail | Clothing and Shoes Stores |
| Ross Stores, Inc. | Pleasanton | Retail | Clothing and Shoes Stores |
| Qualcomm Incorporated | San Diego | Telecommunications | Wireless and Mobile |
| Charles Schwab Corporation | San Francisco | Financial Services | Securities Agents and Brokers |
| Sempra Energy | San Diego | Energy and Utilities | Gas and Electric Utilities |
| Western Digital Corporation | Irvine | Computers and Electronics | Consumer Electronics, Parts and Repair |
| Health Net, Inc. | Woodland Hills | Healthcare, Pharmaceuticals and Biotech | Healthcare, Pharmaceuticals, and Biotech Other |
| Allergan, Inc. | Irvine | Healthcare, Pharmaceuticals and Biotech | Biotechnology |
| The Walt Disney Company | Burbank | Media and Entertainment | Motion Picture and Recording Producers |
| Hewlett-Packard Company | Palo Alto | Computers and Electronics | Consumer Electronics, Parts and Repair |
| URS Corporation | San Francisco | Real Estate and Construction | Architecture,Engineering and Design |
| Cisco Systems, Inc. | San Jose | Computers and Electronics | Networking Equipment and Systems |
| Wells Fargo and Company | San Francisco | Financial Services | Banks |
| Intel Corporation | Santa Clara | Computers and Electronics | Semiconductor and Microchip Manufacturing |
| Applied Materials, Inc. | Santa Clara | Computers and Electronics | Semiconductor and Microchip Manufacturing |
| Sanmina Corporation | San Jose | Computers and Electronics | Semiconductor and Microchip Manufacturing |
| Agilent Technologies, Inc. | Santa Clara | Telecommunications | Telecommunications Equipment and Accessories |
| Avery Dennison Corporation | Pasadena | Manufacturing | Paper and Paper Products |
| The Clorox Company | Oakland | Manufacturing | Chemicals and Petrochemicals |
| Apple Inc. | Cupertino | Computers and Electronics | Consumer Electronics, Parts and Repair |
| Amgen Inc | Thousand Oaks | Healthcare, Pharmaceuticals and Biotech | Biotechnology |
| McKesson Corporation | San Francisco | Healthcare, Pharmaceuticals and Biotech | Pharmaceuticals |
| DIRECTV | El Segundo | Telecommunications | Cable Television Providers |
| Visa, Inc. | San Mateo | Financial Services | Credit Cards and Related Services |
| Google, Inc. | Mountain View | Software and Internet | E-commerce and Internet Businesses |
training details locations, tags and why hsg
The Hartmann Software Group understands these issues and addresses them and others during any training engagement. Although no IT educational institution can guarantee career or application development success, HSG can get you closer to your goals at a far faster rate than self paced learning and, arguably, than the competition. Here are the reasons why we are so successful at teaching:
- Learn from the experts.
- We have provided software development and other IT related training to many major corporations in California since 2002.
- Our educators have years of consulting and training experience; moreover, we require each trainer to have cross-discipline expertise i.e. be Java and .NET experts so that you get a broad understanding of how industry wide experts work and think.
- Discover tips and tricks about Weblogic Administration programming
- Get your questions answered by easy to follow, organized Weblogic Administration experts
- Get up to speed with vital Weblogic Administration programming tools
- Save on travel expenses by learning right from your desk or home office. Enroll in an online instructor led class. Nearly all of our classes are offered in this way.
- Prepare to hit the ground running for a new job or a new position
- See the big picture and have the instructor fill in the gaps
- We teach with sophisticated learning tools and provide excellent supporting course material
- Books and course material are provided in advance
- Get a book of your choice from the HSG Store as a gift from us when you register for a class
- Gain a lot of practical skills in a short amount of time
- We teach what we know…software
- We care…
Interesting Reads Take a class with us and receive a book of your choosing for 50% off MSRP.
