I suspect that many of you are familiar with the term "hard coding a value" whereby the age of an individual or their location is written into the condition (or action) of a business rule (in this case) as shown below:

if customer.age > 21 and customer.city == 'denver'

then ...

Such coding practices are perfectly expectable provided that the conditional values, age and city, never change. They become entirely unacceptable if a need for different values could be anticipated. A classic example of where this practice occurred that caused considerable heartache in the IT industry was the Y2K issue where dates were updated using only the last 2 digits of a four digit number because the first 2 digits were hard-coded to 19 i.e. 1998, 1999. All was well provided that the date did not advance to a time beyond the 1900’s since no one could be certain of what would happen when the millennia arrived (2000). A considerably amount of work (albeit boring) and money, approximately $200 billion, went into revising systems by way of software rewrites and computer chip replacements in order to thwart any detrimental outcomes. It is obvious how a simple change or an assumption can have sweeping consequences.

You may wonder what Y2K has to do with Business Rule Management Systems (BRMS). Well, what if we considered rules themselves to be hard-coded. If we were to write 100s of rules in Java, .NET or whatever language that only worked for a given scenario or assumption, would that not constitute hard-coded logic? By hard-coded, we obviously mean compiled. For example, if a credit card company has a variety of bonus campaigns, each with their own unique list of rules that may change within a week’s time, what would be the most effective way of writing software to deal with these responsibilities?

Python programming language is general purpose open source programming language. One of its main features is flexibility and ease of use. Python has a variety of useful set of utilities and libraries for data processing and analytical tasks. Currently due to the rise in demand of big data processing python has grown in popularity because its features are easy to use which are core to the processing of huge chunks of information.

Guido Van Rossum, the pioneer of python, introduced python in the year 1980 and then implemented it in 1989. The intention behind the development of python was to make it open source language that can also be used for commercial projects. The fundamental principle of python is to write the code that is easy to use, highly readable and embrace writing fewer lines of code for achieving a particular task. One of the most popular standard libraries which have ready to use tools for performing a various work is Python Package Index. It was introduced in January 2016 and contains more than 72,000 packages for third-party software usage.

Python plays a critical role in linking data to customers. Recently python has found few entry barriers and many people have had access to have experienced the power of python in the past. So, what makes python the best language for big data analytics?

One of the reasons to choose python is that python ecosystem is very vibrant, the ratings at Redmonk are a proof of the strength python community. The Redmonk ranking is based on StackOverflow discussions and contribution made in Github to determine the popularity of programming language on the method used by users to ask questions about Python and the number of the open source projects contributions.

Since its foundation, HSG has been a leader in Business Rule Management Systems Training and Consulting services by way of the Blaze Advisor Rule Engine.  Over the years we have provided such services to many of the worlds largest corporations and government institutions whose respective backgrounds include credit card processing, banking, insurance, health and medicine and more, much more.  Such training and consulting services have included:

Create a wrapper object model in either Java, .NET or XML

Identify and catalog business rules

Develop a rule architecture within Blaze Advisor that isolates rule repositories as they relate to functionality and corporate policies

Configure, develop and implement a variety of interfaces to the rule engine from disparate systems ranging from mainframe applications written in Cobol to UNIX/Windows applications using Enterprise Java Beans, Windows Services, Web Services, Fat Clients, Java Messaging Services and Web Applications.

Review and update code to boost efficiency either by way of

    Removing functions calls within conditional statements

    Ensuring that database calls are essential or can be rearchitected in some other manner

    Employing the rete algorithm where necessary

    Paring down extensively large class models

    Deploying such appliations in multi-threaded systems

·         ...

Call us if you:

    are in need of Blaze Advisor Expertise
    are developing SMEs in Blaze
    want to speak directly with an expert (no placement agencies)
    want an affordable alternative to FICO
    want to work with an industry leader

Planning on a little spring cleaning and getting rid of all those old electronics? With the way technology changes so quickly, it seems like we no sooner have one new electronic piece and there’s a new one on the market that we absolutely have to have. The result is a ton of used electronics stored away. Now it’s time to get rid of everything, here are some tips on what to do with all that old tech stuff and how to do it safely.

Save on Landfill Waste

Few people really realize where their electronics wind up when they throw them in the trash or dumpster. They assume they’re getting taken care of and just forget about them. According to the Environmental Protection Agency, one of the fastest growing components of landfill waste is electronics. The unfortunate part of this is that these electronics are filled with lead, mercury and other toxic chemicals – chemicals that find their way into our soil and water.

This results in the contamination of our lakes, streams and soil. The best way to avoid this is by knowing where your electronics are going and to dispose of them safely. Contact the recycling center in your area and ask for the best place to dispose of your old electronics.

Think of Security

Our lives are filled with electronic technology, whether it’s in the form of computers, tablets, smart phones or video games. Most of these electronics allow network sharing with people all over the world. With identity theft such a growing concern, much of our private lives and personal information is entered in the hard drives of these items.

Before you dispose of any electronics, make sure the hard drives are wiped clean of all personal information. This requires more than just “deleting” files. If you’re not sure how to correctly get rid of the information, contact a professional to do this for you. Even if you have to pay to have it done, it’s a minimal expense compared to having your private information stolen.

Find New Homes for Your Electronics

Once you’ve cleaned out your electronics of personal information, they may still be worth something to other individuals. You may know of a child or elderly person who would really appreciate a “new” computer or a tablet. Someone who has never owned a smart phone or laptop may be thrilled to suddenly own one, even if they are obsolete to you.

If you can’t find anyone that wants them, consider selling them on places like eBay, Craigslist or one of the many other online auction sites. The old saying, “one man’s junk is another man’s gold” is especially true when it comes to electronics. Many people like building computers using their own parts.

Students in computer-related programs also utilize used electronics as a form of hands-on training. Most schools also take in donated computers, televisions and cell phones. Contact your electric company or school district to get information on where you can take your old electronics. You’ll be not only cleaning out your house but also helping the environment.

Related:

Cloud Computing: What It Means and How It Could Threaten the Traditional Outsourcing Model