Unless you have a great product, service or idea for which people are willing to wait, chances are highly likely that these potential clients will leave your website should your response time take too long to their incoming requests.  Ignore your application’s performance and you are more likely to be dumped by your users sooner than expected.

To improve the performance of an ASP.Net application you need to optimize your front-end UI (user interface) code as well as the back-end database. You can also think of the following tips as a brief best practices guide for the ASP.net performance optimization. So, whether you are a developer, UI designer or member of the deployment team, the following tips may help you. No matter what’s your role in the project or what you do to boost performance of your application, always remember that your goal should be to:

·          Minimize the amount of data you sent across the network.

·          Reduce the number of server requests.

Here you go (in no particular order)

At Database level

Writing Python in Java syntax is possible with a semi-automatic tool. Programming code translation tools pick up about 75% of dynamically typed language. Conversion of Python to a statically typed language like Java requires some manual translation. The modern Java IDE can be used to infer local variable type definitions for each class attribute and local variable.


Translation of Syntax
Both Python and Java are OO imperative languages with sizable syntax constructs. Python is larger, and more competent for functional programming concepts. Using the source translator tool, parsing of the original Python source language will allow for construction of an Abstract Source Tree (AST), followed by conversion of the AST to Java.

Python will parse itself. This capability is exhibited in the ast module, which includes skeleton classes. The latter can be expanded to parse and source each node of an AST. Extension of the ast.NodeVisitor class enables python syntax constructs to be customized using translate.py and parser.py coding structure.

The Concrete Syntax Tree (CST) for Java is based on visit to the AST. Java string templates can be output at AST nodes with visitor.py code. Comment blocks are not retained by the Python ast Parser. Conversion of Python to multi-line string constructs with the translator reduces time to script.


Scripting Python Type Inference in Java
Programmers using Python source know that the language does not contain type information. The fact that Python is a dynamic type language means object type is determined at run time. Python is also not enforced at compile time, as the source is not specified. Runtime type information of an object can be determined by inspecting the __class__.__name__ attribute.

Python’s inspect module is used for constructing profilers and debugging.
Implementation of def traceit (frame, event, arg) method in Python, and connecting it to the interpreter with sys.settrace (traceit) allows for integration of multiple events during application runtime.

Method call events prompt inspect and indexing of runtime type. Inspection of all method arguments can be conducted. By running the application profiler and exercising the code, captured trace files for each source file can be modified with the translator. Generating method syntax can be done with the translator by search and addition of type information. Results in set or returned variables disseminate the dynamic code in static taxonomy.

The final step in the Python to Java scrip integration is to administer unsupported concepts such as value object creation. There is also the task of porting library client code, for reproduction in Java equivalents. Java API stubs can be created to account for Python APIs. Once converted to Java the final clean-up of the script is far easier.

Related:

 What Are The 10 Most Famous Software Programs Written in Python?

Python, a Zen Poem

Recently, the new iOS update had added Reminders to the iPhone. If you ever found yourself setting notes on your iPhone to remember to do things, such as buying milk while at the grocery store, this process has become leagues upon leagues simpler, and faster. On your iPhone is an application named “Reminders”. Tap on this application and experience the new world of To-Do lists.

Right away, you are greeted by a screen that looks similar to a notepad, where you would be scribbling down reminders for this, and for that. To start off, tap on the plus button, and you are able to input the reminder you want. Say you want to be reminded to “Buy Milk.” Just type that into the application and you’re good to go.

But wait, there’s more. What this new application brings to the table that is extremely useful is the fact that your iPhone can remind you to do that task at a certain location, which, in this case, is buying milk. If you had saved your regular grocery store in your Maps application as a favorite location, you are able to do so. (To save a favorite location, go into your Maps application, search for your nearest grocery store that you regularly shop at, tap on the pin, tap on the blue arrow to get more information, and “Add to Bookmarks.”) In order to remind you to buy milk at your favorite grocery store, slide the “Off” to “On” and you are now able to set where you would like to be reminded at, and at what point in time. Now, you will never leave the grocery store without buying milk!

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.”