A new year filled with data, good wishes and SQL Server! Kicking things off this year at the SS SLUG, are two SQL Server MVPs with sessions on data mining and database development.
More info on the event, including the Agenda can be found here. http://digit.lk/event/ss-slug-january-2014-meet-up/
Team digit, off for now.
Catch you all tomorrow.
Since we are running a little behind schedule, we would like to wrap this up real quick.
Okay folks. thanks for turning up with the Live Blog.
That’s the end of this session.
Gogula will now take question.
In a transaction database, a deadlock happens when two processes each within its own transaction updates two rows of information but in the opposite order. – Gogula
A transaction is something which should have the following properties: atomic, consistent, isolated, and durable. – Gogula.
No matter how correct and precise your code is, some setting tend to screw things up. alwats keep an eye. – Gogula
Error handling is the next topic we are about to discuss with Gogula.
Easiest methods is the Transaction for data modifications. – Gogula.
Gogula carries on with his demonstration.
There re few misconceptions people have about SQL Server. And Gogula is ridding them out..
If wrong assumptations are taken while coding defensively, those assumptions should be corrected. One good way of doing this is via testing the code. – Gogula
Gogula now quickly gets to his laptop and starts the demo he has to present.
Gogula will be continuing from where he left off from November’s session, Gogula will guide you through writing better T-SQL code that is more resilient to unexpected issues and common code failures. This session is based on the book Defensive Database Programming by Alex Kuznetsov.
Programming Defensively is the fist thing in his session he is going to talk about.
Define and understand assumptions
test as many use cases as possible
modulalize into fully testable and fully tested code
reuse code when feasible – Gogula
Gogula will talk to us today on Writing Resilient T-SQL Code – Part II.
He encourages everyone to try out the stuff what is about to be demonstrated here.
We are back.
Next up, we have Gogula G. Aryalingam, who is a Technical Architect at Navantis.
Keep an eye at this space.
Now we break for a small refreshment break for about 10 minutes and we will be back for the 2nd session.
And with that note, Dinesh Asanka concludes his session.
Dinesh will take question now.
And since no one has questions for him, he asks some questions for which there are gifts up for grabs.
Dinesh now shares some reference sites with the audience.
Limitations in Naive Bayes
Attributes are independent from each other
Continuous attributes cannot be used
There are some parameters for this algorithm, such as
-Maximum input attributes
Mostly we use Naive Bayes Databases and data mining to predict our data and to build modules. – Dinesh
Attributes are very critical to define your predictions. – Dinesh
Since the Databse Dinesh was working on has some errors, we move on. He now shows us another actual research database used by the US government to predict the vote casting patterns for democratic and non democratic parties.
Seems like there is an error. Dinesh gives another try at the procedure.
Let the Data mining start.
He already has an old database which he could use for this demo he says.
Now he selects the attributes which he thinks are relevant to this particular database to mine and predict data.
Dinesh is getting ready to show us a small demonstration on the NB Database, where the Database will predict the potential targeted male customers for a Bike purchasing.
This algorithm consumes less computational intense.
In NB algorithm, it will not count interrelated attributes together. – Dinesh
The MS Naive Bayes is based on the Bayes Theorem. – Dinesh
What is a Naive Bayes?
Naive Bayes model will tell you how your attributes are related to each other. – Dinesh.
Catch Dinesh’s writing at his own blog http://www.dbfriend.net/
To start off the proceeding s for tonight, Dinesh Asanka (MVP), Database Specialist Pearson Lanka will take the stage now.
He is about to talk on Business Intelligence (Data Mining).
Catch the conversation on Twitter, under #SQLServerMeetup hashtag.
We are setting up till the speakers are all set, the crowd is all present.
Stay tuned with us for more time to time latest updates.
Agenda for the evening.
Session #1: Predictive Modeling with the Microsoft Naïve Bayes algorithm
Join this session where Dinesh showcases the capabilities of predictive analysis using the Microsoft Naïve Bayes algorithm. Naïve Bayes is a classification algorithm that ships with SQL Server Analysis Services and is used to mine for and predict outcomes based on selected parameters.
CATEGORY: Business Intelligence (Data Mining)
SPEAKER: Dinesh Asanka (MVP), Database Specialist (Pearson Lanka)
DURATION: 45 minutes approx.
Session #2: Writing Resilient T-SQL Code – Part II
Continuing from where he left off from November’s session, Gogula will guide you through writing better T-SQL code that is more resilient to unexpected issues and common code failures. This session is based on the book Defensive Database Programming by Alex Kuznetsov.
SPEAKER: Gogula G. Aryalingam (MVP), Technical Architect (Navantis)
DURATION: 45 minutes approx.
Team diGIT is Live and running from the event, SQL Server Sri Lanka User Group January Meetup.