SLIIT Helper

Load Shape file into PostgreSQL Database

noreply@blogger.com (Ishanka) — Wed, 05 Apr 2017 02:19:00 +0000

Project Spark - Demo of a game tool

noreply@blogger.com (Ishanka) — Sun, 22 Jun 2014 04:56:00 +0000

This is about a game tool from one of our friends in Moratuwa University. Project Spark is an creation game/tool for the Xbox One and Windows 8. This fantasy-themed action game sparks people’s imagination when building their worlds. Create big environments, set the time of day, build characters with their own personalities and create enemies to fight. There’s an infinite number of ways to play, build, and share. Team Dakota’s has developed this game. It’ll be coming to the Xbox one for free. People say “Project Spark is an open-world digital canvas that allows you to create your own games, stories, worlds, and share them with everyone”. The great thing about Project Spark is that it is powered by Microsoft Studios.

SLIIT Convocation 2014

noreply@blogger.com (Ishanka) — Sat, 19 Apr 2014 04:34:00 +0000

SLIIT Convocation 2014

FOSS4G 2014 - Sri Lanka

noreply@blogger.com (Ishanka) — Sat, 08 Feb 2014 03:34:00 +0000

Welcome to FOSS4G Sri Lanka 2014
It will be an event where Sri Lanka geo-spatial community share their ideas related to role of open source GIS tools in Sri Lankan GIS context in a simple and informal setting.

Join us and be part of open source GIS community of Sri Lanka!

Date : Sunday 23rd February 2014

Time : 9.00 a.m- 4.00 p.m.

Venue : Hall D202 , Sri Lanka institute of Information Technology, New Kandy road, Malabe

Visit official web site

Registration Now Open

EAD Lecture Slides

noreply@blogger.com (Ishanka) — Sat, 28 Dec 2013 16:36:00 +0000

EAD Tutorials

noreply@blogger.com (Ishanka) — Tue, 24 Dec 2013 16:20:00 +0000

SEP Project Sample Documents

noreply@blogger.com (Ishanka) — Mon, 11 Nov 2013 15:33:00 +0000

Free Download Head First Book Series!

noreply@blogger.com (Ishanka) — Sun, 27 Oct 2013 08:18:00 +0000

http://uploaded.net/file/n3r67ick/Head%20First%20Books%20Serie%20COLLECTION.part1.rar

http://uploaded.net/file/z4nbven1/Head%20First%20Books%20Serie%20COLLECTION.part2.rar

Panacea's Jacket Wins HSBC Youth Enterprise Awards

noreply@blogger.com (Ishanka) — Sun, 27 Oct 2013 08:02:00 +0000

Congratulations!

Sample Spring MVC Application

noreply@blogger.com (Ishanka) — Thu, 24 Oct 2013 15:10:00 +0000

IDE: Netbeans
Add Spring framework to project classpath before compiling.

Sample Java SE + Spring Application

noreply@blogger.com (Ishanka) — Thu, 24 Oct 2013 15:04:00 +0000

IDE: Netbeans
Add Spring library to project classpath before compiling.

Sample Hibernate Application

noreply@blogger.com (Ishanka) — Thu, 24 Oct 2013 14:56:00 +0000

IDE : Netbeans

Add Hibernate library to project classpath before compiling this project.

DMW Knowledge Base

noreply@blogger.com (Ishanka) — Mon, 21 Oct 2013 15:28:00 +0000

What is a data warehouse?
A data warehouse is a subject oriented, integrated, time variant, non volatile collection of data in support of management's decision making process.

What is data mining?
Explore predictive hidden patterns from a large collection of data.

What is a surrogate key?
In order to isolate the data warehouse from source systems, we will introduce a technical surrogate key instead of re-using the source system's natural (business) key. If a surrogate key generation process is implemented correctly, adding a new source system to the data warehouse processing will not require major efforts.

What are Slowly Changing Dimensions?
Dimensions that change over time are called Slowly Changing Dimensions. For instance, a product price changes over time; People change their names for some reason; Country and State names may change over time. These are a few examples of Slowly Changing Dimensions since some changes are happening to them over a period of time.

Type 1 : Overwriting the old values.
Type 2 : Creating an another additional record.
Type 3 : Creating new fields.

More>>

What is Star Schema?
More>>

What are Snowflake schemas?
Snowflake schemas normalize dimensions to eliminate redundancy. That is, the dimension data has been grouped into multiple tables instead of one large table. For example, a location dimension table in a star schema might be normalized into a location table and city table in a snowflake schema. While this saves space, it increases the number of dimension tables and requires more foreign key joins. The result is more complex queries and reduced query performance.

More>>

Supervised vs. Unsupervised learning
In supervised learning, the classes are predetermined. The data presented to a machine learning algorithm is fully labelled. eg. neural networks

In unsupervised learning, algorithm decides how to group samples into classes that share common properties. eg. Clustering

What is KPI?
KPI stands for Key Performance Indicator.
In Analysis Services, KPI is a collection of calculations that are associated with a measure group in a cube that are used to evaluate business success.
KPIs are created from existing measures. However, not all measures are good candidates for KPI utilization.

What is pivot table?
A pivot table makes it easy to extract information from a large table of data without use of formulas or functions. Extracted data can be sorted, filtered. Viewing data as dimensions are possible.

What is ETL?
In data Warehouse, data from different source systems are integrated, processed and stored. The ETL (Extraction, Transformation and Loading) process handles these data and transforms it into a more consistent, standard formatted data. This ETL process is done with the help of an ETL tool and most widely used tools are Informatica, Data Stage, Ab Inito, Oracle Warehouse Builder etc.

The first step in ETL process is mapping the data between source systems and target database(data warehouse or data mart). The second step is cleansing of source data in staging area. The third step is transforming cleansed source data and then loading into the target system.

ETL Tasks/ Data Transformations

Filtering - The data Filter Operation takes all the row values from the input dataset, tests the value of a field in the row for one a condition, if the condition is false for the attribute value of the row, then we ignore the row else we pass the row into to the result dataset.

Data lookup - Performs lookups by joining data in input columns with columns in a reference dataset.

Derive columns - Values of a column retrieved using other columns

Data cleansing - The process of resolving inconsistencies and fixing the anomalies in source data, typically as part of the ETL process.

Data validation - Data types, formats(email, url, zip code)

What is Meta Data?
Data that describes data and other structures, such as objects, business rules, and processes.
Eg. the schema design of a data warehouse .
A repository contains metadata.

Difference between Relational modeling and Multidimensional modeling

1. Relational models are complex due to joins with other tables. Multidimensional modeling are very simple. Each of the dimension table has a direct relationship with the fact table.

2. Relational models are mostly normalized. Multidimensional modeling are radically denormalized.

3. Standard relational models are optimized for On Line Transaction Processing. OLTP needs the ability to efficiently update data. This is provided in a normalized database that has each value stored only once.
Multidimensional modeling are optimized for On Line Analytical Processing. OLAP needs the ability to retrieve data efficiently. Efficient data retrieval requires a minimum number of joins. This is provided with the simple structure of relationship in a Multidimensional modeling, where each dimension table is only a single join away from the fact table.

4. SQL queries required to manipulate relational data. MDX queries required to manipulate multi-dimensional data

5. Tables are units of relational data storage. Cubes are units of multi-dimensional data storage.

What is a Gini index?
How impure a node is after splitting has occurred.

Data Mining and Warehousing Past Papers

noreply@blogger.com (Ishanka) — Sun, 20 Oct 2013 16:02:00 +0000

Author : Julian Pereira
Year : 2012

Panacea's Jacket Wins Second Place at National Level

noreply@blogger.com (Ishanka) — Sun, 20 Oct 2013 09:42:00 +0000

SLIIT NBQSA REPRESENTATION

Congratulations!

Apriori Algorithm

noreply@blogger.com (Ishanka) — Thu, 17 Oct 2013 16:20:00 +0000

FP Growth tutorial

noreply@blogger.com (Ishanka) — Wed, 16 Oct 2013 16:14:00 +0000

JEE, Hibernate, Spring from scratch

noreply@blogger.com (Ishanka) — Thu, 10 Oct 2013 16:26:00 +0000

Best Java Video Series

There are many resources on the internet to learn Java. Learning with videos is effective most of the times though it is difficult to grasp advanced programming concepts with this technique. If you are going to learn Java EE or any of JEE frameworks, it might be very tough at the beginning as there 's always a big learning curve which is something common in JEE. JavaBrains video series has already helped Java lovers and starters to make this learning curve narrow. Obviously this might be the best JEE online video course available free. Hava a look if you are not familiar with JavaBrains.

SLIIT NBQSA Representation

noreply@blogger.com (Ishanka) — Sat, 05 Oct 2013 15:01:00 +0000

Java Socket Programming [Sinhala Explanation]

noreply@blogger.com (Ishanka) — Sat, 05 Oct 2013 13:58:00 +0000

SQL Server 2008 SSIS, SSAS and SSRS Tutorials

noreply@blogger.com (Ishanka) — Sat, 05 Oct 2013 13:31:00 +0000

SSIS Tutorial

SSAS Tutorial

SSRS Tutorial

SSRS Chart Tutorial

How to install SVN Server

noreply@blogger.com (Ishanka) — Sun, 15 Sep 2013 13:58:00 +0000

Version control is the art of managing changes to information. It has long been a critical tool for programmers, who typically spend their time making small changes to software and then undoing or checking some of those changes the next day. Imagine a team of such developers working concurrently - and perhaps even simultaneously on the very same files! - and you can see why a good system is needed to manage the potential chaos.

VisualSVN is a professional grade Subversion integration plug-in for Visual Studio.
Key benefits of VisualSVN are:
Unbeatable reliability: Visual Studio will never crash or hang because of VisualSVN.
Seamless integration: VisualSVN automatically handles added or renamed files and reflects these operations to Subversion.
Real-time status: VisualSVN carefully tracks and displays all the current changes made to your working copy.
Short learning curve: VisualSVN utilizes TortoiseSVN dialogs and provides a smart wizard to put your sources under Subversion.

TortoiseSVN is a free open-source Windows client for the Apache™ Subversion® version control system. That is, TortoiseSVN manages files and directories over time. Files are stored in a central repository. The repository is much like an ordinary file server, except that it remembers every change ever made to your files and directories. This allows you to recover older versions of your files and examine the history of how and when your data changed, and who changed it. This is why many people think of Subversion and version control systems in general as a sort of “time machine”. Some version control systems are also software configuration management (SCM) systems. These systems are specifically tailored to manage trees of source code, and have many features that are specific to software development - such as natively understanding programming languages, or supplying tools for building software. Subversion, however, is not one of these systems; it is a general system that can be used to manage any collection of files, including source code.

This tutorial demonstrates how to install VisualSVN and TortoiseSVN client.

Sample Mid Review Document for Final Year Research

noreply@blogger.com (Ishanka) — Mon, 26 Aug 2013 18:15:00 +0000

Frequent Patterns Using FP Tree Algorithm

noreply@blogger.com (Ishanka) — Tue, 20 Aug 2013 17:56:00 +0000

FP Tree Tutorial

Apriori algorithm for Data Mining

noreply@blogger.com (Ishanka) — Tue, 20 Aug 2013 16:59:00 +0000

Apriori algorithm Tutorial