Monday, April 28, 2014

IRCTC Railway Launches Train-tracking Mobile and Desktop Applications | IRCTC Apps | Railway Launches Train-tracking Mobile Applications

The process of train tracking is a herculean task for the people. Most of the time, we try to get the real-time information by calling the nearest railway station. If we are lucky enough, you get an answering voice on the other end. Otherwise, we get no answer at all from the other end. Hence, a government organization has come out with an application that tells the users about the real-time information about the trains. The name of the organization that has rolled out this mobile application is Centre for Railway Information Systems (CRIS).
Mobile Application
Railway_app (3)
This application tells the rail users about the expected arrival and departure timings of a particular train of their choice. This application can be freely installed on your mobile for easy tracking of trains. This versatile application is developed by CRIS, the information and technology arm of Indian Railways. According to a senior Railway official, this new application can be used to find out train timings, train position and train location in real-time. Along with the mobile version, CRIS has also released a desktop application that runs on Windows 8 platform. This desktop version also provides the train enquiry results with the help of Microsoft backup.
Features of Application
CRIS has implemented the different features in different tabs in its newly launched mobile and desktop applications. They are listed below.
1. Spot Your Train: This real-time application sports a Spot Your Train tab. This enables the user to query information about the current position of the train, its expected arrival time and its expected departure from a particular station.
2. Train Schedule: The second tab provides the complete list of stations a train is likely to stop enroute to its destination. The exhaustive list also shows the arrival and departure times at each station in the list.
3. Complete Train List: This tab provides the complete list of trains that operate between a source and destination. This list also tells on which days during the week a particular train is available.
4. Cancelled Trains: The fourth tab enlists all trains that are marked as cancelled. It displays trains that are cancelled through the entire root and those that are canceled on partial route.
5. Rescheduled and Diverted Trains: The fifth tab in the application gives the list of rescheduled and diverted trains.

Seventy-Two Big Data Infographics | Big Data Technology | Big Data Questions | Syed Alaudeen

  1. How The USA Federal Government Thinks Big With Data
  2. Are You Ready For The Future of the Internet of Things?
  3. How Big Data Centers Impact the Environment
  4. A Look Into How Data Centers Actually Work
  5. How Big Data Gives Retailers a Competitive Edge and Boosts Growth
  6. How We Are Heading Towards a Smart Planet with The Internet of Things
  7. How Data Disasters Can Seriously Harm Your Company
  8. How Data Mining & Decision Support Systems Can Create A Powerful Marketing Strategy
  9. Five Myths Marketers Believe About Big Data
  10. The Explosion of the Internet of Things
  11. What Are The Trends: A Big Data Survey
  12. How The Internet of Things Will Make Our World Smart- Infographic
  13. Big Data Analytics Trends for 2014
  14. How Big Data Will Improve Decision Making in Your Organisation
  15. How M2M Data Will Have a Major Impact by 2020
  16. The World’s Most Unusual Data Centers
  17. 7 Ways Big Data Could Revolutionize Our Lives by 2020
  18. 5 Ways To Become Extinct As Big Data Evolves
  19. Why Marketers Should Stop Worrying And Start Loving Big Data
  20. In The World Of Digital Storage, Size Does Matter
  21. Maximize Online Sales With Product Recommendations
  22. Understanding The Various Sources of Big Data
  23. The Who, What and Why of Big Data
  24. Customers Sharing Their Personal Data Should Be Cared For
  25. Is Bad Data A Hazard For Your Customer Experience
  26. What The Consumer Really Thinks Of Data Privacy
  27. Financial Services Firms Leveraging Big Data
  28. A Reality Gap Exists With Big Data Initiatives
  29. Smart Cities Turn Big Data Into Insight
  30. Using Big Data To Predict Dengue Fever And Malaria Outbreaks
  31. Keeping Track Of The People Keeping Track Of You
  32. Are European Companies Ready For Big Data?
  33. How Are Companies Organising Their Big Data Initiatives
  34. 10 Greatest Challenges Preventing Businesses From Capitalizing On Big Data
  35. What Will The World Look Like When We Connect The Unconnected
  36. What Is The Value Of The Internet Of Things
  37. What Are The Real Costs Of A Data Breach
  38. How Google Applies Big Data To Know You
  39. How To Put Big Data To Work
  40. How To Become More Competitive With Big Data
  41. How Big Data Can Help To Minimize Attacks On Your Digital Assets
  42. How Can Big Data Improve Education
  43. A Closer Look Into The Future Big Data Ecosystem
  44. Data Lovers vs. Data Haters
  45. How The Internet Of Things Will Create A Smart World
  46. 8 Industries That Could Benefit From Big Data
  47. Five Steps To Data-Driven Marketing
  48. Big Data is Big Business in Banking
  49. The body as a source of big data
  50. Is your data secure?
  51. The viability of big data
  52. A visualization of the world’s largest data breaches
  53. The long road to become a big data scientist
  54. The illustrious big data scientist
  55. What data do the five largest tech companies collect
  56. Getting sales and marketing aligned with big data
  57. Big Data in the Supply Chain
  58. The promise of big personalization
  59. Understanding the growing world of bytes
  60. The history of predictive analytics
  61. The Big Data Industry Atlas
  62. CIOs and Big Data
  63. How retailers can deal with big data
  64. The importance of Big Data Governance
  65. Big Data and the possibilities with Hadoop
  66. Big Data brings big benefits, but what are the costs of too much data?
  67. Giving employees access to big data has big potential
  68. Big Data will transform healthcare
  69. Five opportunities to effectively and efficiently analyze big data
  70. Challenging the traditional RDBMS Status Quo
  71. Big data is Big News
  72. Big Data Snapshot

Friday, April 25, 2014

Microsoft Now Officially Owns Nokia's Phone Business

Microsoft's acquisition of Nokia's Devices and Services business is now complete, both companies have announced.
The $7.2 billion deal, originally announced in September 2013, was approved by Nokia shareholders one month later, and has now been approved by governmental regulatory agencies around the world.
“Today we welcome the Nokia Devices and Services business to our family. The mobile capabilities and assets they bring will advance our transformation,” saidMicrosoft CEO Satya Nadella.
Stephen Elop chimed in with a similarly worded open letter. "As Microsoft and Nokia Devices and Services come together as an expanded family, we will unify our passion, dedication and commitment to bringing you the best of what our joint technologies have to offer (...) From today onwards, the possibilities are endless. As now, we’re one," he wrote.
Microsoft's press release seems to confirm a recently leaked memo, which indicated Microsoft would be renaming Nokia to Microsoft Mobile.
"Microsoft refers to Microsoft Corp. and its affiliates, including Microsoft Mobile Oy, a subsidiary of Microsoft. Microsoft Mobile Oy develops, manufactures and distributes Lumia, Asha and Nokia X mobile phones and other devices," writes Microsoft.
Have something to add to this story? Share it in the comments.
Reference From Linked in:

Tuesday, September 17, 2013

Google Algorithm Update Status | Syed Alaudeen | Digital Marketing Consultant

In 2005 Google published its “Web Authoring Statistics” report, which provided a unique insight into how a large search engine views the Web at the very basic HTML level.

In August 2009 Matt Cutts invited Webmasters to help test a new indexing technology that Google had dubbed Caffeine. The SEO community immediately fell to rampant speculation about how Caffeine would affect rankings (in fact, the only effect was unintentional).

By February 2010 even I had fallen prey to Caffeine Speculationitis. On February 25, 2010 Matt McGee confirmed that Google had not yet implemented the Caffeine technology on more than 1 data center (at this time, in April 2013, there are only 13 Google Data Centers around the world).

On June 8, 2010 Google announced the completion of rolling out its Caffeine indexing technology. Caffeine gave Google the ability to index more of the Web at a faster rate than ever before. This larger, faster indexing technology invariably changed search results because all the newly discovered content was changing the search engine’s frame of reference for millions of queries.

On November 11, 2010 Matt Cutts said that Google might use as many as 50 variations for some of its 200+ ranking signals, a point that Danny Sullivan used to extrapolate a potential 10,000 “signals” Google might use in its algorithm.

On February 24, 2011 Google announced the release of its first Panda algorithm iteration into the index.

On March 2, 2011 Google asked Webmasters to share URLs of sites they believed should not have been downgraded by Panda. The discussion went on for many months and the thread is more than 1000 posts long. Google engineers occasionally confirmed throughout 2011 that they were still watching the discussion and collecting more information.The next day Wired published an interview with Amit Singhal and Matt Cutts (see below).

On May 6, 2011 Amit Singhal published 23 questions that drew much criticism from frustrated Web marketers. The angry mobs did not understand the context in which the questions should be used.

On June 21, 2011 Danny Sullivan suggested that Panda may be a ranking factor more than just a filter (a view that I and others had also come to hold by that time, but Danny was the first to suggest this publicly).

In mid-March 2013 Google announced that the Panda algorithm had been “incorporated into our indexing process”, meaning it was now essentially running on autopilot. Between February 24, 2011 and March 15, 2013 there were more than 20 confirmed and suspected “iterations” of the Panda algorithm that changed the search results for millions of queries.

Friday, August 10, 2012

Google Analytics Segmentations Using User Defined Tracking | Custom variable user defined variable tutorials | Web analytics Consultant Chennai

The Google Analytics’ user defined report allows analyst to compare visitors from segments that you have defined. I will go through several types of segmentations that you could possibly set. You can define these segments by calling a line of code/function in your web page. So every time a page with the code is requested, a custom value is captured and stored in the user defined variable.
The main approach to execute this is to simply apply a code like this:

As an example, one of the sites I work on has English and Japanese sections. Each section will have a value to identify its section, defined as “/viewed/english” or “/viewed/japanese”.
Once you have these segmentations in place, you’ll be able to see how users are behaving differently in each section.

Visitor Type Segmentation
This is a powerful segmentation to get an actionable insight to who is behaving differently. Let’s say you have a form entry that leads to a confirmation page. Assuming the form has a field with values “Engineer”, “Project Manager” and “Director”, and when the form is completed, one these values will be stored into user defined variable.

At certain point, you may learn that Directors are likely to complete the form and convert. This will tell you something about your visitor that you didn’t know.

Landing Page Segmentation

You may have custom landing pages to serve different campaigns or promotions. Identifying the landing pages through user defined report would be a powerful method to analyze effective landing pages or even its campaign effectiveness.
(Example 1) Say you have two different direct mails with different slogan or description. You can have two different friendly URLs set in each of the direct mail. Direct mail A would have a landing page A, and landing page B for direct mail B. When traffic and performance for landing page B performed better than landing page A, that could mean that the direct mail B’s strategy was more effective than version A.
(Example 2) You can have one campaign with a landing page, but a page can receive traffic from other various sources. Setting two different landing pages and segmenting it through user defined variables can show you which landing page is more effective while testing various traffic sources.

A/B Test
Above examples can speak as an example for A/B test. Another example to perform A/B test is testing different call to actions (CTA). Say you have two links; one with an image link and other as a simple text link.
You may distinguish these different CTAs (or possibly link type, position, etc.), and store it into user defined report to assess which criteria performed better.

Referrer Segmentation
Google analytics has a sophisticated campaign tracking method. However, you can choose to use user defined report to parse certain attribute within URLs and apply it to the report. One possible example of such application would be an existing links with identifier in the URL (not compliant to Google’s campaign tracking), where the links are located in two different sites.
Site with a link “″
Site with a link “″
Your landing page could parse these source codes and allocate proper value to a segment. Therefore you should be able to assess the referrers’ performance and its effectiveness.