GadgetZone

Apple Watch … its time to watch this gadget !

THE GOOD The Apple Watch is a beautifully constructed, compact smartwatch. It’s feature-packed, with solid fitness software, hundreds of apps, and the ability to send and receive calls via an iPhone. THE BAD Battery barely lasts a day and recharge time is slow; most models and configurations cost more than they should; requires an iPhone [...]

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GadgetZone

Samsung Galaxy S3 – the ultimate Smartphone

Samsung Galaxy S3 - Smartphone. Samsung is known for smartphones with trendy designs and user-friendly features, but this time they have spared to beat their own record. more...

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GadgetZone

RIM Tablet – Playbook

RIM introduces its own answer to the tablet PC market, an enterprise ready device with a 7-inch screen called Blackberry Playbook. Read More...

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GadgetZone

Dell Streak – World into 5-inch screen

Dell Streak - "We fit the whole world into a 5-inch screen". Dell saying this to be The perfectly-sized, go-anywhere entertainment device.

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GadgetZone

BlackBerry Torch 9800… the new TouchPhone

BlackBerry called the Torch 9800, with both a touchscreen and slide-out QWERTY keyboard, was launched by Research In Motion ...

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Tech Savvy

Big Data …. The Big Picture !

Posted on 09 July 2015

Big data analytics is the process of examining large data sets containing a variety of data types — i.e., big data — to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits.

big-data

 

The primary goal of big data analytics is to help companies make more informed business decisions by enabling data scientists, predictive modelers and other analytics professionals to analyze large volumes of transaction data, as well as other forms of data that may be untapped by conventional business intelligence (BI) programs. That could include Web server logs and Internet clickstream data, social media content and social network activity reports, text from customer emails and survey responses, mobile-phone call detail records and machine data captured by sensors connected to the Internet of Things. Some people exclusively associate big data with semi-structured and unstructured data of that sort, but consulting firms like Gartner Inc. and Forrester Research Inc. also consider transactions and other structured data to be valid components of big data analytics applications.

Big data can be analyzed with the software tools commonly used as part of advanced analytics disciplines such as predictive analytics, data mining, text analytics and statistical analysis. Mainstream BI software and data visualization tools can also play a role in the analysis process. But the semi-structured and unstructured data may not fit well in traditional data warehouses based on relational databases. Furthermore, data warehouses may not be able to handle the processing demands posed by sets of big data that need to be updated frequently or even continually — for example, real-time data on the performance of mobile applications or of oil and gas pipelines. As a result, many organizations looking to collect, process and analyze big data have turned to a newer class of technologies that includes Hadoop and related tools such as YARN, MapReduce, Spark, Hive and Pig as well as NoSQL databases. Those technologies form the core of an open source software framework that supports the processing of large and diverse data sets across clustered systems.

The Challenges of Big Data Analytics:

For most organizations, big data analysis is a challenge. Consider the sheer volume of data and the different formats of the data (both structured and unstructured data) that is collected across the entire organization and the many different ways different types of data can be combined, contrasted and analyzed to find patterns and other useful business information.
The first challenge is in breaking down data silos to access all data an organization stores in different places and often in different systems. A second big data challenge is in creating platforms that can pull in unstructured data as easily as structured data. This massive volume of data is typically so large that it’s difficult to process using traditional database and software methods.

For most organizations, big data analysis is a challenge. Consider the sheer volume of data and the different formats of the data (both structured and unstructured data) that is collected across the entire organization and the many different ways different types of data can be combined, contrasted and analyzed to find patterns and other useful business information.
The first challenge is in breaking down data silos to access all data an organization stores in different places and often in different systems. A second big data challenge is in creating platforms that can pull in unstructured data as easily as structured data. This massive volume of data is typically so large that it’s difficult to process using traditional database and software methods.

In some cases, Hadoop clusters and NoSQL systems are being used as landing pads and staging areas for data before it gets loaded into a data warehouse for analysis, often in a summarized form that is more conducive to relational structures. Increasingly though, big data vendors are pushing the concept of a Hadoop data lake that serves as the central repository for an organization’s incoming streams of raw data. In such architectures, subsets of the data can then be filtered for analysis in data warehouses and analytical databases, or it can be analyzed directly in Hadoop using batch query tools, stream processing software and SQL on Hadoop technologies that run interactive, ad hoc queries written in SQL.

Potential pitfalls that can trip up organizations on big data analytics initiatives include a lack of internal analytics skills and the high cost of hiring experienced analytics professionals. The amount of information that’s typically involved, and its variety, can also cause data management headaches, including data quality and consistency issues. In addition, integrating Hadoop systems and data warehouses can be a challenge, although various vendors now offer software connectors between Hadoop and relational databases, as well as other data integration tools with big data capabilities.

 



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Infotainment

Social Commerce

Posted on 14 May 2013

Social Commerce

.
Social commerce, sometimes abbreviated as “s-ecommerce,” is a term often used to describe new online retail models or marketing strategies that incorporate established social networks and/or peer-to-peer communication to drive sales. Or, as marketing consultant Heidi Cohen more succinctly defines it, it’s “social media meets shopping.”

But several well-known social commerce sites predate the popular rise of social networks by more than half a decade. eBay, a peer-to-peer selling platform founded in 1995, is one of them.

Today, social commerce denotes a wide range of shopping, recommending and selling behaviors. We’ve done our best to group them into seven categories below.

Seven Types of Social Commerce

1. Peer-to-peer sales platforms (eBay, Etsy, Amazon Marketplace): Community-based marketplaces, or bazaars, where individuals communicate and sell directly to other individuals.

2. Social network-driven sales (Facebook, Pinterest, Twitter): Sales driven by referrals from established social networks, or take place on the networks themselves (i.e., through a “shop” tab on Facebook).

3. Group buying (Groupon, LivingSocial). Products and services offered at a reduced rate if enough buyers agree to make the purchase.

4. Peer recommendations (Amazon, Yelp, JustBoughtIt): Sites that aggregate product or service reviews, recommend products based on others’ purchasing history (i.e. “Others who bought item x also bought item y,” as seen on Amazon), and/or reward individuals for sharing products and purchases with friends through social networks.

5. User-curated shopping (The Fancy, Lyst, Svpply): Shopping-focused sites where users create and share lists of products and services for others to shop from.

6. Participatory commerce (Threadless, Kickstarter, CutOnYourBias): Consumers become involved directly in the production process through voting, funding and collaboratively designing products.

7. Social shopping (Motilo, Fashism, GoTryItOn). Sites that attempt to replicate shopping offline with friends by including chat and forum features for exchanging advice and opinions.

The Future of Social Commerce

Social commerce is still in its infancy. None of the major social networks — Facebook, Twitter, Pinterest — have yet figured out how to bring transactions directly to their platforms, instead directing retailers to use earned and paid media to bring customers to their online storefronts.

Online retailers, too, are continually experimenting with new models and marketing methods to allow for greater peer-to-peer and group-based interactions, aware that recommendations from friends (and to a lesser degree, strangers) can play a powerful role in shopping. According to Gartner, 74% of consumers rely on social networks to guide their purchases. We will wait and watch to what extent the Social Commerce gains momentum in this digital century.

credits: mashable



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Tech Savvy

Samsung Galaxy S 4 – compared with other Smartphones

Posted on 15 March 2013

Samsung Galaxy S 4 – compared with other Smartphones

 

Samsung Galaxy S 4 vs. iPhone 5 vs. HTC One vs. Nokia Lumia 920

 

Samsung Galaxy S 4
Galaxy S 4
iPhone 5
iPhone 5
HTC One
HTC One
Nokia Lumia 920
Lumia 920
Screen Size 5 inches 4 inches 4.65 inches 4.5 inches
Resolution 1920×1080 1136×640 1920×1080 1280×768
Screen Type/DPI Super AMOLED, 441 ppi LCD, 326 ppi S-LCD 3, 468 ppi AMOLED, 332 ppi
Weight 4.6 oz 3.9 oz 5 oz 6.5 oz
Chipset Quad-core 1.9GHz Snapdragon 600 in the U.S. (8-core 1.8GHz Exnyos 5 Octa elsewhere) Dual-core Apple A6 Quad-core 1.7GHz Snapdragon 600 Dual-core 1.5 GHz Snapdragon S4
Storage 16GB, 32GB or 64GB +microSD slot 16GB, 32GB or 64GB, no card slot 32GB or 64GB, no card slot 32GB, no card slot
Connectors microUSB Apple Lightning microUSB microUSB
Operating System Android 4.2.2 (Jelly Bean) iOS 6 Android 4.1 (Jelly Bean) Microsoft Windows Phone 8
Battery (in milliamperes/hour) 2,600 mAh 1,434 mAh 2,300 mAh 2,000 mAh
Camera 13MP autofocus, LED flash & zero shutter lag 8MP, autofocus, LED flash 4MP (“ultrapixels”) 8MP, autofocus, optical image stabilization, dual-LED flash
Networking Wi-Fi, 2G, 3G, 4G LTE Wi-Fi, 2G, 3G, 4G LTE Wi-Fi, 2G, 3G, 4G LTE Wi-Fi, 2G, 3G, 4G LTE
U.S. Price (with 2-year contract) Not announced $199 for 16GB, $299 for 32GB, $399 for 64GB $199.99 $99.99

Source: mashable



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Tech Savvy

History and Evolution of ‘Email’

Posted on 29 November 2012

History and Evolution of ‘Email’

Today we probably cannot even imagine our lives without ‘Emails’. Slowly it became a part of our daily life – be it personal or work/business related. Lets see the evolution of this ‘Email’ :

1971: U.S. programmer Raymond Tomlinson allegedly sent “QWERTYUIOP” as the first network email, and he was the first to connect his computer to his mailbox by using an “@” symbol.

1977: Tomlinson’s emailing method worked for networked computers using the same software, but many people began using the Department of Defense’s Advanced Research Projects Agency Network (ARPA) to connect outside networks.

1981: The American Standard Code for Information Interchange adopted a process of letters, punctuation and symbols to digitally store information.

1985: Government and military employees, students and academic professionals were common email users in the mid-1980s.

1991: ISPs allow widespread Internet access, but there were limited options for use until Tim Berners-Lee created the World Wide Web in 1991.

1991: Astronauts Shannon Lucid and James C. Adamson sent the first email from space on a Macintosh Portable: “Hello Earth! Greetings from the STS-43 Crew. This is the first AppleLink from space. Having a GREAT time, wish you were here,…send cryo and RCS! Hasta la vista, baby,…we’ll be back!”

1993: IDM and BellSouth marketed the first PDA-functioning 20-ounce cellphone, which sold for $900 and served as a phone, calculator, fax, email device and pager.

1997: Microsoft purchased Hotmail for approximately $400 million.

1998: The romantic comedy You’ve Got Mail, starring Meg Ryan and Tom Hanks, hit theaters (and the website’s still live).

1998: “Spam” was added to the Oxford English Dictionary after its growth in the mid-1990s — not to be confused with the 3.8 cans of Spam consumed every second in the U.S.

2003: The RIM 850 and 857 original BlackBerry smartphones were released, revolutionizing the mobile platform by concentrating on email.

2004: President George W. Bush signed the Controlling the Assault of Non-Solicited Pornography and Marketing Act of 2003 into law, which gained criticism for its lack of action against spammers.

2008: President Barack Obama became the first president to use mobile email and admit his addiction to his BlackBerry, and despite security concerns, he currently uses it in office.

2011: A study finds the worst email passwords are password and 123456. Others worthy of note: QWERTY, monkey and letmein. The password 123456 was also found to be the most common password during a 2012 email hack.

2012: There are more than 3 billion email accounts across the globe, and approximately 144 billion emails are sent per day. Roughly 78% of them are spam.

Checkout the Interesting facts on Email:

 

Sources: Macworld.com, CNET.com, PCworld.com, Tidbits.com, SendMail.com, Time.com, LivingInternet.com, AntaraNews.com, TheMarketingBit.com, History.com.

 



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Tech Savvy

Internet evolution on 2012

Posted on 24 August 2012

Evolution of Internet from 2002 to 2012 ….

The Infographic shows nicely:

credit: mashable



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Tech Savvy

Google Fiber

Posted on 28 July 2012

A New Technology Concept … from Google !

Google Fiber is a project to build an experimental broadband internet network infrastructure using fiber-optic communication in Kansas City, Kansas, and Kansas City, Missouri, following a selection process.

Over 1,100 communities applied to be the first recipient of the technology. On March 30, 2011, Google announced that Kansas City, Kansas will be the first community where the experimental network would be deployed.On May 17, 2011, Google announced that the service would be expanded to include the Kansas City, Missouri metropolitan area.

On July 24, 2012, Google announced that Fiber would become available that day. Neither pricing nor initial availability were mentioned. Google Fiber will focus to become an alternative to Verizon’s FiOS and AT&T’s U-verse services among other major cable companies.

On July 26, 2012, Google announced that it would roll out a companion fiber optic television service in the Kansas City area that September called Google Fiber TV, which will be offered as a conventional pay television service and will also stream live program content on iPad and Android tablet computers. Neighborhoods that initially receive both the TV and internet services are to be selected through demand from Kansas City area residents.The initial channel lineup for the service does not currently include cable television networks owned by Time Warner, The Walt Disney Company, AMC Networks, and News Corporation, which may eventually be added pending carriage agreements with those companies.



 



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