Retail was one of the earliest industries to utilize big data, as margins are tight and competition is fierce. Retail collects vast amounts of data from points of sale, inventory, marketing, and other sources to drive decisions about products, pricing, placement, and promotion. Advancements in technology have allowed retail to adapt and demand new solutions. The future will see even more real-time data from stores and supply chains, as well as more advanced analytics like path analysis and multivariate testing. These trends will require new data architectures and analytics tools to handle the immense and complex volume of information. Financial constraints are also decreasing, allowing more companies to benefit from big data analytics.
Check out how big data is proving invaluable to finance. Here is the top 10 big data trends in finance. Big data place a vital role in analysing the feeds, Predictive models, forecasts & assess trading impacts
Using Big Data in Finance by Jonah EnglerJonah Engler
How can you utilize Big Data in the Financial Industry? To leverage Big Data - entrepreneur and finance expert Jonah Engler, has put together this presentation to help the slideshare community understand the value - and HOW TO - use big data in the financial campaigns.
Jonah Engler is a financial expert and stock broker based in NYC. Leveraging his experience in finance, Engler has gone on to have success in the franchise, coffee, startup industries and more. To connect with Jonah - checkout his profile on LinkedIn: https://www.linkedin.com/in/jonahengler
How to Ruin your Business with Data Science & Machine Learning by Ingo MierswaData Con LA
Abstract:- Everyone talks about how machine learning will transform business forever and generate massive outcomes. However, it's surprisingly simple to draw completely wrong conclusions from statistical models, and correlation does not imply causation is just the tip of the iceberg. The trend of the democratization of data science further increases the risk for applying models in a wrong way. This session will discuss. How highly-correlated features can overshadow the patterns your machine learning model is supposed to find this leads to models which will perform worse in production than during model building. How incorrect cross-validation lead to over-optimistic estimations of your model accuracy, especially we will discuss the impact of data preprocessing on the accuracy of machine learning models. How feature engineering can lift simple models like linear regression to the accuracy of deep learning but comes with the advantages of understandability & robustness.
Check out how big data is proving invaluable to finance. Here is the top 10 big data trends in finance. Big data place a vital role in analysing the feeds, Predictive models, forecasts & assess trading impacts
Using Big Data in Finance by Jonah EnglerJonah Engler
How can you utilize Big Data in the Financial Industry? To leverage Big Data - entrepreneur and finance expert Jonah Engler, has put together this presentation to help the slideshare community understand the value - and HOW TO - use big data in the financial campaigns.
Jonah Engler is a financial expert and stock broker based in NYC. Leveraging his experience in finance, Engler has gone on to have success in the franchise, coffee, startup industries and more. To connect with Jonah - checkout his profile on LinkedIn: https://www.linkedin.com/in/jonahengler
How to Ruin your Business with Data Science & Machine Learning by Ingo MierswaData Con LA
Abstract:- Everyone talks about how machine learning will transform business forever and generate massive outcomes. However, it's surprisingly simple to draw completely wrong conclusions from statistical models, and correlation does not imply causation is just the tip of the iceberg. The trend of the democratization of data science further increases the risk for applying models in a wrong way. This session will discuss. How highly-correlated features can overshadow the patterns your machine learning model is supposed to find this leads to models which will perform worse in production than during model building. How incorrect cross-validation lead to over-optimistic estimations of your model accuracy, especially we will discuss the impact of data preprocessing on the accuracy of machine learning models. How feature engineering can lift simple models like linear regression to the accuracy of deep learning but comes with the advantages of understandability & robustness.
Big data analytics applies to data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process data in a timely fashion.
Bigdata analysis in supply chain managmentKushal Shah
big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.
supply chain industry need this type of data to survive in every situations.
Big Data is defined as a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.
A brief overview of the use of big data analytics in retail banking. This basic material is an introduction to the video training series: Retail Banking Analytics, available at briastrategy.com.
BIG Data & Hadoop Applications in FinanceSkillspeed
Explore the applications of BIG Data & Hadoop in Finance via Skillspeed.
BIG Data & Hadoop in Finance is a key differentiator, especially in terms of generating greater investment insights. They are used by companies & professionals for risk assessment, fraud detection & forecasting trends in financial markets.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
TechConnex Big Data Series - Big Data in BankingAndre Langevin
TechConnex is an industry forum for Canadian IT executives. This presentation from the fall of 2015 provides a survey of Hadoop adoption in the Canadian banking industry. Most adoption is driven by BCBS-239 implementation projects. The talk provides a broader risk systems perspective on Hadoop and discusses challenges and opportunities around the technology.
Welcome to the Age of Big Data in Banking Andy Hirst
Big Data in banking presentation from Sibos Dubai 2013 . What are use cases driving deployments in Banking ? See the use cases SAP is involved In banking in 2013
A framework that discusses the various elements of Data Monetization framework that could be leveraged by organizations to improve their Information Management Journey.
Big data & analytics for banking new york lars hambergLars Hamberg
BIG DATA & ANALYTICS FOR BANKING SUMMIT, New York, 1 Dec 2015.
Keynote address: "How Predictive Analytics will change the Financial Services Sector”
Speaker : Lars Hamberg
http://www.specialistspeakers.com/?p=8367
Overview & Outlook: Why Big Data will over-deliver on its hype and transform Financial Services; Use cases with Advanced Analytics and Big Data Analytics in Financial Services, in Production & Distribution of banking products; new opportunities for incumbents in tomorrow’s ecosystem; big data, bigdata, analytics, smart data, data analytics, digitization, digitalization, predictive analytics, sentiment analysis, financial services, banking, asset management, distribution, retail, trading, technology, innovation, fintech, wealth, asset management, investment industry, robo advisory, social investing, behavior, profiling, client segmentation, alias matching, semantic memory models, unstructured data, machine learning, pattern recognition
This whitepaper is geared to help
bank marketing professionals
understand the scope of marketing
analytics and also on how it can
contribute value to the various
factions of a bank’s marketing
activities.
KEY CHALLENGES FOR MONETIZING BIG DATA POWERED AI: AN OVERVIEWTyrone Systems
YOU’RE NOT THE ONLY ONE FACING THIS PROBLEM
according to recent articles in the Harvard Business Review and McKinsey. But don’t blame big data for that. It’s all your fault
The data economy is growing globally with more and more organization realizing their core data asset's value. Start here to understand how your company can start your data monetization strategy.
The Impact of Big Data On Marketing Analytics (UpStream Software)Revolution Analytics
Presenter: Tess Nesbitt, Senior Statistician, UpStream Software
Presentation Date: February 26, 2013
This presentation describes how Hadoop and Revolution R Enterprise provide the predictive analytics models for UpStream's revenue attribution application.
Data Driven Disruption - Why Marketing and Advertising in WA lags - ADMA WA 2...Coert Du Plessis (杜康)
WA is in a state of rapid transformation with the changes in Energy, Resources and support industries. At ADMA WA's 2015 annual conference, we explored why disruptive data activity in Marketing and Advertising is lagging the East Coast and Global stage
Big data analytics applies to data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process data in a timely fashion.
Bigdata analysis in supply chain managmentKushal Shah
big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.
supply chain industry need this type of data to survive in every situations.
Big Data is defined as a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.
A brief overview of the use of big data analytics in retail banking. This basic material is an introduction to the video training series: Retail Banking Analytics, available at briastrategy.com.
BIG Data & Hadoop Applications in FinanceSkillspeed
Explore the applications of BIG Data & Hadoop in Finance via Skillspeed.
BIG Data & Hadoop in Finance is a key differentiator, especially in terms of generating greater investment insights. They are used by companies & professionals for risk assessment, fraud detection & forecasting trends in financial markets.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
TechConnex Big Data Series - Big Data in BankingAndre Langevin
TechConnex is an industry forum for Canadian IT executives. This presentation from the fall of 2015 provides a survey of Hadoop adoption in the Canadian banking industry. Most adoption is driven by BCBS-239 implementation projects. The talk provides a broader risk systems perspective on Hadoop and discusses challenges and opportunities around the technology.
Welcome to the Age of Big Data in Banking Andy Hirst
Big Data in banking presentation from Sibos Dubai 2013 . What are use cases driving deployments in Banking ? See the use cases SAP is involved In banking in 2013
A framework that discusses the various elements of Data Monetization framework that could be leveraged by organizations to improve their Information Management Journey.
Big data & analytics for banking new york lars hambergLars Hamberg
BIG DATA & ANALYTICS FOR BANKING SUMMIT, New York, 1 Dec 2015.
Keynote address: "How Predictive Analytics will change the Financial Services Sector”
Speaker : Lars Hamberg
http://www.specialistspeakers.com/?p=8367
Overview & Outlook: Why Big Data will over-deliver on its hype and transform Financial Services; Use cases with Advanced Analytics and Big Data Analytics in Financial Services, in Production & Distribution of banking products; new opportunities for incumbents in tomorrow’s ecosystem; big data, bigdata, analytics, smart data, data analytics, digitization, digitalization, predictive analytics, sentiment analysis, financial services, banking, asset management, distribution, retail, trading, technology, innovation, fintech, wealth, asset management, investment industry, robo advisory, social investing, behavior, profiling, client segmentation, alias matching, semantic memory models, unstructured data, machine learning, pattern recognition
This whitepaper is geared to help
bank marketing professionals
understand the scope of marketing
analytics and also on how it can
contribute value to the various
factions of a bank’s marketing
activities.
KEY CHALLENGES FOR MONETIZING BIG DATA POWERED AI: AN OVERVIEWTyrone Systems
YOU’RE NOT THE ONLY ONE FACING THIS PROBLEM
according to recent articles in the Harvard Business Review and McKinsey. But don’t blame big data for that. It’s all your fault
The data economy is growing globally with more and more organization realizing their core data asset's value. Start here to understand how your company can start your data monetization strategy.
The Impact of Big Data On Marketing Analytics (UpStream Software)Revolution Analytics
Presenter: Tess Nesbitt, Senior Statistician, UpStream Software
Presentation Date: February 26, 2013
This presentation describes how Hadoop and Revolution R Enterprise provide the predictive analytics models for UpStream's revenue attribution application.
Data Driven Disruption - Why Marketing and Advertising in WA lags - ADMA WA 2...Coert Du Plessis (杜康)
WA is in a state of rapid transformation with the changes in Energy, Resources and support industries. At ADMA WA's 2015 annual conference, we explored why disruptive data activity in Marketing and Advertising is lagging the East Coast and Global stage
FirstPartner Data Driven Marketing Market Map 2014FirstPartner
Download a free copy of the map at http://www.firstpartner.net/downloads
The 2014 FirstPartner Data-Driven Marketing Market Map provides an overview of the processes and key players behind the use of consumer data for marketing purposes. The map highlights important trends in the collection and analysis of consumer data and describes how companies are using data to drive marketing initiatives and increase ROI.
The map includes a visual overview of approaches to data collection and analysis, such as by retailers, the data processing ecosystem and the digital advertising ecosystem. It also gives a brief insight into the technologies enabling data processing and discusses the main concerns around the debate on data protection and privacy.
FirstPartner is an independent research and proposition development company specialising in areas including Payments, Mobile Advertising, Mobile Value Added Services and M2M. For more information click on the “Get in touch” button or visit www.firstpartner.net to download a free copy of the map.
Big Data in Data-driven innovation: applications, prospects and limitations ...e-Bi Lab
Ioannis Kopanakis, Konstantinos Vassakis & George Mastorakis. "Big Data in Data-driven innovation: Applications, Prospects and Limitations in Marketing".
Presentation at 4th International Conference on Contemporary Marketing Issues, 22-24 June 2016, Heraklion, Greece.
Presentation template: www.PresentationLoad.com
Content Marketing Is Growing Up, Are You Growing Up With It?Ideas Collide Inc
Matthew Clyde from Ideas Collide explains how brands can expand and evolve their content marketing strategies and campaigns to be more effective in 2016 and beyond.
Oracle approach and solution for the retail industry presented at Retail Summit 2009 conference at Prague on February 4th 2009 by Paul DicksonVice President Retail, Oracle
I was recently shopping at a medium sized retailer and couldn't help but observe how far behind they were in their consumer marketing programs. It got me thinking about the challenge facing many mid-tier retailers. Could Big Data be part of the answer? Maybe. Data itself is no silver bullet. However, if data insights are acted upon in a customer-centric way, it could drive greater retail marketing ROI..
Marketing Mix Models In a Changing EnvironmentAquent
Marketing Mix Models have been used successfully for years at consumer package goods (CPG) companies to increase their marketing effectiveness and efficiency. The four Ps (Product, Placement, Price, and Promotion) were as far as the models needed to go. Broad–based media was and is very expensive, which kept competition to a minimum. However, the marketing environment has changed in many ways and must be considered when looking to these models to improve marketing performance.
Companies that want to turn excellent customer experience into growth need to master Customer Journeys. Customer Journeys (the set of interactions a customer has with a brand to complete a task) and less moments of truth are what matter for a customer. Companies that master not only see an improvement in customer experience, loyalty, and operational productivity; they also see above-market growth.
What is the impact of Big Data on Analytics from a Data Science perspective.
Presented at the Big Data and Analytics Summit 2014, Nasscom by Mamatha Upadhyaya.
Transforming Data into Insights, Decisions, and Actions ศาสตร์ของการใช้ตัวเลขและข้อมูล ใน Business Aspect เพื่อขับเคลื่อนองค์กรและกลยุทธ์ทางการตลาด with Case Studies
In this white paper, we’ll spread the light on such issues as:
- What big data is
- How data science creates a real value in retail
- 5 big data use-cases revealing how retail companies can turn their customers’ data in action
This talk is an introduction to Data Science. It explains Data Science from two perspectives - as a profession and as a descipline. While covering the benefits of Data Science for business, It explaints how to get started for embracing data science in business.
Why Everything You Know About bigdata Is A LieSunil Ranka
As a big data technologist, you can bet that you have heard it all: every crazy claim, myth, and outright lie about what big data is and what it isn't that you can imagine, and probably a few that you can't.If your company has a big data initiative or is considering one, you should be aware of these false statements and the reasons why they are wrong.
Business is running ever faster—generating, collecting and using increas-ing volumes of data about every aspect of the interactions between sup-pliers, manufacturers, retailers and customers. Within these mountains of data are seams of gold—patterns of behavior that can be interpreted, classified and analyzed to allow predictions of real value. Which treat-ment is likely to be most effective for this patient? What can we offer that this particular customer is more likely to buy? Can we identify if that transaction is fraudulent before the sale is closed?
In this presentation, Paul Ballew, D&B's Chief Data and Analytics Officer, explains the three levels of insight needed to gain an informed perspective for smarter decisions involving big data.
How to predict the future of shopping - Ulrich Kerzel @ PAPIs ConnectPAPIs.io
Shopping, or as the people on the other side of the counter call it, retail has become the number one breeding ground for predictive applications in the enterprise. What started as simple recommendation engines has evolved into a complex and powerful ecosystem of predictive applications that affect core processes such as pricing, replenishment and staff planning. In this talk, Ulrich Kerzel will share impact and experiences from building and operating predictive applications for large retailers, and explain why the future of retail is as much a science as an art.
Dr. Ulrich Kerzel is a Senior data scientists at Blue Yonder and renowned scientist with research experience at the University of Cambridge and CERN. Ulrich Kerzel earned his PhD under Professor Dr Feindt at the US Fermi National Laboratory and at that time made a considerable contribution to core technology of NeuroBayes. After his PhD, he went to the University of Cambridge, were he was a Senior Research Fellow at Magdelene College. His research work focused on complex statistical analyses to understand the origin of matter and antimatter using data from the LHCb experiment at the Large Hadron Collider at CERN, the world’s biggest research institute for particle physics. He continued this work as a Research Fellow at CERN before he came to Blue Yonder as a senior data scientist.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
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We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
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We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Retail lessons learned from the first data driven business and future directions presentation 1
1. Retail: Lessons Learned
from the Original Data-
Driven Business and
Future Directions
Presenters:
Marilyn Craig, Senior Director, WW Sales &
Marketing Planning and Analysis, Logitech
Terence Craig, CEO/CTO, PatternBuilders
2. Before We Dive In… A Legal Disclaimer
The views and opinions expressed by Marilyn
Craig in this presentation are hers and do not
necessarily reflect the opinion or any
endorsement from her employer, Logitech.
PatternBuilders is stuck with Terence’s opinion,
whether they like it or not.
Examples of analysis performed within this
presentation are only examples. No actual data
was harmed in making this presentation.
3. Retail—The First Industry to Surf the Big Data Tsunami
Before Big Data was really big, retail data was the “big” measurement standard.
When you factor out
science, government, and
social media, it still is.
t
4. Why was Retail the First to Catch the Big Data Wave?
It’s all about the margins—every penny counts
It’s all about the competition—more market share,
more customers, more sales
It’s all about efficiencies—bottom line improvements
5. Retail is Not Just a Big DataRetail is Not Just a Big Data
Surfer, But aSurfer, But a Technology DriverTechnology Driver
7. What We Now Consider Mainstream, has Retail Roots
RFID VPNs
In-
Transit
Tracking
Real-Time
Logistics
Supply
Chain
Management
Environmental
Sensors
8. Retail’s Gold Standard—No One Does It Better (Yet)
Largest retail company in the world:
Fortune 1 out of 500
Largest sales data warehouse:
RetailLink, a $4 billion project (1991)
One of the largest “civilian” data warehouse in
the world: 2004: 460 terabytes, Internet half as
large
Defines data science:
What do hurricanes, strawberry Pop-Tarts, and beer
have in common?
9. What Keeps Retail Operating on the Technology Edge?
It’s about the 4 P’s creating all
that data and all that data
driving decisions about the 4
P’s.
10. About All That Data…
3 years of historical data
for comparison
10 x 750 x 50 x 52 x 3 =
58,500,000 data points
4 regions to segregate the data
10 x 750 x 50 x 52 x 3 x 7 x 4 =
1,638,000,000 data points
50 states to segregate the data
10 x 750 x 50 x 52 x 3 x 7 x 4 x 50 =
81,900,000,000 data points
7 types of data to monitor (POS,
Inventory, Marketing, Syndicated, etc)
10 x 750 x 50 x 52 x 3 x 7 = 409,500,000
data points
8 categories to aggregate the data
10 x 750 x 50 x 52 x 3 x 7 x 4 x 50 x 8 =
655,200,000,000 data points
10 Retailers
to monitor
10 data points
750 Stores per
retailer to monitor
10 x 750 = 7500
data points
50 products per
store to monitor
10 x 750 x 50 =
375,000 data points
52 weeks of data per
year for trend analysis
10 x 750 x 50 x 52 =
19,500,000 data points
Now, Consider this:
655 Billion+ data points involved with
managing the retail sales channel
12. The Future: Look Out!
Cheap, big analytics is going to
change the world.
13. It’s a Brave New World…
The old rule: new shelf spaces = more sales
The new rule: it’s all about analytic-driven efficiencies
The slow down in new storefronts means growth (and
profitability) will come from efficiencies.
14. There’s More Data From the Store…
Traditional retail dataTraditional retail data
is moving towards real-is moving towards real-
time.time.
15. There’s More Data from the Supply Chain…
Humidity, Vibration,
Temperature,
Ever shortening lead times,
niche targeting, and regulation
drive this. Retailing and
supplying is a team sport.
Are analyzed constantly for
savings and regulatory
compliance.
Both are driving
standardization to an
amazing level.
16. What’s Coming: Big Data = Big Analytics
Path analysis on the store floor.
More aggressive and more complex A/B tests… and lots
and lots of A/B tests.
Deep and constantly updated multivariate analysis
including personal and social media profiles, geo-location
and demographic
All of this makes real-time, targeted ads, discounts, and
offers delivered on the device of choice at the right place
a very profitable reality.
Welcome to
The Minority
Report
18. And This All has an Impact on Your Infrastructure
Sheer volume of data and its complexity is going to require new
data and analytics architectures.
There is a need for both high performance batch (Hadoop) &
streaming/CEP (PatternBuilders, StreamInsight, etc.).
NoSQL approaches are particularly well suited for this problem
domain.
While the public cloud is great, mega-retailer paranoia will
make adoption difficult.
19. The Good News: Financial Constraints are Disappearing
With the advent of:
OSS—who buys database licenses any more?
Moore’s Law
Kryder's Law—10 TBs costs what!
Offshoring—lot of great mathematicians out in the world.
Crowdsourcing —if you have Facebook, Foursquare, POS data and Radian 6, do
you really need Nielsen and NPD?
Bottom Line: You no longer need to make a Wal-MartBottom Line: You no longer need to make a Wal-Mart
size investment to analyze your data.size investment to analyze your data.
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share alike – If you alter, transform, or build upon this work, you may distribute the resulting work only under the same or similar license to this one.
Above each of the sections, want to put the technology enablers.
Free Images:
Environmental Sensors: Anemometer Insides, Creative Commons Attribution-ShareAlike: photo taken by Barney Livingston, (barnoid), January 18, 2009, using Canon EOS 40D.
VPNs: Creative Commons Attribution-ShareAlike: credit to Digital Inspirations
Real-Time Logistics: Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled GNU Free Documentation License. http://commons.wikimedia.org/wiki/File:Geolocation.png
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