Tag: online

Google unleashes Fusion Tables

I just discovered Fusion Tables. There is life beyond the amazing Jeff’s Amazon Ec2/s3 after all!

Check out http://www.google.com/fusiontables/public/tour/index.html

Gather, visualize and share data online

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Create one now

  • Visualize and publish your data as maps, timelines and charts
  • Host your data tables online
  • Combine data from multiple people

data table turns into map

Google Fusion Tables is a modern data management and publishing web application that makes it easy
to host, manage, collaborate on, visualize, and publish data tables online.

What can I do with Google Fusion Tables?

Import your own data
Upload data tables from spreadsheets or CSV files, even KML. Developers can use the Fusion Tables API to insert, update, delete and query data programmatically. You can export your data as CSV or KML too.

Visualize it instantly
See the data on a map or as a chart immediately. Use filters for more selective visualizations.

Publish your visualization on other web properties
Now that you’ve got that nice map or chart of your data, you can embed it in a web page or blog post. Or send a link by email or IM. It will always display the latest data values from your table and helps you communicate your story more easily.

Look at the Fusion Tables Example Gallery

at https://sites.google.com/site/fusiontablestalks/stories

If you are worried about data.gov closing down, heres a snapshot of Fusion Table Public datasets.


 

Save the Data

Breakdown of political party representation in...
Image via Wikipedia

I just read an online cause here-

http://sunlightfoundation.com/savethedata/

Some of the most important technology programs that keep Washington accountable are in danger of being eliminated. Data.gov, USASpending.gov, the IT Dashboard and other federal data transparency and government accountability programs are facing a massive budget cut, despite only being a tiny fraction of the national budget. Help save the data and make sure that Congress doesn’t leave the American people in the dark.

I wonder why the federal government/ non profit agencies can help create a SPARQL database, and in days of cloud computing, why a tech major cannot donate storage space to it, after all despite US corporate tax rate being high, US technological companies do end up paying a lower rate thanks to tax breaks/routing overseas revenue.

In the new age data is power, and the US has led in its mission to use technology to further its own values even especially in Middle East. The datasets should be made public and transitioned to the private sector/academia for research and re designing for data augmentation with out straining the massive deficit /borrowing/ fighting 3 wars. Of particular interest would be datasets of campaign finances  and donors especially given large number of retail/small donors/internet marketing in elections as it will also help serve as an example of democracy and change. Even countries like China can create a corruption/expense efficiency tracking internal dashboard with restricted rights to help with rural and urban governance.

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Protected: Using SAS and C/C++ together

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Heritage Health Prize- Data Mining Contest for 3mill USD

An animation of the quicksort algorithm sortin...
Image via Wikipedia

If Netflix was about 1 mill USD to better online video choices, here is a chance to earn serious money, write great code, and save lives!

From http://www.heritagehealthprize.com/

Heritage Health Prize
Launching April 4

Laptop

More than 71 Million individuals in the United States are admitted to
hospitals each year, according to the latest survey from the American
Hospital Association. Studies have concluded that in 2006 well over
$30 billion was spent on unnecessary hospital admissions. Each of
these unnecessary admissions took away one hospital bed from someone
else who needed it more.

Prize Goal & Participation

The goal of the prize is to develop a predictive algorithm that can identify patients who will be admitted to the hospital within the next year, using historical claims data.

Official registration will open in 2011, after the launch of the prize. At that time, pre-registered teams will be notified to officially register for the competition. Teams must consent to be bound by final competition rules.

Registered teams will develop and test their algorithms. The winning algorithm will be able to predict patients at risk for an unplanned hospital admission with a high rate of accuracy. The first team to reach the accuracy threshold will have their algorithms confirmed by a judging panel. If confirmed, a winner will be declared.

The competition is expected to run for approximately two years. Registration will be open throughout the competition.

Data Sets

Registered teams will be granted access to two separate datasets of de-identified patient claims data for developing and testing algorithms: a training dataset and a quiz/test dataset. The datasets will be comprised of de-identified patient data. The datasets will include:

  • Outpatient encounter data
  • Hospitalization encounter data
  • Medication dispensing claims data, including medications
  • Outpatient laboratory data, including test outcome values

The data for each de-identified patient will be organized into two sections: “Historical Data” and “Admission Data.” Historical Data will represent three years of past claims data. This section of the dataset will be used to predict if that patient is going to be admitted during the Admission Data period. Admission Data represents previous claims data and will contain whether or not a hospital admission occurred for that patient; it will be a binary flag.

DataThe training dataset includes several thousand anonymized patients and will be made available, securely and in full, to any registered team for the purpose of developing effective screening algorithms.

The quiz/test dataset is a smaller set of anonymized patients. Teams will only receive the Historical Data section of these datasets and the two datasets will be mixed together so that teams will not be aware of which de-identified patients are in which set. Teams will make predictions based on these data sets and submit their predictions to HPN through the official Heritage Health Prize web site. HPN will use the Quiz Dataset for the initial assessment of the Team’s algorithms. HPN will evaluate and report back scores to the teams through the prize website’s leader board.

Scores from the final Test Dataset will not be made available to teams until the accuracy thresholds are passed. The test dataset will be used in the final judging and results will be kept hidden. These scores are used to preserve the integrity of scoring and to help validate the predictive algorithms.

Teams can begin developing and testing their algorithms as soon as they are registered and ready. Teams will log onto the official Heritage Health Prize website and submit their predictions online. Comparisons will be run automatically and team accuracy scores will be posted on the leader board. This score will be only on a portion of the predictions submitted (the Quiz Dataset), the additional results will be kept back (the Test Dataset).

Form

Once a team successfully scores above the accuracy thresholds on the online testing (quiz dataset), final judging will occur. There will be three parts to this judging. First, the judges will confirm that the potential winning team’s algorithm accurately predicts patient admissions in the Test Dataset (again, above the thresholds for accuracy).

Next, the judging panel will confirm that the algorithm does not identify patients and use external data sources to derive its predictions. Lastly, the panel will confirm that the team’s algorithm is authentic and derives its predictive power from the datasets, not from hand-coding results to improve scores. If the algorithm meets these three criteria, it will be declared the winner.

Failure to meet any one of these three parts will disqualify the team and the contest will continue. The judges reserve the right to award second and third place prizes if deemed applicable.

 

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IBM and Revolution team to create new in-database R

From the Press Release at http://www.revolutionanalytics.com/news-events/news-room/2011/revolution-analytics-netezza-partnership.php

Under the terms of the agreement, the companies will work together to create a version of Revolution’s software that takes advantage of IBM Netezza’s i-class technology so that Revolution R Enterprise can run in-database in an optimal fashion.

About IBM

For information about IBM Netezza, please visit: http://www.netezza.com.
For Information on IBM Information Management, please visit: http://www.ibm.com/software/data/information-on-demand/
For information on IBM Business Analytics, please visit the online press kit: http://www.ibm.com/press/us/en/presskit/27163.wss
Follow IBM and Analytics on Twitter: http://twitter.com/ibmbizanalytics
Follow IBM analytics on Tumblr: http://smarterplanet.tumblr.com/tagged/new_intelligence
IBM YouTube Analytics Channel: http://www.youtube.com/user/ibmbusinessanalytics
For information on IBM Smarter Systems: http://www-03.ibm.com/systems/smarter/

About Revolution Analytics

Revolution Analytics is the leading commercial provider of software and services based on the open source R project for statistical computing.  Led by predictive analytics pioneer Norman Nie, the company brings high performance, productivity and enterprise readiness to R, the most powerful statistics language in the world. The company’s flagship Revolution R product is designed to meet the production needs of large organizations in industries such as finance, life sciences, retail, manufacturing and media.  Used by over 2 million analysts in academia and at cutting-edge companies such as Google, Bank of America and Acxiom, R has emerged as the standard of innovation in statistical analysis. Revolution Analytics is committed to fostering the continued growth of the R community through sponsorship of the Inside-R.org community site, funding worldwide R user groups and offers free licenses of Revolution R Enterprise to everyone in academia.


Netezza, an IBM Company, is the global leader in data warehouse, analytic and monitoring appliances that dramatically simplify high-performance analytics across an extended enterprise. IBM Netezza’s technology enables organizations to process enormous amounts of captured data at exceptional speed, providing a significant competitive and operational advantage in today’s data-intensive industries, including digital media, energy, financial services, government, health and life sciences, retail and telecommunications.

The IBM Netezza TwinFin® appliance is built specifically to analyze petabytes of detailed data significantly faster than existing data warehouse options, and at a much lower total cost of ownership. It stores, filters and processes terabytes of records within a single unit, analyzing only the relevant information for each query.

Using Revolution R Enterprise & Netezza Together

Revolution Analytics and IBM Netezza have announced a partnership to integrate Revolution R Enterprise and the IBM Netezza TwinFin  Data Warehouse Appliance. For the first time, customers seeking to run high performance and full-scale predictive analytics from within a data warehouse platform will be able to directly leverage the power of the open source R statistics language. The companies are working together to create a version of Revolution’s software that takes advantage of IBM Netezza’s i-class technology so that Revolution R Enterprise can run in-database in an optimal fashion.

This partnership integrates Revolution R Enterprise with IBM Netezza’s high performance data warehouse and advanced analytics platform to help organizations combat the challenges that arise as complexity and the scale of data grow.  By moving the analytics processing next to the data, this integration will minimize data movement – a significant bottleneck, especially when dealing with “Big Data”.  It will deliver high performance on large scale data, while leveraging the latest innovations in analytics.

With Revolution R Enterprise for IBM Netezza, advanced R computations are available for rapid analysis of hundreds of terabyte-class data volumes — and can deliver 10-100x performance improvements at a fraction of the cost compared to traditional analytics vendors.

Additional Resources


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From my bed- POEM

United States Army Center for Health Promotion...
Image via Wikipedia

Tucked in a hospital neatly sanitized

Stowed away from society in a medical compromise

Between the forces of destiny, decay and medical molecular action

Awaiting the prognosis as I am soundly exorcized

Grand delusions realistic illusions and promise of hope

Lift my mood when every other chemical has tried and failed

Prayer helps, so do online afar friendly people,

Hang on buddy, get back on track after being derailed

We need you more than your needers did

We love you more than any of your lovers will

Your dreadful prose, mundane wit, hilarious code

Have made you a daily part of our life though online still

Blog on, dog gone, be inspired, be still

Calmly heal, than slowly mend

We will wait with patience

Till your hospice stint will painless end.

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R Graphs Resources

Relevant GUI-

GrapheR and Deducer

https://rforanalytics.wordpress.com/graphical-user-interfaces-for-r/

Websites-



Graphics by Examples


.  UCLA: Academic Technology Services, 
Statistical Consulting Group. 
from https://www.ats.ucla.edu/stat/R/gbe/default.htm
 (accessed Feb 10, 2011)

https://www.ats.ucla.edu/stat/R/gbe/default.htm

Quick-R

http://www.statmethods.net/graphs/

Graph Gallery

http://addictedtor.free.fr/graphiques/allgraph.php

Frank McCown

https://www.harding.edu/fmccown/r/

Detailed Tutorial

https://math.illinoisstate.edu/dhkim/rstuff/rtutor.html

Advanced Data Visualization

Hadley Wickham

Courses- http://had.co.nz/stat645/

and Package-  http://had.co.nz/ggplot2/

example-

http://had.co.nz/ggplot2/geom_density.html

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