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Bits 'n Pieces on Big Data
Innovative information and insight into Big Data (if you like the content, please consider donating to my bitcoin address #1MhtqfDaAsy4TpYwjS2Kq2DMKrecupbx8c)
Curated by onur savas
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Twitter Data Grants

Twitter Data Grants | Bits 'n Pieces on Big Data | Scoop.it

With more than 500 million Tweets a day, Twitter has an expansive set of data from which we can glean insights and learn about a variety of topics, from health-related information such as when and where the flu may hit to global events like ringing in the new year. To date, it has been challenging for researchers outside the company who are tackling big questions to collaborate with us to access our public, historical data. Twitter Data Grants program aims to change that by connecting research institutions and academics with the data they need.

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1st International Workshop on Scalable Computing For Real-Time Big Data Applications (SCRAMBL'14)

1st International Workshop on Scalable Computing For Real-Time Big Data Applications (SCRAMBL'14) | Bits 'n Pieces on Big Data | Scoop.it

1st International Workshop on Scalable Computing For Real-Time Big Data Applications. This workshop aims at providing a venue for designers, practitioners, researchers, developers, and industrial/governmental partners to come together, present and discuss leading research results, use cases, innovative ideas, challenges, and opportunities that arise from real-time big data applications.

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DARPA Open Catalog

DARPA Open Catalog | Bits 'n Pieces on Big Data | Scoop.it

DARPA Open Catalog contains a curated list of DARPA-sponsored software and peer-reviewed publications.

onur savas's insight:

Mostly from DARPA XDATA.

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Twitter Trends Help Researchers Forecast Viral Memes

Twitter Trends Help Researchers Forecast Viral Memes | Bits 'n Pieces on Big Data | Scoop.it

What makes a meme— an idea, a phrase, an image—go viral? For starters, the meme must have broad appeal, so it can spread not just within communities of like-minded individuals but can leap from one community to the next. Researchers, by mining public Twitter data, have found that a meme's “virality” is often evident from the start. After only a few dozen tweets, a typical viral meme (as defined by tweets using a given hashtag) will already have caught on in numerous communities of Twitter users. In contrast, a meme destined to peter out will resonate in fewer groups.

 


Via Claudia Mihai, Complexity Digest
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june holley's curator insight, January 23, 2014 8:31 AM

Some important ideas here for people interested in change.

Premsankar Chakkingal's curator insight, January 30, 2014 8:58 AM

Forecasting the Future Twitter Trends in hashtags

Christian Verstraete's curator insight, February 3, 2014 4:48 AM

Twitter, what happens when things go viral?

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Apache Spark for Big Analytics

Apache Spark for Big Analytics | Bits 'n Pieces on Big Data | Scoop.it

Via Simon Hunanyan
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Simon Hunanyan's curator insight, December 23, 2013 10:09 PM

Spark, an Apache incubator project, is an open source distributed computing framework for advanced analytics in Hadoop. It's 100X faster than what they are able to achieve with MapReduce. Spark includes a machine learning library (MLLib), a graph engine (GraphX), a streaming analytics engine (Spark Streaming) and much more...

Currently, Spark supports programming interfaces for Scala, Java and Python.  The R interface is under development and this is expected to be released in the first half of 2014.

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It Is Trivially Easy to Match Metadata to Real People

It Is Trivially Easy to Match Metadata to Real People | Bits 'n Pieces on Big Data | Scoop.it

"...We randomly sampled 5,000 numbers from our crowdsourced MetaPhone dataset and queried the Yelp, Google Places, and Facebook directories. With little marginal effort and just those three sources—all free and public—we matched 1,356 (27.1%) of the numbers. Specifically, there were 378 hits (7.6%) on Yelp, 684 (13.7%) on Google Places, and 618 (12.3%) on Facebook..."

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Manifact - Real-time Metrics - Statistics - Alerts

Manifact - Real-time Metrics - Statistics - Alerts | Bits 'n Pieces on Big Data | Scoop.it

Real-time Metrics, Statistics and Alerts. Works with MySql, Oracle, PostgreSql, and Hadoop.

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Big Data – Lessons from Genetics and Bio-Statistics

Big Data – Lessons from Genetics and Bio-Statistics | Bits 'n Pieces on Big Data | Scoop.it
The chimpanzee genome is more than 95 % identical to the human genome – by Roopam

Evolution of Biology & Medicine with Big Data
I learnt a couple…
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Twitter buzz about papers does not mean citations later

Twitter buzz about papers does not mean citations later | Bits 'n Pieces on Big Data | Scoop.it
Analysis of science on social media service finds little correlation with standard measures of academic success.
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Netflix open sources its data traffic cop, Suro

Netflix open sources its data traffic cop, Suro | Bits 'n Pieces on Big Data | Scoop.it
Netflix has open sourced a tool called Suro that collects event data from disparate application servers before sending them to other data platforms such as Hadoop and Elasticsearch.
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Self-Replicating USBs Spread Software Faster than an Internet Connection | MIT Technology Review

Self-Replicating USBs Spread Software Faster than an Internet Connection | MIT Technology Review | Bits 'n Pieces on Big Data | Scoop.it
Downloading free software is hugely time consuming and expensive in the developing world. Now one computer scientist has worked out how to spread it faster and more cheaply without using the internet.
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YouTube Multiview Video Games Dataset

YouTube Multiview Video Games Dataset | Bits 'n Pieces on Big Data | Scoop.it

This dataset contains about 120k instances, each described by 13 feature types, with class information, specially useful for exploring multiview topics (cotraining, ensembles, clustering,..).

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What Big Data Means For Social Science

We've known big data has had big impacts in business, and in lots of prediction tasks. I want to understand, what does big data mean for what we do for science? Specifically, I want to think about the following context:  You have a scientist who has a hypothesis that they would like to test, and I want to think about how the testing of that hypothesis might change as data gets bigger and bigger. So that's going to be the rule of the game. Scientists start with a hypothesis and they want to test it; what's going to happen?

 


Via Alessandro Cerboni, NESS, Complexity Digest
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Evolution: quantity over quality?

Evolution: quantity over quality? | Bits 'n Pieces on Big Data | Scoop.it

When you think about evolution, 'survival of the fittest' is probably one of the first things that comes into your head. However, new research from Oxford University finds that the 'fittest' may never arrive in the first place and so aren’t around to survive.

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Elsevier opens its 11M papers to text-mining

Elsevier opens its 11M papers to text-mining | Bits 'n Pieces on Big Data | Scoop.it

Elsevier says that it has now made it easy for scientists to extract facts and data computationally from its more than 11 million online research papers.

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Ralph Poole's curator insight, February 5, 2014 10:27 AM

This is important news for those of us that work with clients in knowledge intensive scientific industries.  Ingesting and analyzing this content will have profound impact on our ability to make connections and see patterns in scientific literature.

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A co-Relational Model of Data for Large Shared Data Banks - ACM Queue

A co-Relational Model of Data for Large Shared Data Banks - ACM Queue | Bits 'n Pieces on Big Data | Scoop.it
contrary to popular belief, sql and nosql are really just two sides of the same coin.
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Who is Dating Whom: Characterizing User Behaviors of a Large Online Dating Site

Online dating sites have become popular platforms for people to look for potential romantic partners. It is important to understand users' dating preferences in order to make better recommendations on potential dates. The message sending and replying actions of a user are strong indicators for what he/she is looking for in a potential date and reflect the user's actual dating preferences. We study how users' online dating behaviors correlate with various user attributes using a large real-world dateset from a major online dating site in China. Many of our results on user messaging behavior align with notions in social and evolutionary psychology: males tend to look for younger females while females put more emphasis on the socioeconomic status (e.g., income, education level) of a potential date. In addition, we observe that the geographic distance between two users and the photo count of users play an important role in their dating behaviors. Our results show that it is important to differentiate between users' true preferences and random selection. Some user behaviors in choosing attributes in a potential date may largely be a result of random selection. We also find that both males and females are more likely to reply to users whose attributes come closest to the stated preferences of the receivers, and there is significant discrepancy between a user's stated dating preference and his/her actual online dating behavior. These results can provide valuable guidelines to the design of a recommendation engine for potential dates.

 

Who is Dating Whom: Characterizing User Behaviors of a Large Online Dating Site
Peng Xia, Kun Tu, Bruno Ribeiro, Hua Jiang, Xiaodong Wang, Cindy Chen, Benyuan Liu, Don Towsley

http://arxiv.org/abs/1401.5710


Via Complexity Digest
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Urbansocial's curator insight, July 14, 2014 11:41 AM

Urban Social - Online dating for sociable singles www.urbansocial.com

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Solving NetFlow analysis challenges with big data approach

Solving NetFlow analysis challenges with big data approach | Bits 'n Pieces on Big Data | Scoop.it
Admins looking for more efficient NetFlow analysis can find network management tools that offer finer detail and higher performance without relying on relational database platforms.

Via cysap
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The Predictive Power of Big Data

The Predictive Power of Big Data | Bits 'n Pieces on Big Data | Scoop.it
If you wrote out all five zettabytes of data produced by humans produce each year, it would reach the galactic core of the Milky Way.
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Schedule: All sessions: Strata 2014 - O'Reilly Conferences, February 11 - 13, 2014, Santa Clara, CA

Schedule: All sessions: Strata 2014 - O'Reilly Conferences, February 11 - 13, 2014, Santa Clara, CA | Bits 'n Pieces on Big Data | Scoop.it
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The Cloud, Visualizations and Apps: Wikibon’s Big Data Predictions for 2014 | The Big Data Hub

The Cloud, Visualizations and Apps: Wikibon’s Big Data Predictions for 2014 | The Big Data Hub | Bits 'n Pieces on Big Data | Scoop.it
The Cloud, Visualizations and Apps: Wikibon’s Big Data Predictions for 2014
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Big Data Social Science Research Program at Penn State

Big Data Social Science Research Program at Penn State | Bits 'n Pieces on Big Data | Scoop.it

 Integrative Graduate Education and Research Traineeship at Penn State.

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Project ranks billions of drug interactions

Project ranks billions of drug interactions | Bits 'n Pieces on Big Data | Scoop.it

“It’s the largest computational docking ever done by mankind.”


By analysing the chemical structure of a drug, researchers can see if it is likely to bind to, or ‘dock’ with, a biological target such as a protein. Researchers have now unveiled a computational effort that used Google's supercomputers to assesses billions of potential dockings on the basis of drug and protein information held in public databases, finding potentially toxic side effects and allowing researchers to predict how and where a compound might work in the body.

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IBM Data Magazine

IBM Data Magazine | Bits 'n Pieces on Big Data | Scoop.it
Clear, in-depth technical advice and hands-on examples on the latest topics in data management, IBM databases, and big data.
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