Extreme Analytics
Analytics and measurements: A recipe for sustainable food chains
Our food chain is full of waste, but widely distributed sensors and big data analytics together hold potential for dramatic conservation of resources
Busted! The campaign against counterfeit reviews
Fake reviews, either intended to trash a company or artificially inflate its standing, are poisoning the Internet. Here's how machine learning is attempting to stop the counterfeitting
Peeling back the layers of the smarter city
What's the key to making cities smarter? Extend data-driven analytic infrastructure across every aspect of urban existence
When big data is truly better
Take advantage of scale when past experience indicates greater analytic value will result. But big data is not a hammer -- nor is every problem a nail
Can a machine detect sarcasm? Yeah, right
Applying analytics to social media? Good luck -- not all words can be taken at face value. Natural language processing helps, but it's no panacea
What's machine learning? It depends on who you ask
As interest in machine learning has grown, its definition has expanded to include a panoply of techniques for automating knowledge and pattern discovery from fresh data
Big data log analysis thrives on machine learning
Huge quantities of log data generated by all sorts of devices opens immense potential for insight, but machine learning is needed to make sense of it
Too big, too small, or just right? Balancing your social connections
An MIT professor analyzes social graph data to find where influence meets connectedness -- and how to maximize it
Never put everything in one database basket, even if it's Hadoop
Those who recommend putting everything in a Hadoop data lake forget some obvious lessons of database history
Machine learning floats all boats on big data's ocean
Machine learning is the unsung hero that powers many of the most sophisticated big data analytic applications
Cognitive computing can take the semantic Web to the next level
As big data analytics pushes deeper into cognitive computing, it needs to bring the semantic Web into the heart of this new age
Big data demands nonstop experimentation
Predictive modeling needs the dynamic experimentation of big data to discern true, underlying correlations. Some organizations are ready for that disruption -- and some aren't
YARN unwinds MapReduce's grip on Hadoop
Hadoop has been known as MapReduce running on HDFS, but with YARN, Hadoop 2.0 broadens pool of potential applications
Sometimes it's OK to treat people like numbers
After all, the customers' own Web activity fuels analytic models, but a caveat: The data can grow too complex
Devops can take data science to the next level
For data scientists, creating a perfect statistical model is all for naught if the compute power required is prohibitive. We need tools to assess the performance impacts of modeling alternatives
Big data means big challenges in lifecycle management
No matter what its size or variety, data must still be managed through its lifecycle, even when the tools are immature
Graph analysis will make big data even bigger
Social networks transformed the Internet into a complex web of relationships; social graph analysis offers a way to understand those relationships
Big data needs data virtualization
To fulfill the promise of big data, you need to abstract data from its underpinnings -- at both the data and infrastructure layers
There's no shortage of data science smarts
Powerful new big data tools demand analytics expertise -- but a growing body of shared knowledge and a new generation of self-taught experts will fill the gap
When you should put big data in the cloud
Cloud services have an important role to play in big data, especially for short-term jobs or applications where the bulk of data is already in the cloud
