By Peter Kinnaird, Inbal Talgam-Cohen
Magazine: Fall 2012 | Volume 19, No. 1
Big data is everywhere—from financial transactions to Tweets, from ad click throughs to medical records—and is continuously growing. The Information Age has lead to a proliferation of data across all industries. However harnessing all of this data can be a daunting task. There are many challenges facing the big data community. In this issue we provide a comprehensive overview of recent developments affecting big data. The issue is divided into three main themes: theory, systems, and applications. We've also included discussions on how data is used in the real word at IBM and Google. As more businesses use data analytics to make better strategic decisions, there is an increasing need for educated researchers, scientists, and engineers. We hope to encourage our readers to explore this growing field.
By CACM Staff
By Andrew Cron, Huy L. Nguyen, Aditya Parameswaran
By Daniel Gooch
By Anjul Bhambhri
By Ben Deverett
By Matthew Kay, Dimitris Mitropoulos, Wolfgang Richter, Lora Oehlberg, Lea Rosen
The rate at which electronic information is generated in the world is exploding. In this article we explore techniques known as sketching and streaming for processing massive data both quickly and memory-efficiently.
By Jelani Nelson
Approaches from computer science and statistical science for assessing and protecting privacy in large, public data sets.
By Ashwin Machanavajjhala, Jerome P. Reiter
New algorithms for estimating parameters of distributions over big domains need significantly fewer samples.
By Ronitt Rubinfeld
An introduction to designing algorithms for the MapReduce framework for parallel processing of big data.
By Jeffrey D. Ullman
Students working in the big data space get uniquely valuable experiences and perspectives by taking industrial internships, which can help further their research agendas.
By Yanpei Chen, Andrew Ferguson, Brian Martin, Andrew Wang, Patrick Wendell
Surajit Chaudhuri, Distinguished Scientist and head of the Extreme Computing Group (XCG) at Microsoft Research, Redmond provides valuable insights for revisiting data analytics in the context of big data.
By Aditya Parameswaran
How Facebook is analyzing big data.
By Raghotham Murthy, Rajat Goel
Three computer scientists from UC Irvine address the question "What's next for big data?" by summarizing the current state of the big data platform space and then describing ASTERIX, their next-generation big data management system.
By Vinayak R. Borkar, Michael J. Carey, Chen Li
New user interfaces can transform how we work with big data, and raise exciting research problems that span human-computer interaction, machine learning, and distributed systems.
By Jeffrey Heer, Sean Kandel
Many interesting research questions can be explored by studying processes running over networks.
By B. Aditya Prakash
On algorithms for parallel machine learning, and why they need to be more efficient.
By John Langford
An invitation to the digital science of life.
By Cliburn Chan
By Edward Z. Yang, Robert J. Simmons
By Finn Kuusisto
By Colin J. Ihrig
A look at the Luhn algorithm and how it is used in the 21st century for error detection.
By Broderick Causley
To conciliate application logic concerns with event handling performance, we introduce the spChains processing framework.
By Dario Bonino, Luigi De Russis