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Much of the world's most valuable information is trapped in digital sand, siloed in servers scattered around the globe. In this talk I'll discuss the promise of big data, which will come to pass in the coming decade, driven by advances in three principle areas: sensor networks, cloud computing, and machine learning.
by John Fritz
Big Data and predictive analytics can deliver incredible insight that can be used for purposes both good, and not so good. Drawing on real world examples, this session will examine the fine line between competitive advantage and bad behavior, and implications to a complex cast of stakeholders. Let’s begin a dialog on ethics now instead of waiting for our first major crisis.
by Mark Madsen
Big data and analytics have developed a mythology rooted in underlying assumptions. We need to ignore these myths and think clearly about how organizations use data, which means understanding how people use information and make decisions.
by Abhishek Mehta, Mike Olson and Rod Smith
The tools we use play a key role in how we use and respond to big data. Hear about the changes being led by key architects of future big data systems.
by Ed Boyajian
The move to cloud infrastructure and the need to handle big data have created the perfect catalysts for organizations to introduce new infrastructure software and break ties from their expensive incumbent vendors. Ed will share a detailed strategy on how to leverage open source database solutions like PostgreSQL to contain database cost and free budget for other, more valuable initiatives.
A discussion of Big Data approaches to analysis problems in marketing, forecasting, academia and enterprise computing. We focus on practices to enhance collaboration and employ rich statistical methods: a Magnetic, Agile and Deep (MAD) approach to analytics. While the approach is language-agnostic, we show that sophisticated statistics can be easily scaled in traditional environments like SQL.
by Sam Shah
How do you go about building a product around data using Hadoop? This talk will present how LinkedIn builds and maintains such features as People You May Know. We will present our architecture for doing so (open-sourced) as well as knowledge we've gained in the process.
by Rod Cope
Hadoop and HBase make it easy to store terabytes of data, but how do you scale your search mechanism to sift through these mountains of bits and retrieve large result sets in a matter of milliseconds? Careful use of the Solr search server, based on Lucene, made these requirements come to life in our production environment. Come learn how we query terabytes of data in a highly available system.
The convergence of big, open data, ubicomp, and new interfaces will change the way humans work, play, learn, and love. It's a slow transformation that happens one tweet, one blog, and one game at a time -- but it's also an inexorable road towards the singularity. In this panel discussion, we'll look beyond the bytes and algorithms to think about humanity awash in a sea of information.
by Carol McCall
In 2001, the Institutes of Medicine declared that “between the care we have and the care we could have lies not just a gap, but a chasm,” yet nothing’s really changed. Healthcare remains one of the most richly endowed yet poorly equipped knowledge industries anywhere. Using real world examples, we’ll see how BIG DATA may be just what the doctor ordered, but only if we pick the right problems.
by Kevin Weil
Most analytics systems rely on large offline computations, which means results come in hours or days behind. Twitter is all about realtime, but with over 160 million users producing over 90 million tweets per day, we need realtime analytics that scaled horizontally. This talk discusses the development of that infrastructure, as well as the products we are beginning to build on top of it.
The rise of sensor network data and the expectation for low latency query responses combine to obsolete available databases and storage platforms. We have built a platform for web-scale OLAP and in this talk I will cover how we made our infrastructure capable of real-time update and query performance over hundreds of terabytes of multidimensional data.
To many people, Big Data means Open Data: social graphs, voting records, weather patterns, and more. But who owns data? Most of our laws were written for atoms, not bits; they're woefully out of date in an information age. When you share data, does it become more or less valuable? If someone adds to your data, is it still yours? This panel will tackle the gray area of data ownership.
1st–3rd February 2011