Strata in London 2013 Schedule

In January Facebook launched Graph Search in the US which allows users to search their social graph. Ian Hegerty will describe how the Graph Search corpus was built from Facebook's entity graph, and how big data is used to understand users queries and provide relevant results, with minimal initial user behavioral data. Read more.

As big data analytics evolves beyond simple batch jobs, there is a need for both lower-latency processing (interactive queries and steam processing) and more complex analytics (e.g. machine learning, graph algorithms). This talk will introduce Spark and Shark, popular open source projects from Berkeley that address this need through an optimized runtime engine and in-memory computing capabilities. Read more.

I will discuss and showcase polyglot persistence and the lambda architecture. Based on real-world examples and case studies from our customer base and the wider community of practitioners, incl. the financial, energy & media industries and the realm of public data sources (government data), we will elaborate on opportunities and challenges of these new data management and processing memes. Read more.

We're getting better all the time. See how the Cato Institute used responsive design and D3.js to show how human development indicators improve as economic freedom spreads. Read more.

For a two-sided marketplace like Airbnb, the search engine is the main driver of the health of the business. We developed an open-source technology stack and a set of analytical methods to optimize the search experience for our users and search conversion for our business. We’ll discuss the tools we use for data crunching, analysis and reporting, as well as our thoughts on experimental design. Read more.

How Stuff Spreads looks at how two recent memes spread online: Gangnam Style vs Harlem Shake. The talk dissects the memes through the lens of big data to show what made them go viral, what do they have in common, how quantitative and qualitative analysis have to come together to craft insights and tell a story, and finally how to predict future memes and create a data-driven content strategy. Read more.

In May 2013, the O'Reilly Data Sensing Lab collaborated with the Google Cloud Platform and Device Cloud by Etherios, to deploy a network of hundreds of environmental sensors at Google I/O. Learn how the Google Cloud Platform was used to build an end-to-end, scaleable, and high-throughput pipeline for data collection, processing, and analysis. Read more.

Mainframe is Big Data too! Leveraging it in Hadoop creates a remarkable competitive advantage, but exploiting it without the right tools is nearly impossible, requiring you to wrestle with thousands of lines of Java, Pig, Hive, COBOL and more. This session presents a smarter way to ingest and process mainframe data in Hadoop, and how to bridge the technical, skill and cost gaps between the two. Read more.

Maps are powerful tools for people to learn from data. In this project, we combine large-scale data processing with Hadoop and data visualization through CartoDB to make over six years of bi-monthly deforestation data accessible in an interactive map on the web. This talk will tell the story of how large-scale data paired with visualization can make data accessible in important new ways. Read more.

A vending machine that dispenses crisps in response to financial news, laser sketches from 4000 years of solar eclipse data, and a mural created from QR codes acting as cellular automata. Featuring a diverse exploration of data, the ODI will showcase art commissioned and curated for its on-going Data as Culture programme which facilitates artists to explore data as an art material. Read more.

How do we know what we know? Increasingly discoveries are made from computed data, possibly sourced from the internet. If we are to trust these discoveries, how conclusions are reached is critical. Examples from work in Big Data analytics infrastructure for life sciences and social media analysis will illustrate the key issues. Read more.

It has been said by many that 80% data science is scrubbing data. In this talk we'll cover how you can use Cascalog to scrub, transform, manipulate and mangle data into the formats you need, fix things that are wrong and filter out things that are broken. Clojure and Cascalog together provide fantastic tools for this. Learn about using Hadoop with the messy data that exists in the real world. Read more.

In the era of M2M Communication and the Internet of Things on top of traditional 3V's of Big Data - Volume, Variety and Velocity we need to be able to process ephemeral data produced by dispersed sources which needs to be organized and distributed to multiple services. Real-time response, security, compliancy, compatibility, breaking silos - require new approaches to data management. Read more.

Many big data solutions focus on large data analysis that happens in data centers. Or they focus on data visualization in the browser. When you combine both of these techniques, you get amazing and expressive power. This talk will show how to use the Google Maps API with WebGL and Google Big Query, Cloud Storage, App Engine and Compute Engine to deliver amazing, responsive visualizations. Read more.

Farmers in the UK receive subsidies via the Common Agricultural Policy. These form a significant part of their income, approx £2billion/yr total. Changes to the data driving the payments are submitted by farmers as hand drawn imagery. I'll share what the Government Digital Service has learned working with Defra, prototyping ways to capture changes that are sustainable, cheaper and more accurate. Read more.

Medical treatments have have come a long away in the last couple of decades. On the other hand, we could be doing a lot better in monitoring people within their own homes between hospital visits using sensors. Sensors combined with Big Data technologies are set to bring about profound changes for the future of health and social care. Read more.

Fast read and write performance and scalability of distributed in-memory clusters is making it possible to retrain machine learning algorithms in real-time. The application of such algorithms to risk, infrastructure security and other areas can be transformative. Read more.

In this presentation Chris will look at seven Map Reduce techniques that he enjoys playing with – the bits that are fun, exciting, and that can provide valuable insight into your big data. With examples of code (and where you can look for it) for web, image and text processing he'll show things that can quickly allow you to extend your analysis beyond traditional data mining. Read more.

How do you do data journalism when you are not the Guardian, the New York Times or the Washington Post? You don't need a data team, developers, much time or any funding to get started and produce data journalism that grabs headlines and engages readers. This workshop will focus on quick start techniques for getting started and making the most of few resources. Read more.

Crossrail will help deliver a new London. It is one of the largest civil engineering projects -- taking place literally under the feet of Strata London. We'll present how data science is being deployed at Crossrail to fundamentally change the way decisions are made and the operation is being run; from the CEO to the engineers monitoring ground movement in the tunnels. Read more.

Everyday objects are becoming smarter. In ten years’ time, every piece of clothing you own, every piece of jewelry, and every thing you carry with you will be measuring, weighing and calculating your life. In ten years, the world — your world — will be full of sensors. Read more.

"In order to classify documents, simply first convert them to vectors, train, test and finally apply the model." Sounds easy - in theory. Converting documents to vectors usually is the tricky part. This talk walks you through the steps necessary to convert your text documents into feature vectors that Mahout classifiers can use including a few anecdotes on drafting domain specific features. Read more.

How can business and IT users easily explore, analyse and visualise data in Hadoop? Learn about alternatives to manually writing jobs or setting up predefined schemas and how a leading enterprise used Splunk and their Hadoop distribution to empower them with new access to Hadoop data. See how they got up and running in under an hour and enabled their developers to start writing big data apps. Read more.

In Mexico open data journalism is still difficult to exercise. Find In Mexico, an organization to which I belong, we developed a research project in which we use databases and the result was a map (www.mexicoinformate.org/platform) that has managed to draw attention to the slow progress of the law enforcement system in the country. Read more.

The economy is in a mess. But good data can help fix it. Timely analysis of large data sets is beginning to provide insight into what's really happening to business growth, employment and prosperity. We'll look at some of the most exciting examples of how Big Data is changing the way we look at the economy, and how governments and businesses can use them to their advantage. Read more.

In the future we will see huge growth in the amount of traffic data generated through built-in car sensors. This talk presents a case study of analytics on traffic and traffic light data. Methods will be presented that yield a deep understanding of traffic and its characteristics by analyzing past traffic data. These methods could be extended to predict traffic jams and optimize routing systems. Read more.

The value of machine-generated data is highest at the moment it is generated. Operational data, sensor data and video feeds require new automated approaches to maximize the value of these streams of data. In this session, Dr. Volkmar Uhlig will explore how to apply the lessons learned from automated high-frequency trading systems to today’s Big Data problems to monetize information. Read more.

Since it launched just 2 years ago, it has leveraged the open data community to grow to by far the largest open database of companies in the world with over 50 million companies in 70 jurisdictions, and is regularly used by journalists, anti-corruption investigators, civil society, even banks and financial institutions. Read more.

The "smart grid" isn't just about smart meters. Every stage of our electrical power infrastructure has to be "smart," including generation, transmission and distribution. Sophisticated sensors connected to software platforms that continuously gather, visualize and analyze massive amounts of data in real time to produce actionable insights are critical to optimizing our energy assets. Read more.

To keep analyzing more data, and faster, we need a secret weapon: cheating. In this brief survey, learn how you may be doing too much work in your analytics and learning processes, and how giving up a little accuracy can gain a lot of performance. With examples from Apache Hadoop, Mahout, and ML tools from Cloudera. Read more.

To feed LinkedIn's data-driven products, we need to run a complex graph of ETL workflows that deliver the right data to the right systems reliably on a 24x7 basis. To achieve this goal, we have developed a metadata system that captures process dependencies, data dependencies, and execution histories -- this system also lays the foundation for a combined dataflow and workflow engine. Read more.

The large-scale deployment of a big data strategy in the retail financial services sector poses specific challenges in terms of infrastructure, data limitations, organizational structure and portfolio definition and execution. We will share how we are addressing these challenges as well as selected demos and solutions, with focus on promising new financial product lines enabled by big data. Read more.

This session will cover the rapidly changing way that machines are taking on different parts of our lives, making decisions for us and altering our lives with our own data. Shelley Evenson will address how designers need to keep their human focus in order to truly capitalise on the benefits of big data without allowing technology to take over. Read more.

The Lean Startup model showed a generation of founders how to launch companies smarter and faster. At the core of this model is a constant cycle of building, measuring, and learning. In this session, we'll look at the "measure" part of this cycle, and how organizations of all sizes can use data to build a better business faster. Read more.

Analytics is useless if it doesn't lead to action. It is often desirable to put a computer in control of decision making. In this talk I'll discuss bandit algorithms, a class of decision making algorithms that solve a simple but widely applicable decision problem, and have found application in ad serving, content recommendation, and more. Read more.

Apache Hadoop has become popular from its specialization in the execution of MapReduce programs. However, it has been hard to leverage existing Hadoop infrastructure for various other processing paradigms such as real-time streaming, graph processing and message-passing. That was true until the introduction of Apache Hadoop YARN in Apache Hadoop 2.0. Read more.

Attendees will leave this session with a deeper understanding of how organizations are using Hadoop to solve real business problems today, and how recent advancements in the Hadoop ecosystem are expanding the platform's capabilities to serve larger enterprise requirements for a virtual EDW. Read more.

Being good is hard. Being evil is much more fun and gets you paid a lot more. We give a survey of the field of doing high-impact evil with data and analysis. We will look at some of the simplest things you can do to make the maximum (negative) impact on your friends, your business and the world. If you happen to learn something about doing good with data that will be your problem. Read more.

Making data work requires that organisations define success for their company, provide clear business goals, & articulate the right business questions. The best approach to overcoming the cognitive pitfalls that lead to failing to ask the right question come from the intelligence services. This seminar outlines what they do, and suggests how to use it effectively inside a typical business. Read more.

Even if one has big data, sometimes there is a lack of key data. This is a problem for predictive analytics: if there is only a limited amount of training material (e.g. user ratings, categorized documents), then it is hard to generate accurate models. The talk introduces new semi-supervised learning methods to overcome this problem by utilizing the vast amount of unlabeled data. Read more.

Predictive Analytics has emerged as one of the primary use cases for Hadoop, leveraging various Machine Learning techniques to increase revenue or reduce costs. In this talk we provide real-world use cases from several different industries, and then discuss the open source technologies available to companies wishing to implement Predictive Analytics with Hadoop. Read more.

How can companies use social and business data together to gain insight? See how Tableau's native Google BigQuery connector links seamlessly to live data in BigQuery and creates interactive visualizations without writing a single line of code. Find out how to share your results on the web and mobile in minutes. Read more.

Analytics best practices, data feeds and flows between tools and continents are put in parallel with legislation, showing which steps to undertake for legal compliance; how to train for data protection & assure minimal liability. It’s not about security, goes beyond the cookie debate, highlighting how the EU Personal Data Protection Regulation will influence analytics & how Privacy by Design helps Read more.

Some big organisations love the idea of using data to inform decision making but find the reality a little daunting to say the least. How are we demystifying data in the BBC and overcoming editorial fears about it lessening the view of the trusted human in making content decisions? Read more.

As data scientists, uncertainty is all around us: data is noisy, missing, wrong or inherently uncertain. In this talk I want to introduce a branch of statistics called Bayesian reasoning which is a unifying, consistent, logical and practically successful way of handling uncertainty. In short, I'd like to convince people that Bayes rule is the E=MC^2 of data science. Read more.

People want more out of Hive. They want it to be fast, useful, and connect to their tools. Work is being done to reduce start up time, improve the optimizer, extend it to use Tez, process records 50x faster, add support for functions like RANK, add subqueries, and add standard SQL datatypes. We will review this work plus show current benchmarks. Read more.

To take the right decision, you need the right data. As complexity and abundance of data increase, the communication of data analysis results becomes more challenging. Grounding our talk in the pharma R&D arena, we illustrate how animated and interactive graphics can streamline communication on complex data analysis and inform decision making. Read more.

Learn how hadoop is helping TomTom to make fresher maps by continuously processing the incoming GPS data and how hbase is used to present that data to an Operator Read more.

How do you indentify duplicate data and why is it important? What do you do with such data when you find it? Data Matching using the mathematics of probability has been around since the 1950’s. But, how does it actually work? What is the mathematics behind it? How do probabilities allow us to identify duplicate entries? Read more.

NICE Systems is a leading provider of Customer Experience Management software, providing real-time offer management and predictive analytics applications based on HBase. We have recently migrated to HBase from our own custom-built data store, and in this session we will share the challenges we overcame getting HBase to perform to our demanding performance requirements. Read more.

The UK Government team behind the GOV.UK website talk about their work on the Performance Platform, a suite of services and a cultural shift taking people away from immensely detailed value stream maps about a call-centre and paper process (which might be an inherently 5-day long journey), to something that's digital, lightweight, fast and pleasant to use. Read more.

78% of consumers use their smartphone while shopping in-store. What are they doing? More importantly, why? For all the media buzz around showrooming – look in-store, buy online - there is little insight on the issue. SapientNitro explains how key business questions drove hypotheses, data collection using novel instruments, and insights from analytic tools for testing and interpretive analysis. Read more.

This talk will discuss how particle physics research can inform the field of data science. The importance of blind analyses and machine learning algorithms will be discussed as tools for filtering growing bodies of data as the big data trend continues. Read more.

What questions would you ask if you have a Facebook-like graph of what your customer likes, what they bought, and what they viewed? This is what we built at uSwitch by transforming flat data from Hadoop into Neo4J. This talk will walk through how we bridged big data and linked data technologies and the results of such amalgamation. Read more.

Big Data are very interesting for official statistics. Results obtained by analyzing large amounts of Dutch traffic loop detection records, Mobile phone data and Dutch social media messages are discussed to illustrate this. Read more.

How combining quantitative data analysis and qualitative social science work can complement each other, providing deeper understanding of behavior and open new doors of enquiry. Read more.

In this tutorial we'll use the Cloudera Development Kit (CDK) to build a Java web app that logs application events to Hadoop, and then run ad hoc and scheduled queries against the collected data. Read more.

The tutorial will give a first introduction running Big Data Analyses in the statistical software R. R brings together latest Big Data technologies and latest high-level statistical methods. Bring your laptop, use your web browser to access a RStudio based analyses platform in the cloud and leave with a lot of new ideas for efficient Big Data analyses with R. Read more.

This tutorial will describe how to process real-time streams and using the open-source Storm framework. We will define Storm's core concepts whilst focusing on creating a simple topology that counts, in real-time, key-words and hashtags seen in Twitter's public (1%) feed. Read more.

Communicating Data Clearly describes how to draw clear, concise, accurate graphs that are easier to understand than many of the graphs one sees today. The tutorial emphasizes how to avoid common mistakes that produce confusing or even misleading graphs. Graphs for one, two, three, and many variables are covered as well as general principles for creating effective graphs. Read more.