ENTRIES TAGGED "strata"
Sneak Peek at Upcoming Session at Strata Santa Clara 2013
By Robert Munro
Plain text is the world’s largest source of digital information. As the amount of unstructured text grows, so does the percentage of text that is not in English. The majority of the world’s data is now unstructured text outside of English. So unless you’re an exceptional polyglot, you can’t understand most of what’s out there, even if you want to.
Language technologies underlie many of our daily activities. Search engines, spam filtering, and news personalization (including your social media feeds) all employ smart, adaptive knowledge of how we communicate. We can automate many of these tasks well, but there are places where we fall short. For example, the world’s most spoken language, Mandarin Chinese, is typically written without spaces. “解放大道” can mean “Liberation Avenue” or “Solution Enlarged Road” depending on where you interpret the gaps. It’s a kind of ambiguity that we only need to worry about in English when we’re registering domain names and inventing hashtags (something the folk at “Who Represents” didn’t worry about enough). For Chinese, we still don’t get it right with automated systems: the best systems get an error every 20 words or so. We face similar problems for about a quarter of the world’s data. We can’t even reliably tell you what the words are, let alone extract complex information at scale.
Preview of an upcoming session at Strata Santa Clara
In many modern web and big data applications the data arrives in a streaming fashion and needs to be processed on the fly. In these applications, the data is usually too large to fit in main memory, and the computations need to be done incrementally upon arrival of new pieces of data. Sketching techniques allow these applications to be realized with high levels of efficiency in memory, computation, and network communications.
In the algorithms research community, sketching techniques first appeared in the literature in 1980s, e.g., in the seminal work of Philippe Flajolet and G. Nigel Martin, then caught attentions in late 1990s, partially inspired by the award-winning work of Noga Alon, Yossi Matias, and Mario Szegedy, and were/are on fire in 2000’s/2010’s, when sketches got successfully designed not only for fundamental problems such as heavy hitters, but also for matrix computations, network algorithms, and machine learning. These techniques are now at an inflection point in the course of their history, due to the following factors:
1. Untapped potential: Being so new, their huge practical potential has been yet barely tapped into.
2. Breadth and maturity: They are now both broad and mature enough to start to be widely used across a variety of big data applications, and even act as basic building blocks for new highly efficient big data management systems.
Preview of upcoming session "Who is Fake?" at the Strata Conference
By Lutz Finger
In the Matrix, the idea of a computer algorithm determining what we think may seemed far-fetched. Really? Far-fetched? Let’s look at some numbers.
About half of all Americans get their news in digital form. This news is written up by journalists, half of whom at least partially source their stories from social media. They use tools to harvest the real time knowledge of 100,000 tweets per second and more.
But what if someone could influence those tools and create messages that look as though they were part of a common consensus? Or create the appearance of trending?
Preview of upcoming session at Strata Santa Clara
Is your organization considering embracing data science? If so, we would like to give you some helpful advice on organizational and technical issues to consider before you embark on any initiatives or consider hiring data scientists. Join us, Sean Murphy and Marck Vaisman, two Washington, D.C. based data scientists and founding members of Data Community DC, as we walk you through the trials and tribulations of practicing data scientists at our upcoming talk at Strata.
We will discuss anecdotes and best practices, and finish by presenting the results of a survey we conducted last year to help understand the varieties of people, skills, and experiences that fall under the broad term of “Data Scientist”. We analyzed data from over 250 survey respondents, and are excited to share our findings, which will also be published soon by O’Reilly.
Preview of upcoming Strata session on data exploration
Amy Heineike is Director of Mathematics for Quid Inc, where she has been since its inception, prototyping and launching the company’s technology for analyzing document sets. Below is the teaser for her upcoming talk at Strata Santa Clara.
I recently discovered that my favorite map is online. It used to hang on my housemate’s wall in our little house in London back in 2005. At the time I was working to understand how London was evolving and changing, and how different policy or infrastructure changes (a new tube line, land use policy changes) would impact that.
The map was originally published as a center-page pull out from the Guardian, showing the ethnic groups that dominate different neighborhoods across the city. The legend was as long as the image, and the small print labels necessitated standing up close, peering and reading, tracing your finger to discover the Congolese on the West Green Road, our neighbors the Portuguese on the Stockwell Road, or the Tamils in Chessington in the distant south west.
Using data science to predict the Oscars
Sophisticated algorithms are not going to write the perfect script or crawl YouTube to find the next Justin Beiber (that last one I think we can all be thankful for!). But a model can predict the probability of a nominee winning the Oscar, and recently our model has Argo overtaking Lincoln as the likely winner of Best Picture. Every day on FarsiteForecast.com we’ve been describing applications of data science for the media and entertainment industry, illustrating how our models work, and updating the likely winners based on the outcomes of the Awards Season leading up to the Oscars. Just as predictive analytics provides valuable decision-making tools in sectors from retail to healthcare to advocacy, data science can also empower smarter decisions for entertainment executives, which led us to launch the Oscar forecasting project. While the potential for data science to impact any organization is as unique as each company itself, we thought we’d offer a few use cases that have wide application for media and entertainment organizations.
We are simply not good at playing with others when it comes to data
Russia’s railway gauge is different from Western Europe’s. At the border of the former Soviet states, the Russian gauge of 1.524m meets the European & American ‘Standard’ gauge of 1.435m. The reasons for this literal disconnect arise from discussions between the Tsar and his War Minister. When asked the most effective way to prevent Russia’s own rail lines being used against them in times of invasion, the Minister suggested a different gauge to prevent supply trains rolling through the border. The artifact of this decision remains visible today at all rail crossings between Poland and Belarus or Slovakia and Ukraine. The rail cars are jacked up at the border, new wheels inserted underneath, and the car lowered again. It is about a 2-4 hour time burn for each crossing.
Per head, per crossing, over 170 years, is a heck of a lot of resource wasted. But to change it would entail changing the rail stock of the entire country and realigning about 225,000 km (140,000 mi) of track.
Talk about technical debt.
Data suffers from a similar disconnect. It really wasn’t until the advent of XML 15 years ago that we had an agreed (but not entirely satisfactory) mechanism for storing arbitrary data structures outside the application layer. This is as much a commentary on our technical priorities as it is a social indictment. We are simply not good at playing with others when it comes to data.
Sneak peek at my upcoming session at the Strata Conference in Santa Clara
Visualizing data and extracting it from its data store are two activities that go hand in hand. Typically, when you try to use a data visualization toolkit such as Raphael, Protovis or D3 to create a non-trivial visualization, you spend a significant portion of your time writing code to extract the data. The process may involve querying an external database then transforming the resulting data to the correct structure for your visualization.
In his paper introducing plyr, a data manipulation toolkit for R, Hadley Wickham describes a framework, split-apply-combine, for expressing common data operations. The idea is that most data operations can be seen as splitting the data into a series of buckets, applying some aggregation to each bucket to get an aggregate and then combining the results by sorting and limiting. Wickham argues that most data query languages already rely on an equivalent framework whether explicitly or implicitly.
A Call for Industry-Standard Benchmarks for Big Data Platforms at Strata SC 2013
Big data systems are characterized by their flexibility in processing diverse data genres, such as transaction logs, connection graphs, and natural language text, with algorithms characterized by multiple communication patterns, e.g. scatter-gather, broadcast, multicast, pipelines, and bulk-synchronous. A single benchmark that characterizes a single workload could not be representative of such a multitude of use-cases. However, our systematic study of several use-cases of current big data platforms indicates that most workloads are composed of a common set of stages, which capture the variety of data genres and algorithms commonly used to implement most data-intensive end-to-end workloads. Our upcoming session at Strata SC discusses the BigData Top 100 List, a new community-based initiative for benchmarking big data systems.