"data mining" entries

Four short links: 7 March 2016

Four short links: 7 March 2016

Trajectory Data Mining, Manipulating Search Rankings, Open Source Data Exploration, and a Linter for Prose.

  1. Trajectory Data Mining: An Overview (Paper a Day) — This is the data created by a moving object, as a sequence of locations, often with uncertainty around the exact location at each point. This could be GPS trajectories created by people or vehicles, spatial trajectories obtained via cell phone tower IDs and corresponding transmission times, the moving trajectories of animals (e.g. birds) fitted with trackers, or even data concerning natural phenomena such as hurricanes and ocean currents. It turns out, there’s a lot to learn about working with such data!
  2. Search Engine Manipulation Effect (PNAS) — Internet search rankings have a significant impact on consumer choices, mainly because users trust and choose higher-ranked results more than lower-ranked results. Given the apparent power of search rankings, we asked whether they could be manipulated to alter the preferences of undecided voters in democratic elections. They could. Read the article for their methodology. (via Aeon)
  3. Keshif — open source interactive data explorer.
  4. proselint — analyse text for sins of usage and abusage.
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Is 2016 the year you let robots manage your money?

The O’Reilly Data Show podcast: Vasant Dhar on the race to build “big data machines” in financial investing.

Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science.

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In this episode of the O’Reilly Data Show, I sat down with Vasant Dhar, a professor at the Stern School of Business and Center for Data Science at NYU, founder of SCT Capital Management, and editor-in-chief of the Big Data Journal (full disclosure: I’m a member of the editorial board). We talked about the early days of AI and data mining, and recent applications of data science to financial investing and other domains.

Dhar’s first steps in applying machine learning to finance

I joke with people, I say, ‘When I first started looking at finance, the only thing I knew was that prices go up and down.’ It was only when I actually went to Morgan Stanley and took time off from academia that I learned about finance and financial markets. … What I really did in that initial experiment is I took all the trades, I appended them with information about the state of the market at the time, and then I cranked it through a genetic algorithm and a tree induction algorithm. … When I took it to the meeting, it generated a lot of really interesting discussion. … Of course, it took several months before we actually finally found the reasons for why I was observing what I was observing.

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Four short links: 20 November 2015

Four short links: 20 November 2015

Table Mining, Visual Microphones, Platformed Government, and NP-Hard Video Games

  1. DeepDive — Stanford project to create structured data (SQL tables) from unstructured information (text documents) and integrate such data with an existing structured database. DeepDive is used to extract sophisticated relationships between entities and make inferences about facts involving those entities. Code is open source (Apache v2 license). (via Infoworld)
  2. Visual Microphone (MIT) — turn everyday objects — a glass of water, a potted plant, a box of tissues, or a bag of chips — into visual microphones using high-speed photography to detect the small vibrations caused by sound. (via Infoworld)
  3. 10 Rules for Distributed/Networked/Platformed Government (Richard Pope) — Be as vigilant against creating concentrations of power as you are in creating efficiency or bad user experiences. (via Paul Downey)
  4. Classic Nintendo Games are (Computationally) HardWe prove NP-hardness results for five of Nintendo’s largest video game franchises: Mario, Donkey Kong, Legend of Zelda, Metroid, and Pokemon.
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Turning big data into actionable insights

The O’Reilly Data Show podcast: Evangelos Simoudis on data mining, investing in data startups, and corporate innovation.

Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science.

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Can developments in data science and big data infrastructure drive corporate innovation? To be fair, many companies are still in the early stages of incorporating these ideas and tools into their organizations.

Evangelos Simoudis has spent many years interacting with entrepreneurs and executives at major global corporations. Most recently, he’s been advising companies interested in developing long-term strategies pertaining to big data, data science, cloud computing, and innovation. He began his career as a data mining researcher and practitioner, and is counted among the pioneers who helped data mining technologies get adopted in industry.

In this episode of the O’Reilly Data Show, I sat down with Simoudis and we talked about his thoughts on investing, data applications and products, and corporate innovation:

Open source software companies

I very much appreciate open source. I encourage my portfolio companies to use open source components as appropriate, but I’ve never seen the business model as being one that is particularly easy to really build the companies around them. Everybody points to Red Hat, and that may be the exception, but I have not seen companies that have, on the one hand, remained true to the open source principles and become big and successful companies that do not require constant investment. … The revenue streams never prove to be sufficient for building big companies. I think the companies that get started from open source in order to become big and successful … [are] ones that, at some point, decided to become far more proprietary in their model and in the services that they deliver. Or they become pure professional services companies as opposed to support services companies. Then they reach the necessary levels of success.

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Four short links: 20 May 2015

Four short links: 20 May 2015

Robots and Shadow Work, Time Lapse Mining, CS Papers, and Software for Reproducibility

  1. Rise of the Robots and Shadow Work (NY Times) — In “Rise of the Robots,” Ford argues that a society based on luxury consumption by a tiny elite is not economically viable. More to the point, it is not biologically viable. Humans, unlike robots, need food, health care and the sense of usefulness often supplied by jobs or other forms of work. Two thought-provoking and related books about the potential futures as a result of technology-driven change.
  2. Time Lapse Mining from Internet Photos (PDF) — First, we cluster 86 million photos into landmarks and popular viewpoints. Then, we sort the photos by date and warp each photo onto a common viewpoint. Finally, we stabilize the appearance of the sequence to compensate for lighting effects and minimize flicker. Our resulting time-lapses show diverse changes in the world’s most popular sites, like glaciers shrinking, skyscrapers being constructed, and waterfalls changing course.
  3. Git Repository of CS PapersThe intention here is to both provide myself with backups and easy access to papers, while also collecting a repository of links so that people can always find the paper they are looking for. Pull the repo and you’ll never be short of airplane/bedtime reading.
  4. Software For Reproducible ScienceThis quality is indeed central to doing science with code. What good is a data analysis pipeline if it crashes when I fiddle with the data? How can I draw conclusions from simulations if I cannot change their parameters? As soon as I need trust in code supporting a scientific finding, I find myself tinkering with its input, and often breaking it. Good scientific code is code that can be reused, that can lead to large-scale experiments validating its underlying assumptions.
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Four short links: 29 April 2015

Four short links: 29 April 2015

Deceptive Visualisation, Small Robots, Managing Secrets, and Large Time Series

  1. Disinformation Visualisation: How to Lie with DatavisWe don’t spread visual lies by presenting false data. That would be lying. We lie by misrepresenting the data to tell the very specific story we’re interested in telling. If this is making you slightly uncomfortable, that’s a good thing; it should. If you’re concerned about adopting this new and scary habit, well, don’t worry; it’s not new. Just open your CV to be reminded you’ve lied with truthful data before. This time, however, it will be explicit and visual. (via Regine Debatty)
  2. Microtugsa new type of small robot that can apply orders of magnitude more force than it weighs. This is in stark contrast to previous small robots that have become progressively better at moving and sensing, but lacked the ability to change the world through the application of human-scale loads.
  3. Vaulta tool for securely managing secrets and encrypting data in-transit.
  4. iSAX: Indexing and Mining Terabyte Sized Time Series (PDF) — Our approach allows both fast exact search and ultra-fast approximate search. We show how to exploit the combination of both types of search as sub-routines in data mining algorithms, allowing for the exact mining of truly massive real-world data sets, containing millions of time series. (via Benjamin Black)
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Four short links: 11 June 2014

Four short links: 11 June 2014

Right to Mine, Summarising Microblogs, C Sucks for Stats, and Scanning Logfiles

  1. UK Copyright Law Permits Researchers to Data Mine — changes mean Copyright holders can require researchers to pay to access their content but cannot then restrict text or data mining for non-commercial purposes thereafter, under the new rules. However, researchers that use the text or data they have mined for anything other than a non-commercial purpose will be said to have infringed copyright, unless the activity has the consent of rights holders. In addition, the sale of the text or data mined by researchers is prohibited. The derivative works will be very interesting: if university mines the journals, finds new possibility for a Thing, is verified experimentally, is that Thing the university’s to license commercially for profit?
  2. Efficient Online Summary of Microblogging Streams (PDF) — research paper. The algorithm we propose uses a word graph, along with optimization techniques such as decaying windows and pruning. It outperforms the baseline in terms of summary quality, as well as time and memory efficiency.
  3. Statistical Shortcomings in Standard Math Libraries — or “Why C Derivatives Are Not Popular With Statistical Scientists”. The following mathematical functions are necessary for implementing any rudimentary statistics application; and yet they are general enough to have many applications beyond statistics. I hereby propose adding them to the standard C math library and to the libraries which inherit from it. For purposes of future discussion, I will refer to these functions as the Elusive Eight.
  4. fail2ban — open source tool that scans logfiles for signs of malice, and triggers actions (e.g., iptables updates).
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Big data and privacy: an uneasy face-off for government to face

MIT workshop kicks off Obama campaign on privacy

Thrust into controversy by Edward Snowden’s first revelations last year, President Obama belatedly welcomed a “conversation” about privacy. As cynical as you may feel about US spying, that conversation with the federal government has now begun. In particular, the first of three public workshops took place Monday at MIT.

Given the locale, a focus on the technical aspects of privacy was appropriate for this discussion. Speakers cheered about the value of data (invoking the “big data” buzzword often), delineated the trade-offs between accumulating useful data and preserving privacy, and introduced technologies that could analyze encrypted data without revealing facts about individuals. Two more workshops will be held in other cities, one focusing on ethics and the other on law.

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The technical aspects of privacy

The first of three public workshops kicked off a conversation with the federal government on data privacy in the US.

Thrust into controversy by Edward Snowden’s first revelations last year, President Obama belatedly welcomed a “conversation” about privacy. As cynical as you may feel about US spying, that conversation with the federal government has now begun. In particular, the first of three public workshops took place Monday at MIT.

Given the locale, a focus on the technical aspects of privacy was appropriate for this discussion. Speakers cheered about the value of data (invoking the “big data” buzzword often), delineated the trade-offs between accumulating useful data and preserving privacy, and introduced technologies that could analyze encrypted data without revealing facts about individuals. Two more workshops will be held in other cities, one focusing on ethics and the other on law. Read more…

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How did we end up with a centralized Internet for the NSA to mine?

The Internet is naturally decentralized, but it's distorted by business considerations.

I’m sure it was a Wired editor, and not the author Steven Levy, who assigned the title “How the NSA Almost Killed the Internet” to yesterday’s fine article about the pressures on large social networking sites. Whoever chose the title, it’s justifiably grandiose because to many people, yes, companies such as Facebook and Google constitute what they know as the Internet. (The article also discusses threats to divide the Internet infrastructure into national segments, which I’ll touch on later.)

So my question today is: How did we get such industry concentration? Why is a network famously based on distributed processing, routing, and peer connections characterized now by a few choke points that the NSA can skim at its leisure?
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