Data mining curiosities: RSCTC 2010 write-up

In the previous week we had an excellent data mining conference in Warsaw – Rough Sets and Current Trends in Computing (RSCTC). Several months ago, TunedIT had organized the Discovery Challenge for RSCTC: analysis of genetic data for medical purposes. Now, there was a challenge session where the winners presented their solutions to general public. Everyone was really curious how they did it and many questions followed after their talks, so they had no choice but to lift the curtain on their secret tricks. If anyone still wants to learn more, I recommend looking into the challenge paper – to be found here or in conference proceedings (pp. 4-19). We’ll also post shortly an interview with one of the winners, so stay tuned!

Apart from the contest, the conference brought many interesting presentations. First of all, there were four invited keynote talks given by prominent researchers, professors: Roman Słowiński, Sankar Pal, Rakesh Agrawal and Katia Sycara.

Rakesh Agrawal is the head of Microsoft Search Labs, responsible for the development of Microsoft’s Bing search engine. In his talk, Search and Data: The Virtuous Cycle, he sketched what kinds of data mining problems they face when trying to make Bing more “intelligent”, so that search results contain exactly the pages that the user is looking for. It appears that one of the toughest problems is to discover real intentions of the user: what is he really looking for? Search engine knows only the query string, usually very short (1-2 words,often misspelled), say “Ireland”, and must guess what the user expects: travel guide for a tourist or geographical facts about the country? Another problem is that many words have several different meanings: if the user writes “polish” does it mean a verb, “to polish”, or an adjective, “Polish”? Yet another problem: how to deal with numbers in a smart way? The query “$200 camera” gives few sensible results if treated literally – better try “$199 camera” 🙂

Rakesh Agrawal at RSCTC

Many more issues of this kind must be dealt with. Add that the algorithms must dig through petabytes of data in a matter of seconds, and you’ll have no doubts that guys in Microsoft Search Labs never complain about boring assignments. BTW, I must confirm from own experience that data size and performance requirements are critical factors to make data mining fun. With small data and no performance difficulties, data mining is just an interesting thing to do. When performance begins to play a role, you discover that 95% of your fantastic algorithms just don’t catch up and you’ve got to turn all the bright ideas (and software) upside down.

Katia Sycara at RSCTCAnother talk which I really enjoyed – Emergent Dynamics of Information Propagation in Large Networks – was delivered by Katia Sycara from Carnegie Mellon University. It’s interesting to observe how large networks of “agents”, for example people, share information among themselves on a peer-to-peer basis, like through gossiping, and how the information fills the whole network at some point in time or – conversely – suddenly disappears. It’s important that we can predict evolution of such processes, because in real world the “information” distributed may be an infectious disease whose spread should be stopped as soon as possible; or an operator’s request that must be distributed to all computers in a large decentralized network, in a shortest possible time.

Which outcome is observed depends on different parameters of the network: how many connections there are between agents, what’s the topology (uniform connections? separated clusters?), how keen the agents are to pass the gossip further on. But what’s the most interesting is that Read more of this post

What is data science?

An interesting post by Mike Loukides at O’Reilly blogs: What is data science? The title question is hard to answer. Most likely there’s no single answer that everyone would agree upon. But still, Mike makes a couple of good points and observations that are worth quoting:

The web is full of  “data-driven apps.” Almost any e-commerce application is a data-driven application. (…) But merely using data isn’t really what we mean by “data science.” A data application acquires its value from the data itself, and creates more data as a result. It’s not just an application with data; it’s a data product.

I would add that not only the web is full of data. The amount of data grows exponentially in every domain, be it on-line or off-line apps. But the users are moving more and more from off-line to web applications, plus it’s easier and more natural to merge together data from different users when things happen on the web than in an off-line scenario. Some examples of off-line applications: analysis of medical records, bioinformatics & genetics, video surveillance, energy demand forecasting, industrial control systems.

In the last few years, there has been an explosion in the amount of data that’s available. Whether we’re talking about web server logs, tweet streams, online transaction records, “citizen science,” data from sensors, government data, or some other source, the problem isn’t finding data, it’s figuring out what to do with it.

Data Scientist
Yep. Data is the king. I like examples with CDDB and Google. It’s good to realize that 97% of Google revenue actually comes from data mining algorithms: PageRank (smart search engine) combined with AdSense and AdWords (intelligent online advertising). To put it differently, 23 bln $ of Google revenue in 2009 came from data mining algorithms. It’s  data mining and machine learning which make Google search engine so accurate in answering queries and which attract  so many users. It’s data mining and machine learning which allow Google to present digital advertisements in optimal place and time, to users who are potentially most interested in a given product.

At the same time,  intelligent algorithms make up as little as 1% (or less) of their whole code base. Google has lots of other software that has nothing in common with data mining – various web apps (like Google Docs), libraries, widgets, APIs – but the core, the critical code in terms of their revenue, the code that makes Google be Google, is data mining!

This relation – 97% of revenue from 1% of code base – is very typical for data mining applications. On the other hand, this 1% of code is very hard to invent, much harder than the other 99%. I wonder how much do data mining algorithms make for Google in terms of costs? Mainly for paying the specialists who devise them and thoroughly, step by step, over long period of time, tune them up? I would guess for a number that’s closer to 99% than 1%.

The question every company is facing today (…) is how to use data effectively (…). Using data effectively requires something different from traditional statistics, where actuaries in business suits perform arcane but fairly well-defined kinds of analysis. What differentiates data science from statistics is that data science is a holistic approach. We’re increasingly finding data in the wild, and data scientists are involved with gathering data, massaging it into a tractable form, making it tell its story, and presenting that story to others.

Nothing to add.

What is data science?

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The Spirit of TunedIT

Some time ago I spoke to a friend of mine, Paweł Szczęsny from the Polish Academy of Sciences – a biologist, a visionary of Open Science and a pioneer of scientific blogging. When I mentioned about our plans to start a blog for TunedIT, Paweł, after giving it a serious thought, had come up with the following advice: „Remember one thing: do not write about yourself. If someone writes about oneself, the blog becomes terribly boring. Only if you keep writing about something different, it has a chance to be interesting”.

Nerd Ghost

The Nerd Ghost

At first, this tip of advice seemed illogical to me – what’s the point in opening a blog related to the web portal TunedIT, if we are to write about something totally different? All in all, this is so natural: if a new functionality comes up on TunedIT, we will mention it in the blog: „Today a new functionality has been released, which enables … it helps in … you can use it like this …” and so on. If there’s going to be a new competition: „Today we’re launching a new competition … the task is to …” Isn’t it the way you do it? Each of us could instantly list a long series of blogs where similar posts can be encountered. Don’t they sound so familiar, so natural, so conventional, so … banal? Hm, wait a moment. Banal? Actually, … Have I read many blogs like that? Sure! A lot! How many of them have I read further than to the second sentence of the paragraph? I can’t recall any… So maybe writing about yourself is not the best choice for your blog, in fact? But if not, what else then makes it tick? Read more of this post

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