Tag: research
RCOMM 2012 goes live in August
An awesome conference by an awesome software Rapid Miner remains one of the leading enterprise grade open source software , that can help you do a lot of things including flow driven data modeling ,web mining ,web crawling etc which even other software cant.
Presentations include:
- Mining Machine 2 Machine Data (Katharina Morik, TU Dortmund University)
- Handling Big Data (Andras Benczur, MTA SZTAKI)
- Introduction of RapidAnalytics at Telenor (Telenor and United Consult)
- and more
Here is a list of complete program
Program
Time
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Tuesday
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Wednesday
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Thursday
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Friday
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09:00 – 10:30 |
Introductory Speech Ingo Mierswa (Rapid-I)Resource-aware Data Mining or M2M Mining (Invited Talk) Katharina Morik (TU Dortmund University)
Data Analysis
NeurophRM: Integration of the Neuroph framework into RapidMiner |
To be announced (Invited Talk) Andras Benczur Recommender Systems
Extending RapidMiner with Recommender Systems Algorithms Implementation of User Based Collaborative Filtering in RapidMiner |
Parallel Training / Workshop Session
Advanced Data Mining and Data Transformations or |
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10:30 – 11:00 |
Coffee Break |
Coffee Break |
Coffee Break |
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11:00 – 12:30 |
Data Analysis
Nearest-Neighbor and Clustering based Anomaly Detection Algorithms for RapidMiner Customers’ LifeStyle Targeting on Big Data using Rapid Miner Robust GPGPU Plugin Development for RapidMiner |
Extensions
Optimization Plugin For RapidMiner
Image Mining Extension – Year After Incorporating R Plots into RapidMiner Reports |
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12:30 – 13:30 |
Lunch |
Lunch |
Lunch |
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13:30 – 15:30 |
Parallel Training / Workshop Session
Basic Data Mining and Data Transformations or |
Applications
Introduction of RapidAnalyticy Enterprise Edition at Telenor Hungary
Application of RapidMiner in Steel Industry Research and Development A Comparison of Data-driven Models for Forecast River Flow Portfolio Optimization Using Local Linear Regression Ensembles in Rapid Miner |
Extensions
An Octave Extension for RapidMiner
Unstructured Data
Processing Data Streams with the RapidMiner Streams-Plugin Automated Creation of Corpuses for the Needs of Sentiment Analysis
Demonstration: News from the Rapid-I Labs This short session demonstrates the latest developments from the Rapid-I lab and will let you how you can build powerful analysis processes and routines by using those RapidMiner tools. |
Certification Exam |
15:30 – 16:00 |
Coffee Break |
Coffee Break |
Coffee Break |
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16:00 – 18:00 |
Book Presentation and Game Show
Data Mining for the Masses: A New Textbook on Data Mining for Everyone Matthew North presents his new book “Data Mining for the Masses” introducing data mining to a broader audience and making use of RapidMiner for practical data mining problems.
Game Show |
User Support
Get some Coffee for free – Writing Operators with RapidMiner Beans Meta-Modeling Execution Times of RapidMiner operators Conference day ends at ca. 17:00. |
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19:30 |
Social Event (Conference Dinner) |
Social Event (Visit of Bar District) |
and you should have a look at https://rapid-i.com/rcomm2012f/index.php?option=com_content&view=article&id=65
Conference is in Budapest, Hungary,Europe.
( Disclaimer- Rapid Miner is an advertising sponsor of Decisionstats.com in case you didnot notice the two banner sized ads.)
Interview Rob J Hyndman Forecasting Expert #rstats
Here is an interview with Prof Rob J Hyndman who has created many time series forecasting methods and authored books as well as R packages on the same.
Ajay -Describe your journey from being a student of science to a Professor. What were some key turning points along that journey?
Rob- I started a science honours degree at the University of Melbourne in 1985. By the end of 1985 I found myself simultaneously working as a statistical consultant (having completed all of one year of statistics courses!). For the next three years I studied mathematics, statistics and computer science at university, and tried to learn whatever I needed to in order to help my growing group of clients. Often we would cover things in classes that I’d already taught myself through my consulting work. That really set the trend for the rest of my career. I’ve always been an academic on the one hand, and a statistical consultant on the other. The consulting work has led me to learn a lot of things that I would not otherwise have come across, and has also encouraged me to focus on research problems that are of direct relevance to the clients I work with.
I never set out to be an academic. In fact, I thought that I would get a job in the business world as soon as I finished my degree. But once I completed the degree, I was offered a position as a statistical consultant within the University of Melbourne, helping researchers in various disciplines and doing some commercial work. After a year, I was getting bored doing only consulting, and I thought it would be interesting to do a PhD. I was lucky enough to be offered a generous scholarship which meant I was paid more to study than to continue working.
Again, I thought that I would probably go and get a job in the business world after I finished my PhD. But I finished it early and my scholarship was going to be cut off once I submitted my thesis. So instead, I offered to teach classes for free at the university and delayed submitting my thesis until the scholarship period ran out. That turned out to be a smart move because the university saw that I was a good teacher, and offered me a lecturing position starting immediately I submitted my thesis. So I sort of fell into an academic career.
I’ve kept up the consulting work part-time because it is interesting, and it gives me a little extra money. But I’ve also stayed an academic because I love the freedom to be able to work on anything that takes my fancy.
Ajay- Describe your upcoming book on Forecasting.
Rob- My first textbook on forecasting (with Makridakis and Wheelwright) was written a few years after I finished my PhD. It has been very popular, but it costs a lot of money (about $140 on Amazon). I estimate that I get about $1 for every book sold. The rest goes to the publisher (Wiley) and all they do is print, market and distribute it. I even typeset the whole thing myself and they print directly from the files I provided. It is now about 15 years since the book was written and it badly needs updating. I had a choice of writing a new edition with Wiley or doing something completely new. I decided to do a new one, largely because I didn’t want a publisher to make a lot of money out of students using my hard work.
It seems to me that students try to avoid buying textbooks and will search around looking for suitable online material instead. Often the online material is of very low quality and contains many errors.
As I wasn’t making much money on my textbook, and the facilities now exist to make online publishing very easy, I decided to try a publishing experiment. So my new textbook will be online and completely free. So far it is about 2/3 completed and is available at http://otexts.com/fpp/. I am hoping that my co-author (George Athanasopoulos) and I will finish it off before the end of 2012.
The book is intended to provide a comprehensive introduction to forecasting methods. We don’t attempt to discuss the theory much, but provide enough information for people to use the methods in practice. It is tied to the forecast package in R, and we provide code to show how to use the various forecasting methods.
The idea of online textbooks makes a lot of sense. They are continuously updated so if we find a mistake we fix it immediately. Also, we can add new sections, or update parts of the book, as required rather than waiting for a new edition to come out. We can also add richer content including video, dynamic graphics, etc.
For readers that want a print edition, we will be aiming to produce a print version of the book every year (available via Amazon).
I like the idea so much I’m trying to set up a new publishing platform (otexts.com) to enable other authors to do the same sort of thing. It is taking longer than I would like to make that happen, but probably next year we should have something ready for other authors to use.
Ajay- How can we make textbooks cheaper for students as well as compensate authors fairly
Rob- Well free is definitely cheaper, and there are a few businesses trying to make free online textbooks a reality. Apart from my own efforts, http://www.flatworldknowledge.com/ is producing a lot of free textbooks. And textbookrevolution.org is another great resource.
With otexts.com, we will compensate authors in two ways. First, the print versions of a book will be sold (although at a vastly cheaper rate than other commercial publishers). The royalties on print sales will be split 50/50 with the authors. Second, we plan to have some features of each book available for subscription only (e.g., solutions to exercises, some multimedia content, etc.). Again, the subscription fees will be split 50/50 with the authors.
Ajay- Suppose a person who used to use forecasting software from another company decides to switch to R. How easy and lucid do you think the current documentation on R website for business analytics practitioners such as these – in the corporate world.
Rob- The documentation on the R website is not very good for newcomers, but there are a lot of other R resources now available. One of the best introductions is Matloff’s “The Art of R Programming”. Provided someone has done some programming before (e.g., VBA, python or java), learning R is a breeze. The people who have trouble are those who have only ever used menu interfaces such as Excel. Then they are not only learning R, but learning to think about computing in a different way from what they are used to, and that can be tricky. However, it is well worth it. Once you know how to code, you can do so much more. I wish some basic programming was part of every business and statistics degree.
If you are working in a particular area, then it is often best to find a book that uses R in that discipline. For example, if you want to do forecasting, you can use my book (otexts.com/fpp/). Or if you are using R for data visualization, get hold of Hadley Wickham’s ggplot2 book.
Ajay- In a long and storied career- What is the best forecast you ever made ? and the worst?
Rob- Actually, my best work is not so much in making forecasts as in developing new forecasting methodology. I’m very proud of my forecasting models for electricity demand which are now used for all long-term planning of electricity capacity in Australia (see http://robjhyndman.com/papers/peak-electricity-demand/ for the details). Also, my methods for population forecasting (http://robjhyndman.com/papers/stochastic-population-forecasts/ ) are pretty good (in my opinion!). These methods are now used by some national governments (but not Australia!) for their official population forecasts.
Of course, I’ve made some bad forecasts, but usually when I’ve tried to do more than is reasonable given the available data. One of my earliest consulting jobs involved forecasting the sales for a large car manufacturer. They wanted forecasts for the next fifteen years using less than ten years of historical data. I should have refused as it is unreasonable to forecast that far ahead using so little data. But I was young and naive and wanted the work. So I did the forecasts, and they were clearly outside the company’s (reasonable) expectations, and they then refused to pay me. Lesson learned. It’s better to refuse work than do it poorly.
Probably the biggest impact I’ve had is in helping the Australian government forecast the national health budget. In 2001 and 2002, they had underestimated health expenditure by nearly $1 billion in each year which is a lot of money to have to find, even for a national government. I was invited to assist them in developing a new forecasting method, which I did. The new method has forecast errors of the order of plus or minus $50 million which is much more manageable. The method I developed for them was the basis of the ETS models discussed in my 2008 book on exponential smoothing (www.exponentialsmoothing.net)
. And now anyone can use the method with the ets() function in the forecast package for R.
About-
Rob J Hyndman is Professor of Statistics in the Department of Econometrics and Business Statistics at Monash University and Director of the Monash University Business & Economic Forecasting Unit. He is also Editor-in-Chief of the International Journal of Forecasting and a Director of the International Institute of Forecasters. Rob is the author of over 100 research papers in statistical science. In 2007, he received the Moran medal from the Australian Academy of Science for his contributions to statistical research, especially in the area of statistical forecasting. For 25 years, Rob has maintained an active consulting practice, assisting hundreds of companies and organizations. His recent consulting work has involved forecasting electricity demand, tourism demand, the Australian government health budget and case volume at a US call centre.
Interview John Myles White , Machine Learning for Hackers
Here is an interview with one of the younger researchers and rock stars of the R Project, John Myles White, co-author of Machine Learning for Hackers.
Ajay- What inspired you guys to write Machine Learning for Hackers. What has been the public response to the book. Are you planning to write a second edition or a next book?
John-We decided to write Machine Learning for Hackers because there were so many people interested in learning more about Machine Learning who found the standard textbooks a little difficult to understand, either because they lacked the mathematical background expected of readers or because it wasn’t clear how to translate the mathematical definitions in those books into usable programs. Most Machine Learning books are written for audiences who will not only be using Machine Learning techniques in their applied work, but also actively inventing new Machine Learning algorithms. The amount of information needed to do both can be daunting, because, as one friend pointed out, it’s similar to insisting that everyone learn how to build a compiler before they can start to program. For most people, it’s better to let them try out programming and get a taste for it before you teach them about the nuts and bolts of compiler design. If they like programming, they can delve into the details later.
We once said that Machine Learning for Hackers is supposed to be a chemistry set for Machine Learning and I still think that’s the right description: it’s meant to get readers excited about Machine Learning and hopefully expose them to enough ideas and tools that they can start to explore on their own more effectively. It’s like a warmup for standard academic books like Bishop’s.
The public response to the book has been phenomenal. It’s been amazing to see how many people have bought the book and how many people have told us they found it helpful. Even friends with substantial expertise in statistics have said they’ve found a few nuggets of new information in the book, especially regarding text analysis and social network analysis — topics that Drew and I spend a lot of time thinking about, but are not thoroughly covered in standard statistics and Machine Learning undergraduate curricula.
I hope we write a second edition. It was our first book and we learned a ton about how to write at length from the experience. I’m about to announce later this week that I’m writing a second book, which will be a very short eBook for O’Reilly. Stay tuned for details.
Ajay- What are the key things that a potential reader can learn from this book?
John- We cover most of the nuts and bolts of introductory statistics in our book: summary statistics, regression and classification using linear and logistic regression, PCA and k-Nearest Neighbors. We also cover topics that are less well known, but are as important: density plots vs. histograms, regularization, cross-validation, MDS, social network analysis and SVM’s. I hope a reader walks away from the book having a feel for what different basic algorithms do and why they work for some problems and not others. I also hope we do just a little to shift a future generation of modeling culture towards regularization and cross-validation.
Ajay- Describe your journey as a science student up till your Phd. What are you current research interests and what initiatives have you done with them?
John-As an undergraduate I studied math and neuroscience. I then took some time off and came back to do a Ph.D. in psychology, focusing on mathematical modeling of both the brain and behavior. There’s a rich tradition of machine learning and statistics in psychology, so I got increasingly interested in ML methods during my years as a grad student. I’m about to finish my Ph.D. this year. My research interests all fall under one heading: decision theory. I want to understand both how people make decisions (which is what psychology teaches us) and how they should make decisions (which is what statistics and ML teach us). My thesis is focused on how people make decisions when there are both short-term and long-term consequences to be considered. For non-psychologists, the classic example is probably the explore-exploit dilemma. I’ve been working to import more of the main ideas from stats and ML into psychology for modeling how real people handle that trade-off. For psychologists, the classic example is the Marshmallow experiment. Most of my research work has focused on the latter: what makes us patient and how can we measure patience?
Ajay- How can academia and private sector solve the shortage of trained data scientists (assuming there is one)?
John- There’s definitely a shortage of trained data scientists: most companies are finding it difficult to hire someone with the real chops needed to do useful work with Big Data. The skill set required to be useful at a company like Facebook or Twitter is much more advanced than many people realize, so I think it will be some time until there are undergraduates coming out with the right stuff. But there’s huge demand, so I’m sure the market will clear sooner or later.
The changes that are required in academia to prepare students for this kind of work are pretty numerous, but the most obvious required change is that quantitative people need to be learning how to program properly, which is rare in academia, even in many CS departments. Writing one-off programs that no one will ever have to reuse and that only work on toy data sets doesn’t prepare you for working with huge amounts of messy data that exhibit shifting patterns. If you need to learn how to program seriously before you can do useful work, you’re not very valuable to companies who need employees that can hit the ground running. The companies that have done best in building up data teams, like LinkedIn, have learned to train people as they come in since the proper training isn’t typically available outside those companies.
Of course, on the flipside, the people who do know how to program well need to start learning more about theory and need to start to have a better grasp of basic mathematical models like linear and logistic regressions. Lots of CS students seem not to enjoy their theory classes, but theory really does prepare you for thinking about what you can learn from data. You may not use automata theory if you work at Foursquare, but you will need to be able to reason carefully and analytically. Doing math is just like lifting weights: if you’re not good at it right now, you just need to dig in and get yourself in shape.
About-
John Myles White is a Phd Student in Ph.D. student in the Princeton Psychology Department, where he studies human decision-making both theoretically and experimentally. Along with the political scientist Drew Conway, he is the author of a book published by O’Reilly Media entitled “Machine Learning for Hackers”, which is meant to introduce experienced programmers to the machine learning toolkit. He is also working with Mark Hansenon a book for laypeople about exploratory data analysis.John is the lead maintainer for several R packages, including ProjectTemplate and log4r.
(TIL he has played in several rock bands!)
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You can read more in his own words at his blog at http://www.johnmyleswhite.com/about/
He can be contacted via social media at Google Plus at https://plus.google.com/109658960610931658914 or twitter at twitter.com/johnmyleswhite/
Interview Alain Chesnais Chief Scientist Trendspottr.com
Here is a brief interview with Alain Chesnais ,Chief Scientist Trendspottr.com. It is a big honor to interview such a legend in computer science, and I am grateful to both him and Mark Zohar for taking time to write these down.
Ajay- Describe your career from your student days to being the President of ACM (Association of Computing Machinery http://www.acm.org/ ). How can we increase the interest of students in STEM education, particularly in view of the shortage of data scientists.
Alain- I’m trying to sum up a career of over 35 years. This may be a bit long winded…
I started my career in CS when I was in high school in the early 70’s. I was accepted in the National Science Foundation’s Science Honors Program in 9th grade and the first course I took was a Fortran programming course at Columbia University. This was on an IBM 360 using punch cards.
The next year my high school got a donation from DEC of a PDP-8E mini computer. I ended up spending a lot of time in the machine room all through high school at a time when access to computers wasn’t all that common. I went to college in Paris and ended up at l’Ecole Normale Supérieure de Cachan in the newly created Computer Science department.
My first job after finishing my graduate studies was as a research assistant at the Centre National de la Recherche Scientifique where I focused my efforts on modelling the behaviour of distributed database systems in the presence of locking. When François Mitterand was elected president of France in 1981, he invited Nicholas Negroponte and Seymour Papert to come to France to set up the Centre Mondial Informatique. I was hired as a researcher there and continued on to become director of software development until it was closed down in 1986. I then started up my own company focusing on distributed computer graphics. We sold the company to Abvent in the early 90’s.
After that, I was hired by Thomson Digital Image to lead their rendering team. We were acquired by Wavefront Technologies in 1993 then by SGI in 1995 and merged with Alias Research. In the merged company: Alias|wavefront, I was director of engineering on the Maya project. Our team received an Oscar in 2003 for the creation of the Maya software system.
Since then I’ve worked at various companies, most recently focusing on social media and Big Data issues associated with it. Mark Zohar and I worked together at SceneCaster in 2007 where we developed a Facebook app that allowed users to create their own 3D scenes and share them with friends via Facebook without requiring a proprietary plugin. In December 2007 it was the most popular app in its category on Facebook.
Recently Mark approached me with a concept related to mining the content of public tweets to determine what was trending in real time. Using math similar to what I had developed during my graduate studies to model the performance of distributed databases in the presence of locking, we built up a real time analytics engine that ranks the content of tweets as they stream in. The math is designed to scale linearly in complexity with the volume of data that we analyze. That is the basis for what we have created for TrendSpottr.
In parallel to my professional career, I have been a very active volunteer at ACM. I started out as a member of the Paris ACM SIGGRAPH chapter in 1985 and volunteered to help do our mailings (snail mail at the time). After taking on more responsibilities with the chapter, I was elected chair of the chapter in 1991. I was first appointed to the SIGGRAPH Local Groups Steering Committee, then became ACM Director for Chapters. Later I was successively elected SIGGRAPH Vice Chair, ACM SIG Governing Board (SGB) Vice Chair for Operations, SGB Chair, ACM SIGGRAPH President, ACM Secretary/Treasurer, ACM Vice President, and finally, in 2010, I was elected ACM President. My term as ACM President has just ended on July 1st. Vint Cerf is our new President. I continue to serve on the ACM Executive Committee in my role as immediate Past President.
(Note- About ACM
ACM, the Association for Computing Machinery www.acm.org, is the world’s largest educational and scientific computing society, uniting computing educators, researchers and professionals to inspire dialogue, share resources and address the field’s challenges. )
ACM, the Association for Computing Machinery www.acm.org, is the world’s largest educational and scientific computing society, uniting computing educators, researchers and professionals to inspire dialogue, share resources and address the field’s challenges. )
Ajay- What sets Trendspotter apart from other startups out there in terms of vision in trying to achieve a more coherent experience on the web.
Alain- The Basic difference with other approaches that we are aware of is that we have developed an incremental solution that calculates the results on the fly as the data streams in. Our evaluators are based on solid mathematical foundations that have proven their usefulness over time. One way to describe what we do is to think of it as signal processing where the tweets are the signal and our evaluators are like triggers that tell you what elements of the signal have the characteristics that we are filtering for (velocity and acceleration). One key result of using this approach is that our unit cost per tweet analyzed does not go up with increased volume. Using more traditional data analysis approaches involving an implicit sort would imply a complexity of N*log(N), where N is the volume of tweets being analyzed. That would imply that the cost per tweet analyzed would go up with the volume of tweets. Our approach was designed to avoid that, so that we can maintain a cap on our unit costs of analysis, no matter what volume of data we analyze.
Ajay- What do you think is the future of big data visualization going to look like? What are some of the technologies that you are currently bullish on?
Alain- I see several trends that would have deep impact on Big Data visualization. I firmly believe that with large amounts of data, visualization is key tool for understanding both the structure and the relationships that exist between data elements. Let’s focus on some of the key things that are pushing in this direction:
- the volume of data that is available is growing at a rate we have never seen before. Cisco has measured an 8 fold increase in the volume of IP traffic over the last 5 years and predicts that we will reach the zettabyte of data over IP in 2016
- more of the data is becoming publicly available. This isn’t only on social networks such as Facebook and twitter, but joins a more general trend involving open research initiatives and open government programs
- the desired time to get meaningful results is going down dramatically. In the past 5 years we have seen the half life of data on Facebook, defined as the amount of time that half of the public reactions to any given post (likes, shares., comments) take place, go from about 12 hours to under 3 hours currently
- our access to the net is always on via mobile device. You are always connected.
- the CPU and GPU capabilities of mobile devices is huge (an iPhone has 10 times the compute power of a Cray-1 and more graphics capabilities than early SGI workstations)
Put all of these observations together and you quickly come up with a massive opportunity to analyze data visually on the go as it happens no matter where you are. We can’t afford to have to wait for results. When something of interest occurs we need to be aware of it immediately.
Ajay- What are some of the applications we could use Trendspottr. Could we predict events like Arab Spring, or even the next viral thing.
Alain- TrendSpottr won’t predict what will happen next. What it *will* do is alert you immediately when it happens. You can think of it like a smoke detector. It doesn’t tell that a fire will take place, but it will save your life when a fire does break out.
Typical uses for TrendSpottr are
- thought leadership by tracking content that your readership is interested in via TrendSpottr you can be seen as a thought leader on the subject by being one of the first to share trending content on a given subject. I personally do this on my Facebook page (http://www.facebook.com/alain.chesnais) and have seen my klout score go up dramatically as a result
- brand marketing to be able to know when something is trending about your brand and take advantage of it as it happens.
- competitive analysis to see what is being said about two competing elements. For instance, searching TrendSpottr for “Obama OR Romney” gives you a very good understanding about how social networks are reacting to each politician. You can also do searches like “$aapl OR $msft OR $goog” to get a sense of what is the current buzz for certain hi tech stocks.
- understanding your impact in real time to be able to see which of the content that you are posting is trending the most on social media so that you can highlight it on your main page. So if all of your content is hosted on common domain name (ourbrand.com), searching for ourbrand.com will show you the most active of your site’s content. That can easily be set up by putting a TrendSpottr widget on your front page
Ajay- What are some of the privacy guidelines that you keep in mind- given the fact that you collect individual information but also have government agencies as potential users.
Alain- We take privacy very seriously and anonymize all of the data that we collect. We don’t keep explicit records of the data we collected through the various incoming streams and only store the aggregate results of our analysis.
About–
Alain Chesnais is immediate Past President of ACM, elected for the two-year term beginning July 1, 2010.Chesnais studied at l’Ecole Normale Supérieure de l’Enseignement Technique and l’Université de Paris where he earned a Maîtrise de Mathematiques, a Maitrise de Structure Mathématique de l’Informatique, and a Diplôme d’Etudes Approfondies in Compuer Science. He was a high school student at the United Nations International School in New York, where, along with preparing his International Baccalaureate with a focus on Math, Physics and Chemistry, he also studied Mandarin Chinese.Chesnais recently founded Visual Transitions, which specializes in helping companies move to HTML 5, the newest standard for structuring and presenting content on the World Wide Web. He was the CTO of SceneCaster.com from June 2007 until April 2010, and was Vice President of Product Development at Tucows Inc. from July 2005 – May 2007. He also served as director of engineering at Alias|Wavefront on the team that received an Oscar from the Academy of Motion Picture Arts and Sciences for developing the Maya 3D software package.
Prior to his election as ACM president, Chesnais was vice president from July 2008 – June 2010 as well as secretary/treasurer from July 2006 – June 2008. He also served as president of ACM SIGGRAPH from July 2002 – June 2005 and as SIG Governing Board Chair from July 2000 – June 2002.
As a French citizen now residing in Canada, he has more than 20 years of management experience in the software industry. He joined the local SIGGRAPH Chapter in Paris some 20 years ago as a volunteer and has continued his involvement with ACM in a variety of leadership capacities since then.
About Trendspottr.com
TrendSpottr is a real-time viral search and predictive analytics service that identifies the most timely and trending information for any topic or keyword. Our core technology analyzes real-time data streams and spots emerging trends at their earliest acceleration point — hours or days before they have become “popular” and reached mainstream awareness.
TrendSpottr serves as a predictive early warning system for news and media organizations, brands, government agencies and Fortune 500 companies and helps them to identify emerging news, events and issues that have high viral potential and market impact. TrendSpottr has partnered with HootSuite, DataSift and other leading social and big data companies.
Rapid Miner User Conference 2012
One of those cool conferences that is on my bucket list- this time in Hungary (That’s a nice place)
But I am especially interested in seeing how far Radoop has come along !
Disclaimer- Rapid Miner has been a Decisionstats.com sponsor for many years. It is also a very cool software but I like the R Extension facility even more!
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and not very expensive too compared to other User Conferences in Europe!-
http://rcomm2012.org/index.php/registration/prices
Information about Registration
- Early Bird registration until July 20th, 2012.
- Normal registration from July 21st, 2012 until August 13th, 2012.
- Latest registration from August 14th, 2012 until August 24th, 2012.
- Students have to provide a valid Student ID during registration.
- The Dinner is included in the All Days and in the Conference packages.
- All prices below are net prices. Value added tax (VAT) has to be added if applicable.
Prices for Regular Visitors
Days and Event |
Early Bird Rate |
Normal Rate |
Latest Registration |
| Tuesday
(Training / Development 1) |
190 Euro | 230 Euro | 280 Euro |
| Wednesday + Thursday
(Conference) |
290 Euro | 350 Euro | 420 Euro |
| Friday
(Training / Development 2 and Exam) |
190 Euro | 230 Euro | 280 Euro |
| All Days
(Full Package) |
610 Euro | 740 Euro | 900 Euro |
Prices for Authors and Students
In case of students, please note that you will have to provide a valid student ID during registration.
Days and Event |
Early Bird Rate |
Normal Rate |
Latest Registration |
| Tuesday
(Training / Development 1) |
90 Euro | 110 Euro | 140 Euro |
| Wednesday + Thursday
(Conference) |
140 Euro | 170 Euro | 210 Euro |
| Friday
(Training / Development 2 and Exam) |
90 Euro | 110 Euro | 140 Euro |
| All Days
(Full Package) |
290 Euro | 350 Euro | 450 Euro |
Time
|
Tuesday
|
Wednesday
|
Thursday
|
Friday
|
09:00 – 10:30 |
Introductory Speech Ingo Mierswa; Rapid-I
Data Analysis
NeurophRM: Integration of the Neuroph framework into RapidMiner |
To be announced (Invited Talk) To be announced
Recommender Systems
Extending RapidMiner with Recommender Systems Algorithms Implementation of User Based Collaborative Filtering in RapidMiner |
Parallel Training / Workshop Session
Advanced Data Mining and Data Transformations or |
|
10:30 – 12:30 |
Data Analysis
Nearest-Neighbor and Clustering based Anomaly Detection Algorithms for RapidMiner Customers’ LifeStyle Targeting on Big Data using Rapid Miner Robust GPGPU Plugin Development for RapidMiner |
Extensions
Image Mining Extension – Year After Incorporating R Plots into RapidMiner Reports An Octave Extension for RapidMiner |
||
12:30 – 13:30 |
Lunch |
Lunch |
Lunch |
|
13:30 – 15:00 |
Parallel Training / Workshop Session
Basic Data Mining and Data Transformations or |
Applications
Application of RapidMiner in Steel Industry Research and Development A Comparison of Data-driven Models for Forecast River Flow Portfolio Optimization Using Local Linear Regression Ensembles in Rapid Miner |
Unstructured Data
Processing Data Streams with the RapidMiner Streams-Plugin Automated Creation of Corpuses for the Needs of Sentiment Analysis
Demonstration
News from the Rapid-I Labs This short session demonstrates the latest developments from the Rapid-I lab and will let you how you can build powerful analysis processes and routines by using those RapidMiner tools. |
Certification Exam |
15:00 – 17:00 |
Book Presentation and Game Show
Data Mining for the Masses: A New Textbook on Data Mining for Everyone Matthew North presents his new book “Data Mining for the Masses” introducing data mining to a broader audience and making use of RapidMiner for practical data mining problems.
Game Show |
User Support
Get some Coffee for free – Writing Operators with RapidMiner Beans Meta-Modeling Execution Times of RapidMiner operators |
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19:00 |
Social Event (Conference Dinner) |
Social Event (Visit of Bar District) |
Training: Basic Data Mining and Data Transformations
This is a short introductory training course for users who are not yet familiar with RapidMiner or only have a few experiences with RapidMiner so far. The topics of this training session include
- Basic Usage
- User Interface
- Creating and handling RapidMiner repositories
- Starting a new RapidMiner project
- Operators and processes
- Loading data from flat files
- Storing data, processes, and results
- Predictive Models
- Linear Regression
- Naïve Bayes
- Decision Trees
- Basic Data Transformations
- Changing names and roles
- Handling missing values
- Changing value types by discretization and dichotimization
- Normalization and standardization
- Filtering examples and attributes
- Scoring and Model Evaluation
- Applying models
- Splitting data
- Evaluation methods
- Performance criteria
- Visualizing Model Performance
Training: Advanced Data Mining and Data Transformations
This is a short introductory training course for users who already know some basic concepts of RapidMiner and data mining and have already used the software before, for example in the first training on Tuesday. The topics of this training session include
- Advanced Data Handling
- Sampling
- Balancing data
- Joins and Aggregations
- Detection and removal of outliers
- Dimensionality reduction
- Control process execution
- Remember process results
- Recall process results
- Loops
- Using branches and conditions
- Exception handling
- Definition of macros
- Usage of macros
- Definition of log values
- Clearing log tables
- Transforming log tables to data
Development Workshop Part 1 and Part 2
Want to exchange ideas with the developers of RapidMiner? Or learn more tricks for developing own operators and extensions? During our development workshops on Tuesday and Friday, we will build small groups of developers each working on a small development project around RapidMiner. Beginners will get a comprehensive overview of the architecture of RapidMiner before making the first steps and learn how to write own operators. Advanced developers will form groups with our experienced developers, identify shortcomings of RapidMiner and develop a new extension which might be presented during the conference already. Unfinished work can be continued in the second workshop on Friday before results might be published on the Marketplace or can be taken home as a starting point for new custom operators.
Interview Jason Kuo SAP Analytics #Rstats
Here is an interview with Jason Kuo who works with SAP Analytics as Group Solutions Marketing Manager. Jason answers questions on SAP Analytics and it’s increasing involvement with R statistical language.
Ajay- What made you choose R as the language to tie important parts of your technology platform like HANA and SAP Predictive Analysis. Did you consider other languages like Julia or Python.
Jason- It’s the most popular. Over 50% of the statisticians and data analysts use R. With 3,500+ algorithms its arguably the most comprehensive statistical analysis language. That said,we are not closing the door on others.
Ajay- When did you first start getting interested in R as an analytics platform?
Jason- SAP has been tracking R for 5+ years. With R’s explosive growth over the last year or two, it made sense for us to dramatically increase our investment in R.
Ajay- Can we expect SAP to give back to the R community like Google and Revolution Analytics does- by sponsoring Package development or sponsoring user meets and conferences?
Will we see SAP’s R HANA package in this year’s R conference User 2012 in Nashville
Jason- Yes. We plan to provide a specific driver for HANA tables for input of the data to native R. This planned for end of 2012. We’ll then review our event strategy. SAP has been a sponsor of Predictive Analytics World for several years and was indeed a founding sponsor. We may be attending the year’s R conference in Nashville.
Ajay- What has been some of the initial customer feedback to your analytics expansion and offerings.
Jason- We have completed two very successful Pilots of the R Integration for HANA with two of SAP’s largest customers.
About-
Jason has over 15 years of BI and Data Warehousing industry experience. Having worked at Oracle, Business Objects, and now SAP, Jason has been involved in numerous technical marketing roles involving performance management dashboards, information management, text analysis, predictive analytics, and now big data. He has a bachelor’s of science in operations research from the University of Michigan.
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