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Mineknowledge Magazine, Vol. I

The first volume of the magazine of http://mineknowledge.com, a data mining services company

1 of 5
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MINEKNOWLEDGE
December 1, 2008




                   You can visualize data
                   mining as a
                   process of searching
                   for treasure buried in
                   the sand or digging up
                   rock to mine for gold -
                   thus 'mining', but the
                   tools we use
                   do it in a truly
                   systematic and
                   efficient way.




                                             The problem with data
                                             and why you do care
                                             By Anna Skountzou


                                             You are flooded with tons of         knowledge hidden inside? Or           consider as a statistical analysis.
                                             data, day by day.                   how such an input would make          Put aside the fluffy terminology
                                             Spreadsheets, reports,              the difference for you and your       and start thinking of rules and
                                             surveys, you name it. But           company?                              patterns, all illustrated via
                                             you are neither the info                 We bet that, even if it has      expositive writing and
                                             junkie nor the number               already crossed your mind, you        visualization schemes, finally
                                             cruncher. You actually have         have neither the time nor the         contributing the less biased and
                                             no time to invest on it,            right techniques to exploit this      most valuable signals for your
                                             while you know that you’re          data.  And, till now, you were        decisions.
                                             wasting precious insights           forced to hire a connoisseur of            And keep in mind that
                                             and significant potential.           data analysis, something that         latent knowledge presents both a
                                             Despair? No.                        proved to be costly and time-         hidden cost and a tremendous
                                                  The problem is simple. Too     consuming. Yes, that was the best     opportunity, but there is no need
                                             much information in                 case, typically you just did          to be stressed about that
                                             spreadsheets or other formats,      nothing.                              anymore, you may now just mine
                                             laying around your hard disk or          Well, till now. Our team not     your knowledge!
                                             in the cloud. Have you ever tried   only relieves you of all these, but
                                             to imagine the amount of            also extends what you used to



   1
MINEKNOWLEDGE December 1, 2008
                                                          Our solution
                                                          why mineknowledge?
                                                          By Manos Androulakis


                                                          Speaking of taking the most out of your data sets,     “A miner with a mattock in his
                                                          we provide you with a rock solid solution. The         hand is a very rough way to
                                                          process goes like this:                                conceptualize the complexity and
                                                                                                                 state-of-the-art of the
                                                              1. You send your data to us.                       processes we execute.
                                                                                                                 A diverse and extended set of
                                                              2. You sit back, breathe some fresh air and
                                                                                                                 exploration and filtering
                                                          enjoy every moment of your life in between.
                                                                                                                 algorithms, next to a variety of
                                                                                                                 learning and meta-learning
                                                             3. You open your inbox and receive the very
                                                            knowledge and secrets trapped in your data,          techniques, are utilized, optimized
                                                            unveiled.                                            and evaluated, while the problem is
                                                                                                                 a computationally intensive one
                                                               And here are a few more reasons on why to         and demands a highly customized
                                                          select us.                                             approach.
                                                               1. It is easy: Consultants, discussions,          So we’re putting human
                                                               meetings. Forget them all. What you need to       intelligence and our high expertise
                                                               do is just send us an email, with your data set   in between of various advanced
                                                               attached.                                         artificial intelligence algorithms, to
                                                               2. It is fast: Within a week, results and the     finally provide you with the very
                                                               very knowledge of your data set will pop up in    secrets trapped in your data,
                                                               your inbox.                                       unveiled.”
                                                               3. It is fun: Honestly. Working with tons of
                                                               data is so much fun, especially when others do    George Tziralis

                                                               all the work for you.                             6. It is insightful: You already know that,
                                                               4. It is secure: Rest assured that we’ve          we give you the most valuable insights on your
                                                               done our best to keep your data and mining        data in return.
                                                               results safe and private (the latter may not be   7. It is affordable: The cost of a
                                                               valid in our free services).                      datamine.it analysis range, you either pay
                                                               5. It is clear: If statistics sound greek to      nothing, or €500.
                                                               you, we’re speaking your language.




                                 data
                                 stand as the least
                                 biased input to deci-
                                 sion making, a pure
                                 source of insights and
                                 knowledge.




            2
MINEKNOWLEDGE December 1, 2008
                                                                                                    “Think of a simple process. Then, make it
                                                                                                    simpler. Try to find the steps that are still
                                                                                                    vague. Cut them off. Finally, ask your
                                                                                                    grandma what she cannot understand.
                                                                                                    This is how we make things happen in
                                                                                                    MIneKnowledge”
                                                                                                    Iro Zacharidou




                                 The process:
                                 Hassle free
                                 By Eleftheria Kanavou


                                 It’s simple. You just email us your data set, in              balance sheet, you name it). And there are no limitations in
                                 an .xls, .csv or .txt format. And then we take over.          the number of columns and rows of your set.
                                                                                                    Clear enough? Ok, that’s it. As long as you got the data
                                    The file should in a form like the one in the figure. Let us set in that format, you just sent it to us with an email at
                                 make it even more clear.                                      go(AT)mineknowledge.com. We’ll reply with a confirmation
                                                                                               of receiving an appropriate file, plus an invoice via paypal.
                                                                                               And, within a week, you’ll have a fully fledged
                                                                                               mineknowledge report, waiting in your inbox. We think it’s
                                                                                               simple.



                                                                                                   Pricing
                                                                                                       There are two pricing plans:

                                                                                                        FREE: You can have the whole report for free, if your
                                 Columns in the data set are attributes, like color, size, value   data set is of less than 30 columns (attributes) and 300 rows
                                 and purchase decision; whatever your data set is made of.         (instances), plus you agree that we may publish the analysis in
                                 Attributes may be numeric (numbers), or nominal (one or           our blog. In this case, the report may take up to a month.
                                 more words). You also need to define the ‘target’ attribute,
                                 one or more characteristics you want us to focus on and               MAX: No restrictions at all, delivery within a week, full
                                 explain its behavior based on all the other attributes.           fledged analysis for a €500 flat price.
                                      You also may call each row an instance, a case, an
                                 example, in other words a discrete set of values of each              You may take a look at a typical datamine.it results
                                 attribute (let’s say a person’s reply to a survey, a product’s    report in our website, while we do wait for your data sets!
                                 characteristics in a list of products, quarter updates in a

            3
MINEKNOWLEDGE December 1, 2008
                                 An example:
                                 Surveys
                                 By Athina Pandi
                                 You used to think column graphs and pies                   But, the question remains. Is that the most
                                 as the most insightful views you could                 you can expect from a data set analysis? Have you
                                 expect from a data analysis. You’ll                    actually gain deep insights from your data? The
                                 probably change your mind. Let’s take a                answer is a clear no. Let us show you why.
                                                                                            What follows is a set of rules that emerge from
                                 look at a survey example.
                                                                                        a proper datamine.it analysis, even for a data set as
                                                                                        oversimplified as the above example. Try this
                                     A simplistic one. Consider the data set
                                                                                        graph:
                                 described in the previous page.




                                      Let’s say it refers to answers gathered through
                                                                                            or, maybe this set of rules:
                                 a survey, or stored in your enterprise database. A
                                 typical analysis will finally come up with some
                                                                                                    If color = yellow then buy = yes
                                 graphs, like the ones following.                                   If color = red then buy = yes
                                                                                                    If color = white then buy = yes
                                                                                                    If color = green then buy = no
                                                                                                    If color = blue then buy = no
                                                                                                    If color = black then buy = no

                                                                                            See the difference between the almost obvious
                                                                                        and the really insightful?

                                                                                             Go find out more in a complete typical
                                                                                        mineknowledge report. Yes, this is what your own
                                                                                        data set will look like, just after a week. Still
                                                                                        considering it? Check out our blog for more case
                                                                                        studies. And send us your data, now.
                                      And you’re probably used to consider the
                                 analysis contributing a graph like the above as,
                                 well, fruitful. Same for the following one.




            4
MINEKNOWLEDGE December 1, 2008
                                 A few more words
                                 about us
                                 By Eirini Lygkoni
                                 MineKnowledge is a group of young and passionate
                                 data engineers, each of us holding an engineering
                                 diploma from NTUA and an MSc or PhD in Applied
                                 Math, Statistics or Operations Research. We
                                 are located in Athens, Greece and London, UK.

                                      • George Tziralis is clearly a data junkie who
                                   lives on his mac. In the rare case he’s logged off, he
                                   enjoys dancing tango and organizing Open Coffee
                                   meetings around Greece. Apart from that, at 26 he is
                                   a serial entrepreneur, while he also teaches a data
                                   mining post-graduate course via blog and tries to find
                                   some time to write up his PhD Thesis on markets for
                                   forecasting.
                                      • Athina Pandi is -among datamine.it- on her
                                   second MSc at Imperial College. Communications &
                                   Signal Processing is her late interest, next to statistics,
                                   data mining and networks. When she is offline, you
                                   may find her in a pub around Hyde park.
                                      • Eleftheria Kanavou, with a strong tendency
                                   in dancing, is the one who naturally gives rhythm to
                                   the whole team. Her research interests include
                                   stochastic processes and behavioral statistics, while
                                   data mining is the physical outlet of her
                                   entrepreneurial attitude. 
                                      • Manos Androulakis is the algo geek of the
                                   team. The biggest the challenge and the data set, the
                                   most determined he is for the next diamond to mine.
                                   His expertise lies in the areas of statistical designs,
                                   variable selection methods and medical applications,
                                   under the prism of data mining of course.
                                      • Eirini Lygkoni is the epitome of doing magic
                                   under pressure. A multi-tasker by nature, she is                                       data is our passion
                                                                                                                          and mining our joy
                                   addicted to statistics and probabilities, while she                                    We literally can’t wait
                                   literally can’t wait for the next data set to arrive. At                               to put our hands on
                                                                                                                          your data; get ready
                                   the same time, simplicity is her favorite word and                                     to be impressed -or
                                   socializing her selection of choice for her rare free                                  even excited- from the
                                                                                                                          precious insights that
                                   time. Enough said.                                                                     you’ll receive in just a
                                      • Lina Massou stands as the quiet power of the                                      week.
                                   team. With a strong background in information
                                   theory and cryptography, she definitely is the one to
                                   take good care of your data and come up with their
                                   very knowledge, unveiled.
                                      • Anna Skountzou excels at both statistics
                                   research and ecological conscience. That said, she’s
                                   definitely the one to look for, when you are looking at
                                   extracting patterns to let you put your data into
                                   much more efficient use, their green footprint
                                   included. 
                                      • Iro Zacharidou is a true data nut, next to a
                                   party animal, putting her deep statistical expertise
                                   aside. If you wonder about the outcome of these               MINEKNOWLEDGE
                                   coming together, rest assured that the required               Athens, Greece | London, UK
                                   amount of persistence and professionalism to put              go@mineknowledge.com
                                   your data into investigation will be largely outrun.          http://www.mineknowledge.com

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Mineknowledge Magazine, Vol. I

  • 1. MINEKNOWLEDGE December 1, 2008 You can visualize data mining as a process of searching for treasure buried in the sand or digging up rock to mine for gold - thus 'mining', but the tools we use do it in a truly systematic and efficient way. The problem with data and why you do care By Anna Skountzou You are flooded with tons of knowledge hidden inside? Or consider as a statistical analysis. data, day by day. how such an input would make Put aside the fluffy terminology Spreadsheets, reports, the difference for you and your and start thinking of rules and surveys, you name it. But company? patterns, all illustrated via you are neither the info We bet that, even if it has expositive writing and junkie nor the number already crossed your mind, you visualization schemes, finally cruncher. You actually have have neither the time nor the contributing the less biased and no time to invest on it, right techniques to exploit this most valuable signals for your while you know that you’re data.  And, till now, you were decisions. wasting precious insights forced to hire a connoisseur of And keep in mind that and significant potential. data analysis, something that latent knowledge presents both a Despair? No. proved to be costly and time- hidden cost and a tremendous The problem is simple. Too consuming. Yes, that was the best opportunity, but there is no need much information in case, typically you just did to be stressed about that spreadsheets or other formats, nothing. anymore, you may now just mine laying around your hard disk or Well, till now. Our team not your knowledge! in the cloud. Have you ever tried only relieves you of all these, but to imagine the amount of also extends what you used to 1
  • 2. MINEKNOWLEDGE December 1, 2008 Our solution why mineknowledge? By Manos Androulakis Speaking of taking the most out of your data sets, “A miner with a mattock in his we provide you with a rock solid solution. The hand is a very rough way to process goes like this: conceptualize the complexity and state-of-the-art of the 1. You send your data to us. processes we execute. A diverse and extended set of 2. You sit back, breathe some fresh air and exploration and filtering enjoy every moment of your life in between. algorithms, next to a variety of learning and meta-learning 3. You open your inbox and receive the very knowledge and secrets trapped in your data, techniques, are utilized, optimized unveiled. and evaluated, while the problem is a computationally intensive one And here are a few more reasons on why to and demands a highly customized select us. approach. 1. It is easy: Consultants, discussions, So we’re putting human meetings. Forget them all. What you need to intelligence and our high expertise do is just send us an email, with your data set in between of various advanced attached. artificial intelligence algorithms, to 2. It is fast: Within a week, results and the finally provide you with the very very knowledge of your data set will pop up in secrets trapped in your data, your inbox. unveiled.” 3. It is fun: Honestly. Working with tons of data is so much fun, especially when others do George Tziralis all the work for you. 6. It is insightful: You already know that, 4. It is secure: Rest assured that we’ve we give you the most valuable insights on your done our best to keep your data and mining data in return. results safe and private (the latter may not be 7. It is affordable: The cost of a valid in our free services). datamine.it analysis range, you either pay 5. It is clear: If statistics sound greek to nothing, or €500. you, we’re speaking your language. data stand as the least biased input to deci- sion making, a pure source of insights and knowledge. 2
  • 3. MINEKNOWLEDGE December 1, 2008 “Think of a simple process. Then, make it simpler. Try to find the steps that are still vague. Cut them off. Finally, ask your grandma what she cannot understand. This is how we make things happen in MIneKnowledge” Iro Zacharidou The process: Hassle free By Eleftheria Kanavou It’s simple. You just email us your data set, in balance sheet, you name it). And there are no limitations in an .xls, .csv or .txt format. And then we take over. the number of columns and rows of your set. Clear enough? Ok, that’s it. As long as you got the data The file should in a form like the one in the figure. Let us set in that format, you just sent it to us with an email at make it even more clear. go(AT)mineknowledge.com. We’ll reply with a confirmation of receiving an appropriate file, plus an invoice via paypal. And, within a week, you’ll have a fully fledged mineknowledge report, waiting in your inbox. We think it’s simple. Pricing There are two pricing plans: FREE: You can have the whole report for free, if your Columns in the data set are attributes, like color, size, value data set is of less than 30 columns (attributes) and 300 rows and purchase decision; whatever your data set is made of. (instances), plus you agree that we may publish the analysis in Attributes may be numeric (numbers), or nominal (one or our blog. In this case, the report may take up to a month. more words). You also need to define the ‘target’ attribute, one or more characteristics you want us to focus on and MAX: No restrictions at all, delivery within a week, full explain its behavior based on all the other attributes. fledged analysis for a €500 flat price. You also may call each row an instance, a case, an example, in other words a discrete set of values of each You may take a look at a typical datamine.it results attribute (let’s say a person’s reply to a survey, a product’s report in our website, while we do wait for your data sets! characteristics in a list of products, quarter updates in a 3
  • 4. MINEKNOWLEDGE December 1, 2008 An example: Surveys By Athina Pandi You used to think column graphs and pies But, the question remains. Is that the most as the most insightful views you could you can expect from a data set analysis? Have you expect from a data analysis. You’ll actually gain deep insights from your data? The probably change your mind. Let’s take a answer is a clear no. Let us show you why. What follows is a set of rules that emerge from look at a survey example. a proper datamine.it analysis, even for a data set as oversimplified as the above example. Try this A simplistic one. Consider the data set graph: described in the previous page. Let’s say it refers to answers gathered through or, maybe this set of rules: a survey, or stored in your enterprise database. A typical analysis will finally come up with some If color = yellow then buy = yes graphs, like the ones following. If color = red then buy = yes If color = white then buy = yes If color = green then buy = no If color = blue then buy = no If color = black then buy = no See the difference between the almost obvious and the really insightful? Go find out more in a complete typical mineknowledge report. Yes, this is what your own data set will look like, just after a week. Still considering it? Check out our blog for more case studies. And send us your data, now. And you’re probably used to consider the analysis contributing a graph like the above as, well, fruitful. Same for the following one. 4
  • 5. MINEKNOWLEDGE December 1, 2008 A few more words about us By Eirini Lygkoni MineKnowledge is a group of young and passionate data engineers, each of us holding an engineering diploma from NTUA and an MSc or PhD in Applied Math, Statistics or Operations Research. We are located in Athens, Greece and London, UK. • George Tziralis is clearly a data junkie who lives on his mac. In the rare case he’s logged off, he enjoys dancing tango and organizing Open Coffee meetings around Greece. Apart from that, at 26 he is a serial entrepreneur, while he also teaches a data mining post-graduate course via blog and tries to find some time to write up his PhD Thesis on markets for forecasting. • Athina Pandi is -among datamine.it- on her second MSc at Imperial College. Communications & Signal Processing is her late interest, next to statistics, data mining and networks. When she is offline, you may find her in a pub around Hyde park. • Eleftheria Kanavou, with a strong tendency in dancing, is the one who naturally gives rhythm to the whole team. Her research interests include stochastic processes and behavioral statistics, while data mining is the physical outlet of her entrepreneurial attitude.  • Manos Androulakis is the algo geek of the team. The biggest the challenge and the data set, the most determined he is for the next diamond to mine. His expertise lies in the areas of statistical designs, variable selection methods and medical applications, under the prism of data mining of course. • Eirini Lygkoni is the epitome of doing magic under pressure. A multi-tasker by nature, she is data is our passion and mining our joy addicted to statistics and probabilities, while she We literally can’t wait literally can’t wait for the next data set to arrive. At to put our hands on your data; get ready the same time, simplicity is her favorite word and to be impressed -or socializing her selection of choice for her rare free even excited- from the precious insights that time. Enough said.  you’ll receive in just a • Lina Massou stands as the quiet power of the week. team. With a strong background in information theory and cryptography, she definitely is the one to take good care of your data and come up with their very knowledge, unveiled. • Anna Skountzou excels at both statistics research and ecological conscience. That said, she’s definitely the one to look for, when you are looking at extracting patterns to let you put your data into much more efficient use, their green footprint included.  • Iro Zacharidou is a true data nut, next to a party animal, putting her deep statistical expertise aside. If you wonder about the outcome of these MINEKNOWLEDGE coming together, rest assured that the required Athens, Greece | London, UK amount of persistence and professionalism to put go@mineknowledge.com your data into investigation will be largely outrun. http://www.mineknowledge.com