Author Archives: niektemme

About niektemme

Niek Temme is Senior Consultant Business Analytics for DeltIQ Group in the Netherlands.

Using TensorFlow to create your own handwriting recognition engine

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This post describes an easy way to use TensorFlowTM to make your own handwriting engine. It is shown here as an example project.

The full source code can be found on github https://github.com/niektemme/tensorflow-mnist-predict/.

Introduction

I am in the process of writing an article on Machine Learning. When writing on this topic it is hard to ignore TensorFlowTM, a deep learning engine open sourced by Google. Deep learning is a branch of Machine Learning that uses the concept of the human brain in the form of neural networks to solve various problems such as image and speech recognition (Image 1). Problems that are hard to solve using computer ‘traditionally’: using a computer as a big calculator.

Deep Neural Network

Image 1: Deep Neural Network (source: Google)

The fact that TensorFlow is created by Google gives it a lot of traction,especially among the tech sites I follow. To learn more about TensorFlow I joined the local “Coffee & Coding” meetup in Amsterdam who hosted “Get our hands dirty with TensorFlow”.

At the meetup we experimented with tutorials from the TensorFlow website. The tutorials themselves are clear and well written. To me it seems that these examples focus primarily on building and validating the model, but using the created models is not a priority. An exception to this is the ‘Image Recognition’ example. This is, however, one of the more complex examples, making it hard to use when you are not a Machine Learning expert.

While searching the internet–perhaps even using some AI from the same company that created TensorFlow–I saw that more people were trying to find how to apply the created models to solve actual problems.

So I set my goal on how to use a trained model using the easier TensorFlow MNIST tutorials on handwriting recognition.

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Making your own smart ‘machine learning’ thermostat using Arduino, AWS, HBase, Spark, Raspberry PI and XBee

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This blog post describes making your own smart thermostat using machine learning (K-means clustering) and a bunch of hardware: Arduino, Raspberry PI, two XBee’s and an Amazon Cloud sever (see: idea in brief). I have to start with a disclaimer. I am not a programmer good programmer and certainly not an electrical engineer. However, this research project yielded a working thermostat that is able to learn over time how to improve energy efficiency.

For those who want to directly go to the smart ‘learning’ part, you can skip to Part 7: Learning and adapting temperature scenarios in the Amazon cloud (SPARK). Otherwise, the structure of this post, after a short introduction and overview, follows the path of data, control and necessary feedback loops as shown in figure 1. Every part contains specific code examples. The full source code can found on github https://github.com/niektemme/.

  1. Introduction & overview
  2. Reading data form a ‘dumb’ thermostat and various temperature sensors (Arduino)
  3. Sending data, at 1,000 values per second, to a Raspberry PI (Python)
  4. Storing data in the Amazon Cloud (HBase)
  5. Turning the boiler on and off at the right time (Arduino)
  6. Using outside temperature and scenarios to control an Arduino from a Raspberry PI
  7. Learning and adapting temperature scenarios in the Amazon cloud (SPARK)
Idea in brief
This blog post describes building and programming your own smart thermostat. The smart part is based on machine learning in the form of K-means clustering to optimize when, how often and how long the boiler/furnace turns on. The thermostat is built on the concept of feedback loops (figure 1).

  1. The first feedback loop is an Arduino directly controlling the boiler (furnace).
  2. The second feedback loop is a Raspberry PI that uses XBee to wirelessly receive temperature data and boiler status information from the Arduino and send instructions back to the Arduino.
  3. The third and last feedback loop runs on a server in the Amazon cloud. This server uses the Spark Machine Learning Library (MLlib) and HBase to optimize the boiler control model that is running on the Raspberry PI.

schema_loop4
Figure 1: Overview

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Exploring the future of information part 3 – Aristotle on the value of information

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The value of information is the level to which it can extend our ability to sense our environment over time and place.

Idea in brief

In this third post I explore a complex question: ‘What is the value of information?’ I have come to the conclusion that to answer this question we have to use the philosophy of Aristotle. More specifically, I discovered that the value of information is the level to which it can extend our ability to sense our environment.

Drawing from my experience as the founder of a digital media company in the 90’s, being an information consultant in the 00’s and working with today’s big data, I first explore the reinvention of the value of information throughout the last three decades. I find that existing theories s do not provide a sufficient answer. The fundamental value of information is not in its application. Rather, the economic theory about ‘making decisions’ reduces the value to choosing between preset options and the business case theory only explains that we have to find value, not how to find it.

To find the value of information I dive deeper into what it means to be a human being. By discovering that Aristotle defines the human soul as a ‘natural sensory body’.,as a result, the value of information is the level to which it can extend our ability to sense our environment of time and place. Applying this theory to existing uses of information, ranging from paper documents to accounting systems, I come to the conclusion that this not only provides a much better explanation of what the value of information is, but that it also gives us a new way in getting more value out of new technology.

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Exploring the future of information part 2

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Why ancient Egyptian building concepts should not be applied to using information.

This is the second post in a series in which I explore the future of information. In these posts I combine a variety of concepts ranging from the impact of the philosophy of Aristotle on using information to the Internet of Things.

In this second post I explore my own career in the field of Business Intelligence, explain why the pyramid should not be used to represent an organization and how information can be applied in its context instead of a hierarchy. Previous post: Introduction, The rise and deception of post-modernism. Upcoming posts are:

3. Aristotle on the value of information.
4. Consumerization of Information
5. Consumer Data Governance
6. The right app-platform for the Internet of Things
7. The stars in the future field of information technology.

Five years ago I made a career change. I moved from the digital media industry to the field of Business Intelligence (BI). Part of this move was going from an organization of 8 people to a company with 80,000 employees. Starting in this new field I was struck by the abundant use of a particular geometric form: the pyramid. Almost to the extent that the shape itself holds the truth to using information in organizations, which overly simplified what the field of Business Intelligence is about. The shape was used for describing the hierarchy of information itself: data, information, knowledge and wisdom. The shape was used to describe the transformation from raw data to usable information dashboards. Most importantly, the shape was used to represent an organization, often divided into the three levels of operational, tactical and strategic. Concepts often used in the BI paradigm like ‘one version of the truth’, ‘the enterprise data warehouse’ (with an emphasis on enterprise), ‘management information’ and ‘operational BI’ comply with the idea of the organization as a pyramid. When I moved towards to field of Performance Management (PM), I found that here the pyramid had an even greater importance. According do the PM paradigm information flows and narrows to the top by aggregating Key Performance Indicators and targets and actions cascade from the top to the ‘operational’ level.

From the start I felt uneasy with using a pyramid to represent an organization, because it did not fit with the theory from my MBA education. Modern organizations do not function according to a strict hierarchy. Also, the idea of ‘centralized control’ is replaced by empowerment and internal markets. At first I attributed this feeling to moving from a tiny company to one of the largest IT firms in the world. After five years of working as a BI Consultant, however, I have never seen an organization that could be represented by a pyramid. Even when an attempt is made to make it look like one, it always disappoints. One of the best examples comes from when I was working on an assignment for the Academy of a big government institution. This academy, seen as an education related HR department, operated completely independent of the primary activities of the rest of the organization, which dealt mostly with handling large amounts of money. Yet the academy was managed in the same way as any other business unit. Not only did it have to use the same IT systems and other facilities that were designed for these primary activities, but it also had to use the same metrics. This led to such great inefficiencies that any business unit preferred using an external company for training or HR services if they could. This was not only because of cost but also because it was better equipped for doing the task it was set-out to do.

So how should we look at an organization? First a bit of theory. In 1979 the renowned professor Henry Mintzberg published his most famous book ‘The Structuring of Organizations’. In Mintzberg’s model (figure 1) the basic organization consists of five parts: operating core, middle line, strategic apex, techno structure and support staff. To be honest, even here the outline of a pyramid can still be seen. But at leas two parts are added, the techno structure and the support staff. Looking at Mintzberg’s five variations of organizational structure (figure 2) the pyramid almost completely disappears. This is most apparent in the diversified, or divisionalised organization. In the Harvard Business Review article following the book (1981) Minztberg states that most Fortune 500 firms have adopted this divisionalised form. And according to him, “The Divisionalised Form differs from the other four structural configurations in one important respect. It is not a complete structure from the strategic apex to the operating core, but rather a structure superimposed on others”. He further explains that each division has its own structure.

In my article ‘Beyond agility, evolutionary IT-systems and business processes’ I argue that by using divisions organizations can adapt to their environment. Using this form is essential for survival.

Taking a glance on the current organizational structure of the 2012 list of Fortune 500 firms reveals that the divisionalised structure is still most popular amongst these firms. For example ExxonMobil has an organizational structure based on 12 separate global businesses. Philips, the Dutch electronics giant, has three business units based on market sectors and one unit called ‘Innovation & Emerging Businesses’, which contains all support staff. Philips needs a total of six separate organizational charts to explain its organization. Starbucks changed in 2011 to a structure containing five separate organizations: three based on region (China and Asia Pacific, Americas), EMEA and two on the promising brands Seattle’s Best Coffee and Tazo tea. Also in the nonbusiness sector the Divisionalised form is well known. Take for example the Dutch Justice Department. Its organizational “chart” is so complex-containing at least three IT related business units-that it only exists in words and complete sentences. Designing an actual chart would be too complex.

Now what does this mean for the future of information? First of all when using information in organizations the actual complexity of the organization has to be taken into account. This means that the ideal of an ‘Enterprise Data Warehouse’ is no longer attainable. The buying up and selling off of smaller companies by larger corporations not only makes this impossible, but completely integrating business units is also not desirable. Furthermore, ‘one version of the truth’ is a goal that should not be attained. Every business unit is its own organization and operates in its own environment. Every part of an organization, therefore, has its own ‘version of the truth’.

From my own experience I have found it is much better to focus on the actual application of information, regardless of the structure. Information can flow from and to all parts of the organization, as displayed in figure 3. This can be up, down, lateral across business units, diagonal across business units or even from or to outside the organization. A good example of this is the use of information by a well-known large Dutch retailer. They embedded an algorithm in their supply chain to automatically send last minute updates to the warehouses. This algorithm uses data from all corners of the business. By using this diverse information their replenishment process is one of the most efficient in the world. The application of information in its context instead of a hierarchy is something I will further explore in my next posts: ‘Aristotle on the value of information’, ‘the consumerization of information’.

The series continues with:
Part 3 Aristotle on the value of information.

Modified on April 2, 2013 Added link to part 3 in reference to future posts.

Exploring the future of information part 1

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Introduction: The rise and deception of post-modernism

This is the first post in a series in which I explore the future of information. In these posts I combine a variety of concepts ranging from the impact of the philosophy of Aristotle on using information to the Internet of Things.

The introduction explores the deception of post-modernism and how it relates to the future of information. Upcoming posts are:

2. Why ancient Egyptian building concepts should not be applied to using information.
3. Aristotle on the value of information.
4. Consumerization of Information
5. Consumer Data Governance
6. The right app-platform for the Internet of Things
7. The stars in the future field of information technology.

“The future of information” might be one of the most pretentious titles for a series of blog posts. I came up with this title after reading an article in F@stCompany called ’10 Tips From Boing Boing On Making Online Content Sing’. Since I wrote my previous post more then a year ago I felt I definitely needed to use some ‘singing content’. The title is based on tip nr. 4, “get an attitude”. I promise, however, that the content of this series will adhere to the concepts of good Storytelling. These posts will be based on my own stories and ideas about the future of information.

It seems like all current articles or blog post start with a famous quote. Most popular are quotes from Steve Jobs, as if just by quoting the Apple guru will make any piece of text more respectable. Trying to find a quote that is as pretentious as the title of this series I remembered a Mac OS application. This application presented you with a new famous quote every time you restarted your computer. I must admit I have seen this software only once, when my cousin bought a second-hand computer for his mother. He bought it at an Apple event in the mid-90’s from a Filemaker salesman. This was during the ‘sabbatical’ years of Jobs from Apple and most people believed there was little future for Macs. The floor size of the exhibitors hall had been reduced by half from the previous year and Filemaker had a clear strategy of moving their database product to the Windows platform. The salesperson might have thought that selling his Mac made more business sense than selling Apple compatible products. After bringing it to my cousins’ mother we booted the machine to see if we had to clean it up. In addition to the pictures that my cousin deemed unfit for his mother, we noticed that the startup screen had been replaced by software showing a quote from a famous philosopher. We immediately removed both pictures and software. Remembering using quotes as a cheap sales tactic, I decided to use a more personal story as an introduction.

Like all 20 something’s I (re)invented ‘post modernism’. This might sound pretentious as well, but it is not. From my experience, all adolescents at some point in their lives come to the conclusion that ‘all truths are relative’. I distinctly remember when I had my ‘revelation’. When I was 22 my parents took my brother and me on a four-week vacation to Japan. The last city we visited was Kyoto, and at the end of our trip we were completely templed-out. So instead of visiting yet another beautiful ancient temple we went to the Miho museum. This is, in my opinion, one of the most beautiful museums in the world. The art is not particularly exciting and I have almost no memory of the paintings and sculptures I saw. The museum itself however left a lasting impression. The entrance is through a pedestrian tunnel that takes you from an ordinary bus station to the museum building that is literally carved out of a mountain. It must have been the lighting in the museum, which feels like you can actually touch a ray of sunlight-I vaguely remember looking at a painting painted by an Russian artists during the communist era of that country-that I came to the conclusion that people’s belief systems are formed by earlier experiences and that the way people see and judge the world is therefore also based these experiences. As a result people see the world through their own lens, and behold the (re)invention (by my 22-year-old self) of post modernism. Two years later I came to the conclusion, like most people, that this insight is useless. The scene in which I had this insight could be described as the opposite from the one above, as it was the ‘not so clean’ kitchen in my student apartment. Nevertheless, no less true, I found out that both ends of the spectrum, complete dogma and completely individual, are so self evident that they are useless, in and of themselves, to explain anything.

Now what do the rise and deception of post-modernism have to do with the future of information? For that I have to go to a more recent museum visited. This will be my last museum story, I promise. After a long renovation the Stedelijk Museum in Amsterdam reopened. A couple of days ago I revisited the museum, and I was especially drawn to the paintings related to the magazine De Stijl. De Stijl was founded in 1917 in the Netherlands and has had many famous contributors, including the painter Piet Mondrian and architect Gerrit Rietveld. Being Dutch myself I have long had an interest in the painters from ‘De Stijl’, but being redrawn to them I wondered what got me so exited. According to its Wikipedia entry the members of De Stijl aimed for a radical reform of the art to keep pace with the technical, scientific and social changes in the world. After recent technical innovations in information technology, like tablets, smartphones, cheap sensors or technology related to big data I believe that it is worthwhile to aim for a conceptual view on the future of information. Now, the magazine De Stijl was founded many years before the creation of post-modernism and is attributed to have had a significant influence in Dutch modernism. Likewise the ‘big-data’ movement, which almost like a modernistic movement is highly technology driven is being countered by almost post-modernistic ideas that it all depends on the context in which technology is used. This however does not do justice to the potential lasting effect of this technology. Both sides are so self evident that they are useless in explaining anything. So while exploring the future of information I will do my best to present a nuanced view and find the middle ground between new information technological development an the context in which information is applied.

The series continues with:
Part 2 Why ancient Egyptian building concepts should not be applied to using information.
Part 3 Aristotle on the value of information.

Modified on April 2, 2013 Added links to parts 2 and 3 in reference to future posts.

Hadoop as a classic paradigm shift. Thomas Kuhn under the Christmas tree.

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This Christmas I decided to give myself a gift ‘Hadoop: the definitive guide’ by Tom White. As most people who will read this post already know, Apache Hadoop was inspired by Google’s MapReduce to supports data-intensive applications to work with petabytes (1 million gigabytes) of data1. Given the nature of this book I did not put it under the Christmas tree. My wife even questioned why I would give myself such a ‘gift’ instead of something that was more ‘fun’.  For me getting to know a new paradigm in computer technology was actually lot of fun. It was something I have been wanting to do for quite a while, but never got around to actually doing it.

I decided to dive directly into Hadoop itself instead of opting for a more top-level approach from Hive. Hive was developed by Facebook to allow a more SQL-like interface to Hadoop2. And SQL is of course well known within the Analytics or Business Intelligence (BI) community.  Trying to understand a new paradigm I wanted to get to understand the fundamentals of the Hadoop. In my opinion following something that was already familiar to me and was designed to work with the current BI world would not allow me to grasp the full potential of the new world of Hadoop as many of the new possibilities would be lost in translation. Unfortunately this required me to dive deep into the world of Java programming, which is the most common interface to use Hadoop.  Something that I briefly got into in 1996 to develop interactive websites but eventually dropped, because at the time none of the commonly accepted web-browsers (for example Netscape 2) would be able to run the java applets. Talking to Java developers on a regular basis I had already understood that Java had come a long way since 1996, but this was the first time that I had to understand Java code myself.

Going through the examples given in the book and other material from the internet, including the apache project website itself, I realised that Hadoop followed the classic pattern of a paradigm shift as explained by the modern philosopher Thomas Kuhn. As Kuhn explains in his famous work ‘The Structure of Scientific Revolutions’ a new paradigm arises when the current paradigm no longer provides a suitable explanation to all the issues presented to a (scientific) community. Contrary to popular belief, Kuhn describes that a new paradigm does not come from people totally unaware of the current paradigm, or from those who deliberately try to avoid it. Instead it arises when people well aware of the current theories and concepts have to find solutions for problems they can no longer solve using these theories and concepts.

Likewise Hadoop follows the same pattern. Many of the Hadoop examples will give a sense of recognition for many BI proffessionals, especially those who are involved in the Extract Transform and Load or ETL process, which involves getting all the required source data and loading it into a more convenient format. Especially the Mapper function (The Google MapReduce framework is based on two steps Map and Reduce) deals with many issues the ETL developers have to deal with as well. Does dealing with empty values and data quality issues ring a bell? Putting several map and reduce steps in flow gives even more the resemblance of an ETL flow that can be implemented by currently available commercial and open source BI tooling. In this regard Hadoop is well grounded in the existing paradigm.

However Hadoop was clearly designed to solve a different set of needs than are currently served by existing BI applications. Or transaction oriented database technology for that matter. It is in these needs where the Hadoop framework shines. The argument that most people use is that Hadoop can handle more data. As is reflected by the slogan that is printed on the book I bought for Christmas, “Storage and Analysis at Internet Scale”. But for me, this does little justice to Hadoop’s capabilities. Simply because the existing technology can be stretched further to handle more data. An argument many of the existing BI and database vendors and professionals like to give. Which is in line with another concept from Thomas Kuhn, that the new paradigm can never be understood from the old paradigm.

For me the main areas in which the Hadoop shines: to run continuously, to always be on, to serve many users and to quickly do real-time analysis.

  • Run continuously
    Hadoop was clearly designed to run continuously. Of course it had to be. Google’s users live in every time zone and do not want to wait for a batch window to complete before they can use the service again. Also the internet changes every minute, and working on a week old information or even a day old just would not cut it.
  • To always be on
    It is also amazing how much effort was put in to making it fault tolerant so it could always be on. I guess the consumers using google were much more demanding than the average business BI users who have to wait because a nightly load was unsuccessful and the process has to start again.
  • Serve many users
    Hadoop is also designed to server many many users at the same time. Wheras the existing BI paradigm mostly sees a lot of users providing data (through operational systems) and only a few analyzing this data or using the information that is obtained from this data. Often providing information to the select few at the top of the organization is seen as the holy grail in the current BI paradigm.  Hadoop almost has the opposite approach, which obviously can analyze a lot of data from various sources, but can also serve millions of users at the same time.
  • Quickly do real-time analysis
    Instead of calling it ‘big-data’ we might should call it ‘fast-data’.  Hadoop’s is not only able to handle a lot of data, but also to quickly analyze it. This does not only give users real-time information, but it also eliminates the need to use pre-aggregated data to give the information fast enough. As a result Hadoop can be used in situations where a lot of newly created or ‘fresh’ data had to be analyzed in a very short timeframe.  Which is uncommon for many other frameworks thar rely on aggregation and/or indexing of historic data to make information to be delivered faster.

Following the theory presented, the new paradigm will eventually replace the old one. The benefits of Hadoop described above may seem unnecessary for many organizations today.  They could be absolutely vital tomorrow. Increasingly organizations operate 24/7 worldwide, requiring their information to flow continuously. Business users have become more demanding, requiring that their business app’s are always on, as they are accustomed to as a consumer. More and more employees, at all levels of the organization, are demanding that they have access to the information they need for their jobs. And the use of information is increasingly embedded in the daily routines. Strangely enough though existing BI applications struggle to increase their user base. Perhaps a new kind of technology is what is required. Finally an increase of usage of data that comes from outside of the organization, for example social media, or sensor information needs a framework that can analyze vast amounts of new data quickly to provide information quickly enough for decisions to be made in a short enough timeframe.

A couple of years from now we might conclude that new paradigm that Hadoop brings us now will have provided us with the answers for all our issues. Until of course new problems arise that Hadoop can no longer solve. At that time it will be ready to move on the next paradigm, which of course ‘The Structure of Scientific Revolutions’ tells us will continue to happen.

1)    http://en.wikipedia.org/wiki/Apache_Hadoop
2)    Personal interpretation

Beyond agility

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Nestled in the pages of The Economist was a flyer from one of the Big Five Accountancy firms. The topic of this flyer was performance improvement through ‘Business Agility’. Apparently the term (Business) Agility is going mainstream. Recently I even heard a senior executive at a presentation for operational managers saying that this term was already outdated and that we should look beyond agility to a new way of working.

As it so happens (of course not a coincidence), the title of an article I published last April started with ‘Beyond Agility’. Copied below is the introductory paragraph. Click on the title to read the complete article.

Beyond agility, evolutionary IT-systems and business processes
What organizations can do to ‘survive’ and why agility does not work.
The Financial crisis of 2008 and 2009 has strengthened the idea that in order for organizations to ‘survive’ they should be able to quickly adapt to new market circumstances (Heifetz, Grashow & Linskey, 2009). This ability to adapt, is called ‘Agility’ (Hugos, 2009). Agility however, will cause an organization eventually to fail and go ‘extinct’ (Douma & Schreuder, 1998).

Instead of looking at agility we should take an Evolutionary Approach to organizations. We know how organisms survive. Applying the same theory to organizations, will explain more about their survival (Hannan & Freeman, 1989; Nelson & Winter, 1982).

Applying the theory of evolution to IT-systems and business processes tells us what organizations can actually do to stay alive (Davenport, 1993; McAfee, Brynjolfsson, 2008). As a result, an organization’s survival depends on successfully implementing evolutionary IT-systems and business processes.

Hello world!

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Although this is a professional/corporate blog, the words ‘hello world’ make me have to write about my new daughter. Born on the 10th of september the first jersey she wore had the same words printed on it. As anyone who works in the IT industry knows, these two words are of high significance because they stand for ‘it works’ and ‘it was succesful’. This very true for my daughter. She was born four weeks early, but in perfect health. Everything works as it should be she is a very succesful example of a human being.

I promise that the future posts will be purely related tot the topic of Business Information Management and Performance Improvement. Unless there is an other good analogy of course.