Making my dissertation relevant to non-academics calls for a change in style. An invitation to speak at the Open Data Häme workshop, following announcement of funding by the European Regional Development Fund, gave a venue to unveil some normative theory-building from my research potentially useful in the real world.
This talk was fewer slides, and more talk. With 9 content slides to cover in about an hour, the agenda was:
The slides had been posted on the Coevolving Commons in advance of the event.
The audio recording was exceptionally clear, and is downloadable (so boosted volume is probably unnecessary).
|[20170810_Hame_Ing mp3] (58MB)
[20170810_Hame_Ing 3db mp3] (volume boosted 3db, 58MB)
[20170810_Hame_Ing 6db mp3] (volume boosted 6db, 58MB)
Alternatively, downloadable video files may be better for people on the move.
(HD 325Kbps 238MB)
(nHD 109Kkps 97MB)
(HD 470Kbps 212MB)
[20170810_Hame_Ing nHD webm]
(nHD 177Kbps 80MB)
The first part of the talk places open data in the larger context and trend towards the behaviour of open sourcing, and open innovation. Open sourcing enables visibility into system internals, in contrast with private sourcing that makes internals opaque. The rise of open sourcing became more noticeable with the advent of open source licensing in software, but can generalized outside of technology with an example of raising and catching salmon.
daviding September 2nd, 2017
Posted In: innovation
For the Quantitative Methodologies for Design Research (定量研究方法) course for Ph.D. students at Tongji University in spring 2017, Susu Nousala invited me to join the team of instructors in collaborative education in Shanghai. Experts were brought in during the course to guide the graduate students.
While I’m comfortable with the mathematics underlying statistical analysis, I have a lot of practical experience of working with business executives who aren’t. Thus, my approach to working with data relies a lot on presentation graphics to defog the phenomena. While the label of data science began to rise circa 2012, I’ve had the benefit of practical experience that predates that.
In my first professional assignment in IBM Canada in 1985, data science would have been called econometrics. My work included forecasting country sales, based on price-performance indexes (from the mainframe, midrange and personal computer product divisions) and economic outlooks from Statistics Canada. Two years before the Macintosh II would bring color to personal computing, I was an early adopter of GRAFSTAT: “An APL system for interactive scientific-engineering graphics and data analysis” developed at IBM Research. This would eventually become an IBM program product by called AGSS (A Graphical Statistical System) by 1994.
In 1988, I had an assignment where data science would have been called marketing science. I was sent to California to work in the IBM partnership with Metaphor Computer Systems. This was a Xerox PARC spin-off with a vision that predated the first web page on the World Wide Web by a few years. These activities led me into the TIMS Marketing Science Conference in 1990, cofounding the Canadian Centre for Marketing Information Technologies (C2MIT) and contributing chapters to The Marketing Information Revolution published in 1994.
This journey led me to appreciate the selection and use of computer-based tools for quantitative analysis. Today, the two leading platforms in “Data Science 101” are Python (a general purpose language with statistical libraries), and the R Project for Statistical Computing (a specialized package for data analysis and visualization). Both are open source projects, and free to download and use on personal computers. I tried both. R is a higher level programming language more similar to the APL programming language that gets work done more quickly. For statistical work, I recommend R over Python (although APL is a theoretically better implementation).
Since I live in Toronto, I attended the February session of Data Science with R – Bootcamp in person, at Ryerson University. There, I was watched Polong Lin leading a class through R using the Jupyter notebook, both in (i) an interactive version, and (ii) a printable version. Students had the choice to either follow Polong (i) actively, in a step-by-step execution in the Cognitive Class Virtual Lab (formerly called the Data Scientist Workbench) with a cloud-based R session through their web browsers, or (ii) passively, reading the static printable content.
daviding August 26th, 2017
Posted In: universities
The theme of “New Developments of Systems Thinking: From IoT to AI” at the Tenth International Symposium on Service Systems Science presented an opportunity to look at changes currently happening with contemporary technologies. For a short talk, my agenda focused on three assertions:
The relevance of the research for my dissertation (currently in review at Aalto University) became a frame for examining IoT, cloud and cognitive. With both commercial and noncommercial contributors working alongside each other, content creators and makers should think ahead to conditions they wish to place on others who may derive from their works. The previously posted slides on the Coevolving Commons have been synchronized with the digital audio recording.
The lecture and subsequent questions-and-answers are available online as web video.
For those who just want to listen, downloadable audio files (some with digitally boosted volume) are an option.
daviding June 26th, 2017
For the “Understanding Systems & Systemic Design” course in the program for the Master of Design in Strategic Foresight and Innovation at OCAD University, the lecture slides were the same for both the full-time cohort on March 8 and part-time cohort on March 9, while the oral presentation varied. The target, in about 90 minutes, was to cover at least 4 of 5 sections, from:
The students were alerted that some of the arrows in the section headings were double-headed, and some were single-headed — with specific meanings. For each day, the classroom audio was recorded. That digital audio has now been synchronized with slides that had previously been posted on the Coevolving Commons.
This session was #8 of 15 lectures for the OCADU SFI students. They had already done some basic reading on systems approaches. Since they were working towards a Major Research Project (a lighter weight form of a thesis) for their Master of Design degree, my overall agenda for this lecture was to have them reflect on acts of representation. Systems have already been represented to them in a variety of forms: textually, orally and visually. For their Major Research Projects, they would be creating detailed representations, as ways of having their audience appreciate the in-depth study of the world and issues selected for the term.
daviding June 7th, 2017
Teaching methods in a master’s class is different from lecturing on theory. There’s more emphasis on how, with why subsequently provided as the need for that arises. Since I had given a dense 20-minute theoretical talk in the month earlier, the invitation from Satu Teerikangas to the program in International Service Business Management was an opportunity to stretch out at a more leisurely pace with students, as they’re preparing for thesis work.
The 3 hours class was conducted in parts:
The classroom interaction was recorded in audio, and is complemented by slides that had been posted on the Coevolving Commons.
For people who prefer the real-time experience of being in a classroom, video and audio are provided, below.
daviding January 13th, 2017
At the PUARL Conference 2016, a proposal was made on adapting pattern language for service systems thinking. In 1967, Christopher Alexander published Pattern Manual at the founding of the Center for Environmental Structure, describing a pattern format for physical built environments. While we can learn a lot from the nearly 50 years work originating at the CES, service systems have features beyond physicality that suggest reconsidering some of the foundations of pattern language.
An article for discussion was accepted into the proceedings for the PUARL conference. The 20-minute presentation quickly covered the following topics:
Slides have been added over the audio recording to produce a video presentation.
(volume boosted 3db, 20MB, 20m19s)
(volume boosted 6db, 20MB, 20m19s)
|H.264 MP4||[1280×720 384Kbps m4v]
|[1280×720 5000Kbps m4v]
|WebM||[1280×720 110Kbps webm]
|[1280×720 826Kbps webm]
daviding November 17th, 2016