Peg, the well-being indicator system for the community of Winnipeg, recently won the international Community Indicators Consortium Impact Award, presented in Washington, D.C. on Sept. 30. Peg is a joint project of the United Way of Winnipeg (UWW) and the International Institute for Sustainable Development (IISD). Our company, Structured Dynamics, was the lead developer for the project, which is also based on SD’s Open Semantic Framework (OSF) platform.
Peg is an innovative Web portal that helps identify and, on an ongoing basis, track indicators that relate to the economic, environmental, cultural and social well-being of the people of Winnipeg. Datasets may be selected and compared with a variety of charting and mapping and visualization tools at the level of the entire city, neighborhoods or communities. All told, there are now more than 60 indicators within Peg ranging from active transportation to youth unemployment rates representing thousands of individual records and entities. We last discussed Peg upon its formal release in December 2013.
Congratulations to all team members!
I have been dazzled by what Stephen Wolfram has been doing with Wolfram Alpha since Day 1. The mathematical capabilities are exceedingly impressive, the backing knowledge base is exceedingly impressive, and the visualization has also come to be exceedingly impressive. The problem: no open source or anything.
I’ve never met Wolfram and acknowledge he has seen success many times greater than most. But, putting on my VC cap, I see an old school adherence to closed and proprietary.
I would not presume to suggest where the Wolfram folks should draw these lines on open and closed, but I definitely do counsel they work hard to open up as much as they can; it is in their own self interest. The business model of today is premised on leveraging the network effect in various ways. Total proprietary is a turn off and drives the compounding basis for the network effect into the dirt.
Maybe their recent pricing initiatives with Mathematica online are a wink toward this direction. But, better still find a core of functionality and knowledge base and release it as open source. The world will beat a path to what created all of this impressive stuff in the first place.
A few weeks back I reflected on more than a decade of involvement in the semantic Web. I appreciate the many nice comments and compliments received.
In part in reaction to that post and also because it is the retrospective season, Amit Sheth has just posted his own retrospective on the semWeb going back 15 years. Amit has been one of the leaders in the space and (according to his history) the first to obtain a semantic Web patent.
Amit’s wonderful history is more global and informed than my own, and I recommend you check it out. BTW, Amit is asking for comments from those involved in the early years for any corrections or additions.
For years now I have been a huge fan of Pandora (sorry, not apparently available to all across the world due to digital rights issues). Even though Pandora’s Music Genome was not set up as a W3C-compliant ontology, in its use and application it is effectively one. What Pandora shows is that feature selection and characterization trumps language and data structure format.
Given that, I have also been a Web scientist as to how I select and promote music to meet my musical interests.
Thus, based on my own totally unscientific study, here are a few things I’ve found worth passing on. It would be bold to call them secrets; they are really more just observations:
- When a new “seed” artist is chosen, the attributes of that artist (up to 450 different attributes from beat to genre to dominant instruments) set the pure characteristics of that channel
- As similar songs play that meet this profile, when you vote them “up” or “down” you are effectively adding or deleting options for these 450 attributes in your profile criteria
- Thus, continuous expressions of preference as a channel plays acts to “dilute” the purity of the initial seed; these preference expressions lead to a “mixed” seed
- The more that choices are preferred, the more the signal of your original selection gets diluted.
The net result is that I now no longer vote any of my songs on any channel as up or down. Rather, I look to the purest “seeds” that capture my mode or genre preference. If my initial selection does not provide this purity, I delete it, and try to find a better true seed.
This approach has led to some awesome channels for me, that I can then combine together, depending on mood, into mixed shuffle play with randomized channel selection. I no longer vote any song up or down; I rather look for more telling seeds.
As a couple of examples, here is a channel of less-well known 60′s goldies and a jazz guitar channel that are pure, single-seed channels, that, at least for me, provide hours of consistent music in that genre. Roll your own!
We all know the slow decline and zero take-off about the semantic Web writ large, but what we probably did not know is that Big data would gobsmack the concept. Trends do not lie.
Some ten years ago the idea of the semantic Web had about a 4x advantage in Google searches compared to that for “big data”. Today, Big data searches exceed those for the semWeb by 25-fold. The cross-over point occurred in about April 2011:
Within a year, projections are that the Big data interest level will exceed the semantic Web by 35x.
These may not be entirely apples-to-apples cases, but trends don’t often lie. Play yourself with these comparisons on Google Trends.