If I could only attend one conference in 2011 it would have been the Strata Conference in San Francisco last week. Data, the recent entitling of the Data Scientist, big data and data in the cloud all major themes that I follow. But small data and having it networked is the vision we hold at the LifestyleLinking project so I was delighted to read on the ReadWriteWeb blog that Singly had won the startup competition running during the conference.
Singly is the commercial venture behind the open source projects of Jeremie Miller, Locker Project and the TeleHash PeertoPeer JSON data exchanging. The projects are people centric and start by giving individuals the ability to collect their own data on the web, stage two allow it to be shared or networked together for those that wish to and lastly allow developers the ability to offer apps to help individuals use their data to improve peoples lives, from restaurants to figuring out how to prevent cancer, diabetes and other heath matters. It is a vision we adhere to too. Most of the energy in the LL project started out on figuring out how individual data is connected, but this has meant addressing most of the infrastructure questions of building an application to allow individuals to collect their own data and to create a network, in our case a primitive RDF – Dpedia connection of the people apps. in the network. With the hope of more developer resources in the ecosystem via these open source projects then hopefully we can spend more time on understanding the data connections (which only get about 20% of the energy right now) and listening to individual needs in permissioning data access, gathering and sharing through a user interface that is useful to people in their daily lives.
More thoughts on this topic from O’Reilly blog and the UgoTrade Blog.
The best blog on Lifestreaming that I know is written by Mark Krynsky. In this blog post he summaries an interview he recorded with David Galernter, the person regarded as the creator of lifestreaming. At the end of the post Mark gives his take on things going forward and even make a rare prediction:
“With regards to Lifelogging I am seeing a multitude of dedicated devices and smartphone apps that track all sorts of personal data around exercise, sleep, weight, health, etc. As these devices become better, cheaper, and the data collected starts providing large benefits to improving our lives, we will see adoption of them start to surge. I think these two areas show not only the ways that Lifestreaming could recapture the interest of users, but also provide good monetization options for startups. I don’t often make predictions but I see this starting to happen between the next 1-3 years.”
I think his analysis is spot on but the development and delivery of such services to make his predictions true are being built today and tomorrow with increasing intelligence, sharing and hopefully with growing adoption and usage.
There is an informative blog post on O’Reilly today titled, Need faster machine learning? Take a set-oriented approach. In summary it’s about bringing processing efficiency to a ‘machine learning’ algorithm to categorise job vacancies in the field of Electric Medical Records. Even for this ‘small’ categorisation set the calculations are vast. The LifestlyeLinking project is using this approach to classify lifestyles instead of jobs. While the processes read similar the technology applied and categorisation seeding used start from different places. The LL project has chosen Wikipedia to seed lifestyle definitions and an application of wisdom of the crowd mathematics is used instead of a bayes classifier. Regards of these differences, the goals are the same, to give individuals the best information to live their lives by or to find the right job.
As for the question of scaling, smarts in database and managing data is where this post says ‘Set Oriented’ approach has applied dramatic improves in processing times. The LL project has concluded (thus far) to thinking away from this centralized data process to a framework of decentralized peer-to-peer processing and then find away to aggregate (share) the data demanded for each peers needs. We are yet to put this into practice but from our experience to date, scaling in the cloud breaks down and ‘small’ levels of categorisation, let alone all the information that makes up life.
The number one priority for the LifestyleLinking Open Source Project will be to develop usage and evolve a user interface that meets demand. Getting started and finding those early adopters will take committment and hopefully their feedback will provide new challenges that will reveal more value than were are able to do right now. Two start page UI has been added to the wiki. The big idea is have all the options available in the box, text input, display and time preference. Note time will be past or future looking, future time will is referred to as Intention mode and as the year rolls out the plan is to give users more and more information to make their futures around.
The LifestyleLinking Open Source Project has a new focal site, www.lifestylelinking.net. A place to introduce the idea and to link to the range of content on the projects, blog, source code, documentation and videos.
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Tagged connected, exlporing, life, lifestyle, lifestylelinking, lifestylelinking.net, linking, open, php, project, source
I particpated in barcampLondon8 at the weekend and led a session to share where we are at with the LifestyleLinking Open Source Project. Thank you for the feedback during the session and over the whole weekend.
Top identity thinker Drummond Reed has asked to help with the formation of some new wikipedia entries for the following terms:
1. personal data service
2. persona data server
3. personal data store
4. personal data ecosystem
The draft definitions are on the VRM wiki looking for participants.
Drafting down some thoughts:
personal data service
Individual control to put any data they own anywhere
personal data server
The technology layer used enable data control and portability.
personal data store
Understands the range of data stored and acts as data home of homes.
personal data ecosystem
Used to describe a WWW where personal data is the building blocks of communication.