The realm of the universal is the Library of the Commons, a global repository of user-generated and crowd-sourced media and information.
Services that logically nest in the Library include: Amazon, Yelp, YouTube, Craigslist, Wikipedia, Flickr, Twitter tweets, Bit.ly items, Scribd docs, Expedia, Google News, Google Maps, TripAdvisor, iTunes, the App Store and any other services and/or information sources that ‘just work.’
In other words, these are services that have defined the ‘IT’ to the point that we can now pretty much take their utility and availability for granted (typically via API access and/or embed codes with some form of customization wizard).
No one formally deigned it so, but from the countless me-too services borne of the dotcom and Web 2.0 land rushes, the above-referred services are the ones that cultivated the biggest audiences, grew the richest ecosystems and inspired the deepest engagement levels.
In Darwinian terms, these are the survivors, whose structures and workflows have been defined and refined by time/experience.
As such, they are generally well thought out, holistic and integrated, but more to the point, have large, engaged user bases.
Thus, the Commons presents a riddle. Almost as if inspired by Herman Hesse’s ‘The Glass Bead Game‘, the riddle is this.
If all of these services yield a smorgasbord of best practices, why not systematically emulate them so as to…FEDERATE them?
Put another way, what if a time came when people ceased trying to perennially re-create the wheel, and instead, started to ‘decompose’ these services; to empty their function sets from whatever nesting they were contained within; and to re-apply them into new contexts supported by a now federated data flow proxied within the Cloud.
Couldn’t the composite feature set be exposed switchboard-style to enable any number of custom services and client apps?
To put some meat on the conceptual skeleton, consider the following exercise that I recently did:
A decomposition of Craigslist and TripAdvisor yields deep profiles that are accessorized and interconnected via context traversal flows, such as categorization routines, places, events, airfares, posts, pages, ratings, discussion threads, offers, jobs, businesses, products and personal listings.
Craigslist offers up 36 different sub-types of items For Sale; Services represent another 19 sub-types; Jobs 41 more; Discussions, another 72. And so it goes (including Housing, Personals and Community) across 175+ geo-locales.
TripAdvisor is an instance of this model that overlays a set of time-tested workflows specific to the relatively complex task of planning a vacation.
These workflows make it easy to match a travel plan to specific tastes, requirements and budget – regardless of the information traversal path you pursued to being ready to get pricing on desired travel dates.
Could these same workflows be re-purposed for researching and then purchasing other similarly complex products or services?
I will come back to that thought, in a moment.
The Infodex is a kind of next-generation Rolodex, with aspirations to grow into a real-time marketplace.
What exactly is the Infodex? It is comprised of three parts.
Part one is a listing tool for linking to content, creating a metadata wrapper around media items and encapsulating the above-referenced services (i.e., Yelp, YouTube, WIkipedia) into listing containers that define and expose the methods that one can interface to the media item (framework integrity stuff).
Part two is an indexing engine so that, once simple rules are defined, your media libraries and the information in the listings themselves becomes ‘self-organizing.’
Named picture types (globes, animals, historic or famous images), for example, could be a federation of multiple picture services (Flickr, Photobucket, Getty Images) and ‘discovered’ pictures from past queries.
Looked at from this perspective, the goal, in part, is to establish a cloud-based, crowd-sourced Dewey Decimal System built around the outcome of facilitating better searching, compositing, cross-indexing, sharing, archiving, and analytics functions for specific media and information ‘types.’
One simple example of a basic type of function that might be propagated across all of these environments is the Three Item Topical List (e.g., Top Three Favorites or Three Most Related Items). Define once, propagate everywhere.
A core assumption of the model is that both the media player and the service integration layers are open-sourced. This ensures that the user experience is uniformly good across all of these services, and pushes proprietary-ness higher up the stack, thus raising the floor for all comers.
A final thought. Google became Google by indexing the web. Couldn’t the next generation extend this approach by being federated, crowd-sourced and context-specific (i.e., media, information and service aware)?
Are their obvious best practices for The Commons? Obvious gotchas? What about the Infodex?