Open Geospatial Consortium OGC adopts OWS Context Conceptual Model and ATOM Encoding Standards

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OGC has made a release today. There is a new standard for, if implemented, lets say if you make a map in Google Maps, and want to show it in Bing or an advanced GIS tool, it would be like opening a Word document in another tool like OpenOffice. 

This seems simple enough, but as you can imagine, we cannot do this now. Actually quite the opposite – most viewers require coding to make a mashup or workflow configuration work in its own application. This standard paves the way for sharing across OGC services.

Xentity staff supported input into the ATOM XML encoded standard, and provided support to drive for the JSON encoding, which is more HTML5 and browser friendly, which can help for tablet, light viewer solutions as well.

Full article: The OGC adopts OWS Context Conceptual Model and ATOM Encoding Standards

Some favorite TED talks

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– “Migrated to Confluence 5.3”

A business partner last night said “I don’t wake up and turn on my phone, or watch TV, or check email right away. I try to keep it simple… ” he said as several of us waxed rhapsodic of the pre-pocket tech and internet days and how teenagers patterns know no other world. But yet he continued, “OK, well that’s not true, I do get my morning dose of TED for inspiration”. 

Its just one more to add to the many morning intake mediums. People seeking personal philosophical guidance in the morning through religion, scripture, reading a story, meditation, prayer, mind-body engagement or quiet time. People seeking temporal context in morning news TV, newspaper, internet and feeds, websurfing (can I still use that term?), tablet time. People seeking social engagement with morning coffeee at the diner with the guys/gals, spouse or/and kid quality time, the facebook rise-and-shiner, or other social media digests. People seeking inspiration in either of the above

Personally, I have yet to ever find my morning ritual and I bounce in different mediums. Sometimes, its playing trains or toys or some activity with the family when we get a good rhythm going that morning, sometimes it is tablet browsing when feeling curious on various news or video feeds, sometimes it is mindless TV news digestion, and probably more rare than I should, sometimes it is outside quiet time in a run, bike, walk, or reading or whanot. Other times, the day gets going to fast, and there is no interstitial time, and an east coast call to this mountain time zone starts right up.

Though, I haven’t found my rhythm, but over the last partial decade here are a few of the greatest TED hits I’ve tweeted out as greatest hits and found inspirational :

Hans Rosling: Stats that reshape your world-view (Jun 2007)

Geoffrey West: The surprising math of cities and corporations (July 2011)

TEDxUofM – Jameson Toole – Big Data for Tomorrow (May 2011)

Eli Pariser: Beware online “filter bubbles” (Mar 2011)

Sugata Mitra: Build a School in the Cloud (Feb 2013)

Deb Roy: The birth of a word (Mar 2011)

-mt

Xentity Hosting Geo Colorado Meetup

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A Geo Colorado Meetup is scheduled for this Thursday 8/8 at 7PM in Golden near Colo Mills/NREL at Xentity Headquarters. 

The Geo CO group – http://www.meetup.com/Geo-CO/ – formed in early 2012 with a goal of:
This is a meetup group for those in the Colorado area who like maps, GIS, OpenStreetMap, cartography, shapefiles, cartodb, tilemill, and anything in between. Based on the idea to help connect each other for learning and socializing about mapping, software, projects, datasets, biodiversity and anything else that sounds related.
Meet some other folks and hear what interesting projects, tech, problems they are working on. 
Invite others, more the merrier. If you aren’t available, but think of others that may be, feel free to pass this on.

How can we help geoscience to move their data to shared services

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Most data that is generated out of science is not intended to be used on broader scale problems outside of their own research or their own specific domain. Let’s stick with geoscience to check this ‘hypothesis’ out and check on the ‘so what?’ factor.

Current grant and programmatic funding models are not designed to develop shared services or interoperable data for the geosciences.  There are few true shared services that are managed and extended to the community as products and services should be. “We broadly estimate that 20% of users of a dataset might be experts in the field for which it was created, while 80% might be others.” There is currently limited to no incentive for most geoscientists to think beyond immediate needs. “The culture of collaborative Science is just being established.”  Finally, there is no current clear way to build and sustain the large and diverse geosciences community.

The key we believe starts not with the tech, the money, the management, the govnernance, but with stakeholder alignment which can be “the extent to which interdependent stakeholder orient and connect with one another to advance their separate and shared interests”. 

The Geoscientist community – Big Head and Long Tail. Geoscientists with affiliated government institutions, academic and international partners. Data Scientists, Data and Information Stewards, Curators and Administrators (content and metadata), Data Product and Service Managers , Citizen Earth Science participants, Emerging Geoscientists found in the STEM community – (K-12)

Additionally, the supply chain roles from:

  • Data and Information Suppliers – NSF funded centers and systems.  Programmatic producers of geoscience related data and information – i.e. Earth Observation systems like Landsat or MODIS or specialized information systems that produce value added products like NAWQA. Indonesia NSDI, DOI Authoritative data sources and services
  • Cyber Infrastructure community/Development Collaborators – Basic and Applied Research and Development, Software and System Engineers, Data Manager and Analysts
  • Infrastructure  Management /Collaborators – IT Service Management (ITSM) – Managers and Operators of the shared infrastructure and key software services in industry, commercial, government, Research, and Cost-Sharing FFRDCs
  • Consumers reached out to via end user workshops., Public Policy, Regulatory, Legal and Administrative Analysts, Private Sector , Academia (non-participating state), Other science disciplines
  • Executive Sponsorship and Geoscience Various and Cross-Cutting Governance Community too numerous to get into in this blog at least

The stakeholder themes we have seen in data are generally the same. 

These challenge echoes the organizing themes of the Xentity supported developing 7-years ago for DOI Geospatial Services Architecture:

  • “I know the information exists, but I can’t find it or access it conveniently”, has its analog in “Considerable difficulties exist in finding and accessing data that already exists” 
  • “I don’t know who else I could be working with or who has the same needs”, has its analog in “Duplication of efforts across directorates and disciplines, disconnect between data and science; data graveyard –useless collection of data…”
  • “If I can find it, can I trust it?”, has its analog in “There is a need to evaluate consistency /accuracy of existing data. 

A start on this, to jump into boring consulting theory, is to Develop a clear line of sight to address stakeholder needs and community objectives. 

This ensures the analysis engages all the necessary dimensions and relationships within the architecture. Without a strategy like this, good solutions, business or technical, often suffer from lack of adoption or have unintended consequences and introduce unwanted constraints. The reason for this is the lack of alignment. Technology innovators tend not to share the same view of what is beneficial nor does the Geoscientist who is accustomed to enabling a single or small set of technology directives.

How does one create the shared enterprise view? Using the Line of Sight, our approach at least to architecture transformation and analysis creates the framework and operating model. It connects business drivers, objectives, stakeholders, products and services, data assets, systems, models, services, components and technologies.  Once the linkages have been established, the team will create the conceptual design using 40-50 geoscience domain investment areas. This will effectively describe the capabilities of the existing IT portfolio.  The architecture and the portfolio will be designed to support governance, future transition planning.

Sample Ecosystem Edge Analysis

Human Edges (adaptive systems)

Data and Information Edges

Computing and Infrastructure Edges

Citizen scientists/ STEM and Professional scientists

Data Supply and Information Product/Services

Centralization and federation of computing infrastructure

Geoscience as consumer and producer of data and information

Possessing the data and access the data

Commodity Computing vs. Analytical Computing

Individual science and collaborative science

Macro Scale data  vs. Micro Scale data

Mission driven systems and shared services access

Science Ideation: Piecemeal or segmented vs. holistic

Five data dimensions – spatial (x,y,z), temporal and scale

Domain Systems vs. Interoperability Frameworks

Individual vs. Collective Impact and credit

Authoritative sources vs. free for all data

Systems vs. Managed Services

Governance rigidity and flexibility

Data and models vs. Product

Big Head and Systematic Data Collection, vs. project components

Earth Science and Cyber-infrastructure and Engineering

Long Tail vs.  Big Head Data

 

The Line of Sight allows for exploring the complexities of geoscientist “ecosystem edges” and architect for greater interaction and production in the geosciences.   Those in the “Long Tail” encounter the same cross domain access, interoperability, management barriers as the “Big Head”. Neither have the incentive to develop common enabling data interoperability services, scalable incentive solutions, common planning approaches or increase the participation of the earth science community. Xentity’s believes architecture is an enabling design service.  It is used to empower the user community with the tools to expand its capacities. In this case, Xentity will provide the operating model and architecture framework in a conceptual design to bring together the currently unattended edges.  In the long run, the models will provide the emerging governance system the tools to develop investments strategies for new and legacy capabilities.

The Broader Impact

At its core, we believe the geoscience integration challenge is to exploit the benefits and possibilities of the current and future geoscience “ecosystems edge effect”.  In the ecosystem metaphor, the conceptual design approach will target the boundary zones lying between the habitats of the various geoscience disciplines and systems.   What is needed is an operating model, architectural framework and governance system that can understand the complexities of a geoscientist shared environment and successfully induce the “edge effect”.  It needs to balance the well performing aspects of the existing ecosystem with new edges to generate greater dynamism and diversification for all geosciences. 

An Operating Model example: Collaborative geoscience planning could make a good demonstration case for the benefits of the “edge effect”.  A lot of science efforts are driven by large scale programs or individual research groups who have very little knowledge of who else may be working in the same environmental zones, geographies or even on related topics.  A shared planning service could put disparate projects into known time, location and subject contexts and accelerate cross domain project resource savings and develop the resulting interdisciplinary cross pollination required to understand the earth’s systems.  An Enterprise geoscience initiative could provide a marketplace for geoscientist to shop around for collaborative opportunities.  The plans can be exposed in a market place to other resources like citizen scientists or STEM institutions.  The work can be decomposed so that environments like Amazon’s Mechanical Turk can post, track and monitor, distributed tasks.

By recognizing these edges, the architecture will create greater value or energy from the disciplines and improve the creativity, strength and diversity of ideas, and mitigate disruption.  The ecosystem-like design that balances the Big Head with the Long Tail will enable more cost effective geoscience projects and create a higher return on IT investments while collapsing the time to conduct quality impactful science. Most importantly, this will accelerate the realization of the sciences’ impact on other dependent scientific initiatives or time to develop and implement policy.  Xentity sees the potential to use this and other “ecosystem edges” to transform how geoscience is currently conducted. 

Xentity believes a geoscientist, emerging (STEM) or emeritus would be willing to participate in cross-cutting, shared service model based on how well these edges are architected and governed.  If designed and operated effectively, the edges will create an environment that will address the two key barriers to adoption: trust and value.  In essence, we see the scientists as consumers and producers.

  • As consumers of data, information and knowledge products and technology services, they are continuously looking to create more knowledge and contribute to social benefit. 
  • As producers they contribute data, information and knowledge back into their colleagues’ knowledge processes.  

In fact, the predominant challenge for such an approach is that the share-service will be develoepd by the community who themselves are a consumer. Just like any other consumer, they will have expectations when they purchase or use a product or a service.  If one cannot uphold the terms and conditions of product quality or a service agreement; you lose the consumer. So, how does the architecture ensure these “edges” develop and evolve?  It must ensure:

How to earn Geoscientists’ Trust

The scientists need to know that they will have highly reliable technical services and authoritative data that are available and perform well when they request them.  Most importantly, they will need to influence and control who and how they conduct the work within the shared environment. They need to ensure the quality of the science and appropriate credit.

How to demonstrate the value to the Geoscientist:

The scientists need the provider to correct products or services that will eliminate the most significant barriers and constraints to doing more and higher quality science – research, analysis and experimentation – with less effort.

In the short term, the shared service challenge is to earn the scientists trust and identify the optimal suite of products and services to provision value from the “community resources” as defined in Layered Architecture. For land elevation products up to 80% of the requests are for standardized products. If done correctly, the governance system, operating model and architecture framework will develop the trust and value recognition from the shared community.  In the longer term, the models and framework will guide the redirection of its limited resources towards an interoperable set of systems, processes and data.

Great, but even if we create this, how do we fund? 

See the next part on “Will geoscience go for a shared service environment” which discusses ways to address funding, ways to engage, encourage, enable, and support execution of these enterprise capabilities for geoscientists. 

Why we focus on spatial data science

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The I in Information Technology is so broad – why is our first integrated data science problem focus on spatial data? It doesnt fit when looking on face of our Services Catalog . We get asked this a lot and this is our reason, and like Geospatial, its multi-dimensional spanning different ways of thinking, audiences, maturity, progressions, science, modeling, and time:

 

In green, x-axis, is the time progression of public web content. The summary point is data took the longest period – about 10-15 years. And data can only get better as it matures into being popular 25 years old on the web. We are in the information period now, but moving swiftly into the knowledge period. Just see how much more scientific data visualizations, and dependence we are on the internet. Just think how much you were on the web in 1998 compared to 15 years later – IT IS IN YOUR POCKET now. 

This isn’t just our theory.

RadarNetworks put together the visual of progressing through the web eras. Web 1.0 was websites or Content and early Commerce sites. Web 2.0 raised the web community with blogs and the web began to link collaboratively built information with wikis. Web 3.0 is ushering in the semantic direction and building integrated knowledge.

Even scarier, Public Web Content progression lags several business domains, but not necessarily in this leading order: Intelligence, Financial, Energy, Retail, and Large Corporate Analytics. Meaning, this curve reflects the Public maturity, and those other domains have different and faster curves. 

The recent discussions on intelligence analysis linking social/internet data with profile, Facebook/Google Privacy and use for personalized advertising, level of detail SalesForce knows about you and why companies pay so much for a license/seat, how energy exploration is optimizing where to drill in some harder to find areas, or the absolute complexity and risk of the financial derivatives as the world market goes – these technologies usually lag in how we integrate public content for googling someone, or using the internet to learn more and faster. Reason: Those do not make money. Same reason why the DoD invented the internet – it was driven by security of the U.S. which makes money which makes power. 

So, that digression aside (as we have been told “well, my industry is different”), the public progression does follow a parabolic curve that matches Moore’s Law driving factor in IT capability – every 2 years, computing power doubles in power, at same cost (paraphrasing). The fact that we can do more faster at quality levels means we can continue to increase our complexity of analysis in red. And there appears to be a stall not moving towards wisdom, but as we move toward knowledge. Its true our knowledge will continue to increase VERY fast, but what we do with that as a society is the “fear” as we move towards this singularity so fast. 

Fast is an understatement, very fast even for logarithmic progression as its hard to emote and digest the magnitude of just how fast it is moving. We moved from

  • The early 90s simply placing history up there and experimentation and having general content with loose hyperlinking and web logs
  • to the late 90s conducting eCommerce and doing math/financial interaction modeling and simulations and building product catalogs with metadata that allowed us to relate and say if a user found that quality or metdata in something, it might liek something else over here
  • to the early 2000s to engineering solutions including social and true community solutions that began to build on top of relational and the network effect and use semantics and continually share content on timelines and where a photo was taken as GPS devices began to appear in our pockets
  • To the 2010s or today where we are looking for new ways to collaborate, find new discoveries in cloud, and use the billions and billions on sensors and data streams to create more powerful more knowledgable applications

Another way to digest this progression is via the table below.

Web VersionTimeDIKWWeb MaturityKnowledge Domain Leading WebData Use Model on WebData Maturity on Web
.9early 90sDataContentHistoryExperimentalLogs
1.01995+Info HistoryExperimentalContent
1.11997  MathExperimentalRelational
1.21999 +CommerceMathHypotheticalMetadata
1.32002  EngineeringHypotheticalSpatial
2.02005+Knowledge+CommunityEngineeringComputationalTemporal
2.12010s  EngineeringComputationalSemantic
3.02015 and predictable webKnowledge+CollaborationScienceData as 4th paradigm notTempoSpatial (goes public)
4.02020 -2030Wisdom in sectorsAdvancing Collaboration with 3rd world coreAdvancing Science into Shared Services – Philsophical is out yearRobot/Ant data qualitySentiment and Predictive (goes public/useful) – Sensitive is out year

Now, think of the last teenager that could maintain eye contact in a conversation with an adult while holding phone in their hand and not be distracted by the pavlovian response of a text, tweet, instagram, etc. Now imagine, ten years from now, when its not tidbits of data, but as a call comes up, auto-searching on terms they arent aware of come up in augmented reality. Advice on how to react on the sentiment they just received – not just the information. The emotional knowledge quotient will be google now – “What do I do when?” versus critical thinking and live and learn.

So, taking it back to the “now”, though this blog is lacking the specific citations (blogs do allow us to cheat, but our research sources will make sure to detail and source our analysis), if you agree that spatial mapping for professional occurred in early 2000s and agree now that it has hit the public and understand that spatially tagging data has pass the tipping points with advent of smartphones, map apps, local scouts, augmented reality directions, and multi-dimensionl modeling integrating GIS and CAD with web, then you can see the data science maturity stage we are in that has the largest impact right now is – Geospatial.

Geospatial data is different. Prior to geospatial, data is non-dimension-based. It has many attributable and categorical facets, but prior to spatial data, that data does not have to be stored as a mathematical or picture form with specific relation to earth position. Spatial data – GIS, CAD, Lat/Longs, have to be stored in numerical fashion in order to calculate upon it. Further more, it hasnt be be related to a grounding point. Essentially, geospatial is storing vector maps or pixel maps. When you begin to put that together for 10s of millions of streams, you get a a very large complicated spatially referenced hydrography dataset. It gets even more complicated when you overlay 15-minute time-based data such as water attributes (flow, height, temperature, quality, changes, etc.) with that. Even more complicated when you combine that data with other dimensions such as earth elevations and need to relate across domains of science, speaking different languages to be able to calculate how fast water may flow a certain contaniment down a slope after a river bank or levy collapses.

Before we can get to those more complex scenarios, geospatial data is the next progression in data complexity .

That said, definitely check out our Geospatial Integrated Services and Capabilities