Saturday, December 1, 2012

The Semantic Common Operating Picture

One of the most important achievements in analytics during the 2000's was the development of what can be described as Common Operating Picture architectures or COPS. These solutions combine data fusion technology along with advanced reporting functionality and role-based dashboards to provide an integrated view of various types of data. COPs are often used in the DoD or Homeland Security to support threat analysis or other Cyber Security missions. However, the current COP architectures are lacking in several key areas including:
  • Scalability (e.g. it's costly to make changes to the underlying integration)
  • Ability to manage Big Data
  • Accuracy across larger sets of disparate data (or domains)
For these reasons a new generation of COP solutions is needed. We're going to discuss one such idea today - the Semantic Common Operating Picture.

A standard COP Architecture
This approach represents a fairly ambitious, evolutionary step to the family of solutions already referred to as Common Operating Pictures or COPs. Sometimes these types of systems are referred to by their functional role, which in this case most often is described as Situation Awareness. Most COPs include a number of technologies within their solution architectures and thus represent a both a product (or set of products) and an integration activity.

The Semantic Common Operating Picture (S-COP) approach builds upon previous COP architectures but differs from them in several critical ways:

  1. It will take advantage of new standards in data management and message manipulation that weren't yet defined or matured when standard COPs first began appearing more than a decade ago. Many of these new standards are associated with the concept collectively referred to as the “Semantic Web.”
  2. It will allow for flexibility in how data is presented with more and richer opportunities for complex visualizations.
  3. It will support collaborative activities by design (and not as an afterthought). The potential associated with Collaborative Analytics was discussed in more depth in the previous section of this proposal.
  4. It will provide more opportunities for data discovery through a more flexible approach towards management of data sources. In other words, this solution will have access to more unstructured data and will give users the ability to add meaning to that unstructured data (and to structured data as well).   
  5. It will support the emerging field (and technical standards related to) Big Data.

The S-COP concept is based on a set of seven principles, listed in the mindmap below. 

These core principles supporting the S-COP concept are as follows:

·       The solution must support multiple levels of analysis – this implies the ability to manage analytical tasks at the personal level, the group level and the organizational level. This in essence is not too different from the traditional COP architecture. However, we can also view this principle in the context of data temporality. It is important that the solution be able to differentiate chronologically sensitive data. 
·       The solution must support complex collaboration – this implies that the solution must be able to facilitate not just simple messaging between participants in various domains, but must also facilitate complex collaborative analytics.
·       The solution must support interactive annotation – what this means in the context of this particular solicitation is the ability for end-users or subject matter experts to add meaning to existing data elements or sources through data tagging or adding other notes.
·       The solution must support multiple visualization capabilities – in the context of this project this means the ability to display both traditional dashboard-like graphics as well as complex concept visualizations. For example, it would be beneficial to be able to illustrate relationships between data without necessarily having to define specific queries or analytic representations.
·       The solution must support intelligent discovery – This means that the solution must have the capacity to learn from previous heuristic paths and results. Intelligent discovery must be able to harness existing subject matter expertise across partner domains as well as existing knowledge bases. It must also be able to exploit patterns.
·       This solution must support pattern management capabilities – This implies the ability to discern, record and manage patterns from what has been previously discovered to more quickly assess current situation awareness. Pattern management must be able to build patterns out of both structured and unstructured data.
·       The solution must support rules management capabilities – Having Rules Management allows for discovery and analysis to be linked in real-time to mitigation options. It also helps to automate more of the analytics processes thus allowing resources to focus their attention on only those areas which really need it.
These principles taken together represent a basic framework or requirements hierarchy which can be used both to assess possible technologies and to build a solutions architecture. We will examine the architecture of the S-COP in greater detail in future posts...

Copyright 2012  - Technovation Talks, Semantech Inc.


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