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Enable distributed device situational awareness through cloud-based data mgmt

Posted: 15 Jun 2012 ?? ?Print Version ?Bookmark and Share

Keywords:situational awareness? real-time distributed system? Quality of Service?

In the book Moneyball, author Michael Lewis detailed the use of Sabermetrics to guide strategy in managing a baseball team. Though focused on analytical aspects of assembling a baseball team, the basic idea was the use of historical information (advanced statistics) to influence the current strategy (an at-bat, a defensive situation, or lineup decisions).

The book's focus was on the unexpected decisions that came out of the statistical analysis, often going against the 'gut feel' of the long time baseball experts. But it also showed that the successful use of Sabermetrics required (1) knowing what happened in the past, (2) recognizing the current situation, and (3) applying the historical knowledge to drive the current strategy.

This statistics trend can be found in many other domains. For example, financial trading applications are based on models that are calibrated using historical data. Much like in the baseball world, the success of the model depends on how quickly the opportunity can be recognized based on current market activity. If the real-time market data is delayed for any reason, the opportunity is diminished, or worse, lost.

At a recent Big Data conference at the Stanford GSB, the panelists discussed the growing trend of Big Data in all industries, including industrial, automotive, consumer, medical and not just baseball or finance. Or, more accurately, that Big Data is already the next big thing and businesses would be wise to analyze the tremendous amount of information generated within their systems.

However, what's overlooked is how to actually collect this tremendous data; or, more precisely, how to move the data from where it is created to all of the different places it needs to be.

This ability to correlate real-time and historical data to achieve situational awareness and transform raw data into information is fast becoming a necessary capability of many large-scale distributed systems.

The rise of Big Data has shown that traditional RDBMS databases cannot keep up with the data rates present in these large distributed systems, and are inefficient at running the kinds of queries needed to retrieve important information. The recent explosion of virtualization and cloud based data management systems allows us to approach these problems in an innovative way.

The rest of this article outlines how to achieve situational awareness by connecting a distributed network of connected devices and systems to cloud-based data management tools, using so-called NoSQL database methods. Examples from several domains will be used to show how real-time and historical data can be combined to give the analyst a complete picture of emerging situations, as well as post-event analysis.

Persistent real-time data
Whenever persistent data management is added to a real-time distributed system the primary concern is maintaining the critical performance characteristics of operational technology (OT). the physical equipment-oriented technology implemented and supported by engineering organizations, typical of many device and embedded systems designs.

In any large enterprise, OT is usually done independent from the Information Technology (IT) groups who are involved in data management, including that generated by the embedded devices on the production line or within deployed systems, with little real time interaction between the two.

In such distributed systems, the performance of persistent storage lags behind that of volatile storage, although there are signs that the two may be converging (i.e. Solid State Disks).

Real-time data management consists of several simultaneous activities:
1. Storage (write)
2. Querying, Correlation & Retrieval
3. Distribution

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