Forging Our Historian in Fire

We are "forging" our historian by creating a personal and commercial form for real-time financial market data. In this scenario, a reasonably large load is put on the historian as hundreds or even 1,000's of transactions per second stream in for each of numerous "tickers" from numerous feeds simultaneously. That's a lot of data. This helps not only our financial customers get and store data for historical analysis, it helps us harden our historian. This is more demanding than most industrial applications, though less so than some high-speed research environments, such as particle accelerators.

A ticker is a stock, fund, currency pair or other security. Many times per second people are bidding and asking to work out a price to buy and sell. In each of these actions, we receive a "Quote". On each exchange, there are literally hoards of traders electronically bidding each millisecond. We get each of these offers in on the wire.

A "Quote" is a complex object with up to 15 associated values, not a single numeric or text value, so each transaction is a block of data values. Some "mainstream" historians might be challenged by this because they are oriented to archiving single values, and thus a "Quote" would have to be split up, disassociated and written to 15 "Tags". Not so with our Historian, which archives Objects and thus the Quote objects themselves, directly.

Now objects take a bit more overhead when stored, a bit more disk space, but in exchange the data is all properly associated, the processing is less and it is faster.

Let the flames roar, as we harden the steel of our Historian.

Catch Up

Sorry to have fallen behind here, but business and new product development have been brisk. We are also over on Facebook...

http://www.facebook.com/pages/IntelliDynamics/345638741076

Give us a "Like" if you use Facebook !

So here are some highlights...

• based on prospective customer request, we are preparing a data managment only solution including data access, synchronization, validation, conditioning, pre-processing and transport in batch and real-time, including visualization.
• We have successfully installed Intellect 3.0 at a consumer paper products manufacturing facility where they are now doing "cascaded" models, the output of one model feeds as an input to another, integrated through either a SQL database or our on-board object historian.
• We successfully completed a "Virtual Metering" project for multi-national oil and gas corporation.
• We held a meeting in South East Asia with a local national oil company regarding field-wide asset management, including modeling, prediction and optimization of a complete oil field.

These are a few highlights.