The evolution of written language started in earnest in 3500 BC with Cuneiform, spurring a step-change in the volume of information that could be recorded and transmitted over large distances.
This evolved into wide spectrum of other methods of information transmission. The first transatlantic telegraph cables, for example, were laid in the mid-to-late nineteenth century by information pioneers – industrialists who saw the vast benefit in increasing the rate of information exchange by many orders of magnitude. This led to a Cambrian explosion in the sheer volume of information transmitted internationally, increasing trade and commerce to hitherto unseen levels.
Key parts of the communication infrastructure we exploit today are based on the work of the early information pioneers; for example, I video-conference with my nephews and niece through the Internet, transmitting information from my laptop in Canada via one of several submarine communication cables that snake across the Atlantic to the UK.
Connectivity is growing in all its forms, and the subsea information pipes pioneered one hundred years ago have now spawned communication satellites, 3G phones, and Wi-MAX, enabling us to interrogate the vast databases of information on the other side of the world, whether we’re at home or between places. It’s humbling to know that the low-cost access to data I have today is simply an evolutionary descendent of the early information transmission technology.
Connectivity and access to data is a theme that’s increasingly important for users of technical software. As engineers, scientists and financial engineers, we need to calibrate our models, assumptions and theories with data.
For example, I recently engaged in a lively online conversation about a worksheet I wrote a couple of years ago, and blogged about last year; it allowed Maple users to import stock quotes from Yahoo. I’ve also used the same Maple principles to download chemical kinetics data from the NIST website, historical weather data from Wunderground.com, and winning lottery numbers (not that the latter’s made me anymore richer).
Of course, the vast amount of data at my fingertips is daunting, and it’s all simply a few commands away. We need, however, practical methods of managing this avalanche of information.
Take the problem and solution I stumbled upon when developing the Stock Importer worksheet. Stock tickers have many associated quantities, and having to remember the ticker symbol-variable name combinations would be daunting. To make accessing the appropriate data simpler, ticker symbol-variable names combinations are added to the command completion menu so you only have to remember the ticker symbols.
That’s a simple example, but it demonstrates how Maple has evolved to help users manage and explore data.
In case you need a data fix, I’ve attached a brief primer on the Sockets package; this is a suite of tools for network communication. It demonstrates how to automatically download and import stock quotes and weather data from the Internet into Maple.
One of the reasons I enjoy using Maple so much is that it supports every stage of the problem solving process - from importing networked data, to its management, analysis, and visualization. While Maple hasn’t made me a stock market millionaire just yet, it provides a fun distraction while I wait.