``Pattern Analyzers''
Dr. Lawrence P. Raymond
Oracle Corporation, Special Projects
Kingwood, TX 77345
E-mail: lraymond@us.oracle.com

Forget death and taxes. Change is perhaps the only process one can depend on. This includes change in natural properties such as alterations of state, condition, location, composition, dynamics, and impact. And it also includes to changes in markets, resources, costs, prices, rules, and regulations, geopolitical status, and other similar risk and opportunity measures. Those able to measure and monitor change meaningful to their purposes are the most likely to find their missions realized. Those able to relate the dynamics in business and natural properties to root causes, potentially can manage risk and foresee opportunities to great advantage.

This explains the interest in data management, computerized simulations and analytical advances. Many growth interests in science and business are strategic. Business future is dependent upon sustainability not of things, but of dynamic balance. For man to advance quality of life, he must learn to balance resource and business consumption with productivity. And we have much to learn. We need a mechanism that allows timely comparison of decisions and policy with their consequences. Suggested here is that we need real-time measures of the change resulting from actions we can control, in context with those events we cannot. Given this, it should be possible to start with what we think we know, compare results against expectations, and make rapid adjustments as results dictate. This should lead to continuous learning with minimal redundancy and maximum justification.

How close are we to achieving this? The answer is indicated by the current state of technologies for data collection, management, and transformation, as well as reduction, modeling, and interpretation techniques. This perhaps is why we are here.

Current State-of-the-Art

Technology in database management no longer is limiting creativity. It is now possible to create databases directly from data models using computerized tools that write much of the script directly. Tools also exist for loading legacy data ranging from character, audio, video, temporal, image, text and spatial; these may be in flat files or multi-dimensional. Data can be housed in universal servers, and tapped by a variety of application servers directly by network computers or client-server environments. Network servers can push files and programs through the system, or can link directly to a single client sharing software throughout the enterprise. Data storage has become almost limitless, whether in co-located or distributed systems, such that the risk of data loss is approaching zero. With the advent of Java and other open-architectures, restrictions placed by operating systems potentially are removed. With the advent of the Internet, any data can now be accessed anywhere at any time. And, through data warehousing and metadata, data can be mined and manipulated in a variety of ways to build upon our knowledge base.

Speed, capacity, and system flexibility will continue to evolve, but none of these are limiting knowledge evolution at this time. Limitations appear in the amount of knowledge that can be gained from these huge data stores, as well as in the efficiency and utility of data acquisition. These, in turn, depend upon the number of persons educated in science, systems, and data analysis, as well as upon advancements in ways to capture and analyze data.

Changing Paradigms

Several fundamental changes are occurring in the ways we think about information. One is that patterns reflected in acquired data sets are now being investigated instead of parameter trend analyses. This derives from concepts that organization exits in chaos, and strings of data exist that depict new relations when sufficient numbers are brought together and viewed in new ways.

It is now recognized that all natural phenomena oscillate within ranges measured by levels, frequency and rate, and that within a given time frame, are characteristic of normal conditions. Thus, all parameters can be viewed as zero deviations from normal, with corresponding values zeroed as floating points. Any changes from normal conditions can be arbitrarily defined as significant, and described by a multi-dimensional control surface. Data patterns shown by these control surface images likely reflect environmental perturbations that can be defined through research.

Examples are patterns in pH, temperature, conductivity, and redox potential measured in biological fluids during normal and diseased states. Each of these parameters normally cycles over a given range, dependent primarily on time, food source, and fluid intake. Natural system regulators maintain this condition in homeostatic state. When abnormal factors are introduced, such as particles, antigens, and antagonists, these natural regulators are stressed. The first indication of stress occurs as spikes in parameter values that manifest as change in standard deviation. Standard deviation increases until the capacity of the regulatory system is surpassed. At this point, the rate of parameter change increases and continues as normal level thresholds are exceeded, without return to normal. This occurs most notably for temperature, with smaller, related changes occurring in other parameters.

The time interval varies between increased standard deviation and increased rate, dependent upon causal agents. Similarly, characteristic patterns develop for the other parameters, creating different surface contours based upon both magnitude and direction of response. Viewed as a multi-dimensional surface, each pattern was associated with specific causes, some with remarkable resolution. The outcome was an ability to quickly predict many root causes, based upon a comparative analysis of multi-dimensional response curves. This analytical approach could be improved by coupling with mathematical models; this may extend and improve predictability, as well as help address elements of uncertainty.

Technology Transfer

Migrating technology to the commercial is facilitated by building upon existing industry standards in ways that help answer difficult problems in a timely fashion. The above suggest that principal opportunities exist in sensor system research and in coupling pattern-based analytical methods with current modeling efforts.

This research was conducted in relatively small, controlled environments, up to warehouse size and neonatal ward complexity. It related only cause and effect. What needs to be done now is to expand this to real world regions, greater amounts of data, sensor systems able to measure and resolve regional data, all within an analytical framework that helps identify and justify further research needs.