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Data Mining Archives - Mark Reynolds

Tag: Data Mining

 

This Digital Transformation Web Site and Blog

[updated May 17, 2018] DigitalTransformation.Engineer This www.DigitalTransformation.Engineer website and blog site is a personal creation; a personal labor-of-love project. Through this site and these blogs, I hope to shed a little light on the evolving industry – software, analytics, machine learning, and the digital oilfield. The software, analytics, and digital oilfield landscape has changed in the past few years and I’ve been blessed to be a part of it. Background In 2010-2011, we were undertaking the 24-7 operations center. AtRead More …

SPE Digital Energy Conference

Energy Forum: BP keynoter Clint Wood, “Communication continuum goes as follows: chaos – noise – data – information – knowledge – wisdom.” Interesting quotes heard during the conference. Data Mining ==> How to pull the data together, then listen to it. Artificial intelligence is no match for natural stupidity. Reasoning ==> Inductive — Platonic — Data Mining Deductive — Aristotle — Statistical Neural Network according to Robert Hecht-Nielson ==> a neural network is a computing system made up of aRead More …

Thoughts on Data Mining

Data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information (see prior blogs including The Data Information Hierarchy series). The term is overused and conjures impressions that do not reflect the true state of the industry. Knowledge Discovery from Databases (KDD) is more descriptive and not as misused – but the base meaning is the same. Nevertheless, this definition of data mining is a very general definitionRead More …

Artificial Intelligence vs Algorithms

I first considered aspects of artificial intelligence (AI) in the 1980s while working for General Dynamics as an Avionics Systems Engineer on the F-16. Over the following 3 decades, I continued to follow the concept until I made a realization – AI is just an algorithm. Certainly the goals of AI will one day be reached, but the manifestation metric of AI is not well defined. Mark Reynolds is currently at Southwestern Energy where he works in the Fayetteville Shale DrillingRead More …

The Value of Real-Time Data

Real-Time data is a challenge to any process-oriented operation. But the functionality of the data is difficult to describe in such a way that team members not well versed in data management. Toward that end, four distinct phases of data have been identified: Real-Time: streaming data visualized and considered – system responds Forensic: captured data archived, condensed – system learns Data Mining: consolidated data hashed and clustered – system understands Predictive Analytics: patterned data compared and matched – system anticipatesRead More …

The Big Crew Change

“The Big Crew Change” is an approaching event within the oil and gas industry when the mantle of leadership will move from the “calculators and memos” generation to the “connected and Skype” generation. In a blog 4 years ago, Rembrandt observes: “The retirement of the workforce in the industry is normally referred to as “the big crew change”. People in this sector normally retire at the age of 55. Since the average age of an employee working at a majorRead More …

Real-Time Data in an Operations/Process Environment

The operations/process environment differs from the administrative and financial environments in that operations is charged with getting the job done. As such, the requirements placed on computers, information systems, instrumentation, controls, and data is different too. Data is never ‘in balance’, data always carries uncertainty, and the process cannot stop. Operations personally have learned to perform their job while waiting for systems to come online, waiting for systems to upgrade, or even waiting for systems to be invented. Once online, systemsRead More …

Multi-Nodal, Multi-Variable, Spatio-Temporal Datasets

Multi-Nodal, Multi-Variable, Spatio-Temporal Datasets are large-scale datasets encountered in real-world data-intensive environments. Example Dataset #1 A basic example would be the heat distribution within a chimney at a factory. Heat sensors are distributed throughout the chimney and readings are taken are periodic intervals. Since the laws of Thermodynamics within a chimney are well understood, the interaction between the monitoring devices can be modeled. Predictive analysis could, conceivably be performed on the dataset and chimney cracks could be detected, or even predicted, in real-time.Read More …

The Data-Information Hierarcy, Part 2

Data, as has been established is the organic, elemental source quantities. Data, by itself, does not produce cognitive information and decision-making ability. But without it, the chain Data –> Information –> Knowledge –> Understanding –> Wisdom is broken before it starts. Data is recognized for its discrete characteristics. Information is the logic grouping and presentation of the data. Information, in a more general sense, is the structure and encoded explanation of phenomena. It answers the who, what, when, where questions (http://www.systems-thinking.org/dikw/dikw.htm)Read More …

Information Theory and Information Flow

(originally posted on blogspot January 28, 2010) Information is the core, the root, of any business. But exactly what is information? Many will immediately begin explaining computer databases. But only a small portion of information theory is actually computer databases. Information is a concrete substance in that it is a quantity that is sought, it is a quantity that can be sold, and it is a quantity that is protected. Wikipedia’s definition: “Information is any kind of event that affectsRead More …