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58 SMT Magazine • November 2015 nomenon adds new challenges to data analy- sis, search, integration, reporting, and system maintenance that must be met to keep pace with the exponential growth of data. And the sources of data are many. As a result, these chal- lenges unique to big analog data have provoked three technology trends in the widespread field of data acquisition. Contextual Data mining The physical characteristics of some real- world phenomena prevent information from being gleaned unless acquisition rates are high enough, which makes small data sets an impos- sibility. Even when the characteristics of the measured phenomena allow more information gathering, small data sets often limit the accu- racy of conclusions and predictions in the first place. Consider a gold mine where only 20% of the gold is visible. The remaining 80% is in the dirt where you can't see it. Mining is required to re- alize the full value of the contents of the mine. This leads to the term "digital dirt," meaning digitized data can have concealed value. Hence, data analytics and data mining are required to achieve new insights that have never before been seen. Data mining is the practice of using the contextual information saved along with data to search through and pare down large data sets into more manageable, applicable volumes. By storing raw data alongside its original context, or "metadata," it becomes easier to accumulate, locate, and later manipulate and understand. For example, examine a series of seemingly random integers: 5126838937. At first glance, it is impossible to make sense of this raw in- formation. However, when given context like (512) 683-8937, the data is much easier to rec- ognize and interpret as a phone number. Descriptive information about measurement data context provides the same benefits and can detail anything from sensor type, manufactur- er, or calibration date for a given measurement channel to revision, designer, or model number for an overall component under test. In fact, the more context that is stored with raw data, the more effectively that data can be traced throughout the design life cycle, searched for or located, and correlated with other measure- ments in the future by dedicated data post-pro- cessing software. Intelligent DAQ Node Data acquisition applications are incredibly diverse. But across a wide variety of industries and applications, data is rarely acquired sim- ply for the sake of acquiring it. Engineers and scientists invest critical resources into building advanced acquisition systems, but the raw data produced by those systems is not the end game. Instead, raw data is collected so it can be used as an input to analysis or processing algorithms that lead to the actual results system designers seek. For example, automotive crash tests can col- lect gigabytes of data in a few tenths of a sec- ond that represent speeds, temperatures, forc- es of impact, and acceleration. But one of the key pieces of pertinent knowledge that can be computed from this raw data is the Head Injury Criterion (HIC), a single scalar, calculated value representing the likelihood of a crash dummy to experience a head injury in the crash. Additionally, some applications—particular- ly in the environmental, structural, or machine condition monitoring spaces—avail themselves to periodic, slow acquisition rates that can be drastically increased in bursts when a notewor- thy condition is detected. This technique keeps acquisition speeds low and minimizes logged data while allowing sampling rates that are ad- mANAGING bIG DATA From AN ANALoG WorLD FeaTure " Data mining is the practice of using the contextual information saved along with data to search through and pare down large data sets into more manageable, applicable volumes. "