31:, or other mechanism without the active intervention of a human. While the term dates back over fifty years, there is some current indecision as to the scope of the term. Monash Research's Curt Monash defines it as "data that was produced entirely by machines OR data that is more about observing humans than recording their choices." Meanwhile, Daniel Abadi, CS Professor at
35:, proposes a narrower definition, "Machine-generated data is data that is generated as a result of a decision of an independent computational agent or a measurement of an event that is not caused by a human action." Regardless of definition differences, both exclude data manually entered by a person. Machine-generated data crosses all
51:, and frequency respond to some particular business purpose. Machines often create it on a defined time schedule or in response to a state change, action, transaction, or other event. Since the event is historical, the data is not prone to be updated or modified. Partly because of this quality, the
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published that data will grow by 650% over the following five years. Most of the growth in data is the byproduct of machine-generated data. IDC estimated that in 2020, there will be 26 times more connected things than people. Wikibon issued a forecast of $ 514 billion to be spent on the
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98:/columns. With these data points, the challenge lies mostly with analyzing the data. Given high performance requirements along with large data sizes, traditional
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Given the fairly static yet voluminous nature of machine-generated data, data owners rely on highly scalable tools to process and analyze the resulting
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94:. Almost all machine-generated data is unstructured but then derived into a common structure. Typically, these derived structures contain many
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and partitioning limits the size and history of the dataset for processing. Alternative approaches exist with
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247:"Machine-generated data is the lifeblood of the Internet of Things (#IoT): a key but missing point"
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as only particular "columns" of the dataset would be accessed during particular analysis.
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Control
Systems Functions and Programming Approaches: Applications by Dimitris N Chorafas
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Federal
Evidence Review, Machine Generated Data was Not Statement and Raised no Hearsay
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347:"Machine Generated Data Was Not Statement and Raised no Hearsay or Confrontation"
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Machine-generated data has no single form; rather, the type, format,
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Logs transmitted from security, network and OS sources to
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consider machine-generated data as highly reliable.
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61:Machine-generated data is the lifeblood of the
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313:Monash, Examples of Machine Generated Data
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139:Security information and event management
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217:Monash, Three Broad Categories of Data
365:"Examples of Machine Generated Data"
383:"Gartner Ten Technologies to Watch"
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329:"Machine vs. Human generated data"
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356:"Three Broad Categories of Data"
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174:. Academic Press. 1966-01-01.
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245:Seth Grimes (8 March 2016).
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23:automatically generated by a
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147:collected by the government
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345:Federal Evidence Review.
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338:"Machine Generated Data"
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17:Machine-generated data
126:Financial instrument
121:Call detail records
80:Industrial Internet
376:. Monash Research.
367:. Monash Research.
358:. Monash Research.
340:. Infobright, Inc.
194:Monash, 12/30/2010
104:columnar databases
63:Internet of Things
181:978-0-08-095534-6
100:database indexing
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157:Reference List
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331:. BlogSpot.
96:data points
29:application
21:information
133:event logs
86:Processing
145:Telemetry
82:in 2020.
73:In 2009,
43:Relevance
395:Category
131:Network
110:Examples
49:metadata
294:Wikibon
255:Twitter
92:dataset
75:Gartner
65:(IoT).
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128:trades
69:Growth
251:Tweet
203:Abadi
152:Notes
176:ISBN
53:U.S.
33:Yale
19:is
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