SlideShare a Scribd company logo
Big Data
Presenting a Historical Knowledge Breakthrough at UC B
Variant Data
Hugh Ching
Chien Yi Lee
Post-Science Institute
August 2015
NEXT
1
Two Advantages of Computers over Humans
Speed
Size
2
Speed
The computer
has
demonstrated its speed advantage.
3
How Does Computer Demonstrate Size?
Big Data.
(Referring to the ability of the
computer to handle an unlimited
amount of information)
4
The Evolution of Computer Usage
1. Speed
2. Size
Computers have yet to catch up to humans in Complete
Automation, which eliminates the maintenance and update
cost and is, therefore, far more important than speed and
size, and Range of Tolerance, which deals directly with the
survival of a creation in an uncertain future.
3. Automation
4. Tolerance
5
Analyses of Big Data
 The concept of Big Data is a natural progression of computer
development and represents a new phenomenon, not a fad.
 Big Data can be classified into four types:
1. Invariant Data (e.g. speed of light, Planck Constant)
2. Fuzzy Invariant Data (e.g. medical survey, languages)
3. Variant Data (e.g. prices, calculated, not surveyed, data)
4. Approximately-Invariant Variant Data (e.g. human
decisions within the range of tolerance, market
comparable inputs in social science, such as rate of
return, growth rate and interest rate.)
6
Invariant Data
 Science deals with invariant data and phenomena,
which never change and are considered non-
violable laws of nature in science. A small amount
of data is needed.
 Science is based on empirical verification.
 There is no “reason” in science or invariant data.
 Reason, logic, and mathematics in science do not
change anything and merely allow the same
phenomenon to be described from different
perspectives. NEXT
7
Variant Data
 Variant Data change continuously to infinity in time and/or
space. Variant Data originally refer to historical price data.
 Approximately-Invariant Variant Data are used as inputs.
 Variant Data are calculated from a mathematically rigorous
relationship, where the inputs are obtained from the market
survey of Approximately-Invariant Variant Data.
 Since Variant Data are obtained from calculation, they are
generally in conflict with data obtained from market surveys.
 The calculated price is the solution of financial crises,
and the market comparison price is the cause of
financial crises. NEXT
8
Science, Social Science, & Life Science
 Science is based on empirical verification and faith in the law
of uniformity (what happened in the past will happen in the
future.). Science deals with Invariant Data and 5 variables.
 Due to the consideration of infinity, which, by definition, never
arrives, social science, which deals with around 50 variables,
must be based on mathematical rigor.
 When the final variable is Variant Data, it is calculated from a
deterministic system and is not empirically verifiable.
 Life or computer science, such as DNA and computer
software, dealing with around 500 variables, must be based
on logic due to unlimited complexity and the involvement of
infinity. DNA itself is Big Data, and its effect is not
subjected to empirical verification.
NEXT
9
Conclusion
 The number of man-made laws in science is exactly zero. Social
science should replace man-made laws with fuzzy laws of nature.
 In social science, life or computer science, theory is just as
important as collected empirical data (Big Data).
 Reality is infinite and fuzzy rather than finite and exact.
 Finally, it can be concluded that mathematics is for social
science, and logic is for life science or computer science.
 Mathematics and logic are not just for playing games or
intellectual exercise.
 Big Data will be the next advancement in human knowledge, but
will contribute to financial crises and hasten complexity crises, if
the solutions of value and complete automation are not available.
NEXT
10
References
 Paul Feyerabend: Farewell to Reason and Against Method, UC B
 Gerard Debreu (& Kenneth Arrow): Theory of Value: AN AXIOMATIC
ANALYSIS OF ECONOMIC EQUILIBRIUM, UC B
 Lotfi A. Zadeh: Fuzzy Logic, UC B
 Ta-You Wu: Jumpulse
 Chitoor V. Ramamoorthy: von Neumann Syndrome, UC B
 Tosiyasu L. Kunii: Homotopy Theory
 Sumner Davis: First to remark: “Science does not have Variant Data.”
UC B
 Hugh Ching: “Quantitative Supply and Demand Model Based on
Infinite Spreadsheet” (Pat. No. 6,078,901) and “Completely Automated
and Self-generating Software System” (Pat. No. 5,485,601) UC B
NEXT
11
Thank You.
Chien Yi Lee
Presented at UC Berkeley August 26, 2015
Unfinished work to be continued…
12

More Related Content

Viewers also liked

后科学工业 Post science industry
后科学工业 Post science industry后科学工业 Post science industry
后科学工业 Post science industry
Hugh Ching Jumpulse
 
Heaven
HeavenHeaven
Universal Permanent Number
Universal Permanent NumberUniversal Permanent Number
Universal Permanent Number
Hugh Ching Jumpulse
 
Steiner-Lehmus Direct Proof
Steiner-Lehmus Direct Proof Steiner-Lehmus Direct Proof
Steiner-Lehmus Direct Proof
Hugh Ching Jumpulse
 
Big Data Innovations Post-Science
Big Data Innovations Post-ScienceBig Data Innovations Post-Science
Big Data Innovations Post-Science
Hugh Ching Jumpulse
 
Infinite Spreadsheet
Infinite SpreadsheetInfinite Spreadsheet
Infinite Spreadsheet
Hugh Ching Jumpulse
 
Hugh Ching Resume Chinese/English
Hugh Ching Resume Chinese/EnglishHugh Ching Resume Chinese/English
Hugh Ching Resume Chinese/English
Hugh Ching Jumpulse
 

Viewers also liked (7)

后科学工业 Post science industry
后科学工业 Post science industry后科学工业 Post science industry
后科学工业 Post science industry
 
Heaven
HeavenHeaven
Heaven
 
Universal Permanent Number
Universal Permanent NumberUniversal Permanent Number
Universal Permanent Number
 
Steiner-Lehmus Direct Proof
Steiner-Lehmus Direct Proof Steiner-Lehmus Direct Proof
Steiner-Lehmus Direct Proof
 
Big Data Innovations Post-Science
Big Data Innovations Post-ScienceBig Data Innovations Post-Science
Big Data Innovations Post-Science
 
Infinite Spreadsheet
Infinite SpreadsheetInfinite Spreadsheet
Infinite Spreadsheet
 
Hugh Ching Resume Chinese/English
Hugh Ching Resume Chinese/EnglishHugh Ching Resume Chinese/English
Hugh Ching Resume Chinese/English
 

Similar to Big Data--Variant Data Concept

Post-Science knowledge revolution
Post-Science knowledge revolutionPost-Science knowledge revolution
Post-Science knowledge revolution
Chien Yi Lee
 
Computing, cognition and the future of knowing,. by IBM
Computing, cognition and the future of knowing,. by IBMComputing, cognition and the future of knowing,. by IBM
Computing, cognition and the future of knowing,. by IBM
Virginia Fernandez
 
Learning to trust artificial intelligence systems accountability, compliance ...
Learning to trust artificial intelligence systems accountability, compliance ...Learning to trust artificial intelligence systems accountability, compliance ...
Learning to trust artificial intelligence systems accountability, compliance ...
Diego Alberto Tamayo
 
A Primer on Big Data taken by the book: "Big Data" by Schoenberger and Cukier
A Primer on Big Data taken by the book: "Big Data" by Schoenberger and CukierA Primer on Big Data taken by the book: "Big Data" by Schoenberger and Cukier
A Primer on Big Data taken by the book: "Big Data" by Schoenberger and Cukier
Mauro Meanti
 
Smart machines IBM’s watson and the era of cognitive computing
Smart machines IBM’s watson and the era of cognitive computingSmart machines IBM’s watson and the era of cognitive computing
Smart machines IBM’s watson and the era of cognitive computing
Wirehead Technology
 
What Data Can Do: A Typology of Mechanisms . Angèle Christin
What Data Can Do: A Typology of Mechanisms . Angèle Christin What Data Can Do: A Typology of Mechanisms . Angèle Christin
What Data Can Do: A Typology of Mechanisms . Angèle Christin
eraser Juan José Calderón
 
BDW17 London - Totte Harinen, Uber - Why Big Data Didn’t End Causal Inference
BDW17 London - Totte Harinen, Uber - Why Big Data Didn’t End Causal InferenceBDW17 London - Totte Harinen, Uber - Why Big Data Didn’t End Causal Inference
BDW17 London - Totte Harinen, Uber - Why Big Data Didn’t End Causal Inference
Big Data Week
 
Issues in Information SystemsVolume XII, No. 2, pp. 67-73-.docx
Issues in Information SystemsVolume XII, No. 2, pp. 67-73-.docxIssues in Information SystemsVolume XII, No. 2, pp. 67-73-.docx
Issues in Information SystemsVolume XII, No. 2, pp. 67-73-.docx
vrickens
 
Some Take-Home Message about Machine Learning
Some Take-Home Message about Machine LearningSome Take-Home Message about Machine Learning
Some Take-Home Message about Machine Learning
Gianluca Bontempi
 
ARTIFICIAL INTELLIGENCE & ROBOTICS – SYNTHETIC BRAIN IN ACTION
ARTIFICIAL INTELLIGENCE & ROBOTICS – SYNTHETIC BRAIN IN ACTIONARTIFICIAL INTELLIGENCE & ROBOTICS – SYNTHETIC BRAIN IN ACTION
ARTIFICIAL INTELLIGENCE & ROBOTICS – SYNTHETIC BRAIN IN ACTION
ijaia
 
NG2S: A Study of Pro-Environmental Tipping Point via ABMs
NG2S: A Study of Pro-Environmental Tipping Point via ABMsNG2S: A Study of Pro-Environmental Tipping Point via ABMs
NG2S: A Study of Pro-Environmental Tipping Point via ABMs
Kan Yuenyong
 
Roger hoerl say award presentation 2013
Roger hoerl say award presentation 2013Roger hoerl say award presentation 2013
Roger hoerl say award presentation 2013
Roger Hoerl
 
Can we morally justify the replacement of humans by artificial intelligence i...
Can we morally justify the replacement of humans by artificial intelligence i...Can we morally justify the replacement of humans by artificial intelligence i...
Can we morally justify the replacement of humans by artificial intelligence i...Kai Bennink
 
Debate on Artificial Intelligence in Justice, in the Democracy of the Future,...
Debate on Artificial Intelligence in Justice, in the Democracy of the Future,...Debate on Artificial Intelligence in Justice, in the Democracy of the Future,...
Debate on Artificial Intelligence in Justice, in the Democracy of the Future,...
AJHSSR Journal
 
Data Science definition
Data Science definitionData Science definition
Data Science definition
CarloLauro1
 
Let's talk about Data Science
Let's talk about Data ScienceLet's talk about Data Science
Let's talk about Data Science
Carlo Lauro
 
Argumentation in Artificial Intelligence.pdf
Argumentation in Artificial Intelligence.pdfArgumentation in Artificial Intelligence.pdf
Argumentation in Artificial Intelligence.pdf
Sabrina Baloi
 
Cognitive Computing : IBM Watson
Cognitive Computing : IBM WatsonCognitive Computing : IBM Watson
Cognitive Computing : IBM Watson
Amit Ranjan
 

Similar to Big Data--Variant Data Concept (20)

Post-Science knowledge revolution
Post-Science knowledge revolutionPost-Science knowledge revolution
Post-Science knowledge revolution
 
Computing, cognition and the future of knowing,. by IBM
Computing, cognition and the future of knowing,. by IBMComputing, cognition and the future of knowing,. by IBM
Computing, cognition and the future of knowing,. by IBM
 
Learning to trust artificial intelligence systems accountability, compliance ...
Learning to trust artificial intelligence systems accountability, compliance ...Learning to trust artificial intelligence systems accountability, compliance ...
Learning to trust artificial intelligence systems accountability, compliance ...
 
A Primer on Big Data taken by the book: "Big Data" by Schoenberger and Cukier
A Primer on Big Data taken by the book: "Big Data" by Schoenberger and CukierA Primer on Big Data taken by the book: "Big Data" by Schoenberger and Cukier
A Primer on Big Data taken by the book: "Big Data" by Schoenberger and Cukier
 
Smart machines
Smart machinesSmart machines
Smart machines
 
Smart machines IBM’s watson and the era of cognitive computing
Smart machines IBM’s watson and the era of cognitive computingSmart machines IBM’s watson and the era of cognitive computing
Smart machines IBM’s watson and the era of cognitive computing
 
resume-19-11-2015
resume-19-11-2015resume-19-11-2015
resume-19-11-2015
 
What Data Can Do: A Typology of Mechanisms . Angèle Christin
What Data Can Do: A Typology of Mechanisms . Angèle Christin What Data Can Do: A Typology of Mechanisms . Angèle Christin
What Data Can Do: A Typology of Mechanisms . Angèle Christin
 
BDW17 London - Totte Harinen, Uber - Why Big Data Didn’t End Causal Inference
BDW17 London - Totte Harinen, Uber - Why Big Data Didn’t End Causal InferenceBDW17 London - Totte Harinen, Uber - Why Big Data Didn’t End Causal Inference
BDW17 London - Totte Harinen, Uber - Why Big Data Didn’t End Causal Inference
 
Issues in Information SystemsVolume XII, No. 2, pp. 67-73-.docx
Issues in Information SystemsVolume XII, No. 2, pp. 67-73-.docxIssues in Information SystemsVolume XII, No. 2, pp. 67-73-.docx
Issues in Information SystemsVolume XII, No. 2, pp. 67-73-.docx
 
Some Take-Home Message about Machine Learning
Some Take-Home Message about Machine LearningSome Take-Home Message about Machine Learning
Some Take-Home Message about Machine Learning
 
ARTIFICIAL INTELLIGENCE & ROBOTICS – SYNTHETIC BRAIN IN ACTION
ARTIFICIAL INTELLIGENCE & ROBOTICS – SYNTHETIC BRAIN IN ACTIONARTIFICIAL INTELLIGENCE & ROBOTICS – SYNTHETIC BRAIN IN ACTION
ARTIFICIAL INTELLIGENCE & ROBOTICS – SYNTHETIC BRAIN IN ACTION
 
NG2S: A Study of Pro-Environmental Tipping Point via ABMs
NG2S: A Study of Pro-Environmental Tipping Point via ABMsNG2S: A Study of Pro-Environmental Tipping Point via ABMs
NG2S: A Study of Pro-Environmental Tipping Point via ABMs
 
Roger hoerl say award presentation 2013
Roger hoerl say award presentation 2013Roger hoerl say award presentation 2013
Roger hoerl say award presentation 2013
 
Can we morally justify the replacement of humans by artificial intelligence i...
Can we morally justify the replacement of humans by artificial intelligence i...Can we morally justify the replacement of humans by artificial intelligence i...
Can we morally justify the replacement of humans by artificial intelligence i...
 
Debate on Artificial Intelligence in Justice, in the Democracy of the Future,...
Debate on Artificial Intelligence in Justice, in the Democracy of the Future,...Debate on Artificial Intelligence in Justice, in the Democracy of the Future,...
Debate on Artificial Intelligence in Justice, in the Democracy of the Future,...
 
Data Science definition
Data Science definitionData Science definition
Data Science definition
 
Let's talk about Data Science
Let's talk about Data ScienceLet's talk about Data Science
Let's talk about Data Science
 
Argumentation in Artificial Intelligence.pdf
Argumentation in Artificial Intelligence.pdfArgumentation in Artificial Intelligence.pdf
Argumentation in Artificial Intelligence.pdf
 
Cognitive Computing : IBM Watson
Cognitive Computing : IBM WatsonCognitive Computing : IBM Watson
Cognitive Computing : IBM Watson
 

Recently uploaded

Q1’2024 Update: MYCI’s Leap Year Rebound
Q1’2024 Update: MYCI’s Leap Year ReboundQ1’2024 Update: MYCI’s Leap Year Rebound
Q1’2024 Update: MYCI’s Leap Year Rebound
Oppotus
 
一比一原版(TWU毕业证)西三一大学毕业证成绩单
一比一原版(TWU毕业证)西三一大学毕业证成绩单一比一原版(TWU毕业证)西三一大学毕业证成绩单
一比一原版(TWU毕业证)西三一大学毕业证成绩单
ocavb
 
哪里卖(usq毕业证书)南昆士兰大学毕业证研究生文凭证书托福证书原版一模一样
哪里卖(usq毕业证书)南昆士兰大学毕业证研究生文凭证书托福证书原版一模一样哪里卖(usq毕业证书)南昆士兰大学毕业证研究生文凭证书托福证书原版一模一样
哪里卖(usq毕业证书)南昆士兰大学毕业证研究生文凭证书托福证书原版一模一样
axoqas
 
standardisation of garbhpala offhgfffghh
standardisation of garbhpala offhgfffghhstandardisation of garbhpala offhgfffghh
standardisation of garbhpala offhgfffghh
ArpitMalhotra16
 
Best best suvichar in gujarati english meaning of this sentence as Silk road ...
Best best suvichar in gujarati english meaning of this sentence as Silk road ...Best best suvichar in gujarati english meaning of this sentence as Silk road ...
Best best suvichar in gujarati english meaning of this sentence as Silk road ...
AbhimanyuSinha9
 
一比一原版(CU毕业证)卡尔顿大学毕业证成绩单
一比一原版(CU毕业证)卡尔顿大学毕业证成绩单一比一原版(CU毕业证)卡尔顿大学毕业证成绩单
一比一原版(CU毕业证)卡尔顿大学毕业证成绩单
yhkoc
 
Criminal IP - Threat Hunting Webinar.pdf
Criminal IP - Threat Hunting Webinar.pdfCriminal IP - Threat Hunting Webinar.pdf
Criminal IP - Threat Hunting Webinar.pdf
Criminal IP
 
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
nscud
 
一比一原版(ArtEZ毕业证)ArtEZ艺术学院毕业证成绩单
一比一原版(ArtEZ毕业证)ArtEZ艺术学院毕业证成绩单一比一原版(ArtEZ毕业证)ArtEZ艺术学院毕业证成绩单
一比一原版(ArtEZ毕业证)ArtEZ艺术学院毕业证成绩单
vcaxypu
 
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
pchutichetpong
 
一比一原版(YU毕业证)约克大学毕业证成绩单
一比一原版(YU毕业证)约克大学毕业证成绩单一比一原版(YU毕业证)约克大学毕业证成绩单
一比一原版(YU毕业证)约克大学毕业证成绩单
enxupq
 
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
Ch03-Managing the Object-Oriented Information Systems Project a.pdfCh03-Managing the Object-Oriented Information Systems Project a.pdf
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
haila53
 
一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单
一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单
一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单
ewymefz
 
一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
一比一原版(UniSA毕业证书)南澳大学毕业证如何办理一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
slg6lamcq
 
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Subhajit Sahu
 
【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】
【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】
【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】
NABLAS株式会社
 
一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单
一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单
一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单
ewymefz
 
Machine learning and optimization techniques for electrical drives.pptx
Machine learning and optimization techniques for electrical drives.pptxMachine learning and optimization techniques for electrical drives.pptx
Machine learning and optimization techniques for electrical drives.pptx
balafet
 
The affect of service quality and online reviews on customer loyalty in the E...
The affect of service quality and online reviews on customer loyalty in the E...The affect of service quality and online reviews on customer loyalty in the E...
The affect of service quality and online reviews on customer loyalty in the E...
jerlynmaetalle
 
Criminal IP - Threat Hunting Webinar.pdf
Criminal IP - Threat Hunting Webinar.pdfCriminal IP - Threat Hunting Webinar.pdf
Criminal IP - Threat Hunting Webinar.pdf
Criminal IP
 

Recently uploaded (20)

Q1’2024 Update: MYCI’s Leap Year Rebound
Q1’2024 Update: MYCI’s Leap Year ReboundQ1’2024 Update: MYCI’s Leap Year Rebound
Q1’2024 Update: MYCI’s Leap Year Rebound
 
一比一原版(TWU毕业证)西三一大学毕业证成绩单
一比一原版(TWU毕业证)西三一大学毕业证成绩单一比一原版(TWU毕业证)西三一大学毕业证成绩单
一比一原版(TWU毕业证)西三一大学毕业证成绩单
 
哪里卖(usq毕业证书)南昆士兰大学毕业证研究生文凭证书托福证书原版一模一样
哪里卖(usq毕业证书)南昆士兰大学毕业证研究生文凭证书托福证书原版一模一样哪里卖(usq毕业证书)南昆士兰大学毕业证研究生文凭证书托福证书原版一模一样
哪里卖(usq毕业证书)南昆士兰大学毕业证研究生文凭证书托福证书原版一模一样
 
standardisation of garbhpala offhgfffghh
standardisation of garbhpala offhgfffghhstandardisation of garbhpala offhgfffghh
standardisation of garbhpala offhgfffghh
 
Best best suvichar in gujarati english meaning of this sentence as Silk road ...
Best best suvichar in gujarati english meaning of this sentence as Silk road ...Best best suvichar in gujarati english meaning of this sentence as Silk road ...
Best best suvichar in gujarati english meaning of this sentence as Silk road ...
 
一比一原版(CU毕业证)卡尔顿大学毕业证成绩单
一比一原版(CU毕业证)卡尔顿大学毕业证成绩单一比一原版(CU毕业证)卡尔顿大学毕业证成绩单
一比一原版(CU毕业证)卡尔顿大学毕业证成绩单
 
Criminal IP - Threat Hunting Webinar.pdf
Criminal IP - Threat Hunting Webinar.pdfCriminal IP - Threat Hunting Webinar.pdf
Criminal IP - Threat Hunting Webinar.pdf
 
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
 
一比一原版(ArtEZ毕业证)ArtEZ艺术学院毕业证成绩单
一比一原版(ArtEZ毕业证)ArtEZ艺术学院毕业证成绩单一比一原版(ArtEZ毕业证)ArtEZ艺术学院毕业证成绩单
一比一原版(ArtEZ毕业证)ArtEZ艺术学院毕业证成绩单
 
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
 
一比一原版(YU毕业证)约克大学毕业证成绩单
一比一原版(YU毕业证)约克大学毕业证成绩单一比一原版(YU毕业证)约克大学毕业证成绩单
一比一原版(YU毕业证)约克大学毕业证成绩单
 
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
Ch03-Managing the Object-Oriented Information Systems Project a.pdfCh03-Managing the Object-Oriented Information Systems Project a.pdf
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
 
一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单
一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单
一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单
 
一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
一比一原版(UniSA毕业证书)南澳大学毕业证如何办理一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
 
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
 
【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】
【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】
【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】
 
一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单
一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单
一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单
 
Machine learning and optimization techniques for electrical drives.pptx
Machine learning and optimization techniques for electrical drives.pptxMachine learning and optimization techniques for electrical drives.pptx
Machine learning and optimization techniques for electrical drives.pptx
 
The affect of service quality and online reviews on customer loyalty in the E...
The affect of service quality and online reviews on customer loyalty in the E...The affect of service quality and online reviews on customer loyalty in the E...
The affect of service quality and online reviews on customer loyalty in the E...
 
Criminal IP - Threat Hunting Webinar.pdf
Criminal IP - Threat Hunting Webinar.pdfCriminal IP - Threat Hunting Webinar.pdf
Criminal IP - Threat Hunting Webinar.pdf
 

Big Data--Variant Data Concept

  • 1. Big Data Presenting a Historical Knowledge Breakthrough at UC B Variant Data Hugh Ching Chien Yi Lee Post-Science Institute August 2015 NEXT 1
  • 2. Two Advantages of Computers over Humans Speed Size 2
  • 4. How Does Computer Demonstrate Size? Big Data. (Referring to the ability of the computer to handle an unlimited amount of information) 4
  • 5. The Evolution of Computer Usage 1. Speed 2. Size Computers have yet to catch up to humans in Complete Automation, which eliminates the maintenance and update cost and is, therefore, far more important than speed and size, and Range of Tolerance, which deals directly with the survival of a creation in an uncertain future. 3. Automation 4. Tolerance 5
  • 6. Analyses of Big Data  The concept of Big Data is a natural progression of computer development and represents a new phenomenon, not a fad.  Big Data can be classified into four types: 1. Invariant Data (e.g. speed of light, Planck Constant) 2. Fuzzy Invariant Data (e.g. medical survey, languages) 3. Variant Data (e.g. prices, calculated, not surveyed, data) 4. Approximately-Invariant Variant Data (e.g. human decisions within the range of tolerance, market comparable inputs in social science, such as rate of return, growth rate and interest rate.) 6
  • 7. Invariant Data  Science deals with invariant data and phenomena, which never change and are considered non- violable laws of nature in science. A small amount of data is needed.  Science is based on empirical verification.  There is no “reason” in science or invariant data.  Reason, logic, and mathematics in science do not change anything and merely allow the same phenomenon to be described from different perspectives. NEXT 7
  • 8. Variant Data  Variant Data change continuously to infinity in time and/or space. Variant Data originally refer to historical price data.  Approximately-Invariant Variant Data are used as inputs.  Variant Data are calculated from a mathematically rigorous relationship, where the inputs are obtained from the market survey of Approximately-Invariant Variant Data.  Since Variant Data are obtained from calculation, they are generally in conflict with data obtained from market surveys.  The calculated price is the solution of financial crises, and the market comparison price is the cause of financial crises. NEXT 8
  • 9. Science, Social Science, & Life Science  Science is based on empirical verification and faith in the law of uniformity (what happened in the past will happen in the future.). Science deals with Invariant Data and 5 variables.  Due to the consideration of infinity, which, by definition, never arrives, social science, which deals with around 50 variables, must be based on mathematical rigor.  When the final variable is Variant Data, it is calculated from a deterministic system and is not empirically verifiable.  Life or computer science, such as DNA and computer software, dealing with around 500 variables, must be based on logic due to unlimited complexity and the involvement of infinity. DNA itself is Big Data, and its effect is not subjected to empirical verification. NEXT 9
  • 10. Conclusion  The number of man-made laws in science is exactly zero. Social science should replace man-made laws with fuzzy laws of nature.  In social science, life or computer science, theory is just as important as collected empirical data (Big Data).  Reality is infinite and fuzzy rather than finite and exact.  Finally, it can be concluded that mathematics is for social science, and logic is for life science or computer science.  Mathematics and logic are not just for playing games or intellectual exercise.  Big Data will be the next advancement in human knowledge, but will contribute to financial crises and hasten complexity crises, if the solutions of value and complete automation are not available. NEXT 10
  • 11. References  Paul Feyerabend: Farewell to Reason and Against Method, UC B  Gerard Debreu (& Kenneth Arrow): Theory of Value: AN AXIOMATIC ANALYSIS OF ECONOMIC EQUILIBRIUM, UC B  Lotfi A. Zadeh: Fuzzy Logic, UC B  Ta-You Wu: Jumpulse  Chitoor V. Ramamoorthy: von Neumann Syndrome, UC B  Tosiyasu L. Kunii: Homotopy Theory  Sumner Davis: First to remark: “Science does not have Variant Data.” UC B  Hugh Ching: “Quantitative Supply and Demand Model Based on Infinite Spreadsheet” (Pat. No. 6,078,901) and “Completely Automated and Self-generating Software System” (Pat. No. 5,485,601) UC B NEXT 11
  • 12. Thank You. Chien Yi Lee Presented at UC Berkeley August 26, 2015 Unfinished work to be continued… 12