Towards analytical techniques for optimizing knowledge acquisition, processing, propagation, and use in cyberinfrastructure and big data

146 Pages·2018·1.9 MB·English

by  Kreinovich

Studies in Big Data 29
L. Octavio Lerma
Vladik Kreinovich
Towards Analytical
Techniques for
Optimizing Knowledge
Acquisition, Processing,
Propagation, and Use
in Cyberinfrastructure
and Big Data
Studies in Big Data
Volume 29
Series editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
e-mail: [email protected]
About this Series
Theseries“StudiesinBigData”(SBD)publishesnewdevelopmentsandadvances
in the various areas of Big Data-quickly and with a high quality. The intent is to
coverthetheory,research,development,andapplicationsofBigData,asembedded
inthefieldsofengineering,computerscience,physics,economicsandlifesciences.
The books of the series refer to the analysis and understanding of large, complex,
and/or distributed data sets generated from recent digital sources coming from
sensorsorotherphysicalinstrumentsaswellassimulations,crowdsourcing,social
networks or other internet transactions, such as emails or video click streams and
other. The series contains monographs, lecture notes and edited volumes in Big
Data spanning the areas of computational intelligence incl. neural networks,
evolutionary computation, soft computing, fuzzy systems, as well as artificial
intelligence, data mining, modern statistics and Operations research, as well as
self-organizing systems. Of particular value to both the contributors and the
readership are the short publication timeframe and the world-wide distribution,
which enable both wide and rapid dissemination of research output.
More information about this series at http://www.springer.com/series/11970
L. Octavio Lerma Vladik Kreinovich
(cid:129)
Towards Analytical
Techniques for Optimizing
Knowledge Acquisition,
Processing, Propagation,
and Use in Cyberinfrastructure
and Big Data
123
L. OctavioLerma VladikKreinovich
Department ofComputer Science, College Department ofComputer Science, College
ofEngineering ofEngineering
TheUniversity of Texasat ElPaso TheUniversity of Texasat ElPaso
ElPaso, TX ElPaso, TX
USA USA
ISSN 2197-6503 ISSN 2197-6511 (electronic)
Studies in BigData
ISBN978-3-319-61348-2 ISBN978-3-319-61349-9 (eBook)
DOI 10.1007/978-3-319-61349-9
LibraryofCongressControlNumber:2017944300
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Preface
Motivation.Formanydecades,therehasbeenacontinuousprogressinscienceand
engineering applications. A large part of this progress comes from the new
knowledge that researchers acquire, propagate, and use. This new knowledge has
revolutionized many aspects of our life, from driving to communications to
shopping.
Somewhat surprisingly, there is one area of human activity which is the least
impacted by the modern technological progress: the very processes of acquiring,
processing, and propagating information. When we decide where to place sensors,
which algorithm to use for processing the data—we rely mostly on our own intu-
itionandontheopinionoftheexperts.Asaresult,knowledge-relatedmethodsthat
we select are often far from optimal. To make effective recommendations, it is
necessary to build realistic models of the corresponding processes and then use
these models to find optimal ways of controlling these processes.
Theneedforsuchmodelsiswellunderstood.Therearemanynumericalmodels
of knowledge acquisition, processing, and propagations. Some of these models
have been successfully used to enhance the corresponding processes. However,
these applications are limited by the fact that most of these models are based on
detailed numerical simulation of the corresponding processes, which make the
resulting models very time-consuming to use. This is especially important in situ-
ationsofbigdata,whentheamountofdataissohugethatthetraditionalnumerical
methods are not applicable. It is therefore necessary to develop analytical models
forthecorrespondingknowledge-relatedprocesses,modelsthatwouldalloweasier
optimization and application.
Structure of the Book. The main purpose of this book is to develop analytical
models for all the knowledge-related processes, from knowledge acquisition to
knowledge processing and knowledge propagation. In this book:
(cid:129) inChap.2,weprovideanalyticalmodelsfordataacquisition,i.e.,fortheuseof
sensors;
v
vi Preface
(cid:129) in Chap. 3, we provide analytical models for data and knowledge processing;
specifically, we explain how to best organize computing power, how to best
organize research, and how to best organize research teams;
(cid:129) in Chap. 4, we provide analytical models for knowledge propagation and
resulting knowledge enhancement; these models describe how the knowledge
propagates, how to assess the students’ initial knowledge level, how to present
the material, and what is the effect offeedback;
(cid:129) finally, in Chap. 5, we provide case studies explaining how the corresponding
models can be used in science, in control, in design, and in maintenance.
Acknowledgements. Our thanks to Dr. Miguel Argaez, Dr. Ann Gates, Dr. Olga
Kosheleva, Dr. Ming-Ying Leung, Dr. Deana Pennington, Dr. Andrzej Pownuk,
Dr. Scott Starks, Dr. Craig Tweedie, and Dr. Leticia Velasquez for their help,
valuable suggestions, and inspiring ideas. We would also like to express our
appreciationtoPaulSalopekandhisOutofEdenWalkProjectteamfortheirhelp.
Thanks as well to all the participants of:
(cid:129) 8th International Joint Conference on Autonomous Agents and Multiagent
Systems AAMAS’09 (Budapest, Hungary, May 10–15, 2009),
(cid:129) 30th Annual Conference of the North American Fuzzy Information Processing
Society NAFIPS’2011 (El Paso, Texas, March 18–20, 2011),
(cid:129) WorldConference onSoft Computing(SanFrancisco,CA,May 23–26,2011),
(cid:129) AAAI Workshop on Applied Adversarial Reasoning and Risk Modeling
AARM’11 (San Francisco, California, August 7, 2011),
(cid:129) 11th International Sun Conference on Teaching and Learning (El Paso, Texas,
March 5–7, 2014),
(cid:129) International Conference on Risk Analysis in Meteorological Disasters
RAMD’2014 (Nanjing, China, October 12–13, 2014),
(cid:129) Annual Conference of the North American Fuzzy Information Processing
Society NAFIPS’2015 and 5th World Conference on Soft Computing
(Redmond, Washington, August 17–19, 2015),
for valuable discussions.
Grant support. This work was supported in part by the US National Science
Foundation Grants:
(cid:129) Grants No. HRD-0734825 and HRD-1242122 (Cyber-ShARE Center of
Excellence),
(cid:129) Grant No. DUE-0926721 (Bioinformatics), and
(cid:129) Grant No. OCI-1135525 (CI-Team Diffusion project: The Virtual Learning
Commons).
El Paso, TX, USA L. Octavio Lerma
August 2017 Vladik Kreinovich
Contents
1 Introduction.... .... .... ..... .... .... .... .... .... ..... .... 1
References.. .... .... .... ..... .... .... .... .... .... ..... .... 4
2 Data Acquisition: Towards Optimal Use of Sensors. .... ..... .... 7
2.1 Optimal Use of Stationary Sensors: Case Study of Optimal
Placement of Bio-Weapon Detectors .. .... .... .... ..... .... 7
2.2 Optimal Use of Stationary Sensors: Case Study of Optimal
Placement of Environmental Sensors .. .... .... .... ..... .... 15
2.3 OptimalUseofMobileSensors:CaseStudyofUnmannedAerial
Vehicles Patrolling the Border ... .... .... .... .... ..... .... 22
2.4 Efficient Algorithms for Optimizing Sensor Use: Case Study
of Security Problems . ..... .... .... .... .... .... ..... .... 30
References.. .... .... .... ..... .... .... .... .... .... ..... .... 42
3 Data and Knowledge Processing .... .... .... .... .... ..... .... 45
3.1 Data and Knowledge Processing: How to Best Organize
Computing Power.... ..... .... .... .... .... .... ..... .... 45
3.2 Data and Knowledge Processing: How to Best Organize
Research... .... .... ..... .... .... .... .... .... ..... .... 52
3.3 Data and Knowledge Processing: How to Best Organize
Research Teams . .... ..... .... .... .... .... .... ..... .... 54
3.3.1 Diversity Is Important: Main Argument .. .... ..... .... 54
3.3.2 Diversity Is Important: Additional Argument .. ..... .... 57
3.3.3 Caution: Excessive Diversity May Be Detrimental ... .... 59
3.3.4 Towards Finding the Optimal Level of Diversity .... .... 60
References.. .... .... .... ..... .... .... .... .... .... ..... .... 62
vii
viii Contents
4 Knowledge Propagation and Resulting Knowledge
Enhancement... .... .... ..... .... .... .... .... .... ..... .... 65
4.1 Analyzing the Early Stages of Idea Propagation.. .... ..... .... 65
4.1.1 Power Law Model of Knowledge Propagation:
Theoretical Explanation... .... .... .... .... ..... .... 66
4.1.2 Testing a Power Law Model of Knowledge Propagation:
Case Study of the Out of Eden Walk Project .. ..... .... 72
4.2 Analyzing the Assessment of the Students’ Initial
Knowledge Level.... ..... .... .... .... .... .... ..... .... 82
4.3 Analyzing the Way the Material Is Presented: Global Aspects.... 94
4.3.1 In What Order Should We Present the Material ..... .... 95
4.3.2 How Much Flexibility Should We Allow Students... .... 97
4.4 Analyzing the Way the Material Is Presented: Local Aspects. .... 102
4.5 Analyzing the Effect of Feedback. .... .... .... .... ..... .... 105
References.. .... .... .... ..... .... .... .... .... .... ..... .... 109
5 Knowledge Use . .... .... ..... .... .... .... .... .... ..... .... 113
5.1 Use of Knowledge in Science: A Case Study.... .... ..... .... 113
5.2 Use of Knowledge in Control: A Case Study.... .... ..... .... 120
5.3 Use of Knowledge in Design: A Case Study .... .... ..... .... 121
5.4 Use of Knowledge in Maintenance: A Case Study.... ..... .... 125
References.. .... .... .... ..... .... .... .... .... .... ..... .... 135
6 Conclusions .... .... .... ..... .... .... .... .... .... ..... .... 137
Index .... .... .... .... .... ..... .... .... .... .... .... ..... .... 139
Chapter 1
Introduction
Abstract Inthischapter,weexplainwhatisthemainproblemswithwhichwedeal
inthisbook,howexactlywedealwiththem,andwhatisthebook’sstructure.
Knowledge-related processes are important. For many decades, there has been
acontinuousprogressinscienceandengineeringapplications.Alargepartofthis
progress comes from the new knowledge that researchers acquire, propagate, and
use. This new knowledge has revolutionized many aspects of our life. We rely on
automated computer-based systems when we drive our cars, when we fly planes,
whenwecommunicatewithpeopleovertheweb.
Knowledge-relatedprocessesneedtobeenhanced.Somewhatsurprisingly,there
isoneareaofhumanactivitywhichistheleastimpactedbythemoderntechnological
progress:theveryprocessesofacquiring,processing,andpropagatinginformation.
Whenwedecidewheretoplacesensors,whichalgorithmtouseforprocessingthe
dataacquiredviathesesensors,whatisthebestwaytopropagateinformation(e.g.,
which teaching techniques is the best for each task)—we rely mostly on our own
intuition and on the opinion of the experts, and not—as in many other areas—on
computer-basedsystems.
Asaresult,themethodsofdataacquisition,processing,andpropagationthatwe
selectareoftenfarfrombeingoptimal.Thisnon-optimalityisnotsurprising:when
adrivernavigatesinanewtown,clearlyaGPS-basednavigatorwillselectamuch
betterroutethanadriverwouldcomeupbasedonhisorherintuition.
State-of-the-artinmodelingandenhancingknowledge-relatedprocesses:suc-
cesses and limitations. To make effective recommendation on how to acquire,
process, and propagate knowledge, it is necessary to build realistic models of the
correspondingprocesses,andthenusethesemodelstofindoptimalwaysofcontrol-
lingtheseprocesses.
This need is well understood. There are many numerical models of knowledge
acquisition, processing, and propagations; see, e.g., [1–12]. Some of these models
havebeensuccessfullyusedtoenhancethecorrespondingprocesses.
©SpringerInternationalPublishingAG2018 1
L.O.LermaandV.Kreinovich,TowardsAnalyticalTechniquesforOptimizing
KnowledgeAcquisition,Processing,Propagation,andUseinCyberinfrastructure
andBigData,StudiesinBigData29,DOI10.1007/978-3-319-61349-9_1