Damss scurt v2 dss an evolving class ...

Several Decades Ago…
Peter Drucker (1967a)
stated: “The computer
makes no decisions; it
only carries out orders.
It’s a total moron, and
therein lies its strength.
It forces us to think, to
set the criteria. The
stupider the tool, the
brighter the master has
to be—and this is the
dumbest tool we have
ever had”..
12/26/18 F. G. Filip 2
Photo
https://en.wikipedia.org/w/index.
php?curid=34221009

(P.Drucker, 1967b) “This danger is being
aggravated today by the advent of the computer
and of the new information technology. The
computer, being a mechanical moron, can handle
only quantifiable data. These it can handle with
speed, accuracy, and precision. It will, therefore,
grind out hitherto unobtainable quantified
information in large volume. One can get, however,
by and large quantify only what goes on inside an
organization – costs and production figures,
patient statistics in the hospital, or training
reports. The relevant outside events are rarely
available in quantifiable form until it is much too
late to do anything about them.”
12/26/18 F. G. Filip 3

Umberto Eco
(1986)
Il computer non è una
macchina intelligente
che aiuta le persone
stupide, anzi è una
macchina stupida che
funziona solo nelle
mani delle persone
intelligenti”
(“The comuter is not an intelligent
machine that helps the stupid
people , but is a stupid tool that
functions only in the hands of
intelligent people“) .
Photo://www.napocanews.ro/
2016/02/a-murit-scriitorul-
umberto-eco.htmltorul-
umberto-eco.html
12/26/18 F. G. Filip 4

DSS-An Evolving Class of Information
Systems
or
(The Computer: from Peter Drucker’s Moron
to Cognitive Systems)
by F. G. Filip
The Romanian Academy and INCE
Bucharest
12/26/18 5F. G. Filip

This talk is about
modern Information and Communication
Technologies ( I&C T) such as Big Data,
Cloud Computing, and Cognitive Systems)
and
their usage in Decision Support Systems
(DSS) used in management applications
It also
reveals possible open problems and
concerns
12/26/18 F. G. Filip 6

Paul Morris Fitts ( 1912-1965)
12/26/18 F. G. Filip 7
http:/
Photo: /fww.few.vu.nl/hci/interactive/fitts/9.html

The Original Fitts’ (1951) MABA-MABA
( Men Are Best At-Machines Are Best At) List
• “Humans appear to surpass present-day machines in respect
• to the following:
• 1. Ability to detect a small amount of visual or acoustic energy
• 2. Ability to perceive patterns of light or sound
• 3. Ability to improvise and use flexible procedures
• 4. Ability to store very large amounts of information for
long periods and to recall relevant facts at the appropriate
time
• 5. Ability to reason inductively
• 6. Ability to exercise judgment”
•
• “Present-day machines appear to surpass humans in respect
• to the following:
• 1. Ability to respond quickly to control signals and to apply great force smoothly and
precisely
• 2. Ability to perform repetitive, routine tasks
• 3. Ability to store information briefly and then to erase it completely
• 4. Ability to reason deductively, including computational ability
• 5. Ability to handle highly complex operations, i.e. to do many different things “at once.
•
12/26/18 8F. G. Filip

• A new business lansdcape !
12/26/18 F. G. Filip 9

New Manufacturing Paradigmes
( Vernadat et al, 2018)
• “Industry 4.0, or technologies for the factories of
the future, […] is the logical progression from the
CIM […] trends that started at the beginning of
the 80s…and takes advantage of the new I&C
Technologies”( see part II);
• “S^3 Enterprises means enhancing sensing, smart
and sustainability capabilities of manufacturing or
service companies”;
• Cloud Manufacturing (CMfg) that takes full
advantage of networked organization and Cloud
Computing (CC)”12/26/18 F. G. Filip 10

Enabling Modern I&C Technologies (I)
(adapted from Vernadat et al, 2018)
• Web Technology, Social Media.
• Internet/Web of Things (I/WoT) enables objects and machines such
as sensors, actuators, robots or users’ mobile devices (e.g. mobile
phones, tablets) to communicate with each other as well as with human
agents in order to carry out tasks or to find solutions to problems.
• Internet of Services (IoS) enables a service to be performed as a as
a commercial transaction where one party gets temporary access to
resources of another party (human workforce or skills/knowledge, IT-
based operations) to perform a certain function at a given/agreed cost.
• Big Data and associated Data Analytics/Business Intelligence and
Analytics enable processing the massive volumes of data generated by
IoT to discover new facts or extract relevant information or knowledge
by using data warehousing , statistics, data mining, knowledge discovery
, machine learning techniques, data visualization.
• Cloud Computing (CC) enables ubiquitous access to shared pools of
configurable I&CT resources and services that can be quickly
provisioned with minimal management effort.
12/26/18 F. G. Filip 11

Enabling Modern I&C Technologies (II)
(adapted from Vernadat et al, 2018)
• Artificial Intelligence- based tools.
• Cyber-Augmented Interaction and Collaboration combines I&CT real-time
control, cybernetic brain models and operate in a parallel cyber-space with
multi-agent systems, task administration protocols and algorithms with a view
to provide streamlined, harmonized and optimized workflows for interactions
of humans and machines.
• Augmented Reality (AR) complements real data (usually live direct or indirect
video images) with computer-processed data (sound, 2D or 3D images, videos,
charts, etc.) and using various sensory modalities, including visual, auditory,
haptic, somato-sensory sensations.
• Ambient Intelligence (AmI) as defined by ISTAG ( Information Society
Technology Program Advisory Group to the European Community) as "the
convergence of ubiquitous computing, ubiquitous communication, and
interfaces adapted to the user”.
• Advanced ( intelligent/real-time/collaborative ) Decision Support
Systems(DSS
12/26/18 F. G. Filip 12

Automation[at Large]
Definition:We speak about automation when
a computer or another device executes
certain functions that the human agent
would normally perform.
Remark: Automation is present not only in
most safety or time critical systems, such
as aviation, power plants or refineries, but
also in transportation, libraries, medicine,
robotized homes, and even intelligent
cloths.
12/26/18 F. G. Filip 13

Substitution or Transformation ?
( Dekker , Woods 2002)
“Substitution assumes a fundamentally
uncooperative system architecture in which
the interface between human and machine has
been reduced to a trivial "you do this, I do
that" barter.”
“Quantitative “who does what” allocation does
not work because the real effects of
automation are qualitative: it transforms
human practice and forces people to adapt
their skills and routines.”
12/26/18 F. G. Filip 14

Keywords
• Division of work between human and
machine
• Substitution of human by a machine
• Adaptation and transformation of the
human practice
• A new issue
Collaboration of humans with various
agents such as other humans, robots,
computers, communication systems
12/26/18 F. G. Filip 15

Collaboration
• Definition:In general, two or more entities
collaborate because each one working
independently cannot deliver the expected
output such as a product, a service, a
decision ( Nof et al , 2015)
• Other Related Concepts: shared control,
cooperative control, human-machine
cooperation, collaborative automation,
adaptive / shared automation (Flemisch et al,
2016)12/26/18 F. G. Filip 16

Evolutions
• “After years of promise and hype[…], computers are
replacing skilled practitioners in fields such as
architecture, aviation, the law, medicine, and
petroleum geology—and changing the nature of
work in a broad range of other jobs and professions.
Deep Knowledge Ventures, a Hong Kong venture-
capital firm, has gone so far as to appoint a decision-
making algorithm to its board of directors
( Dewhurst and Willmott, 2014) .
• [However] ”…the hardest activities to automate with
currently available technologies are those that
involve managing and developing people (9%
automation potential or that apply expertise to
decision–making, planning or creative work
(18%)”(Chui, 2016)
12/26/18 F. G. Filip 17

DSS ( Decision Support System): A
Definition(Filip 2008)
• An anthropocentric and evolving
information system which is meant to
implement the functions of a human
support system that would otherwise
be necessary to help the decision-maker
to overcome his/her limits and
constraints he/she may encounter when
trying to solve complex and complicated
decision problems that count
12/26/18 F. G. Filip 18

DSS in Three Data Bases (Suduc, 2017)
12/26/18 F. G. Filip 19

Group DSS
• Attributes of a collaborating group of humans
(Briggs, 2015)
– Congruence of goals and methods used of members
with the agreed common goals and procedures;
– Group effectiveness in attaining the common goals;
– Group efficiency in saving member resources
consumed;
– Group cohesion: preserving member willingness to
collaborate in the future.
12/26/18 F. G. Filip 20

Major Desired Characteristic Features of a
Group DSS (Nunamaker et al , 2015)
• Parallelism meant to avoid the waiting time of participants
who want to speak in an unsupported meeting by enabling all
users to add, in a simultaneous manner, their ideas and
points of view;
• Anonymity that makes possible an idea be accepted based
on its value only, no matter what position or reputation has
the person who has proposed it;
• Memory of the group that is based on long term and
accurate recording of the ideas expressed by individual
participants and conclusions that were reached;
• Improved precision of the contributions which were typed-
in compared with their oral presentation;
• Unambiguous display on computer screen of the ideas.
• Any time and/or any place operation that enable the
participation of all relevant persons, no matter their
location
12/26/18 F. G. Filip 21

An Early GDSS: Claremont Graduate Univ.
Decision Room in 1987 ( Schuff et al 2011)
12/26/18 F. G. Filip 22

A Typical GDSS at NTT in 1996
(source: http://www.wtec.org/loyola/hci/c3_s1.htm )
12/26/18 F. G. Filip 23

University of Arizona GDSS
( Schuff 2011)
12/26/18 F. G. Filip 24

Data-driven DSS Features (Power, 2008)
• Ad-hoc data filtering and retrieval: drilling-down, changeing the
aggregation level from the most summarized data one to more detailed
ones;
• Creating data displays allowing the user to choose the desired format
(Scatter diagrams, bar and pie charts and so on) or/and to perform
various effects, such as animation, playing back historical data and so
on;
• Data management;
• Data summarization: possibility to customize the data aggregation
format, perform the desired computations, examine the data from
various perspectives;
• Spreadsheet integration;
• Metadata creation and retrieval;
• Report designing, generation and storing in order to be used or
distributed via electronic documents or posted on webpages;
• Statistical analysis including data mining for discovering useful
relationships.12/26/18 F. G. Filip 25

Data
• At present
– Data are accumulated from various internal
and external sources such as:
• sensors,
• transactions,
• web searches,
• human communication+ Crowdsourceing,
• Internet/Web of Things
– They contain information useful for
• finance, trade, manufacturing, education…
– The amounts of data are too big for people’s
storing capacities12/26/18 F. G. Filip 26

Big Data Attributes
(adapted from Kaisler et al 2013)
• Volume measures the amount of data available and
accessible to the organization.
• Velocity is a measure of the speed of data creation,
streaming and aggregation.
• Variability of data flows that might be inconsistent with
periodic peaks( introduced by SAP).
• Variety measures the richness of data representation:
numeric, textual, audio,video, structured, unstructured a.s.o.
• Value isusefulness and usability in decision making (ORACLE)
• Complexity measures the degree of interconnectedness,
interdependence in data structures and sensitivity of the
whole to local changes.( introduced by SAP)
• Veracity measures the confidence in the accuracy of data
( introduced by IBM).12/26/18 F. G. Filip 27

Big Data Views
• Various Bloggers ( apud Power, 2013)
– Andrew Brust (2012):"The excitement around
Big Data is huge; the mere fact that the
term is capitalized implies a lot of
respect” , ZDNet, March, 1.
– Barry Devlin ( 2013) of 9sight Consulting
(https://tdwi.org/pages/upside.aspx ): “Big
data as a technological category is
becoming an increasingly meaningless
name.” , B-eye-Network blog.
12/26/18 F. G. Filip 28

Challenges of Big Data
( Shi, 2015; Filip & Herrera Viedma, 2014)
• Transforming semi-structured and unstructured
data collected into a structured format to be
processed by the available data mining tools.
• Exploring the complexity, uncertainty and
systematic modeling of big data.
• Exploring the relationship of data heterogeneity,
knowledge heterogeneity and decision
heterogeneity .
• Engineering decision-making by using cross-industry
standards such as the six-step CRISP-DM (Cross
Industry Standard Process for Data Mining),
recommended by the EU
12/26/18 F. G. Filip 29

Yong Shi
• University of CAS
(http://people.ucas.edu.cn/~000
2041?language=en )
• University of Nebraska
at Omaha
(https://www.unomaha.edu/coll
ege-of-information-science-and-
technology/about/faculty-
staff/yong-shi.php )
• ITQM president
• (http://www.iaitqm.org/)
12/26/18 F. G. Filip 30

Business Intelligence (BI) as A
Software Platform ( Chen et al, 2012)
Three classes of functionalities:
• Integration : BI infrastructure, metadata
management, development tools and enabling
collaboration;
• Analysis: OLAP (OnLine Analytical Processing),
interactive visualization, predictive modeling, data
mining and scorecards.
• Information delivery/presentation: reporting,
dashboards, ad-hoc query, Microsoft Office
integration, search-based BI, and mobile BI;
12/26/18 F. G. Filip 31

BI&A Generations (I)
( Chen et al, 2012)
BI&A 1.0
• adopted by industry in the 1990s,
• predominance of structured data collected by existing
legacy systems and stored and processed by RDBM
(Relational Data Base Management Systems);
• the majority of analytical techniques use well established
statistical methods and data mining tools developed in the
1980s
• The ETL (Extract, Transformation and Load) of data
warehouses, OLAP (On Line Analytical Processing) and
simple reporting tools are common aspects.
. 12/26/18
F. G. Filip

BI&A Generations (II)
BI&A 2.0
• triggered by advances in Internet and Web
technologies, in particular text mining and web
search engines;
• main technologies: text and web mining
techniques associated with social networks, Web
2.0 technology,
• crowdsourcing business practice allow making
better decisions concerning both product and
service offered by companies and recommended
applications for the potential customers12/26/18 F. G. Filip 33

BI&A Generations (III)
BI&A 3.0
• characterized by the large-scale usage
of mobile devices and applications such
as iPhone and iPad
• the effective data collection enabled by
the Internet of Thing
12/26/18 F. G. Filip 34

Big Data Analytics (Gandomi, Haider 2015)
• Text Analytics (mining)
– Information extraction (IE) techniques extract
structured data from unstructured text with two sub-
tasks : Entity Recognition (ER) and Relation Extraction
(RE)
– Text summarization techniques meant to automatically
produce a succinct summary of a single or multiple
documents.
– Question answering (QA) techniques provide answers to
ques-tions posed in natural language.( => cognitive
systems) ; Examples : Siri ( Apple) and Watson( IBM)
– Opinion mining techniques analyze the texts which
contain people’s opinions toward various entities such as
products, services. organizations, peoples, events a.s.o12/26/18 F. G. Filip 35

Other Big Data Analytics
• Audio Analytics (AA):
– Transcript-based approach (called Large Vocabulary
Continuous Speech Recognition- LVCSR)
– Phonetic-based approach.
• Video Content Analytics (VCA): techniques meant to
monitor, analyze, and extract meaningful information from
video streams.
• Social Media Analytics (SMA)
– Community detection/discovery meant to identifyimplicit
communities within a network
– Social influence analysis
– Prediction of future links12/26/18 F. G. Filip 36

Big Data in Romania
• A community of BIG DATA is formed : http://bigdata.ro
• Big Data Events: https://bigdata.ro/big-data-events/
• Big international companies are active and promote their
products and services
– ORACLE: EXALITYCS and ADVANCED ANALYTICS
• https://www.oracle.com/big-data/index.html
– IBM: Big Data Platform: Apache Hadoop, Stream
computing:
• https://www-01.ibm.com/software/data/bigdata
12/26/18 F. G. Filip 37

Big Data Public Projects in Romania
(Alexandru et al, 2016)
• Electronic Public Procurement System (SEAP) is a unified I&C T
infrastructure which ensures the development of the procurement
processes;
• Electronic Document Filing System (DEDOC) is used for reporting
individual budgets by means of electronic declarations and forms;
• The Integrated Information System of the National TradeRegister
Office (NTRO) that provides online services for the business
community;
• Project for Modernizing Revenue Administration (RAMP): implemented
by the National Agency for Fiscal Administration (ANAF) intended to
generate premises to achieve a higher level of revenue collection,
• The Integrated System of the National House of Pensions (Orizont)
manages the systems for public pensions and insurances;
• National Cadastral and Estate Registry System (E-Terra).
12/26/18 F. G. Filip 38

An Application: Labour Market-DSS
( Brandas et al, 2016)
• Import.io (https://www.import.io/) to allow the extraction
and conversion of semi-structured data into structured
data. The collected data can be exported as CSV (Comma-
Separated Values), Excel, Google Sheets or JSON
(JavaScript Object Notation).
• Waikato Environment for Knowledge Analysis (WEKA) is a
machine learning software for data mining processes. It
contains tools for data pre-processing, classification,
regression, association rules, clustering and visualization
(http://www.cs.waikato.ac.nz/ml/weka/).
• Google Fusion Tables
(https://support.google.com/fusiontables/answer/2571232?
hl=en) : an an experimental data visualization web
application that allows the gathering, visualization, and
sharing of data tables using Google Maps12/26/18 F. G. Filip 39

Data Spatialization of The Academic
Jobs (Brandas et al 2016)
12/26/18 F. G. Filip 40

Cloud Computing:
The NIST view
• Definition: “CC is a model for enabling ubiquitous,
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g., networks, servers,
storage, applications, and services) that can be rapidly
provisioned and released with minimal management effort
or service provider interaction “(Mell & Grance, 2011)
• Service Models
– Infrastructure as a Service (IaaS).
– Platform as a Service (PaaS).
– Software as a Service (SaaS)
12/26/18 F. G. Filip 41

Early Skeptical Views (apud Aburst et
al, 2009)
• Larry Ellison, ORACLE CEO: “The interesting thing about Cloud
Computing is that we’ve redefined Cloud Computing to include
everything that we already do. . . . I don’t understand what we
would do differently in the light of Cloud Computing other than
change the wording of some of our ads”. the Wall Street Journal,
September 26, 2008.
• Andy Isherwood, H P Vice President of European Software Sales : “A
lot of people are jumping on the [cloud] bandwagon, but I have
not heard two people say the same thing about it. There are
multiple definitions out there of the cloud”., ZDnet News,
December 11, 2008.
• Richard Stallman of Free Software Foundation: “It’s stupidity. It’s
worse than stupidity: it’s a marketing hype campaign.
Somebody is saying this is inevitable — and whenever you hear
somebody saying that, it’s very likely to be a set of businesses
campaigning to make it true”. The Guardian, September 29, 2008
12/26/18 F. G. Filip 42

From Mainframe Computer Centre to
Cloud Computing
12/26/18 F. G. Filip 43
Mainframe
ComputerCentre
PC
Server
Cluster
Data
Centre
Cloud

A Remark
• After all, cloud computing is just
mainframe computing in a
different way, which is how I
learned how to compute when I
was a young boy”.
• From “The impact of disruptive technology: A
conversation with Google executive chairman
Eric Schmidt “(McKinsey&Company ,2013)
12/26/18 F. G. Filip 44

From Mainframe Computer Centre to
Cloud Computing
12/26/18 F. G. Filip 45
Mainframe
ComputerCentre
PC
Server
Cluster
Data
Centre
Cloud

BIG DATA and the CLOUD
• Big Data as A Service (BDaAS)
( http://www.clubitc.ro/2016/02/01/big-data-ca-serviciu/ )
• Remark : At present BDaAS
– For SME ( Small and Medium Enterprisese) hich
cannot afford
• a private cloud
• A own in house BIG DATA solution,
Only in a private cloud environment of a service provider
– Barriers :Various billing schemes, bandwidth
F. G. Filip 46

iDSS an integrated platform for Group
Decision Support:the Server (Candea,Filip,2016)
12/26/18 F. G. Filip 47

iDSS Levels
12/26/18 F. G. Filip 48
a) B( business) aaS level; b) S( Software) aaS level; c)P( latform)aaS
level; d) I( infrastructure) aaS level (Radu et al. 2014)

• At present, Big Data as a Service
– is recommended for SME ( Small and
Medium Enterprises)which cannot afford
• a private cloud
• their own in house BIG DATA solution,
– There are barriers :
• Various billing schemes,
• Insufficient bandwidth
12/26/18 F. G. Filip 49

http://diva.library.cmu.edu/Simon/biography.htmlF. G. Filip 50

Herbert Simon ( 1916-2001) Photo source:
http://diva.library.cmu.edu/Simon/biography.htmlF. G. Filip 51

The Time for AI
• H. Simon (1987):“The MS/OR profession has,
in a single generation, grown from birth to a
lively adulthood and is playing an important
role in the management of our private and
public institutions. This success should rise
our aspirations. We should aspire to increase
the impact of MS/OR by incorporating the
AI kit of tools that can be applied to ill-
structured , knowledge rich, nonquantitative
decision domains … “(Two heads are better than
one; the collaboration between AI and OR.
Interfaces , 17(4): 8-15)12/26/18 F. G. Filip 52

Technologies of AI 2.0 (Pan, 2016)
• Internet crowd intelligence;
• Big-data-based AI ( transforming Big Data into
knowledge);
• Cross-media intelligence/ computing;
• Autonomous-intelligent systems;
• New forms of hybrid-augmented intelligence, from
the pursuit of an intelligent machine to high-level
human-machine collaboration and fusion=> Cognitive
Systems.
12/26/18 F. G. Filip 53

Cognitive Computing: An Early
Vision of J.C.R. Licklider ( 1960 )
“Man-computer symbiosis is an expected development
in cooperative interaction between men and
electronic computers. It will involve very close
coupling between the human and the electronic
members of the partnership. The main aims are:
• to let computers facilitate formulative thinking as
they now facilitate the solution of formulated
problems, and
• to enable men and computers to cooperate in making
decisions and controlling complex situations without
inflexible dependence on predetermined
programs[…] “
Man-Computer Symbiosis. IRE Transactions on Human Factors
in Electronics , HFE-1(1), March 1960 : p.4-11 )12/26/18 F. G. Filip,, 54

J.C.R. Licklider (1915-1990)
( nicknamed “Johnny Appleseed”)
12/26/18 F. G. Filip 55

“Cognitive Systems Engineering: New
wine in new bottles”(Hollnagel, Woods, 1983)
• A Cognitive System
– behaves in a goal oriented way;
– is adaptive and able to view the problem in more
than one ways;
– is able to plan and modify its actions based on
knowledge about itself and environment;
– is data driven and concept driven as well
The goal is improving the overall operation of the
system rather than replacing as many as possible
functions of the operator.
12/26/18 F. G. Filip 56

• “Now we are at the dawn of a much bigger
shift in the evolution of technology—a new
era affecting nearly every aspect of the
field. The changes that are coming over the
next two decades will transform the way
we live and work just as the computing
revolution has transformed the human
landscape over the past half century…. call
this the era of cognitive computing “ (John
E. Kelly III & SE Hamm, 2013)
12/26/18 F. G. Filip 57

Cognitive Systems: The Adopted
Definition ( Kelly III, 2015)
• “Cognitive computing refers to systems that a)learn at scale, b)reason
with purpose , and c)interact with humans naturally.
• Rather than being explicitly programmed, they learn and reason from
their interactions with us and from their experiences with their
environment.
• They are made possible by advances in a number of scientific fields over
the past half-century, and are different in important ways from the
information systems that preceded them. Those systems have been
deterministic; cognitive systems are probabilistic. They generate not
just answers to numerical problems, but hypotheses, reasoned
arguments and recommendations about more complex — and meaningful
— bodies of data.
• They can make sense of the 80 percent of the world’s data that
computer scientists call “unstructured.” This enables them to keep pace
with the volume, complexity and unpredictability of information and
systems in the modern world.”
12/26/18 F. G. Filip 58

Two Examples
1. IBM Watson- The first cognitive system (High, 2012)
– Natural language processing by helping to understand the
complexities of unstructured data;
– Hypothesis generation and evaluation by applying advanced analytics
to weigh and evaluate a panel of responses based on only relevant
evidence;
– Dynamic learning by helping to improve learning based on
outcomes to get smarter with each iteration and interaction
1. DISCIPLE Metodology and set of cognitive agents
(Tecuci, 2016)
– The agents play the role of intelligent assistants that use the
evidence-based reasoning;
– They learn the expertise in problem solving and decision-making
directly, from human experts, support people and non-experts;
– The users may not be computer people but possess application and
problem solving knowledge
12/26/18 F. G. Filip 59

IBM Watson:How It Works
(High, 2012)
1. When a question is first presented to Watson, it parses
the question to extract the major features of the
question
2. It generates a set of hypotheses by looking across the
corpus for passages that have some potential for
containing a valuable response.
3. It performs a deep comparison of the language of the
question and the language of each potential response by
using various reasoning algorithms.
4. Each reasoning algorithm produces one or more scores,
indicating the extent to which the potential response is
inferred by the question based on the specific area of
focus of that algorithm.12/26/18 F. G. Filip 60

IBM Watson:How It Works
(High, 2012)
• 5. Each resulting score is then weighted against a
statistical model that captures how well that algorithm
did at establishing the inferences between two similar
passages for that domain during the “training period” for
Watson. That statistical model can then be used to
summarize a level of confidence that Watson has about
the evidence that the candidate answer is inferred by the
question.
• 6. Watson repeats this process for each of the candidate
answers until it can find responses that surface as being
stronger candidates than the others”
12/26/18 F. G. Filip 61

Cognition as a Service (CaaS)
• “Engineers predict that by 2055, nearly
everyone has 100 cognitive assistants that
“work for them[….]
• Almost all the people including doctors,
physicians, patients, bankers, policymakers,
tourists, customers, as well as community
people greatly augmented their capabilities
by the cognitive mediators or cognition as a
service CaaS[…]
• Cognitive systems can potentially progress
from tools to assistants to collaborators
to coaches “. ( Spohrer et al, 2017)12/26/18 F. G. Filip 62

12/26/18 F. G. Filip 63
J. Spohrer

Cautious Voices
• Stephan Hawking (2017) : Humans who are limited
by slow biological evolution couldn't compete
and would be superseded. ... The development of
full artificial intelligence could spell the end of
the human race. ... It would take off on its own,
and re-design itself at an ever increasing rate.“
• “A I could be the worst event in the history our
civilization” CNBC
(https://www.cnbc.com/2017/11/06/stephen-hawking-ai-could-
be-worst-event-in-civilization.html )
12/26/18 F. G. Filip 64

• Elon Musk of Tesla Motors : "I think we should be very careful
about artificial intelligence. If I had to guess at what our
biggest existential threat is, it's probably that. So we need
to be very careful. ..
.
• Bill Gates "I am in the camp that is concerned about super
intelligence. ... I agree with Elon Musk and some others
on this and don't understand why some people are not
concerned.“
• Steve Wozniak: "Computers are going to take over from
humans, no question ... Like people including Stephen
Hawking and Elon Musk have predicted, I agree that the
future is scary and very bad for people ... If we build these
devices to take care of everything for us, eventually they'll
think faster than us and they'll get rid of the slow
humans t to run companies efficiently ... Will we be the
gods? Will we be the family pets? Or will we be the ants
that stepped on? I don't know …"
12/26/18 F. G. li ””” 65

A Balanced View
• Sundar Pichai, Google CEO: “AI is one of the
most important things humanity is
working on. It is more profound than, I
dunno, electricity or fire [….]We have
learned to harness fire for the benefits of
humanity but we had to overcome its
downsides too. So my point is, AI is really
important, but we have to be concerned
about it.”
https://www.cnbc.com/2018/02/01/google-
ceo-sundar-pichai-ai-is-more-important-than-
fire-electricity.html
12/26/18 F. G. Filip 66

Choosing an Application
Software/Platform/Service:
General Criteria
• Adequacy: informational transparency, accuracy of
expected results, robustness to errors and low
quality uncertain input data, response time;
• Quality of implementation:scalability,flexibility,
functional transparency, documentation
completeness
• Delivery quality: price, delivery time, provider’s
general reputation, easy adaptation, degree of
dependence on the technical assistance from the
provider’s specialists for implementation and usage
12/26/18 F. G. Filip 67

MADM( Multi Attribute Decision
Models): Input Data
• na possible courses of actions
(alternatives), Ai ( i = 1,2,…, na) ;
• nc evaluation criteria, ECj ( j = 1,…, nc)
– Upper and lower limits
– Weights wj
• Scores sij ( i = 1,2,…, na ; j = 1,…, nc)
• nd individual decision-makers, Dk ( k =
1,2,…, nd);
12/26/18 F. G. Filip 68

General Methods
• Dzemyda G., Saltenis V (1994) Multiple criteria
decision support system: methods, user's interface
and applications. INFORMATICA, 5 ( 1-2), pp.31-42
• Zavadskas, E.K., et al (2014). State of art surveys of
overviews on MCDM/MADM methods. Technological
and economic development of economy, 20(1), pp.165-
179.
• Cochran, J.K. & Chen, H.N.( 2005). Fuzzy multi-
criteria selection of object-oriented simulation
software for production system analysis. Computers &
operations research, 32(1), pp.153-168
12/26/18 F. G. Filip 69

An Example : Choosing A Cloud
Computing Solution (I); The CSP
Problem
The Cloud Service Provider - CSP selection problem:
A decision-maker - DM (or a group of DMs), which is a cloud user or
cloud broker, wants to select from a set of Cloud Service Providers -
CSPs, available on the Cloud market, a CSP that fits better with his
requirements. In order to do that the DM have to evaluate and rank
the set of candidates CSPs with respect to his requirements.
The CSP selection problem is a complex decision-making problemcomplex decision-making problem
which involves multiple criteriamultiple criteria, often conflicting one to another.
Some uncertainty is involved in the decision-making process of
selection.
12/26/18 F. G. Filip 70

Computing Solution (II): Standards
Quality of Service (QoS)Quality of Service (QoS) standards for CSPs.
• The “Service Measurement Index (SMI)Service Measurement Index (SMI)”. Proposed by Cloud
Services Measurement Initiative Consortium (CSMIC)
• SMI defines a framework and method for the calculation of
a relative index, which may be used to compare IT Services
against one another, or to track services over time.
• The SMI cloud quality criteria can be qualitative or
quantitative.
12/26/18 F. G. Filip 71

Computing Solution (III): The SMI
• The SMI starts with a hierarchical frameworka hierarchical framework.
• The top level is divided into seven clustersseven clusters and each cluster
is further refined by three or more criteria. The seven
clusters are (http://csmic.org/):
– Accountability, Agility,
– Assurance,
– Financial,
– Performance,
– Security and Privacy,
– Usability.
12/26/18 F. G. Filip 72

An Example: Choosing A Cloud
Computing Solution (IV): References
• *** Cloud Services Measurement Initiative Consortium.
http://csmic.org/. "Selecting a cloud provider, defining widely
accepted measures for cloud services", Accessed October 2018
• Rădulescu, C.Z., Rădulescu, M., (2018), “Group Decision Support
Approach for Cloud Quality of Service Criteria Weighting,” Studies in
Informatics and Control, ISSN 1220-1766, vol. 27(3), pp. 275-284, 20
• Radulescu, CZ., Radulescu, I. (2017), “An Extended TOPSIS Approach
for Ranking Cloud Service Providers, Studies in Informatics and
Control,” Volume: 26, Issue: 2, pp: 183-192
• Radulescu, C.Z., (2017) “A cloud providers' services evaluation using
triangular fuzzy numbers,” Proceedings of The 21th International
Conference on Control Systems and Computer Science (CSCS21), May
29 - 31, 2017, Bucharest, Romania, pp.123-128.12/26/18 F. G. Filip 73

General Conclusions
• BI&A make more effective the Intelligence phase
of the decision –making process model of H. Simon
• Mobile computing makes possible locating and
calling the best experts to perform the evaluation
of alternatives
• Cloud computing enables complex computation
during the Choice phase
• Cognitive systems can be effective in the Choice
phase
• Social networks enable crowd problem solving
• The combination of Cloud computing and BI&A is
the solution for Big Data problems12/26/18 F. G. Filip 74

References (I)
• Alexandru A., et al (2016) Big Data: Concepts, Technologies and Applications in the Public
Sector. Intern J/ of Computer and Information Engineering, 10(10)
• Ambrust M et al (2009)Above the Clouds: A Berkeley View of Cloud Computing. Technical
Report No. UCB/EECS-2009-28
• Chen H, Chiang R H L, Storey V C. (2012) Business Intelligence and Analytics: from Big
Data to Big Impact. MIS Quarterly, 36(4), December: 1-24
• Candea, C, Filip F G (2016) Towards intelligent collaborative decision support platforms.
Studies in Informatics and Control, 25(2): 143-152
• Chui, M., Manyika, J., & Miremadi, M. (2015). Four fundamentals of workplace automation.
McKinsey Quarterly ,November 2015
• Dekker SW, Woods DD ( 2002) MABA-MABA or abracadabra? Progress on human–
automation co-ordination. Cognition, Technology&Work,4(4), p.240-244.
• Drucker P F(1967a) The manager and the moron. In: Drucker P, Technology, Management
and Society: Essays by Peter F. Drucker, Harper & Row, New York: p. 166-177
• Drucker P .F(1967b). The Effective Executive. Butterworth-Heinemann,* republished by
Rutlege ), p.15
• Eco U (1986) Prefazione al libro "Come scrivere una tesi di laurea di laurea con il personal
computer" di Claudio Pozzoli, Rizzoli
• Filip F.G. ( 2008) Decision support and control for large-scale complex systems. Annual
Reviews in Control, 32(1), p.62-70
• Filip F G , E. Hererra Viedma (2014) Big Data in Europe . The Bridge, Winter, , 33-37

References (II)
• Fitts, P. M. (1951) Human engineering for an effective air navigation and traffic
control system. Washington, DC: National Research Council
• Flemisch F et al. / IFAC-PapersOnLine 49(19 ) 072–077
• Gandomi A, Haider M (2015) Beyond the hype: Big data concepts, methods, and analytics.
International Journal of Information Management 35 : p.137–144
• High R ( 2012) The Era of Cognitive Systems: An Inside Look at IBM Watson and How it
Works
• Hu H, Wen Y. Chua T-S, Li X (2014) Toward Scalable Systems for Big Data
Analytics: A Technology Tutorial. IEEE Access: 652-687.
• Hollnagel E,Woods DD(1983) Cognitive Systems Engineering: New wine in new bottles.
International Journal of Man-Machine Studies. 18:583–600 ( Intern. J. Human-Comp.
Studies, 51, p. 339-356)
• Kaisler S, Armour F, Espinosa J. A, Money W (2013) Big Data: Issues and Challenges
Moving Forward. In: 46th Hawaii International Conference in System Sciences, IEEE
Computer Society: p. 995-104
• Kelly III J.E ( 2015) Computing, cognition and the future of knowing How humans and
machines are forging a new age of understanding. IBM Global Services
• Kelly III JE , SE Hamm (2013), Smart machines: IBM’s Watson and the era of cognitive
computing, Columbia University Press, New York..
• Mell, P., Grance, T. (2011). The NIST definition of Cloud Computing. Special publication
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References (III)
• Nof S Y (2017). Collaborative control theory and decision support systems.
Computer Science Journal of Moldova. 25 (2), 15-144
• Pan Y (2016 ) Heading toward Artificial Intelligence 2.0. Engineering, 2, p. 400-413
• Shi (2015). Challenges to engineering management in the Big Data Era. Frontiers of
Engineering Management, 293-303
• Shi Y(2018)Big Data Analysis and the Belt and Road Initiative
• Schuff D Paradice D, Burstein F,Power D J, Sharda R eds (2011) Decision
Support : An Examination of the DSS Discipline. Springer, New York:
• Spohrer J, Siddike MAK, Khda Y (2017) Rebuilding Evolution: A Service Science
Perspective Proceedings of the 50th Hawaii International Conference on System
Sciences , p. 1663-1672
• Spohrer J.(2018)- Future of AI and Service Scidnce. ITQM Keynote Omaha Nebraska
• Suduc, A. M. (2017). SSD in trei baze de date ştiinţifice. Personal Communication
• Tecuci G et al (2016) Building Intelligent Agents ;An Aprenticenship Multistrategy
Learning, Theory ,Tools and Case Studies. Cambridge Univ. Press, San Diego
• Vernadat FB et al ( 2018) Information systems and knowledge management in
industrial engineering: recent advances and new perspectives. Intern. J. Prod. Res,
56(8): p.2707-2713
12/26/18 F. G. Filip 77

Thank-you for your
attention!
12/26/18 F. G. Filip 78

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