Intro to social network analysis | What is Network Analysis? | History of (So...Gaditek
Social network analysis is a method by which one can analyze the connections across individuals or groups or institutions. That is, it allows us to examine how political actors or institutions are interrelated.
This presentation on evaluating public relations campaigns is an excerpt from a presentation conducted by Shrita Sterlin of Penn Strategies. Shrita conducted this presentation at a forum held by the Center for Nonprofit Success on November 3, 2011 in Washington, DC. The presentation explores best practices for creating data-driven public relations campaigns in nonprofit organizations; provides tips for quantifying social change efforts; and demonstrates ways to measure the process and results of public relations campaigns.
Intro to social network analysis | What is Network Analysis? | History of (So...Gaditek
Social network analysis is a method by which one can analyze the connections across individuals or groups or institutions. That is, it allows us to examine how political actors or institutions are interrelated.
This presentation on evaluating public relations campaigns is an excerpt from a presentation conducted by Shrita Sterlin of Penn Strategies. Shrita conducted this presentation at a forum held by the Center for Nonprofit Success on November 3, 2011 in Washington, DC. The presentation explores best practices for creating data-driven public relations campaigns in nonprofit organizations; provides tips for quantifying social change efforts; and demonstrates ways to measure the process and results of public relations campaigns.
Indexing theory of political mass communication - Prepared by Fiza Zia Ul HannanDr. Fiza Zia Ul Hannan
Inspired by the work of Hallin, W. Lance Bennett introduced the “Indexing” theory in his article “Toward a Theory of Press-State Relations in the United States” (1990). The theory also known as indexing hypothesis and indexing model was proposed on the basis of a study that was conducted on the New York Times’ coverage of the United States’ involvement with Nicaraguan contras. Bennett’s preliminary indexing hypothesis states: “mass-media news professionals tend to ”index” the range of voices and viewpoints in both news and editorials according to the range of views expressed in the mainstream government debate about foreign affairs topics” - (Bennett 1990).
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. SNA provides both a visual and a mathematical analysis of human relationships.
The sole purpose of sharing these slides are to educate the beginners of IT and Computer Science/Engineering. Credits should go to the referred material and also CICRA campus, Colombo 4, Sri Lanka where I taught these in 2017.
Social Network Analysis power point presentation Ratnesh Shah
Basics of social network analysis,Application and also explain interesting study done by facebook , twitter, youtube and many more social media network ,slide contains some of interesting study to get knowledge about online social network analysis.
Goes with this video
Brands 3 - Brand Management in Politics
https://www.youtube.com/watch?v=tvotlu4wpv8
Building brand equity in politics is very similar to what is done in the commercial space
A high-level overview of social network analysis using gephi with your exported Facebook friends network. See more network analysis at http://allthingsgraphed.com.
Indexing theory of political mass communication - Prepared by Fiza Zia Ul HannanDr. Fiza Zia Ul Hannan
Inspired by the work of Hallin, W. Lance Bennett introduced the “Indexing” theory in his article “Toward a Theory of Press-State Relations in the United States” (1990). The theory also known as indexing hypothesis and indexing model was proposed on the basis of a study that was conducted on the New York Times’ coverage of the United States’ involvement with Nicaraguan contras. Bennett’s preliminary indexing hypothesis states: “mass-media news professionals tend to ”index” the range of voices and viewpoints in both news and editorials according to the range of views expressed in the mainstream government debate about foreign affairs topics” - (Bennett 1990).
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. SNA provides both a visual and a mathematical analysis of human relationships.
The sole purpose of sharing these slides are to educate the beginners of IT and Computer Science/Engineering. Credits should go to the referred material and also CICRA campus, Colombo 4, Sri Lanka where I taught these in 2017.
Social Network Analysis power point presentation Ratnesh Shah
Basics of social network analysis,Application and also explain interesting study done by facebook , twitter, youtube and many more social media network ,slide contains some of interesting study to get knowledge about online social network analysis.
Goes with this video
Brands 3 - Brand Management in Politics
https://www.youtube.com/watch?v=tvotlu4wpv8
Building brand equity in politics is very similar to what is done in the commercial space
A high-level overview of social network analysis using gephi with your exported Facebook friends network. See more network analysis at http://allthingsgraphed.com.
This is a slide on Information Systems. Information systems are interrelated set of components working together to collect, process, store and provide information for decision making. Every organisation needs an information system hence the need for this slide. The target audience are students in high school (Ghana) studying elective ICT, however, anybody interested in Information and Communications Technology will find this useful.
YouTube Video: https://www.youtube.com/watch?v=j_onwdSFjxA&t=3314s
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
3. 3
Decision Support Systems:
An Overview
Capabilities
Structure
Classifications
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
4. 4
DSS Configurations
Supports individuals and teams
Used repeatedly and constantly
Two major components: data and models
Web-based
Uses subjective, personal, and objective data
Has a simulation model
Used in public and private sectors
Has what-if capabilities
Uses quantitative and qualitative models
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
5. 5
DSS Definitions
Little (1970)
“model-based set of procedures for processing
data and judgments to assist a manager in his
decision making”
Assumption: that the system is computer-based
and extends the user’s capabilities.
Alter (1980)
Contrasts DSS with traditional EDP systems
(Table 3.1)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
6. 6
TABLE 3.1 DSS versus EDP.
Dimension DSS EDP
Use Active Passive
User Line and staff
management
Clerical
Goal Effectiveness Mechanical
efficiency
Time
Horizon
Present and future Past
Objective Flexibility Consistency
Source: Alter [1980].
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
7. 7
Moore and Chang (1980)
1. Extendible systems
2. Capable of supporting ad hoc data analysis and
decision modeling
3. Oriented toward future planning
4. Used at irregular, unplanned intervals
Bonczek et al. (1980)
A computer-based system consisting of
1. A language system -- communication between the user
and DSS components
2. A knowledge system
3. A problem-processing system--the link between the
other two components
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
8. 8
Keen (1980)
DSS apply “to situations where a ‘final’ system can
be developed only through an adaptive process of
learning and evolution”
Central Issue in DSS
support and improvement of decision making
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
9. 9
TABLE 3.2 Concepts Underlying DSS Definitions.
Source DSS Defined in Terms of
Gorry and Scott Morton [1971] Problem type, system function (support)
Little [1970] System function, interface
characteristics
Alter [1980] Usage pattern, system objectives
Moore and Chang [1980] Usage pattern, system capabilities
Bonczek, et al. [1996] System components
Keen [1980] Development process
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
10. 10
Working Definition of DSS
A DSS is an interactive, flexible, and adaptable CBIS,
specially developed for supporting the solution of a
non-structured management problem for improved
decision making. It utilizes data, it provides easy user
interface, and it allows for the decision maker’s own
insights
DSS may utilize models, is built by an interactive
process (frequently by end-users), supports all the
phases of the decision making, and may include a
knowledge component
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
11. 11
Characteristics and Capabilities of
DSS(Figure 3.1)
1. Provide support in semi-structured and unstructured
situations, includes human judgment and
computerized information
2. Support for various managerial levels
3. Support to individuals and groups
4. Support to interdependent and/or sequential decisions
5. Support all phases of the decision-making process
6. Support a variety of decision-making processes and
styles
(more)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
12. 12
7. Are adaptive
8. Have user friendly interfaces
9. Goal: improve effectiveness of decision making
10. The decision maker controls the decision-making
process
11. End-users can build simple systems
12. Utilizes models for analysis
13. Provides access to a variety of data sources,
formats, and types
Decision makers can make better, more consistent
decisions in a timely manner
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
13. 13
DSS Components
1. Data Management Subsystem
2. Model Management Subsystem
3. Knowledge-based (Management) Subsystem
4. User Interface Subsystem
5. The User
(Figure 3.2)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
15. 15
The Data Management Subsystem
DSS database
Database management system
Data directory
Query facility
(Figure 3.3)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
16. 16
DSS In Focus 3.2: The Capabilities of DBMS in a DSS
Captures/extracts data for inclusion in a DSS database
Updates (adds, deletes, edits, changes) data records and files
Interrelates data from different sources
Retrieves data from the database for queries and reports
Provides comprehensive data security (protection from unauthorized access, recovery
capabilities, etc.)
Handles personal and unofficial data so that users can experiment with alternative
solutions based on their own judgment
Performs complex data manipulation tasks based on queries
Tracks data use within the DSS
Manages data through a data dictionary
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
17. 17
DSS Database Issues
Data warehouse
Data mining
Special independent DSS databases
Extraction of data from internal, external, and private
sources
Web browser data access
Web database servers
Multimedia databases
Special GSS databases (like Lotus Notes / Domino
Server)
Online Analytical Processing (OLAP)
Object-oriented databases
Commercial database management systems (DBMS)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
18. 18
The Model Management
Subsystem
Analog of the database management subsystem
(Figure 3.4)
Model base
Model base management system
Modeling language
Model directory
Model execution, integration, and command
processor
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
19. 19
Model Management Issues
Model level: Strategic, managerial (tactical), and
operational
Modeling languages
Lack of standard MBMS activities. WHY?
Use of AI and fuzzy logic in MBMS
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
20. 20
The Knowledge Based
(Management) Subsystem
Provides expertise in solving complex
unstructured and semi-structured problems
Expertise provided by an expert system or other
intelligent system
Advanced DSS have a knowledge based
(management) component
Leads to intelligent DSS
Example: Data mining
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
21. 21
The User Interface (Dialog)
Subsystem
Includes all communication between a user and
the MSS
Graphical user interfaces (GUI)
Voice recognition and speech synthesis possible
To most users, the user interface is the system
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
22. 22
The User
Different usage patterns for the user, the
manager, or the decision maker
Managers
Staff specialists
Intermediaries
1. Staff assistant
2. Expert tool user
3. Business (system) analyst
4. GSS Facilitator
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
23. 23
DSS Hardware
Evolved with computer hardware and
software technologies
Major Hardware Options
Mainframe
Workstation
Personal computer
Web server system
– Internet
– Intranets
– Extranets
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
24. 24
Distinguishing DSS from
Management Science and MIS
DSS is a problem-solving tool and is
frequently used to address ad hoc and
unexpected problems
Different than MIS
DSS evolve as they develop
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
25. 25
DSS Classifications
Alter’s Output Classification (1980)
Degree of action implication of system outputs
(supporting decision) (Table 3.3)
Holsapple and Whinston’s Classification
1. Text-oriented DSS
2. Database-oriented DSS
3. Spreadsheet-oriented DSS
4. Solver-oriented DSS
5. Rule-oriented DSS
6. Compound DSS
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
26. 26
Intelligent DSS Categories
Descriptive
Procedural
Reasoning
Linguistic
Presentation
Assimilative
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
27. 27
Alternate Categories of
Intelligent DSS
Symbiotic
Expert-system based
Adaptive
Holistic
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
28. 28
Other Classifications
Institutional DSS vs. Ad Hoc DSS
Institutional DSS deals with decisions of a recurring
nature
Ad Hoc DSS deals with specific problems that are
usually neither anticipated nor recurring
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
29. 29
Other Classifications (cont’d.)
Degree of nonprocedurality (Bonczek et al., 1980)
Personal, group, and organizational support
(Hackathorn and Keen, 1981)
Individual versus group support systems (GSS)
Custom-made versus ready-made systems
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
30. 30
Summary
Fundamentals of DSS
Components of DSS
Major capabilities of the DSS components
Major DSS categories
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ