The document contains questions from a Decision Support Systems exam for a B.Tech program. It covers various topics related to DSS including:
1) Human and Kepner-Tregoe decision making methods
2) Types of DSS software and client/server computing
3) Components of corporate models and electronic meeting styles
4) Expert systems, knowledge bases, and queuing disciplines
5) Data warehouses, extraction/loading stages, and multi-dimensional databases
The questions require explanations of concepts, comparison of approaches, and short answers testing understanding of key DSS topics.
BPSC Previous Year Question for Assistant Programmer, Assistant Maintenance Engineer, Assistant Network Engineer by Stack IT job Solution
Book : Stack IT job Solution (A pattern Based IT job solution)
বুয়েট, কুয়েট, রুয়েট, ডুয়েট, পিএসসি, টেলিকম, আইবিএ, এমআইএস
সহ যেকোন প্যার্টানে জব প্রস্তুতির একমাত্র বই।
Order Link: https://stackvaly.com/product/stack-it-job-solution-p3qxo
WhatsApp: 01789741518
BPSC Previous Year Question for Assistant Programmer, Assistant Maintenance Engineer, Assistant Network Engineer by Stack IT job Solution
Book : Stack IT job Solution (A pattern Based IT job solution)
বুয়েট, কুয়েট, রুয়েট, ডুয়েট, পিএসসি, টেলিকম, আইবিএ, এমআইএস
সহ যেকোন প্যার্টানে জব প্রস্তুতির একমাত্র বই।
Order Link: https://stackvaly.com/product/stack-it-job-solution-p3qxo
WhatsApp: 01789741518
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Knowledge engineering: from people to machines and back
Decision Support Systems Jntu Model Paper{Www.Studentyogi.Com}
1. www.studentyogi.com www.studentyogi.com
Code No: RR420506
Set No. 1
IV B.Tech II Semester Regular Examinations, Apr/May 2007
DECISION SUPPORT SYSTEMS
( Common to Computer Science & Engineering and Information
Technology)
Time: 3 hours Max Marks: 80
Answer any FIVE Questions
All Questions carry equal marks
1. Explain the following. [16]
(a) Kepner- Tregoe decision-making metho d
(b) Human decision-making process
2. Short notes on [16]
(a) Information Quality
(b) Institutional Versus Adho c DSS
3. (a) Explain the DSS software categories based on application and rm size.
(b) Explain why client / server computing is popular? [8+8]
4. What are the ma jor segments used in corporate mo del and explain each segment .
[16]
5. (a) What do es work ow system do? What are its key characteristics?
(b) What are the three electronic meeting styles and explain each? [8+8]
6. Consider a simple expert system that can follow three rules:
(a) Rule 1) If order can be satis ed within a normal work schedule, then the
factory should operate 40 Hours next week.
(b) Rule 2) If order cannot satis ed within a normal work schedule and overtime
has not been scheduled, then schedule overtime work.
(c) Rule 3) If order cannot satis ed within a normal work schedule and overtime
has been scheduled, then notify customers that order will be delayed. Develop
VP-Expert form of simple knowledge base. [16]
7. (a) Describe four kinds of data that data warehouse use.
(b) List the stages involved in getting a data into data warehouse and explain
each stage. [8+8]
8. (a) List and describe brie y the eight stages of any information system pro ject.
(b) What are the four types of data that the data warehouse architecture must
specify?
[8+8]
2. www.studentyogi.com www.studentyogi.com
Code No: RR420506
Set No. 2
IV B.Tech II Semester Regular Examinations, Apr/May 2007
DECISION SUPPORT SYSTEMS
( Common to Computer Science & Engineering and Information
Technology)
Time: 3 hours Max Marks: 80
Answer any FIVE Questions
All Questions carry equal marks
1. (a) What is a decision? Give three examples.
(b) Classify the types of decisions based on the degree of the structure and orga-
nizational impact. [8+8]
2. (a) De ne a system? What are the key characteristics of a system?
(b) How information system defer from a system in general? [8+8]
3. Explain the following terms [16]
(a) Fat clients
(b) Thin clients
(c) Network computers
(d) An Intranet
4. Draw the cobweb model of a market economy with 0 = 5.0, A = 10, B = 0.9, C
= -2.4, D = 1.2, by considering demand (D) = A ? B X P (Price) and Supply (S)
= C + D X -1. [16]
5. (a) Explain the characteristics and components of GDSS.
(b) Explain the value analysis and cost bene t analysis relating to GDSS. [8+8]
6. Consider a simple expert system that can follow three rules:
(a) Rule 1) If order can be satis ed within a normal work schedule, then the
factory should operate 40 Hours next week.
(b) Rule 2) If order cannot satis ed within a normal work schedule and overtime
has not been scheduled, then schedule overtime work.
(c) Rule 3) If order cannot satis ed within a normal work schedule and overtime
has been scheduled, then notify customers that order will be delayed. Develop
VP-Expert form of simple knowledge base. [16]
7. (a) Explain how a relational database can be organized for a data warehouse.
(b) Explain the concept of a multi dimensional database and why they are well
suited to data warehouse. [8+8]
8. Explain the need of DSS at present and future of DSS. [16]
3. www.studentyogi.com www.studentyogi.com
Code No: RR420506
Set No. 3
IV B.Tech II Semester Regular Examinations, Apr/May 2007
DECISION SUPPORT SYSTEMS
( Common to Computer Science & Engineering and Information
Technology)
Time: 3 hours Max Marks: 80
Answer any FIVE Questions
All Questions carry equal marks
1. What do the intelligence, design and choice phase consists of for the following
decisions? [16]
(a) buying a car
(b) faculty recruitment
2. (a) What are the fundamental operations involved in turning data into informa-
tion?
(b) Give an example for the di erence between the data and information. [8+8]
3. (a) Give eight factors that a DSS architecture must take into account.
(b) Draw the blo ck diagrams for conceptual DSS and speci c DSS architectures.
[8+8]
4. What are the ma jor segments used in corporate mo del and explain each segment .
[16]
5. Write a General Purpose Systems Simulation (GPSS) program for a manufacturing
shop. [16]
6. De ne data warehouse and state three characteristics of data warehouse.
[16]
7. (a) Explain how a relational database can be organized for a data warehouse.
(b) Explain the concept of a multi dimensional database and why they are well
suited to data warehouse. [8+8]
8. (a) List and describe brie y the eight stages of any information system pro ject.
(b) What are the four types of data that the data warehouse architecture must
specify?
[8+8]
4. www.studentyogi.com www.studentyogi.com
Code No: RR420506
Set No. 4
IV B.Tech II Semester Regular Examinations, Apr/May 2007
DECISION SUPPORT SYSTEMS
( Common to Computer Science & Engineering and Information
Technology)
Time: 3 hours Max Marks: 80
Answer any FIVE Questions
All Questions carry equal marks
1. Explain the following. [16]
(a) Kepner- Tregoe decision-making metho d
(b) Human decision-making process
2. (a) De ne a system? What are the key characteristics of a system?
(b) How information system defer from a system in general? [8+8]
3. (a) What are the ma jor DSS hardware environments?
(b) Describe the types of hardware environments used for DSS. [8+8]
4. What are the ma jor segments used in corporate mo del and explain each segment .
[16]
5. Explain the following terms relating to the Queuing discipline. [16]
(a) FIFO
(b) LIFO
(c) Random
(d) Polling
(e) Priority
(f ) Interrupt
(g) Reneging
(h) Balking
6. (a) What are the query tools of data warehouse and explain in detail.
(b) Explain the pros and cons of expert system. [8+8]
7. Considering an example of MBA admissions of Business School based on GPA and
GMAT, explain the following: [8+4+4]
(a) Classi cation and Regression Tree (CART)
(b) Neural Network.
(c) Nearest Neighborhood
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Code No: RR420506
Set No. 4
(b) Justify a data warehouse interms of both tangible and intangible bene ts. [16]