The document provides an overview of enterprise systems and their key components. It discusses how traditional systems operate with compartmentalized departments that work in isolation, whereas enterprise systems treat the entire organization as a single, integrated system. It describes integrated management information, business modeling, and integrated data modeling as important aspects of enterprise systems that allow for seamless information sharing across departments. Overall, the summary emphasizes how enterprise systems facilitate coordination and informed decision-making through an integrated approach compared to traditional compartmentalized systems.
System Development Life Cycle
Data, Function, Network, People, Time, Motivation What constitutes the “enterprise”?
Key enterprise architecture terms Enterprise Architecture Terms
How do you achieve perfect alignment?
Importance of alignment
Lack of Alignment
Nature of Complexity
Architectural Principles
System Development Life Cycle
Data, Function, Network, People, Time, Motivation What constitutes the “enterprise”?
Key enterprise architecture terms Enterprise Architecture Terms
How do you achieve perfect alignment?
Importance of alignment
Lack of Alignment
Nature of Complexity
Architectural Principles
The Deadlock Problem
System Model
Deadlock Characterization
Methods for Handling Deadlocks
Deadlock Prevention
Deadlock Avoidance
Deadlock Detection
Recovery from Deadlock
A brief introduction to Process synchronization in Operating Systems with classical examples and solutions using semaphores. A good starting tutorial for beginners.
ANALYSIS OF SYSTEM ON CHIP DESIGN USING ARTIFICIAL INTELLIGENCEijesajournal
Automation is a powerful word that lies everywhere. It shows that without automation, application will not
get developed. In a semiconductor industry, artificial intelligence played a vital role for implementing the
chip based design through automation .The main advantage of applying the machine learning & deep
learning technique is to improve the implementation rate based upon the capability of the society. The
main objective of the proposed system is to apply the deep learning using data driven approach for
controlling the system. Thus leads to a improvement in design, delay ,speed of operation & costs.
Through this system, huge volume of data’s that are generated by the system will also get control.
ANALYSIS OF SYSTEM ON CHIP DESIGN USING ARTIFICIAL INTELLIGENCEijesajournal
Automation is a powerful word that lies everywhere. It shows that without automation, application will not
get developed. In a semiconductor industry, artificial intelligence played a vital role for implementing the
chip based design through automation .The main advantage of applying the machine learning & deep learning technique is to improve the implementation rate based upon the capability of the society. The main objective of the proposed system is to apply the deep learning using data driven approach for controlling the system. Thus leads to a improvement in design, delay ,speed of operation & costs.Through this system, huge volume of data’s that are generated by the system will also get control.
ANALYSIS OF SYSTEM ON CHIP DESIGN USING ARTIFICIAL INTELLIGENCEijesajournal
Automation is a powerful word that lies everywhere. It shows that without automation, application will not get developed. In a semiconductor industry, artificial intelligence played a vital role for implementing the chip based design through automation .The main advantage of applying the machine learning & deep learning technique is to improve the implementation rate based upon the capability of the society. The main objective of the proposed system is to apply the deep learning using data driven approach for controlling the system. Thus leads to a improvement in design, delay ,speed of operation & costs. Through this system, huge volume of data’s that are generated by the system will also get control.
The Deadlock Problem
System Model
Deadlock Characterization
Methods for Handling Deadlocks
Deadlock Prevention
Deadlock Avoidance
Deadlock Detection
Recovery from Deadlock
A brief introduction to Process synchronization in Operating Systems with classical examples and solutions using semaphores. A good starting tutorial for beginners.
ANALYSIS OF SYSTEM ON CHIP DESIGN USING ARTIFICIAL INTELLIGENCEijesajournal
Automation is a powerful word that lies everywhere. It shows that without automation, application will not
get developed. In a semiconductor industry, artificial intelligence played a vital role for implementing the
chip based design through automation .The main advantage of applying the machine learning & deep
learning technique is to improve the implementation rate based upon the capability of the society. The
main objective of the proposed system is to apply the deep learning using data driven approach for
controlling the system. Thus leads to a improvement in design, delay ,speed of operation & costs.
Through this system, huge volume of data’s that are generated by the system will also get control.
ANALYSIS OF SYSTEM ON CHIP DESIGN USING ARTIFICIAL INTELLIGENCEijesajournal
Automation is a powerful word that lies everywhere. It shows that without automation, application will not
get developed. In a semiconductor industry, artificial intelligence played a vital role for implementing the
chip based design through automation .The main advantage of applying the machine learning & deep learning technique is to improve the implementation rate based upon the capability of the society. The main objective of the proposed system is to apply the deep learning using data driven approach for controlling the system. Thus leads to a improvement in design, delay ,speed of operation & costs.Through this system, huge volume of data’s that are generated by the system will also get control.
ANALYSIS OF SYSTEM ON CHIP DESIGN USING ARTIFICIAL INTELLIGENCEijesajournal
Automation is a powerful word that lies everywhere. It shows that without automation, application will not get developed. In a semiconductor industry, artificial intelligence played a vital role for implementing the chip based design through automation .The main advantage of applying the machine learning & deep learning technique is to improve the implementation rate based upon the capability of the society. The main objective of the proposed system is to apply the deep learning using data driven approach for controlling the system. Thus leads to a improvement in design, delay ,speed of operation & costs. Through this system, huge volume of data’s that are generated by the system will also get control.
In this research brief, we talk about the role of machine learning and artificial intelligence in Observability. The market is still in early stages and we expect mainstream adoption in the next 2-3 years. It is time for Modern IT Operations/SRE/DevOps teams to understand how Observability is different from traditional marketing and how it can help run resilient services with cloud native architectures.
The present study emphasis on the importance of management information system which forms the backbone for digitalizing the organization. It comprises of information useful for smooth functioning by evaluating and synchronizing both hardware and software technologies. The management information system has gained popularity in last few decades with almost every sector has implemented its principles to manage the economic flow and functioning. It has greatly influenced the world connectivity with business point of view and has uplifted the management strategies. The presented mini review describe the elements and usage of management information system along with its advantages. The study provides, insight on the category of management information system with its possible applicative sectors.
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.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality
Chapter 2 enterprise an overview - alexis leon
1. 7/3/2010
2. Enterprise- An
Overview
Sonali Chauhan
UDIT 2009-10
Topic
Introduction
Integrated Management Information
Business Modeling
Integrated Data Model
University Department Of
Information Technology Sonali C. 2
Introduction
What is Enterprise?
Enterprise is a group of people with a common
goal, Which has certain resources at its disposal
to achieved that goal.
Enterprise acts PEOPLE GOAL AND
OBJECTIVE
as an entity.
Traditional system
RESOURCES
The Enterprise
University Department Of
Information Technology Sonali C. 3
1
2. 7/3/2010
Cont…
Organization where there is no or little
Traditional Approach communication betw departments
Each of this
department are
compartmentalized
and have their own
goals and
objectives, (from their
point of view are in the
line with organization’s
objective
University Department Of
Information Technology Sonali C. 4
Cont…
Work in isolation
Has their own system of data collection and analysis.
So information is available only TOP Management (only
summary) and not to other department.
Ultimately, various department end up in different direction
instead taking orgniz towards common Goal.
Coz other department does not know what other department
is doing.
Eg sales n market people want more product variety to satisfy
needs of customers, but production department wants to limit
the product variety to cut down costs.
University Department Of
Information Technology Sonali C. 5
Cont…
For Enterprise
Entire organization is
ONE SYSTEM,
department is
SUBSYSTEM
Information is store
centrally n available to
all department
University Department Of
Information Technology Sonali C. 6
2
3. 7/3/2010
Cont…
Each subsystem knows what other is
doing, and why they r doing and what
should be done to achieve common goal.
In ERP, information is integrated with
smooth and seamless flow of information
across department
University Department Of
Information Technology Sonali C. 7
Integrated Management Information
In information system there is logical flow of
information, that is
Input Processed Output
data supplied to system – manipulated –
transformed into information
University Department Of
Information Technology Sonali C. 8
Cont…
Management Information System is an integrated
information system
One of the popular subsystem/ technology used in
ERP
It provided information system for decision-
making in organization
MIS elements
Transaction process (collecting, storing, processing)
Reporting System (gives reports. Charts, graphs)
Decision support system
University Department Of
Information Technology Sonali C. 9
3
4. 7/3/2010
Cont…
This satisfies the need of managers at
operational level.
But they work at departmental level.
Each department has its own db and inf
system
This will produce its own report.
University Department Of
Information Technology Sonali C. 10
DISADVANTAGES OF INTEGRATED
MANAGEMENT INFORMATION:
People in one department do not have any
information what is happening in other department
Top level mangers will circulate the report to other
department but that department will fail to capture
real picture.
These system gives only the information which is
predetermined and processed on given data
Eg. A manger fails to have extra information which report
does not contain n MIS cannot help
University Department Of
Information Technology Sonali C. 11
DISADVANTAGES OF INTEGRATED
MANAGEMENT INFORMATION: (cont)
These system lacks integrated approach.
Accounting department-Account Managmnt
System, finance department-Finance Managmnt
System etc.
Work in isolation
If person wants report from 2 deprtmnt he has to
go thru both system n correlate them and
combine the data
University Department Of
Information Technology Sonali C. 12
4
5. 7/3/2010
DISADVANTAGES OF INTEGRATED
MANAGEMENT INFORMATION: (cont)
As system works in isolation , analysis of
data from various department is difficult
Causes stress in decision making…
basically tedious job
If no effective and efficient mechanism for
decision –
will affect working of organization
Lost valuable time
University Department Of
Information Technology Sonali C. 13
Cont…
INFORMATION - key resource of
organization
Accuracy
Relevancy
Timeliness
Organization should have mechanism to
automate information gathering and
analysis process will beat the competition.
University Department Of
Information Technology Sonali C. 14
Business Modeling
Creation of business model is must in ERP
systems- First activities in ERP.
It should be mirror the business process.
It is built on the basis of organization’s goal,
objective and strategic plan.
Business model is built based on business
processing which is handled by individual to
achieve common goal
Representation of business as 1 large system
(interconnection and interdependency of various
subsystem)
University Department Of
Information Technology Sonali C. 15
5
6. 7/3/2010
Business Modeling (cont…)
Based on business model ERP is designed.
In BM, model a business as integrated system
Information is important resource
Business Model is basically represented as
graphical form using flow chart or flow diagram
University Department Of
Information Technology Sonali C. 16
Integrated Data Model
Critical step in ERP implementation- creation of
Integrated Data Model
Organization can use ths integrated data for
analysis n decision making
No departmental information system and no
departmental database
Has to be in integrated database.
Approach will reduce redundancy n provide
update information to entire organization.
University Department Of
Information Technology Sonali C. 17
Integrated Data Model (cont…)
For integrated database to be effective
Clearly Depict organization
Reflect day to day transaction
update continuously
Should give snap-shot of organization any time
Eg. Order is entered, sale is done, goods is
dispatched, db shld reflect those changes.
Inventory shld be reduced and account shld be
increased. (reflect instantaneously and
automatically)
University Department Of
Information Technology Sonali C. 18
6
7. 7/3/2010
Integrated Data Model (cont…)
Integrated model is
derived from business
model
Major Constituents
Information integration
Process/procedure
automation
University Department Of
Information Technology Sonali C. 19
7