The document discusses the system development life cycle (SDLC), which consists of 6 phases: 1) recognition of need, 2) feasibility study, 3) analysis, 4) design, 5) implementation, and 6) post-implementation and maintenance. It provides details on each phase, including that analysis involves defining system boundaries and collecting data, design determines how the problem will be solved through technical specifications, and implementation includes user training, testing, and file conversion. The overall SDLC process gives a system project meaning and direction by thoroughly understanding user needs from recognition through ongoing maintenance.
A presentation that contains an introduction to the whole concept of System Life Cycle. System Life Cycle - A methodology used for improving a system / process.
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Stage 1: Preliminary Investigation Report
Before you begin this assignment, be sure you have read the Case Study and all assignments for this class, especially Stage 4: Final System Report.
Stage 1: Preliminary Investigation Report
Before you begin this assignment, be sure you have read the Case Study and all assignments for this class, especially Stage 4: Final System
For more classes visit
www.snaptutorial.com
Stage 1: Preliminary Investigation Report
Before you begin this assignment, be sure you have read the Case Study and all assignments for this class, especially Stage 4: Final System Report.
For more classes visit
www.snaptutorial.com
Stage 1: Preliminary Investigation Report
Before you begin this assignment, be sure you have read the Case Study and all assignments for this class, especially Stage 4: Final System Report.
A presentation that contains an introduction to the whole concept of System Life Cycle. System Life Cycle - A methodology used for improving a system / process.
For more classes visit
www.snaptutorial.com
Stage 1: Preliminary Investigation Report
Before you begin this assignment, be sure you have read the Case Study and all assignments for this class, especially Stage 4: Final System Report.
Stage 1: Preliminary Investigation Report
Before you begin this assignment, be sure you have read the Case Study and all assignments for this class, especially Stage 4: Final System
For more classes visit
www.snaptutorial.com
Stage 1: Preliminary Investigation Report
Before you begin this assignment, be sure you have read the Case Study and all assignments for this class, especially Stage 4: Final System Report.
For more classes visit
www.snaptutorial.com
Stage 1: Preliminary Investigation Report
Before you begin this assignment, be sure you have read the Case Study and all assignments for this class, especially Stage 4: Final System Report.
Online auction system is web based application, in which the seller can sell the goods by sitting in his own house ,so the main advantage of this application is that there is no more system compatibility requirement problem. The main advantage of the online auction system is that the user can have the better choices for their investment and also it is time saving , and through this system user can invest in their own selected firm.
Information System (IS) is a collection of components that work together to provide information to help in the operations and management of an organization.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
2. System development life cycle is also referred as
system study. The system analyst gives a system
development project meaning and direction.
A candidate system is approached after the
analyst has a thorough understanding of user
needs and problems.
3. System development life cycle has 6 phases. They are
1.Recognition of need
2.Feasibility study
3.Analysis
4.Design
5.Implementation
6.Post-implementation and maintenance
5. RECOGNITION OF NEED
One must know what the problem is before it can be solved. The basis for a
candidate system is recognition of a need for improving an information system or a
procedure.
For e.g: a supervisor may want to investigate the system flow in purchasing or a
bank president has been getting complains about the long lines in the drive-in. This
need leads to a preliminary survey or an initial investigation to determine whether
alternative systems can solve the problems. It entails looking into the duplication of
efforts, bottleneck, inefficient existing in procedures or whether parts of the existing
system would be candidates for computerization.
6. FEASIBILITY STUDY.
Depending on the results of the initial investigations the survey is
expanded to a more feasibility study.
We can define feasibility study as a test to a proposed system
according to its workability, impact on the organization,
ability to meet user needs and effective use of resources.
The objective of a feasibility study is not to solve the problem but
to acquire a sense of its scope. During this study, the problem
definition is crystallized and aspects of the problem to be included
in the system are determined.
7. The result of the feasibility study is a formal proposal. This is like a report. This
report summarizes what is known and what is going to be done. It consists of the
following.
1.Statement of the problem:- a carefully worded statement of the problem that led
to analysis.
2.Summary of findings and recommendations:- a lists of the major findings and
recommendations of the study
3.Details of findings:-an outline of the methods and procedures undertaken by the
existing system
4.Recommendations and conclusions:-specific recommendations regarding the
candidate system, including personnel assignments, costs, project schedules and
target dates.
Then the management reviews this report. After the proposal is reviewed, it
becomes a formal agreement that paves the way for actual design and
implementation.
8. ANALYSIS
Analysis is a detailed study of the various operations performed by a system and
their relationships within and outside of the system.
A key question is: what must be done to solve the problem?.
One aspect of analysis is defining the boundaries of the system and determining
whether or not a candidate system should consider other related systems. During
analysis data are collected on the available files, decision points and transactions
handled by the present system.
Data flow diagrams, interviews, on site observations and questionnaires are
some tools that are used in analysis.
9. DESIGN
The most creative and challenging phase of system life cycle is
system design. The term design describes a final system and the process by
which it is developed. It refers to the technical specifications that will be applied
in implementing the candidate system. It also includes the construction of
programs and program testing.
The key question here is how should the problem be solved?
10. Finally details related to justification of the system and an estimate of the impact
of the Candidate system on the user and the organization are documented and
evaluated by management as a step to word implementation. The final report
prior to the implementation phase includes procedural flow charts , report layout
and a workable plan for implementing the candidate system.
There are four steps in design phase. They are
Output design: This step is to determine how the output is to be produced and in
what format.
Input design: In this step the samples of input and its format is finalized.
File design: In this step the master file , transaction files have to be designed.
Processing design: This is also called Operational Phase. In this step the
program construction and testing is performed.
11. IMPLEMENTATION:
The implementation phase is less creative than system design. It
is primarily concerned with the user training , site preparation and file conversion .
During the final testing user acceptance is tested followed by user training.
Depending on the nature of the system extensive user training may be required
.Conversion usually take place at about the same time the user is being trained or
later.
System testing checks the readiness and accuracy of the system to access,
update and retrieve data from new files. Once the programs become available
test data are read into the computer and processed against the files provided for
testing. If successful the programs is then run with live data . Otherwise a
diagnostic procedure is used to locate and correct errors in the program.
12. POST IMPLEMENTATION AND MAINTENANCE:
Like any system there is an aging process that requires periodic
maintenance of h/w and s/w . If the new information is inconsistent
with the design specification, the changes have to be made.
Hardware also requires periodic maintenance to keep into with the
design specification. The importance of maintenance is to continue to
bring new systems to standards