The document provides information on entity relationship diagrams (ERDs), including their objectives, components, and how to create them. An ERD is a graphical representation of entities and their relationships that can be used to design databases. The key steps to developing an ERD are: 1) identifying entities, 2) finding relationships between entities, 3) drawing a rough ERD, 4) defining cardinalities, 5) identifying primary keys, 6) adding attributes, and 7) checking the results. ERDs help database designers understand information and communicate logical database structures to users.
● Data Modeling and Data Models.
● Business Rules (Translating Business Rules into Data Model Components).
● Emerging Data Models: Big Data and NoSQL.
● Degrees of Data Abstraction (External, Conceptual, Internal and Physical model).
When a software program is modularized, there are measures by which the quality of a design of modules and their interaction among them can be measured. These measures are called coupling and cohesion.
You can get clear knowledge about the functional dependencies in "Normalization". And also the rules, types of FDs and finally the closure and its applications
● Data Modeling and Data Models.
● Business Rules (Translating Business Rules into Data Model Components).
● Emerging Data Models: Big Data and NoSQL.
● Degrees of Data Abstraction (External, Conceptual, Internal and Physical model).
When a software program is modularized, there are measures by which the quality of a design of modules and their interaction among them can be measured. These measures are called coupling and cohesion.
You can get clear knowledge about the functional dependencies in "Normalization". And also the rules, types of FDs and finally the closure and its applications
An overview of ER-diagrams including entity sets, relationship sets, and attributes. The four attributes types are covered and cardinality constraints. Further partial or full participation is discussed.
Download at http://DavidHubbard.net/powerpoint - This Introduction to Business Intelligence gives an overview of how Business Intelligence fits into business strategy in general. It does not go into the specific technologies of Business Intelligence. It is meant to be used to explain Business Intelligence to those not already familiar with Business Intelligence.
Business Intelligence made easy! This is the first part of a two-part presentation I prepared for one of our customers to help them understand what Business Intelligence is and what can it do...
This presentation will help you understand the basic building blocks of Business Intelligence. Learn how decisions are triggered, the complete decision process and who makes decisions in the corporate world.
More importantly, understand core components of a Business Intelligence architecture such as a data warehouse, data mining, OLAP (Online analytical procession) , OLTP (Online Transaction Processing) and data reporting. Each component plays an integral part which enables today's managers and decision makers collect, analyze and interpret data to make it actionable for decision making.
Business intelligence has become an integral part that needs to be incorporated to ensure business survival. It is a tool that helps analyze historical data and forecast future so that your are always one step ahead in your business.
Please feel free to like, share and comment as you please!
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
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The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
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/
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
2. Objectives
• Define terms related to entity relationship
modeling, including entity, entity instance,
attribute, relationship and cardinality, and
primary key.
• Describe the entity modeling process.
• Discuss how to draw an entity relationship
diagram.
• Describe how to recognize entities,
attributes, relationships, and cardinalities.
3. Database Model
A database can be modeled as:
– a collection of entities,
– relationship among entities.
Database systems are often modeled using
an Entity Relationship (ER) diagram as the
"blueprint" from which the actual data is
stored — the output of the design phase.
4. Entity Relationship Diagram (ERD)
• ER model allows us to sketch database designs
• ERD is a graphical tool for modeling data.
• ERD is widely used in database design
• ERD is a graphical representation of the logical
structure of a database
• ERD is a model that identifies the concepts or
entities that exist in a system and the
relationships between those entities
5. Purposes of ERD
An ERD serves several purposes
• The database analyst/designer gains a
better understanding of the information to
be contained in the database through the
process of constructing the ERD.
• The ERD serves as a documentation tool.
• Finally, the ERD is used to communicate
the logical structure of the database to
users. In particular, the ERD effectively
communicates the logic of the database to
users.
6. Components of an ERD
An ERD typically consists of four different
graphical components:
1. Entity
2. Relationship
3. Cardinality
4. Attribute
7. Classification of Relationship
• Optional Relationship
– An Employee may or may not be assigned
to a Department
– A Patient may or may not be assigned to a
Bed
• Mandatory Relationship
– Every Course must be taught by at least
one Teacher
– Every mother have at least a Child
8. Cardinality Constraints
• Express the number of entities to which another
entity can be associated via a relationship set.
• Cardinality Constraints - the number of instances
of one entity that can or must be associated with
each instance of another entity.
• Minimum Cardinality
– If zero, then optional
– If one or more, then mandatory
• Maximum Cardinality
– The maximum number
9. Cardinality Constraints (Contd.)
• For a binary relationship set the mapping
cardinality must be one of the following types:
–One to one
• A Manager Head one Department and vice versa
–One to many ( or many to one)
• An Employee Works in one Department or One
Department has many Employees
–Many to many
• A Teacher Teaches many Students and A
student is taught by many Teachers
11. Cardinality Constraints Example
• In our model, we wish to indicate that each
school may enroll many students, or may not
enroll any students at all.
• We also wish to indicate that each student
attends exactly one school. The following
diagram indicates this optionality and
cardinality:
12. Cardinality Constraints Example (Contd.)
SCHOOL
STUDENT
Each school enrolls
at least zero
and at most many
students
Each student attends
at least one
and at most one
school
13. General Steps to create an ERD
• Identify the entity
• Identify the entity's attributes
• Identify the Primary Keys
• Identify the relation between entities
• Identify the Cardinality constraint
• Draw the ERD
• Check the ERD
15. Developing an ERD
The process has ten steps:
1. Identify Entities
2. Find Relationships
3. Draw Rough ERD
4. Fill in Cardinality
5. Define Primary Keys
6. Draw Key-Based ERD
7. Identify Attributes
8. Map Attributes
9. Draw fully attributed ERD
10. Check Results
16. A Simple Example
A company has several departments. Each
department has a supervisor and at least one
employee. Employees must be assigned to at
least one, but possibly more departments. At
least one employee is assigned to a project,
but an employee may be on vacation and not
assigned to any projects. The important data
fields are the names of the departments,
projects, supervisors and employees, as well
as the supervisor and employee number and
a unique project number.
17. Identify entities
• One approach to this is to work through the
information and highlight those words which you think
correspond to entities.
• A company has several departments. Each
department has a supervisor and at least one
employee. Employees must be assigned to at least
one, but possibly more departments. At least one
employee is assigned to a project, but an employee
may be on vacation and not assigned to any projects.
The important data fields are the names of the
departments, projects, supervisors and employees, as
well as the supervisor and employee number and a
unique project number.
• A true entity should have more than one instance
18. Find Relationships
• Aim is to identify the associations, the
connections between pairs of entities.
• A simple approach to do this is using a
relationship matrix (table) that has rows and
columns for each of the identified entities.
19. Find Relationships (Contd.)
• Go through each cell and decide whether or not
there is an association. For example, the first cell
on the second row is used to indicate if there is a
relationship between the entity "Employee" and
the entity "Department".
20. Identified Relationships
Names placed in the cells are meant to
capture/describe the relationships. So you
can use them like this
• A Department is assigned an employee
• A Department is run by a supervisor
• An employee belongs to a department
• An employee works on a project
• A supervisor runs a department
• A project uses an employee
21. Draw Rough ERD
Draw a diagram and:
• Place all the entities in rectangles
• Use diamonds and lines to represent the
relationships between entities.
• General Examples
25. Fill in Cardinality
• Supervisor
– Each department has one supervisor.
• Department
– Each supervisor has one department.
– Each employee can belong to one or more departments
• Employee
– Each department must have one or more employees
– Each project must have one or more employees
• Project
– Each employee can have 0 or more projects.
26. Fill in Cardinality (Contd.)
The cardinality of a relationship can only
have the following values
–One and only one
–One or more
–Zero or more
–Zero or one
28. Cardinality Examples
A
A
A
A
B
B
B
B
Each instance of A is related to a minimum of
zero and a maximum of one instance of B
Each instance of B is related to a minimum of
one and a maximum of one instance of A
Each instance of A is related to a minimum of
one and a maximum of many instances of B
Each instance of B is related to a minimum of
zero and a maximum of many instances of A
34. Identify Attributes
• In this step we try to identify and name all the attributes
essential to the system we are studying without trying to
match them to particular entities.
• The best way to do this is to study the forms, files and reports
currently kept by the users of the system and circle each data
item on the paper copy.
• Cross out those which will not be transferred to the new
system, extraneous items such as signatures, and constant
information which is the same for all instances of the form
(e.g. your company name and address). The remaining
circled items should represent the attributes you need. You
should always verify these with your system users.
(Sometimes forms or reports are out of date.)
• The only attributes indicated are the names of the
departments, projects, supervisors and employees, as well
as the supervisor and employee NUMBER and a unique
project number.
35. Map Attributes
• For each attribute we need to match it with exactly
one entity. Often it seems like an attribute should
go with more than one entity (e.g. Name). In this
case you need to add a modifier to the attribute
name to make it unique (e.g. Customer Name,
Employee Name, etc.) or determine which entity an
attribute "best' describes.
• If you have attributes left over without
corresponding entities, you may have missed an
entity and its corresponding relationships. Identify
these missed entities and add them to the
relationship matrix now.
38. Check ERD Results
• Look at your diagram from the point of view of
a system owner or user. Is everything clear?
• Check through the Cardinality pairs.
• Also, look over the list of attributes associated
with each entity to see if anything has been
omitted.