This document discusses entity relationship (ER) modeling and how ER diagrams (ERDs) are used to represent the main components of a conceptual database, including entities, relationships, and attributes. It covers key aspects of ERDs such as connectivity, cardinality, and how to handle many-to-many relationships. The document also notes that database design involves reconciling conflicting goals through compromises.
● 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).
Data Models [DATABASE SYSTEMS: Design, Implementation, and Management]Usman Tariq
In this PPT, you will learn:
• About data modeling and why data models are important
• About the basic data-modeling building blocks
• What business rules are and how they influence database design
• How the major data models evolved
• About emerging alternative data models and the needs they fulfill
• How data models can be classified by their level of abstraction
Author: Carlos Coronel | Steven Morris
● 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).
Data Models [DATABASE SYSTEMS: Design, Implementation, and Management]Usman Tariq
In this PPT, you will learn:
• About data modeling and why data models are important
• About the basic data-modeling building blocks
• What business rules are and how they influence database design
• How the major data models evolved
• About emerging alternative data models and the needs they fulfill
• How data models can be classified by their level of abstraction
Author: Carlos Coronel | Steven Morris
A database model refers to the structure of a database and determines how the data within the database can be organized and manipulated. Let’s explore some common types of database models:
Relational Model: The most popular example, the relational model, organizes data into tables (also known as relations). Each table contains rows representing records and columns representing attributes. Relationships between tables are established using keys.
Hierarchical Model: Developed by IBM for IMS (Information Management System), this model arranges data in a tree-like structure. Each record is a tree node, and relationships follow a one-to-many pattern. It’s predictable and efficient for data access.
Network Model: This model allows many-to-many relationships between records. It’s more flexible than the hierarchical model but less common.
Entity–Relationship Model (ER Model): It represents entities, their attributes, and the relationships between them. ER diagrams visually depict these components.
Object Model: Used in object-oriented databases, it treats data as objects with properties and methods. It’s suitable for complex data structures.
Document Model: Commonly used in NoSQL databases, it stores data as documents (e.g., JSON or XML). Each document can have varying attributes.
Entity–Attribute–Value (EAV) Model: A flexible model where data is stored in a sparse matrix. It’s useful for handling dynamic attributes.
Star Schema: Primarily used in data warehousing, it simplifies complex data structures into a central fact table connected to dimension tables.
ESOFT Metro Campus - Diploma in Software Engineering - (Module IV) Database Concepts
(Template - Virtusa Corporate)
Contents:
Introduction to Databases
Data
Information
Database
Database System
Database Applications
Evolution of Databases
Traditional Files Based Systems
Limitations in Traditional Files
The Database Approach
Advantages of Database Approach
Disadvantages of Database Approach
Database Management Systems
DBMS Functions
Database Architecture
ANSI-SPARC 3 Level Architecture
The Relational Data Model
What is a Relation?
Primary Key
Cardinality and Degree
Relationships
Foreign Key
Data Integrity
Data Dictionary
Database Design
Requirements Collection and analysis
Conceptual Design
Logical Design
Physical Design
Entity Relationship Model
A mini-world example
Entities
Relationships
ERD Notations
Cardinality
Optional Participation
Entities and Relationships
Attributes
Entity Relationship Diagram
Entities
ERD Showing Weak Entities
Super Type / Sub Type Relationships
Mapping ERD to Relational
Map Regular Entities
Map Weak Entities
Map Binary Relationships
Map Associated Entities
Map Unary Relationships
Map Ternary Relationships
Map Supertype/Subtype Relationships
Normalization
Advantages of Normalization
Disadvantages of Normalization
Normal Forms
Functional Dependency
Purchase Order Relation in 0NF
Purchase Order Relation in 1NF
Purchase Order Relations in 2NF
Purchase Order Relations in 3NF
Normalized Relations
BCNF – Boyce Codd Normal Form
Structured Query Language
What We Can Do with SQL ?
SQL Commands
SQL CREATE DATABASE
SQL CREATE TABLE
SQL DROP
SQL Constraints
SQL NOT NULL
SQL PRIMARY KEY
SQL CHECK
SQL FOREIGN KEY
SQL ALTER TABLE
SQL INSERT INTO
SQL INSERT INTO SELECT
SQL SELECT
SQL SELECT DISTINCT
SQL WHERE
SQL AND & OR
SQL ORDER BY
SQL UPDATE
SQL DELETE
SQL LIKE
SQL IN
SQL BETWEEN
SQL INNER JOIN
SQL LEFT JOIN
SQL RIGHT JOIN
SQL UNION
SQL AS
SQL Aggregate Functions
SQL Scalar functions
SQL GROUP BY
SQL HAVING
Database Administration
SQL Database Administration
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.
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
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
2. Database Systems, 8th
Edition 2
Objectives
• In this chapter, you will learn:
– The main characteristics of entity relationship
components
– How relationships between entities are defined,
refined, and incorporated into the database
design process
– How ERD components affect database design
and implementation
– That real-world database design often requires
the reconciliation of conflicting goals
3. Database Systems, 8th
Edition 3
The Entity Relationship (ER) Model
• ER model forms the basis of an ER diagram
• ERD represents conceptual database as
viewed by end user
• ERDs depict database’s main components:
– Entities
– Attributes
– Relationships
4. Database Systems, 8th
Edition 4
Entities
• Refers to entity set and not to single entity
occurrence
• Corresponds to table and not to row in
relational environment
• In Chen and Crow’s Foot models, entity
represented by rectangle with entity’s name
• Entity name, a noun, written in capital letters
5. Database Systems, 8th
Edition 5
Attributes
• Characteristics of entities
• Chen notation: attributes represented by ovals
connected to entity rectangle with a line
– Each oval contains the name of attribute it
represents
• Crow’s Foot notation: attributes written in
attribute box below entity rectangle
Attribute : - ciri-ciri yang terdapat pada entiti
7. Database Systems, 8th
Edition 7
Attributes (continued)
• Required attribute: must have a value
• Optional attribute: may be left empty
• Domain: set of possible values for an attribute
– Attributes may share a domain
• Identifiers: one or more attributes that uniquely
identify each entity instance
• Composite identifier: primary key composed
of more than one attribute
setiap attribute haruslah mempunyai nilai
Domain merupakan ruang yang menghad data yang dimasukkan
merujuk kepada key (sesuatu yang boleh menerangkan secara unit suatu entiti). Eg: no. matrik
9. Database Systems, 8th
Edition 9
Attributes (continued)
• Composite attribute can be subdivided
• Simple attribute cannot be subdivided
• Single-value attribute can have only a single
value
• Multivalued attributes can have many values
11. Database Systems, 8th
Edition 11
Attributes (continued)
• M:N relationships and multivalued attributes should
not be implemented
– Create several new attributes for each of the
original multivalued attributes components
– Create new entity composed of original
multivalued attributes components
• Derived attribute: value may be calculated
from other attributes
– Need not be physically stored within database
13. Database Systems, 8th
Edition 13
Relationships
• Association between entities
• Participants are entities that participate in a
relationship
• Relationships between entities always operate
in both directions
• Relationship can be classified as 1:M
• Relationship classification is difficult to establish
if only one side of the relationship is known
14. Database Systems, 8th
Edition 14
Connectivity and Cardinality
• Connectivity
– Describes the relationship classification
• Cardinality
– Expresses minimum and maximum number of
entity occurrences associated with one
occurrence of related entity
• Established by very concise statements known
as business rules
CONNECTIVITY dab CARDINALITY banyak di tunjuk dalam
crow foot model
15. Database Systems, 8th
Edition 15
minimum
maximum
CARA BACA: satu professor mengajar sekurang2nya 1 kelas
dan maksimum 4 kelas.
16. Database Systems, 8th
Edition 16
Existence Dependence
• Existence dependence
– Entity exists in database only when it is
associated with another related entity
occurrence
• Existence independence
– Entity can exist apart from one or more related
entities
– Sometimes such an entity is referred to as a
strong or regular entity
17. Database Systems, 8th
Edition 17
Relationship Strength
• Weak (non-identifying) relationships
– Exists if Primary Key (PK) of related entity does
not contain PK component of parent entity
• Strong (identifying) relationships
– Exists when PK of related entity contains PK
component of parent entity
20. Database Systems, 8th
Edition 20
Weak Entities
• Weak entity meets two conditions
– Existence-dependent
– Primary key partially or totally derived from
parent entity in relationship
• Database designer determines whether an
entity is weak based on business rules
26. Database Systems, 8th
Edition 26
Relationship Degree
• Indicates number of entities or participants
associated with a relationship
• Unary relationship
– Association is maintained within single entity
• Binary relationship
– Two entities are associated
• Ternary relationship
– Three entities are associated
29. Database Systems, 8th
Edition 29
Recursive Relationships
• Relationship can exist between occurrences of
the same entity set
– Naturally found within unary relationship
32. Database Systems, 8th
Edition 32
Associative (Composite) Entities
• Also known as bridge entities
• Used to implement M:N relationships
• Composed of primary keys of each of the
entities to be connected
• May also contain additional attributes that play
no role in connective process
33. Database Systems, 8th
Edition 33
MANY TO MANY tak boleh ada dalam database. Jadi, kalau ada
MANY TO MANY relationship, kena wujudkan INTERSECTION untuk
jadikan MANY TO MANY kepada ONE TO MANY
35. Database Systems, 8th
Edition 35
Developing an ER Diagram
• Database design is an iterative process
– Create detailed narrative of organization’s
description of operations
– Identify business rules based on description of
operations
– Identify main entities and relationships from
business rules
– Develop initial ERD
– Identify attributes and primary keys that
adequately describe entities
– Revise and review ERD
47. Database Systems, 8th
Edition 47
Database Design Challenges:
Conflicting Goals
• Database designers must make design
compromises
– Conflicting goals: design standards, processing
speed, information requirements
• Important to meet logical requirements and
design conventions
• Design of little value unless it delivers all
specified query and reporting requirements
• Some design and implementation problems do
not yield “clean” solutions
49. Database Systems, 8th
Edition 49
Summary
• Entity relationship (ER) model
– Uses ERD to represent conceptual database as
viewed by end user
– ERM’s main components:
• Entities
• Relationships
• Attributes
– Includes connectivity and cardinality notations
50. Database Systems, 8th
Edition 50
Summary (continued)
• Connectivities and cardinalities are based on
business rules
• M:N relationship is valid at conceptual level
– Must be mapped to a set of 1:M relationships
• ERDs may be based on many different ERMs
• Database designers are often forced to make
design compromises