This document provides an overview of fundamentals of database design. It discusses what a database is, the difference between data and information, why databases are needed, how to select a database system, basic database definitions and building blocks, quality control considerations, and data entry methods. The overall purpose of a database management system is to transform data into information, information into knowledge, and knowledge into action.
This presentation is part of my work for the course 'Heterogeneous and Distributed Information Systems' at TU Berlin within the IT4BI (Information Technology for Business Intelligence) master programme.
A data dictionary is a “virtual database” containing metadata (data about data). Data dictionary holds information about the database and the data that it stores.
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
Explain growth and importance of databases
Name limitations of conventional file processing
Identify five categories of databases
Explain advantages of databases
Identify costs and risks of databases
List components of database environment
Describe evolution of database systems
Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations.
This presentation is part of my work for the course 'Heterogeneous and Distributed Information Systems' at TU Berlin within the IT4BI (Information Technology for Business Intelligence) master programme.
A data dictionary is a “virtual database” containing metadata (data about data). Data dictionary holds information about the database and the data that it stores.
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
Explain growth and importance of databases
Name limitations of conventional file processing
Identify five categories of databases
Explain advantages of databases
Identify costs and risks of databases
List components of database environment
Describe evolution of database systems
Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations.
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4rth Complete book Database systems Handbook dbms rdbms by Muhammad Sharif
Database management systems handbook by Muhammad Sharif
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Information Systems For Business and BeyondChapter 4Data a.docxjaggernaoma
Information Systems For Business and Beyond
Chapter 4
Data and Databases
IST
5500
1
Objectives
Describe differences between data, info & knowledge
Define database & identify steps to create one
Describe role of a database management system
Describe characteristics of a data warehouse; and
Define data mining & describe its role in an organization
2
Data, Information & Knowledge
Data: raw bits & pieces of info
Quantitative or qualitative
Data alone not useful
Needs context to be information
Aggregate & analyze: knowledge
Knowledge used for decisions
Wisdom includes experience!
NOTE: We will not be discussing older, hierarchical databases during this class
Databases
Relational database most popular
Limit our discussion to them
Examples: MS Access, MySQL & Oracle
Data organized into one or more tables
Each table contains set of fields
A record is one instance of a set of fields
Tables related by one or more fields: primary key
Database Design
Needs, requirements & goals?
Define data requiring tracking
Determine tables needed
Specifically which fields
Data to which they will relate
Establish primary key (unique)
Normalize: avoid duplicates & achieve flexibility
Designing a Database
Example: a university wants to create an information system to track participation in student clubs
Goal to give insight into how university funds clubs
Track number of club members & club activeness
Must keep track of the clubs, members & events
Following tables needed:
Clubs: club name, club president, short description of club
Students: student name, e-mail, year of birth
Memberships: correlates students with clubs, any given student can join multiple clubs
Events: when clubs meet & attendance
Designing a Database continued
Primary key must be selected for each table to create a relationship
unique identifier for each record in a table
Designing a Database Table Details
Designing a Database Table Details cont.
Designing a Database continued
Normalization
Design database in a way that:
reduces duplication of data between tables
gives table as much flexibility as possible
Purpose of creating Memberships table separate from Students & Clubs tables
Makes it simple to change design without major modifications to existing structure
Data Types
Each field in a database table needs a data type
Text, Number, Yes/No, Date/Time, Currency, Object, etc.
Importance of properly defined data types
tells database what functions can be performed
proper amount of storage space is allocated for data
Data Types: Assigned by Fields
Text – generally under 256 characters
Numbers* – usually different types
Yes/No – decisions (*special type)
Date/Time – formats (*special type)
Currency – types (*special type)
Paragraphs - allows text over 256
Objects – images, music, etc.
Database Tables 1NF (1st normal form)
Database Demonstration
Time permi.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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.
Enhancing Performance with Globus and the Science DMZGlobus
ESnet has led the way in helping national facilities—and many other institutions in the research community—configure Science DMZs and troubleshoot network issues to maximize data transfer performance. In this talk we will present a summary of approaches and tips for getting the most out of your network infrastructure using Globus Connect Server.
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.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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.
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.
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
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
1. Fundamentals of
Database Design
John Villamil-Casanova
Executive Vice President & CIO
The Aspira Association
202.835.3600 ext. 123
jvillamil@aspira.org
2. Agenda
Introduction and participants needs
We will review “what is a database;”
Understand the difference between data
and information;
What is the purpose of a database
system;
How to select a database system;
Database definitions and fundamental
building blocks;
4. What is a database
A database is any organized collection of
data. Some examples of databases you
may encounter in your daily life are:
a telephone book
T.V. Guide
airline reservation system
motor vehicle registration records
papers in your filing cabinet
files on your computer hard drive.
5. Data vs. information:
What is the difference?
What is data? What is information?
Data can be defined in
Information is data that
many ways. Information have been organized and
science defines data as communicated in a
unprocessed information. coherent and meaningful
manner.
Data is converted into
information, and
information is converted
into knowledge.
Knowledge; information
evaluated and organized
so that it can be used
purposefully.
6. Why do we need a database?
Keep records of our:
Clients
Staff
Volunteers
To keep a record of activities
and interventions;
Keep sales records;
Develop reports;
Perform research
Longitudinal tracking
7. What is the ultimate purpose of
a database management
system?
Is to transform
Data Information Knowledge Action
8. More about database definition
What is a database?
Quite simply, it’s an organized collection of data.
A database management system (DBMS) such
as Access, FileMaker, Lotus Notes, Oracle or
SQL Server which provides you with the
software tools you need to organize that data in
a flexible manner. It includes tools to add,
modify or delete data from the database, ask
questions (or queries) about the data stored in
the database and produce reports summarizing
selected contents.
9. Let’s explore some examples
Outlook contacts
Aspira Association MIS
KidTrax
GIS-GPS systems
10. Types of Databases
Non-relational databases
Non-relational databases place information in field categories that we create so
that information is available for sorting and disseminating the way we need it.
The data in a non-relational database, however, is limited to that program and
cannot be extracted and applied to a number of other software programs, or
other database files within a school or administrative system. The data
can only be "copied and pasted.“ Example: a spread sheet
Relational databases
In relational databases, fields can be used in a number of ways (and
can be of variable length), provided that they are linked in tables. It is
developed based on a database model that provides for logical
connections among files (known as tables) by including identifying
data from one table in another table
11. Selecting a Database
Management System
Database management systems (or DBMSs) can be divided into
two categories -- desktop databases and server databases.
Generally speaking, desktop databases are oriented toward
single-user applications and reside on standard personal
computers (hence the term desktop).
Server databases contain mechanisms to ensure the reliability
and consistency of data and are geared toward multi-user
applications.
12. Selecting a database system:
Need Analysis
The needs analysis process will be specific to your organization but, at
a minimum, should answer the following questions:
How many records we will warehouse and for how long?
Who will be using the database and what tasks will they perform?
How often will the data be modified? Who will make these
modifications?
Who will be providing IT support for the database?
What hardware is available? Is there a budget for purchasing
additional hardware?
Who will be responsible for maintaining the data?
Will data access be offered over the Internet? If so, what level of
access should be supported?
13. Some Definitions
A File: A group or collection of similar records, like INST6031 Fall
Student File, American History 1850-1866 file, Basic Food Group
Nutrition File
A record book: a "rolodex" of data records, like address lists,
inventory lists, classes or thematic units, or groupings of other
unique records that are combined into one list (found in
AppleWorks, FileMaker Pro software).
A field: one category of information, i.e., Name, Address, Semester
Grade, Academic topic
A record: one piece of data, i.e., one student's information, a recipe,
a test question
A layout: a design for a database that contains field names and
possibly graphics.
Database glossary
14. Fundamental building blocks
Tables comprise the fundamental building blocks of any database. If you're familiar with
spreadsheets, you'll find database tables extremely similar. Take a look at this example of
a table sample database:
The table above contains the employee information for our organization -- characteristics
like name, date of birth and title. Examine the construction of the table and you'll find that
each column of the table corresponds to a specific employee characteristic (or attribute in
database terms). Each row corresponds to one particular employee and contains his or her
information. That's all there is to it! If it helps, think of each one of these tables as a
spreadsheet-style listing of information.
15. Where do we start?
Let’s explore your “paper
system”
Client intake forms
Job application form
Funders reports
Database modeling:
Define required fields from
“forms” or required reports
Avoid repetition
Keep it simple
Identify a unique identifier
or primary key
16. Some Quality Control
Considerations
Remember “garbage in –
garbage out”. Some examples
and how to prevent this.
Quality management
encompasses three distinct
processes: quality planning,
quality control, and quality
improvement
Quality Planning in relation to
database systems design:
Who will perform data
entry?
Training? On-line help?
How data entry will be
performed?
17. Data entry considerations
Define “must” enter fields – no record is complete
unless: such and such is entered;
Make data entry fool proof. Example: Grade level can
be entered as a number (8 or 8th or eight). By using a
pull-down menu with the correct data format these
mistakes can be avoided.
18. Data Entry – additional
considerations
Barcode scanners
USB or
Wireless attached to a
Palm or Pocket PC
Pocket PC
WiFi 802.11g,
Bluetooth
Wireless networks
(real-time on demand
systems)