Database Management Systems (DBMS) allow users to define, construct, and manipulate databases. A DBMS provides facilities to define data structures and constraints, store data, and retrieve or update data through queries. Common examples of databases include company records, airline reservation systems, and library catalogs. It is important to distinguish between a database schema, which describes the database structure, and a database instance, which contains the actual stored data. Popular DBMS languages include DDL for defining data structures and DML for manipulating data. DBMSs can be classified based on their data model, number of users, distribution, and cost.
A public member is visible from anywhere in the system. In class diagram, it is prefixed by the symbol '+'. Private − A private member is visible only from within the class.
A public member is visible from anywhere in the system. In class diagram, it is prefixed by the symbol '+'. Private − A private member is visible only from within the class.
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.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
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We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
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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.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
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
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
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
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One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
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2. Definitions
• Data: known facts that can be recorded
• Database: a collection of data
• represents some aspect of the real world
• logically coherent collection (not a random collection)
• designed, built & populated for a specific purpose
• Database Management System: the software
that manages the data
3. DBMSs provide...
• Facilities to:
– Define – specify data types, structures &
constraints for the data to be stored in the
database
– Construct – store the data
– Manipulate – pose queries to retrieve specific
data, update data or generate reports based
on the data
4. Popular Examples
• Company Databases
– employees, departments, projects …
• Airline Reservation Systems
– flights, fares, customers, reservations ..
• Library Databases
– authors, titles, publishers, videos …
• Bank Databases
– accounts, customers ...
5. Schemas & Instances
• Important to distinguish between
– database schema: the description of the
database
– database: the stored data
6. Financial Records
(Company, Type, Name, Date, Amt, NumShares, Broker)
Company Type Name Date Amt NumShares Broker
Trimark Mutual Trimark 01/01/84 49.75 100 C. Harris
Fund Fund
AGF Mutual Foreign 01/01/94 62.25 1000 C. Harris
Fund Equity
7. Database States
• Empty State -- database is empty when we
first define the database schema
• Initial State -- database is first populated or
loaded with data
• Current State -- snapshot in time
3753 7
8. DBMS Languages
• DDL: Data Definition Language
– used to define/change the structure of the
database
• DML: Data Manipulation Language
– used to query the database, insert data,
change data or delete data
3753 8
9. Classification of DBMSs
• Data Model Classification
– relational, network, hierarchical, object-
oriented …
• Number of users
– single user or multi-user
• Number of Sites
– centralized vs distributed
• Cost of the DBMS
3753 9
10. Data Model
• A collection of concepts that can be used
to define the structure (data, data types,
relations and constraints) of a database.
• Examples:
– Entity Relationship model
– Relational Model
– hierarchical & network models
– object-data models
12. Entity-Relationship Model
• Most popular conceptual model for
database design
• Basis for many other models
• Describes the data in a system and how
that data is related
• Describes data as entities, attributes
and relationships
3753 X1
13. Database requirements
• We must convert the written database
requirements into an E-R diagram
• Need to determine the entities, attributes
and relationships.
– nouns = entities
– adjectives = attributes
– verbs = relationships
3753 X1
15. Entities
• Entity – basic object of the E-R model
– Represents a “thing” with an independent
existence
– Can exist physically or conceptually
• a professor, a student, a course
• Entity type – used to define a set of
entities with the same properties.
3753 X1
16. Entity and Entity Types
Name
Number Topic
Entity Type
Course
Number: 3753
Entity
Name: Database Management Systems
Topic: Introduction to DBMSs
3753 X1
17. Attributes
• Each entity has a set of associated properties
that describes the entity. These properties are
known as attributes.
• Attributes can be:
– Simple or Composite
– Single or Multi-valued
– Stored or Derived
– NULL
3753 X1
21. Attributes (cont’d)
• NULL attributes have no value
– not 0 (zero)
– not a blank string
• Attributes can be “nullable” where a null
value is allowed, or “not nullable” where
they must have a value.
3753 X1
22. Primary Keys
Professor Employee ID
• Employee ID is the primary key
• Primary keys must be unique for the
entity in question
3753 X1
23. Relationships
• defines a set of associations between
various entities
• can have attributes to define them
• are limited by:
– Participation
– Cardinality Ratio
3753 X1
25. Participation
• Defines if the existence of an entity depends on
it being related to another entity with a
relationship type.
– Partial
– Total
Section part of Course
3753 X1
26. Cardinality
• The number of relationships that an entity
may participate in.
– 1:1, 1:N, N:M, M:1
N 1
Section part of Course
3753 X1
27. Weak entity
• Weak entities do not have key attributes of their
own.
• Weak entities cannot exist without another a
relationship to another entity.
• A partial key is the portion of the key that comes
from the weak entity. The rest of the key comes
from the other entity in the relationship.
• Weak entities always have total participation as
they cannot exist without the identifying
relationship.
3753 X1
28. Weak Entity (cont’d)
Section ID Section
Descriminator
Identifying Relationship part of
Number
Course
3753 X1
29. Acadia Teaching Database
Design an E-R schema for a database to store info about professors,
courses and course sections indicating the following:
• The name and employee ID number of each professor
• The salary and email address(es) for each professor
• How long each professor has been at the university
• The course sections each professor teaches
• The name, number and topic for each course offered
• The section and room number for each course section
• Each course section must have only one professor
• Each course can have multiple sections
3753 X1
30. Visual View of the Database
Employee ID Start Date Years Teaching Section ID Room
1 N
Professor teaches Section
Email
N
Salary First
Name Part of
Last
1
Number Course
Topic Name
31. University DB Case Study
• Maintain the following information about
undergraduate students:
– Name, address, student number, date of
birth, year of study, degree program (BA, BSc,
BCS), concentration (Major, Honours, etc) and
department of concentration.
• Note: An address is composed of a street, city,
province and postal code; the student number is
unique for each student
3753 X1
32. University Case Study (cont’d)
• Maintain information about departments
– Name, code (CS, Phy), office phone, and faculty
members
• Maintain information about courses:
– Course number (3753), title, description,
prerequisites.
• Maintain information about course sections:
– Section (A, B, C), term (X1), slot #, instructor
3753 X1
33. University Case Study (cont’d)
• Maintain information about faculty:
– Name, rank, employee number, salary, office
number, phone number and email address.
– Note: employee number is unique
• Maintain a program of study for the current
year for each student:
– i.e. courses that each student is enrolled in
3753 X1
34. Address Street
Number N M
Section Enrolled Student
Number City
Term
Name Province
Slot N
N
1
Teaches DOB Postal Code
Has
Salary Name
1
N
Faculty Number
Number Prereq
Office
1 N
Title Course M
Phone
Start Date Head Member
Description Email
1 1
End Date Code Rank
N
1
Name
Offer Dept
Phone
35. Extended E-R Model
• E-R model is sufficient for traditional
database applications
• Nontraditional applications (CAD,
multimedia) have more complex
requirements
• Can extend traditional E-R diagrams with
semantic data modeling concepts
3753 X1
36. IS-A Relationship
Name Employee S.S.N.
IS-A
Staff Faculty Teaching Assistant
Position Rank Student #
3753 X1
37. Specialization & Generalization
• Specialization
– process of taking an entity and creating
several specialized subclasses
• Generalization
– process of taking several related entities
and creating a general superclass
• We will talk mainly of specialization, but
most information will also apply to
generalization
3753 X1
38. Specialization constraints
• Specializations can be predicate-defined
or attribute-defined or user-defined
• Disjointness constraint – specialization is
disjoint or overlapping
• Completeness constraint – specialization
is total or partial
3753 X1
39. Predicate-defined subclass
• An attribute value is used to determine the
members of a subclass
• Not all members of every subclass can be
determined by the attribute value
• In the following example, the Pension Plan type
can be used to determine faculty from staff, but
has no effect on students or those who opted out
of the pension plan.
3753 X1
41. Attribute-defined subclass
• There is one defining attribute for all
subclasses
• Each member of the superclass can be
assigned to the appropriate subclass
based on this one attribute
3753 X1
43. User-defined subclass
• When there is no condition to automatically
determine membership in a subclass, it must
be done at the discretion of the user.
3753 X1
44. Disjointness constraint
• Specifies that an entity can be a member
of at most one subclass
• There can be no overlap between the
subclasses
• We use the notation of a d in a circle to
symbolize that the subclasses are disjoint
3753 X1
45. Disjoint constraint
Name Employee S.S.N.
d
Staff Teaching Assistant
Faculty
Position Rank Student #
3753 X1
46. Overlap
• Entities are able to belong to more than
one subclass
• Notation is an o inside of a circle
3753 X1
47. Overlap
Jobtype Employee S.S.N.
A staff member may
o also be a student
Staff Students Faculty
Rank Year Rank
3753 X1
48. Completeness Constraint
• May be total or partial
• for total, every entity in the superclass
must belong to a subclass
• for partial, entities in the superclass do not
need to be part of any subclass
• notation for total and partial are the same
as in a regular E-R diagram – single and
double lines
3753 X1
49. Partial
Pension Person S.S.N.
Plan Type
d
Staff Faculty
Rank Rank
3753 X1
50. Total
Jobtype Employee S.I.N.
o
Staff Students Faculty
Rank Year Rank
3753 X1
51. Hierarchies and Lattices
• Hierarchies
– a tree-like structure where each subclass
belongs to only one superclass
• Lattices
– a graph-like structure where a subclass can
belong to more than one superclass
3753 X1
52. Lattice
name Person
o student #
Employee Student
salary
Teaching Assistant course
3753 X1
54. Union Types and Lattice
• Lattice
– Subset of the Intersection of the superclasses.
– A shared subclass (Teaching Assistant) is the
subclass in two distinct superclass relatioships
• Union Types
– Subset of the unoin of distinct Entity Types
3753 X1
56. Relationships of Higher Degree
q Relationship types of degree 2 are called binary
q Relationship types of degree 3 are called ternary
and of degree n are called n-ary
q In general, an n-ary relationship is not equivalent to
n binary relationships
Chapter 3-56
59. Problem with constraints on
higher order relationship types
m
n
p
What does it mean to put m:n:p on the three arms of the relationship ?
It is essentially meaningless.
Chapter 3-59
61. The (min,max) notation for
higher order relationship type
constraints
(1,2) (1,3)
(1,5)
A Teacher can offer min 1 and max 2 Offerings
A Course may have 1 to 3 Offerings
A Student may enroll in from 1 to 5 Offerings
Chapter 3-61