Conceptes varis sobre el model entitat relació: Conjunt d'entitats, entitats, atributs i tipus d'atributs, tipus de relacions,
cardinalitat, entitats dèbils i fortes
Entity sets, attribute, relationship, type of attribute, cardinality, relationship type, weak and strong entities. Null values, identifier
The small words that manage the grammar in English have two different pronunciations. These are called weak and strong forms. The weak forms are unstressed and the strong forms stressed. Most weak forms have either schwa or short 'i' vowel sounds and they are difficult to hear. These words are very important for the pronunciation of English grammar--they are like the gluer in the phonetic system.
The small words that manage the grammar in English have two different pronunciations. These are called weak and strong forms. The weak forms are unstressed and the strong forms stressed. Most weak forms have either schwa or short 'i' vowel sounds and they are difficult to hear. These words are very important for the pronunciation of English grammar--they are like the gluer in the phonetic system.
Language education reflects largely unstated government policies, mainstream cultural values, and minority group aspirations. Their diverse aims result in monolingualism or various types of bilingual education, weak or strong forms in terms of bilingual outcomes among students. This presentation shows how 10 cases of school systems in Japan and the world can be analyzed into types of bilingual education.
Com es pot realitzar la monitorització d'un sistema informàtic amb sistema operatiu Windows i Linux. Classificació del monitoratge. Tipus. Comandes i fitxers de monitorització. Registre de Windows.
Language education reflects largely unstated government policies, mainstream cultural values, and minority group aspirations. Their diverse aims result in monolingualism or various types of bilingual education, weak or strong forms in terms of bilingual outcomes among students. This presentation shows how 10 cases of school systems in Japan and the world can be analyzed into types of bilingual education.
Com es pot realitzar la monitorització d'un sistema informàtic amb sistema operatiu Windows i Linux. Classificació del monitoratge. Tipus. Comandes i fitxers de monitorització. Registre de Windows.
Conceptes bàsics del model relacional i correspòndències amb el model entitat relació extès (EER).
Aplicació de les 3 formes normals i la forma normal Boyce-Codd. Normalització de taules amb exemples de les diferents situacions.
Regles per a transformar el model entitat-relació al model relacional. Transformació de relacions 1:N, N:M i 1:1 segons diferents criteris. Transformació de jerarquies disjuntes i solapades i amb participació parcial o global
Atributs i tipus d'atributs: multivalor, identificador, compost, derivat o calculat, valor Null. Relacions entre conjunts d'entitats: tipus de relacions (1:1, N:1 o 1:N, N:M), cardinalitat mínima i màxima. Participació total i parcial. Relacions reflexives. Tipus de conjunts d'entitats fortes i dèbils.
Característiques de les bases de dades distribuides. Bloqueig de registres. Rèpliques. Fragmentació. Transaccions. Disseny de bases de dades distribuides
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.
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.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
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!
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Database design
What do we need? / Specifications
Conceptual model
Entity-Relationship(ER)
Relational model
Data definition language
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Model ER - Elements
●
Entity sets/entities
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Relationship sets/Relationship
●
Attributes
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Entity
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Object (concrete or abstract) distinguishable from other objects.
●
Example
–40232423, Artur Mas (concrete: particular person)
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Example
–Vacances a la Costa Brava, des del 03/08/2015 fins el 21/08/2015. (abstract)
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Entity sets
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Set of entities of the same type
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Examples: Workers, vehicles, buildings ...
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Examples: Departments, Salaries, holidays ...
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Exercicis
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Indica els conjunts d'entitats necessàries per a la gestió d'una botiga d'informàtica.
●
Indica els conjunts d'entitats necessàries per a la gestió del club de Handbol de
Granollers.
●
Indica els conjunts d'entitats necessàries per a la gestió d'un parking.
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Indica els conjunts d'entitats necessàries per a la gestió d'un joc de rol (informàtic).
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Attributes
●
Describes a property or characterstic of an entity.
●
An entity set is composed by one or more attributes.
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It's identified by a name.
●
Two attributes with the same name are not allowed in the same entity
set.
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NULL values
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Special name for «unknown» value.
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Space or zero is not NULL (space and zero is a known values).
●
Examples:
–Qualification: NULL. (No results yet).
–Final match result: NULL (Not started).
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Derived attribute
●
His value can be obtained from other attributes or related entities.
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Examples:
–Age → if an attribute «date of birth» exists.
–Matches won → can get this value from an entity set «results».
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Composite attribute
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An attribute is composite if we can divide it on some other attributes.
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The model ER is clearer.
●
Example:
–Name (person): name , surname.
–Direction: city, address, number ...
–Temperature: value, units.
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Attribute hierarchy
●
A composite attribute can be formed by other
composite attributes.
Direction
City
Postal code
Name
Address Number
Simple attributes
Composite attributes
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Univalued / multivalued
●
Can store one value => univalued attribute.
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Can store many values => multivalued attribute.
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Examples univalued:
–Name, date of birth (person), model (vehicle) …
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Examples multivalued:
–Telephone, colors (product), languages, ...
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Fictitious attributes
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They don't add any meaning to the entity.
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They are used like fictious identifiers attributes
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Their use is not recommended.
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Their use is acceptable when an entity is identified for 4 or more attributes.
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Their implementation is based on the use of autonumeric values (1, 2, 3, ….)
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Relationship
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Is a correlation between two or more entities.
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Examples
–Pau Pi is learning databases systems.
–Volkswagen is producing a new car model.
–Pau Pi has bought a HP 14z Laptop computer.
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Relationship sets
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Is a correlation between two or more entity sets.
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Examples
–Teachers and students.
–Customers and invoices.
–Warehouses and articles.
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Relationship sets type (1:1)
●
One entity can be related to only one entity, and viceversa.
●
It is not frequent.
●
Example:
–Country and president
a1
a2
a3
a4
...
an
Entity set A
b1
b2
b3
b4
...
bn
Entity set B
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Relationship sets type (1:N) / (N:1)
●
One entity can be related to many entities.
●
(1:N/N:1) depends on the order of entities sets.
●
Example:
–Country and monuments
a1
a2
a3
a4
...
an
Entity set A
b1
b2
b3
b4
...
bn
Entity set B
(1:N)
a1
a2
a3
a4
...
an
Entity set A
b1
b2
b3
b4
...
bn
Entity set B
(N:1)
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Relationship sets type (N:M)
●
One entity can be related to many entities and
viceversa.
●
Example:
–Films and actors
a1
a2
a3
a4
...
an
Entity set A
b1
b2
b3
b4
...
bn
Entity set B
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Participation
●
Total: all entities are related with another entity.
●
Partial: there're any entities not realet with any entity.
–«A» has a total participation in «B».
–«B» has a partial participation in «A».
a1
a2
a3
a4
...
an
Entity set A
b1
b2
b3
b4
...
bn
Entity set B
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Participation - Examples
●
Total
–Vehicle and insurance.
–Book and author.
●
Partial
–Students and companies.
–Workers and computers.
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Degree
●
Number of entity sets involved in a relationship.
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Examples:
–Team has players: binary relationship.
–Players commit fouls in a match: ternary relationship.
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Cardinality
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Cardinality: set a minimun and maximun value to the relationship.
–Basket player commits fouls in a match.
–Relationship set: commit.
–Relationship set type: 1:N (player-fouls)
–Minimun value: 0
–Maximun value: 5
–
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Candidate key
●
An entity can be identified by using differents attributes.
●
All the attributes that identifies an entity is a candidate key.
●
Example:
–A worker could be identified by: SSN, NIF. Both attributes are
candidate.
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Weak Entities
●
The existence of a weak entity depends on the existence of an identifying entity type (strong entity).
●
A weak entity has a relationship set (1:N or N:1), where 1 is strong entity and N weak entity.
●
Examples:
–Course (strong) and course-offering (weak).
–Match (strong) and goals (weak).
–Customer (strong) and sales (weak).
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Graphical representation
Entity set Rel. set
Weak
entity set
Identifying
rel. set
Entity set Rel. setEntity set Rel. set
Entity set Rel. set
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Exemple 1
Es necessita dissenyar un model ER que permeti controlar quins mòduls i
unitats formatives fa cada alumne.
Cada mòdul s'identifica per un codi. Altra informació que conforma el mòdul és
el nom associat i el total d'hores de les unitats formatives que el conformen.
De cada unitat formativa s'emmagatzemarà el seu codi, el nom de la unitat
formativa i les hores que la componen.
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Biblioteca
Es necessita un model ER per a controlar la gestió d'una
biblioteca. Els socis de la biblioteca poden agafar llibres per un
període determinat. Es vol saber quins llibres té cada soci.
El temps que es pot agafar cada un dels llibres deprendrà del
tipus de llibre.
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Reflexive relationship
●
Two entities belonging to the same entity set can be related. When
this occurs , we have an reflexive relationship.
●
Examples:
–An employee is supervised by one manager
–A course requires previous courses.
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Ternary relationship sets
●
A ternary relationship occurs when 3 entity sets are
involved in a relationship.
●
The relationship sets type is set taking two entities
sets and determining the type respect the other one.
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Example
SOME ENTITY SETS VALUES
Supplier Part Vehicle
Michelin Wheel Volkswagen
Firestone Motor Renault
Electronic Motors Ford
Tesla Motors
Donada una peça (ex.: roda) i un proveïdor,
es pot suministrar a molts vehicles?
Resposta sí: Supplier/Part → tipus relació N
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Example
SOME ENTITY SETS VALUES
Supplier Part Vehicle
Michelin Wheel Volkswagen
Firestone Motor Renault
Electronic Motors Ford
Tesla Motors
Donat un tipus de vehicle i una peça, aquesta
pot ser suministrada per molts proveïdors?
Resposta sí: Vehicle/Part → tipus relació M
N
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Example
SOME ENTITY SETS VALUES
Supplier Part Vehicle
Michelin Wheel Volkswagen
Firestone Motor Renault
Electronic Motors Ford
Tesla Motors
Un proveïdor pot suministrar moltes peces per un tiNM
Resposta sí: Vehicle/Supplier → tipus relació P
NM
P
M:N:P
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Generalization / Specialization
●
Two or more entity sets combines to form a higher level entity sets (generalization)
●
1 entity sets breaks in 2 or more entity sets to form a lower level entity sets
(specialization)
●
Examples:
–An entity sets teacher and student combines to form an entity sets Person (generalization)
–An entity sets vehicle breaks in different entity sets: car, truck, bus ... (specialization)
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Generalization / Specialization
●
The attributes of the higher entity sets are inherited to the lower
level entity sets.
●
Each lower level entity sets has they own attribute sets.
–Example: dni is an attribute for person entity sets, while average
qualification is an attribute for student entity sets.
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Projectes
Es necessita un model ER per a controlar els projectes d'una empresa. Cada projecte està associat a un tipus (nous
productes, informàtica,...). Cada tipus de projecte té associat a un responsable, que serà l'encarregat de vetllar pel
bon funcionament de tots els projectes associats al tipus en concret. Apart del cap de projectes, a cada projecte hi
estan assignades una sèrie de persones. Una persona pot treballar en varis projectes al mateix temps.
Apart des recursos humans, per a cada un dels projectes es necessiten una sèrie de materials. Cada material
s'identifica per un codi. Un material només es podrà utilitzar en un projecte. Altra informació que es vol
emmagatzemar del material és la quantitat i el preu unitari.
De cada projecte és important saber el total de persones que té associades i el cost del material del mateix.