This document provides an overview of the relational model for databases. It discusses how data is organized in tables called relations and how the tables are related to each other. The key aspects covered include how the relational model was introduced, how relations represent data as tables with rows and columns, properties of relations like primary keys, and relational algebra operations like select, project, join, union and more.
The document discusses the relational data model and mapping ER/EER diagrams to relational schemas. It covers basic concepts of the relational model like relations, attributes, tuples, keys, and integrity constraints. It also describes the main phases of database design including conceptual, logical, and physical design. Logical design involves mapping ER/EER diagrams to relational schemas through steps like mapping regular entity types, weak entity types, and relationships. The goal is to construct a relational schema that is independent of specific DBMS considerations.
Islamic University Previous Year Question Solution 2019 (ADBMS)Rakibul Hasan Pranto
Database administrators (DBAs) manage databases and are responsible for tasks like creating the database schema, defining storage structures and access methods, modifying the schema or physical organization when needed, granting authorization for data access, and specifying integrity constraints. Other database roles include application programmers, sophisticated users, specialized users, standalone users, native users, and system analysts who are responsible for database design, structure, and properties.
This document provides an overview of databases and database management systems (DBMS). It discusses what a database is, components of a database system like users and applications, and examples of DBMS like MySQL and Oracle. It also summarizes key database concepts such as data models, relationships between data using keys, and relational algebra operations for querying databases.
A database is a collection of logically related data organized for convenient access, usually by programs for specific purposes. A DBMS is software that allows users to define, construct and manipulate databases for various applications. The database and DBMS together form a database system. A DBMS provides advantages like reducing data redundancy and inconsistency, restricting unauthorized access, and enforcing data integrity and security.
The document discusses the Entity-Relationship (ER) model for conceptual database design. It describes the key components of the ER model including entities, attributes, relationships, and keys. It also explains how the ER model maps to a relational schema and database, including the use of tables, rows, columns, primary keys, foreign keys, and integrity constraints. Referential integrity constraints are defined to link tables through foreign key to primary key relationships.
This document discusses relational database management systems and SQL. It covers basic SQL queries involving selection, projection, and joins. It also discusses set operations in SQL like union, intersect, and except. Further, it describes complex queries, integrity constraints, domains, and data definition language commands for creating, altering, and dropping tables.
A collection of conceptual tools for describing
data
data relationships
data semantics
data constraints
Relational model
Entity-Relationship model
Other models:
object-oriented model
semi-structured data models
Older models: network model and hierarchical model
The document discusses the relational data model and its key concepts. The relational model represents a database as a collection of relations (tables). Each row in a relation represents a tuple of related data values. Attributes describe the columns and domains define the possible values for each attribute. Relations have schemas that define the relation name and attributes. Relation states contain sets of tuples that must satisfy integrity constraints defined on the schema.
The document discusses the relational data model and query languages. It provides the following key points:
1. The relational data model organizes data into tables with rows and columns, where rows represent records and columns represent attributes. Relations between data are represented through tables.
2. Relational integrity constraints include key constraints, domain constraints, and referential integrity constraints to ensure valid data.
3. Relational algebra and calculus provide theoretical foundations for query languages like SQL. Relational algebra uses operators like select, project, join on relations, while relational calculus specifies queries using logic.
The document discusses the relational database model. It was introduced in 1970 and became popular due to its simplicity and mathematical foundation. The model represents data as relations (tables) with rows (tuples) and columns (attributes). Keys such as primary keys and foreign keys help define relationships between tables and enforce integrity constraints. The relational model provides a standardized way of structuring data through its use of relations, attributes, tuples and keys.
Chapter 6 relational data model and relationalJafar Nesargi
The document discusses the relational data model and relational algebra. It describes key concepts of the relational model including relations, tuples, domains, attributes, and constraints. It defines domains as sets of atomic values, relation schemas made up of relation names and attribute lists, and tuples as ordered lists of values. It discusses characteristics of relations such as ordering, null values, and interpretation. It also covers relational model notation, constraints including domain, key, entity integrity, referential integrity constraints and foreign keys, and update operations such as insert, delete, and modify operations.
Chapter 6 relational data model and relationalJafar Nesargi
This document discusses the relational data model and relational algebra. It defines key concepts of the relational model such as relations, tuples, domains, attributes, and constraints. It also covers relational algebra operations like SELECT, PROJECT, JOIN, and set operations. Relational algebra provides a foundation for implementing queries in relational database management systems.
This document provides an overview of a database management systems course. It outlines the course name, outcomes, and topics to be covered, which include relational model concepts, integrity constraints, relational algebra, relational calculus, and SQL. The document then provides details on several relational model and algebra topics through a series of slides, including definitions of relations, tuples, domains, keys, constraints, and relational algebra operations.
Chapter 6 relational data model and relationalJafar Nesargi
This document discusses the relational data model and relational algebra. It defines key concepts of the relational model such as relations, tuples, domains, attributes, and constraints. It also covers relational algebra operations like SELECT, PROJECT, JOIN, and set operations. Updates, deletes and inserts are discussed along with how they can violate constraints and how violations are handled.
The document discusses concepts related to the relational model and relational algebra. It defines key concepts like relations, attributes, tuples, domains, and integrity constraints. It describes operations on relations like select, project, join, union and set operations. It explains how these operations are used to retrieve and manipulate data in relational databases and how insert, delete and update operations must adhere to integrity constraints. It provides examples to illustrate relational algebra operations and how they are applied to relations.
The Relational Data Model and Relational Database Constraints Ch5 (Navathe 4t...Raj vardhan
The Relational Data Model and Relational Database Constraints
Ch5 (Navathe 4th edition)/ Ch7 (Navathe 3rd edition)
Example of STUDENT Relation(figure 5.1)
The document summarizes key concepts of the relational database model including:
1. The relational model uses tables to represent data and relationships, with each table having columns and rows.
2. Key characteristics are that it is the primary commercial data model, provides a simple way to represent data, and uses a record-based structure with fixed-format records and fields.
3. Relational databases have a schema defining relations (tables) and attributes (columns), with each relation made up of tuples (rows) that contain values from the defined domains.
Purpose of the data base system, data abstraction, data model, data independence, data definition
language, data manipulation language, data base manager, data base administrator, data base users,
overall structure.
ER Models, entities, mapping constrains, keys, E-R diagram, reduction E-R diagrams to tables,
generatio, aggregation, design of an E-R data base scheme.
Oracle RDBMS, architecture, kernel, system global area (SGA), data base writer, log writer, process
monitor, archiver, database files, control files, redo log files, oracle utilities.
SQL: commands and data types, data definition language commands, data manipulation commands,
data query language commands, transaction language control commands, data control language
commands.
Joins, equi-joins, non-equi-joins, self joins, other joins, aggregate functions, math functions, string
functions, group by clause, data function and concepts of null values, sub-querries, views.
PL/SQL, basics of pl/sql, data types, control structures, database access with PL/SQL, data base
connections, transaction management, data base locking, cursor management.
The document discusses key concepts of the relational database model including:
- Relations are represented as tables with rows and columns.
- Entities have attributes with domains and tuples represent rows.
- Relations have keys like candidate keys and foreign keys for integrity.
- Views are virtual relations defined by queries on base relations and allow customized access.
Raish Khanji GTU 8th sem Internship Report.pdfRaishKhanji
This report details the practical experiences gained during an internship at Indo German Tool
Room, Ahmedabad. The internship provided hands-on training in various manufacturing technologies, encompassing both conventional and advanced techniques. Significant emphasis was placed on machining processes, including operation and fundamental
understanding of lathe and milling machines. Furthermore, the internship incorporated
modern welding technology, notably through the application of an Augmented Reality (AR)
simulator, offering a safe and effective environment for skill development. Exposure to
industrial automation was achieved through practical exercises in Programmable Logic Controllers (PLCs) using Siemens TIA software and direct operation of industrial robots
utilizing teach pendants. The principles and practical aspects of Computer Numerical Control
(CNC) technology were also explored. Complementing these manufacturing processes, the
internship included extensive application of SolidWorks software for design and modeling tasks. This comprehensive practical training has provided a foundational understanding of
key aspects of modern manufacturing and design, enhancing the technical proficiency and readiness for future engineering endeavors.
This document provides an overview of databases and database management systems (DBMS). It discusses what a database is, components of a database system like users and applications, and examples of DBMS like MySQL and Oracle. It also summarizes key database concepts such as data models, relationships between data using keys, and relational algebra operations for querying databases.
A database is a collection of logically related data organized for convenient access, usually by programs for specific purposes. A DBMS is software that allows users to define, construct and manipulate databases for various applications. The database and DBMS together form a database system. A DBMS provides advantages like reducing data redundancy and inconsistency, restricting unauthorized access, and enforcing data integrity and security.
The document discusses the Entity-Relationship (ER) model for conceptual database design. It describes the key components of the ER model including entities, attributes, relationships, and keys. It also explains how the ER model maps to a relational schema and database, including the use of tables, rows, columns, primary keys, foreign keys, and integrity constraints. Referential integrity constraints are defined to link tables through foreign key to primary key relationships.
This document discusses relational database management systems and SQL. It covers basic SQL queries involving selection, projection, and joins. It also discusses set operations in SQL like union, intersect, and except. Further, it describes complex queries, integrity constraints, domains, and data definition language commands for creating, altering, and dropping tables.
A collection of conceptual tools for describing
data
data relationships
data semantics
data constraints
Relational model
Entity-Relationship model
Other models:
object-oriented model
semi-structured data models
Older models: network model and hierarchical model
The document discusses the relational data model and its key concepts. The relational model represents a database as a collection of relations (tables). Each row in a relation represents a tuple of related data values. Attributes describe the columns and domains define the possible values for each attribute. Relations have schemas that define the relation name and attributes. Relation states contain sets of tuples that must satisfy integrity constraints defined on the schema.
The document discusses the relational data model and query languages. It provides the following key points:
1. The relational data model organizes data into tables with rows and columns, where rows represent records and columns represent attributes. Relations between data are represented through tables.
2. Relational integrity constraints include key constraints, domain constraints, and referential integrity constraints to ensure valid data.
3. Relational algebra and calculus provide theoretical foundations for query languages like SQL. Relational algebra uses operators like select, project, join on relations, while relational calculus specifies queries using logic.
The document discusses the relational database model. It was introduced in 1970 and became popular due to its simplicity and mathematical foundation. The model represents data as relations (tables) with rows (tuples) and columns (attributes). Keys such as primary keys and foreign keys help define relationships between tables and enforce integrity constraints. The relational model provides a standardized way of structuring data through its use of relations, attributes, tuples and keys.
Chapter 6 relational data model and relationalJafar Nesargi
The document discusses the relational data model and relational algebra. It describes key concepts of the relational model including relations, tuples, domains, attributes, and constraints. It defines domains as sets of atomic values, relation schemas made up of relation names and attribute lists, and tuples as ordered lists of values. It discusses characteristics of relations such as ordering, null values, and interpretation. It also covers relational model notation, constraints including domain, key, entity integrity, referential integrity constraints and foreign keys, and update operations such as insert, delete, and modify operations.
Chapter 6 relational data model and relationalJafar Nesargi
This document discusses the relational data model and relational algebra. It defines key concepts of the relational model such as relations, tuples, domains, attributes, and constraints. It also covers relational algebra operations like SELECT, PROJECT, JOIN, and set operations. Relational algebra provides a foundation for implementing queries in relational database management systems.
This document provides an overview of a database management systems course. It outlines the course name, outcomes, and topics to be covered, which include relational model concepts, integrity constraints, relational algebra, relational calculus, and SQL. The document then provides details on several relational model and algebra topics through a series of slides, including definitions of relations, tuples, domains, keys, constraints, and relational algebra operations.
Chapter 6 relational data model and relationalJafar Nesargi
This document discusses the relational data model and relational algebra. It defines key concepts of the relational model such as relations, tuples, domains, attributes, and constraints. It also covers relational algebra operations like SELECT, PROJECT, JOIN, and set operations. Updates, deletes and inserts are discussed along with how they can violate constraints and how violations are handled.
The document discusses concepts related to the relational model and relational algebra. It defines key concepts like relations, attributes, tuples, domains, and integrity constraints. It describes operations on relations like select, project, join, union and set operations. It explains how these operations are used to retrieve and manipulate data in relational databases and how insert, delete and update operations must adhere to integrity constraints. It provides examples to illustrate relational algebra operations and how they are applied to relations.
The Relational Data Model and Relational Database Constraints Ch5 (Navathe 4t...Raj vardhan
The Relational Data Model and Relational Database Constraints
Ch5 (Navathe 4th edition)/ Ch7 (Navathe 3rd edition)
Example of STUDENT Relation(figure 5.1)
The document summarizes key concepts of the relational database model including:
1. The relational model uses tables to represent data and relationships, with each table having columns and rows.
2. Key characteristics are that it is the primary commercial data model, provides a simple way to represent data, and uses a record-based structure with fixed-format records and fields.
3. Relational databases have a schema defining relations (tables) and attributes (columns), with each relation made up of tuples (rows) that contain values from the defined domains.
Purpose of the data base system, data abstraction, data model, data independence, data definition
language, data manipulation language, data base manager, data base administrator, data base users,
overall structure.
ER Models, entities, mapping constrains, keys, E-R diagram, reduction E-R diagrams to tables,
generatio, aggregation, design of an E-R data base scheme.
Oracle RDBMS, architecture, kernel, system global area (SGA), data base writer, log writer, process
monitor, archiver, database files, control files, redo log files, oracle utilities.
SQL: commands and data types, data definition language commands, data manipulation commands,
data query language commands, transaction language control commands, data control language
commands.
Joins, equi-joins, non-equi-joins, self joins, other joins, aggregate functions, math functions, string
functions, group by clause, data function and concepts of null values, sub-querries, views.
PL/SQL, basics of pl/sql, data types, control structures, database access with PL/SQL, data base
connections, transaction management, data base locking, cursor management.
The document discusses key concepts of the relational database model including:
- Relations are represented as tables with rows and columns.
- Entities have attributes with domains and tuples represent rows.
- Relations have keys like candidate keys and foreign keys for integrity.
- Views are virtual relations defined by queries on base relations and allow customized access.
Raish Khanji GTU 8th sem Internship Report.pdfRaishKhanji
This report details the practical experiences gained during an internship at Indo German Tool
Room, Ahmedabad. The internship provided hands-on training in various manufacturing technologies, encompassing both conventional and advanced techniques. Significant emphasis was placed on machining processes, including operation and fundamental
understanding of lathe and milling machines. Furthermore, the internship incorporated
modern welding technology, notably through the application of an Augmented Reality (AR)
simulator, offering a safe and effective environment for skill development. Exposure to
industrial automation was achieved through practical exercises in Programmable Logic Controllers (PLCs) using Siemens TIA software and direct operation of industrial robots
utilizing teach pendants. The principles and practical aspects of Computer Numerical Control
(CNC) technology were also explored. Complementing these manufacturing processes, the
internship included extensive application of SolidWorks software for design and modeling tasks. This comprehensive practical training has provided a foundational understanding of
key aspects of modern manufacturing and design, enhancing the technical proficiency and readiness for future engineering endeavors.
Analysis of reinforced concrete deep beam is based on simplified approximate method due to the complexity of the exact analysis. The complexity is due to a number of parameters affecting its response. To evaluate some of this parameters, finite element study of the structural behavior of the reinforced self-compacting concrete deep beam was carried out using Abaqus finite element modeling tool. The model was validated against experimental data from the literature. The parametric effects of varied concrete compressive strength, vertical web reinforcement ratio and horizontal web reinforcement ratio on the beam were tested on eight (8) different specimens under four points loads. The results of the validation work showed good agreement with the experimental studies. The parametric study revealed that the concrete compressive strength most significantly influenced the specimens’ response with the average of 41.1% and 49 % increment in the diagonal cracking and ultimate load respectively due to doubling of concrete compressive strength. Although the increase in horizontal web reinforcement ratio from 0.31 % to 0.63 % lead to average of 6.24 % increment on the diagonal cracking load, it does not influence the ultimate strength and the load-deflection response of the beams. Similar variation in vertical web reinforcement ratio leads to an average of 2.4 % and 15 % increment in cracking and ultimate load respectively with no appreciable effect on the load-deflection response.
ELectronics Boards & Product Testing_Shiju.pdfShiju Jacob
This presentation provides a high level insight about DFT analysis and test coverage calculation, finalizing test strategy, and types of tests at different levels of the product.
"Boiler Feed Pump (BFP): Working, Applications, Advantages, and Limitations E...Infopitaara
A Boiler Feed Pump (BFP) is a critical component in thermal power plants. It supplies high-pressure water (feedwater) to the boiler, ensuring continuous steam generation.
⚙️ How a Boiler Feed Pump Works
Water Collection:
Feedwater is collected from the deaerator or feedwater tank.
Pressurization:
The pump increases water pressure using multiple impellers/stages in centrifugal types.
Discharge to Boiler:
Pressurized water is then supplied to the boiler drum or economizer section, depending on design.
🌀 Types of Boiler Feed Pumps
Centrifugal Pumps (most common):
Multistage for higher pressure.
Used in large thermal power stations.
Positive Displacement Pumps (less common):
For smaller or specific applications.
Precise flow control but less efficient for large volumes.
🛠️ Key Operations and Controls
Recirculation Line: Protects the pump from overheating at low flow.
Throttle Valve: Regulates flow based on boiler demand.
Control System: Often automated via DCS/PLC for variable load conditions.
Sealing & Cooling Systems: Prevent leakage and maintain pump health.
⚠️ Common BFP Issues
Cavitation due to low NPSH (Net Positive Suction Head).
Seal or bearing failure.
Overheating from improper flow or recirculation.
Passenger car unit (PCU) of a vehicle type depends on vehicular characteristics, stream characteristics, roadway characteristics, environmental factors, climate conditions and control conditions. Keeping in view various factors affecting PCU, a model was developed taking a volume to capacity ratio and percentage share of particular vehicle type as independent parameters. A microscopic traffic simulation model VISSIM has been used in present study for generating traffic flow data which some time very difficult to obtain from field survey. A comparison study was carried out with the purpose of verifying when the adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and multiple linear regression (MLR) models are appropriate for prediction of PCUs of different vehicle types. From the results observed that ANFIS model estimates were closer to the corresponding simulated PCU values compared to MLR and ANN models. It is concluded that the ANFIS model showed greater potential in predicting PCUs from v/c ratio and proportional share for all type of vehicles whereas MLR and ANN models did not perform well.
π0.5: a Vision-Language-Action Model with Open-World GeneralizationNABLAS株式会社
今回の資料「Transfusion / π0 / π0.5」は、画像・言語・アクションを統合するロボット基盤モデルについて紹介しています。
拡散×自己回帰を融合したTransformerをベースに、π0.5ではオープンワールドでの推論・計画も可能に。
This presentation introduces robot foundation models that integrate vision, language, and action.
Built on a Transformer combining diffusion and autoregression, π0.5 enables reasoning and planning in open-world settings.
Concept of Problem Solving, Introduction to Algorithms, Characteristics of Algorithms, Introduction to Data Structure, Data Structure Classification (Linear and Non-linear, Static and Dynamic, Persistent and Ephemeral data structures), Time complexity and Space complexity, Asymptotic Notation - The Big-O, Omega and Theta notation, Algorithmic upper bounds, lower bounds, Best, Worst and Average case analysis of an Algorithm, Abstract Data Types (ADT)
"Feed Water Heaters in Thermal Power Plants: Types, Working, and Efficiency G...Infopitaara
A feed water heater is a device used in power plants to preheat water before it enters the boiler. It plays a critical role in improving the overall efficiency of the power generation process, especially in thermal power plants.
🔧 Function of a Feed Water Heater:
It uses steam extracted from the turbine to preheat the feed water.
This reduces the fuel required to convert water into steam in the boiler.
It supports Regenerative Rankine Cycle, increasing plant efficiency.
🔍 Types of Feed Water Heaters:
Open Feed Water Heater (Direct Contact)
Steam and water come into direct contact.
Mixing occurs, and heat is transferred directly.
Common in low-pressure stages.
Closed Feed Water Heater (Surface Type)
Steam and water are separated by tubes.
Heat is transferred through tube walls.
Common in high-pressure systems.
⚙️ Advantages:
Improves thermal efficiency.
Reduces fuel consumption.
Lowers thermal stress on boiler components.
Minimizes corrosion by removing dissolved gases.
We introduce the Gaussian process (GP) modeling module developed within the UQLab software framework. The novel design of the GP-module aims at providing seamless integration of GP modeling into any uncertainty quantification workflow, as well as a standalone surrogate modeling tool. We first briefly present the key mathematical tools on the basis of GP modeling (a.k.a. Kriging), as well as the associated theoretical and computational framework. We then provide an extensive overview of the available features of the software and demonstrate its flexibility and user-friendliness. Finally, we showcase the usage and the performance of the software on several applications borrowed from different fields of engineering. These include a basic surrogate of a well-known analytical benchmark function; a hierarchical Kriging example applied to wind turbine aero-servo-elastic simulations and a more complex geotechnical example that requires a non-stationary, user-defined correlation function. The GP-module, like the rest of the scientific code that is shipped with UQLab, is open source (BSD license).
RICS Membership-(The Royal Institution of Chartered Surveyors).pdfMohamedAbdelkader115
Glad to be one of only 14 members inside Kuwait to hold this credential.
Please check the members inside kuwait from this link:
https://www.rics.org/networking/find-a-member.html?firstname=&lastname=&town=&country=Kuwait&member_grade=(AssocRICS)&expert_witness=&accrediation=&page=1
Lidar for Autonomous Driving, LiDAR Mapping for Driverless Cars.pptxRishavKumar530754
LiDAR-Based System for Autonomous Cars
Autonomous Driving with LiDAR Tech
LiDAR Integration in Self-Driving Cars
Self-Driving Vehicles Using LiDAR
LiDAR Mapping for Driverless Cars
Fluid mechanics is the branch of physics concerned with the mechanics of fluids (liquids, gases, and plasmas) and the forces on them. Originally applied to water (hydromechanics), it found applications in a wide range of disciplines, including mechanical, aerospace, civil, chemical, and biomedical engineering, as well as geophysics, oceanography, meteorology, astrophysics, and biology.
It can be divided into fluid statics, the study of various fluids at rest, and fluid dynamics.
Fluid statics, also known as hydrostatics, is the study of fluids at rest, specifically when there's no relative motion between fluid particles. It focuses on the conditions under which fluids are in stable equilibrium and doesn't involve fluid motion.
Fluid kinematics is the branch of fluid mechanics that focuses on describing and analyzing the motion of fluids, such as liquids and gases, without considering the forces that cause the motion. It deals with the geometrical and temporal aspects of fluid flow, including velocity and acceleration. Fluid dynamics, on the other hand, considers the forces acting on the fluid.
Fluid dynamics is the study of the effect of forces on fluid motion. It is a branch of continuum mechanics, a subject which models matter without using the information that it is made out of atoms; that is, it models matter from a macroscopic viewpoint rather than from microscopic.
Fluid mechanics, especially fluid dynamics, is an active field of research, typically mathematically complex. Many problems are partly or wholly unsolved and are best addressed by numerical methods, typically using computers. A modern discipline, called computational fluid dynamics (CFD), is devoted to this approach. Particle image velocimetry, an experimental method for visualizing and analyzing fluid flow, also takes advantage of the highly visual nature of fluid flow.
Fundamentally, every fluid mechanical system is assumed to obey the basic laws :
Conservation of mass
Conservation of energy
Conservation of momentum
The continuum assumption
For example, the assumption that mass is conserved means that for any fixed control volume (for example, a spherical volume)—enclosed by a control surface—the rate of change of the mass contained in that volume is equal to the rate at which mass is passing through the surface from outside to inside, minus the rate at which mass is passing from inside to outside. This can be expressed as an equation in integral form over the control volume.
The continuum assumption is an idealization of continuum mechanics under which fluids can be treated as continuous, even though, on a microscopic scale, they are composed of molecules. Under the continuum assumption, macroscopic (observed/measurable) properties such as density, pressure, temperature, and bulk velocity are taken to be well-defined at "infinitesimal" volume elements—small in comparison to the characteristic length scale of the system, but large in comparison to molecular length scale
its all about Artificial Intelligence(Ai) and Machine Learning and not on advanced level you can study before the exam or can check for some information on Ai for project
ADVXAI IN MALWARE ANALYSIS FRAMEWORK: BALANCING EXPLAINABILITY WITH SECURITYijscai
With the increased use of Artificial Intelligence (AI) in malware analysis there is also an increased need to
understand the decisions models make when identifying malicious artifacts. Explainable AI (XAI) becomes
the answer to interpreting the decision-making process that AI malware analysis models use to determine
malicious benign samples to gain trust that in a production environment, the system is able to catch
malware. With any cyber innovation brings a new set of challenges and literature soon came out about XAI
as a new attack vector. Adversarial XAI (AdvXAI) is a relatively new concept but with AI applications in
many sectors, it is crucial to quickly respond to the attack surface that it creates. This paper seeks to
conceptualize a theoretical framework focused on addressing AdvXAI in malware analysis in an effort to
balance explainability with security. Following this framework, designing a machine with an AI malware
detection and analysis model will ensure that it can effectively analyze malware, explain how it came to its
decision, and be built securely to avoid adversarial attacks and manipulations. The framework focuses on
choosing malware datasets to train the model, choosing the AI model, choosing an XAI technique,
implementing AdvXAI defensive measures, and continually evaluating the model. This framework will
significantly contribute to automated malware detection and XAI efforts allowing for secure systems that
are resilient to adversarial attacks.
2. Chapter 5(5.1 to 5.3)
Relational Model: Relational Model Concepts,
Relational Model Constraints and relational database schemas,
Update operations,
Transactions, and
Dealing with constraint violations.
Chapter 8(8.1 to 8.5)
Relational Algebra: Unary and Binary relational operations,
additional relational operations (aggregate, grouping, etc.)
Examples of Queries in relational algebra.
Chapter 9(9.1 to 9.2)
Mapping Conceptual Design into a Logical Design:
Relational Database Design using ER-to-Relational mapping
3. 5.1 Relational Model Concepts
• The relational model is a way of structuring and querying data in a
database
• The relational model represents the database as a collection of
relations.
• Informally, each relation resembles a table of values or, to some
extent, a flat file of records .
• A relation is thought of as a table of values, each row in the table
represents a collection of related data values.
• A row represents a fact that typically corresponds to a real-world
entity or relationship.
• The table name and column names are used to help to interpret
the meaning of the values in each row.
• In the formal relational model terminology, a row a tuple, a
column header an attribute, and the table a relation.
• The data type describing the types of values that can appear in
each column is represented by a domain of possible values.
4. 5.1.1 Domains, Attributes, Tuples, and Relations
• A domain D is a set of atomic values.
• By atomic means each value in the domain is indivisible in formal
relational model.
• A common method of specifying a domain is to specify a data type
from which the data values forming the domain are drawn.
Some examples of domains follow:
• USA_phone_number: string of digits of length ten
• SSN: string of digits of length nine
• Name: string of characters beginning with an uppercase letter
• GPA: a real number between 0.0 and 4.0
• Gender: a member of the set { female, male }
• Dept_Code: a member of the set { CMPS, MATH, ENGL, PHYS,
PSYC, ... }
5. • A relation schema R, denoted by R(A1, A2, … , An), is
made up of a relation name R and a list of attributes, A1,
A2, … , An.
• Attribute: Ai is the name of a role played by some domain D
in the relation schema R.
• D is called the domain of Ai and is denoted by dom(Ai).
• Tuple: A tuple is a mapping from attributes to values drawn
from the respective domains of those attributes.
• A tuple is intended to describe some entity (or relationship
between entities) in the miniworld.
6. • R is called the name of this relation.
• The degree (or arity) of a relation is the number of attributes n of
its relation schema.
• A relation of degree seven, which stores information about
university students, would contain seven attributes describing each
student as follows: STUDENT(Name, Ssn, Home_phone,
Address, Office_phone, Age, Gpa)
• Relational Database: A collection of relations, each one consistent
with its specified relational schema.
• A relation (or relation state) r of the relation schema R(A1, A2, …
, An), also denoted by r(R), is a set of n-tuples r = {t1, t2, … , tm}.
Each n-tuple t is an ordered list of n values t =<v1,v2….vn>.
11. 5.2 Relational Model Constraints and Relational Database Schemas
Relational Model Constraints on databases can generally be divided into
three main categories:
1. Constraints that are inherent in the data model known as inherent
model-based constraints or implicit constraints.
2. Constraints that can be directly expressed in the schemas of the data
model, typically by specifying them in the DDL known as schema-
based constraints or explicit constraints.
3. Constraints that cannot be directly expressed in the schemas of the
data model, and hence must be expressed and enforced by the
application programs or in some other way known as application-based
or semantic constraints or business rules.
12. The schema-based constraints include domain constraints, key
constraints, constraints on NULLs, entity integrity constraints, and
referential integrity constraints.
14. 5.2.4 Entity Integrity, Referential Integrity, and Foreign Keys
Entity Integrity:
Entity Integrity ensures that each table has a primary key and that the values in this
primary key are unique and not null. This guarantees that each record (tuple) in the
table can be uniquely identified.
Primary Key: A set of one or more columns whose combined values uniquely
identify each row in the table. A table can have only one primary key, which
enforces both uniqueness and non-nullability on its columns.
15. A foreign key :
Is a column or a set of columns in a relational database table that
provides a link between data in two tables. It acts as a cross-
reference between tables by referencing the primary key of another
table, thereby ensuring referential integrity.
16. Referential Integrity
Referential Integrity ensures that a foreign key value always
refers to an existing, valid row in another table. This maintains
the logical consistency between related tables.
17. In the EMPLOYEE relation,
the attribute Dno refers to the
department for which an
employee works; hence, it is
designated Dno to be a
foreign key of EMPLOYEE
referencing the
DEPARTMENT relation
In this definition, EMPLOYEE is
called the referencing relation and
DEPARTMENT is the referenced
relation
19. 5.2.5 Other Types of Constraints
The salary of an employee should not exceed the salary of the
employee’s supervisor and the maximum number of hours an
employee can work on all projects per week is 56.
Such constraints can be specified and enforced within the
application programs that update the database, or by using a
general-purpose constraint specification language. Sometimes
called as Semantic Integrity constraint.
23. The Delete Operation :
The Delete operation can violate only referential integrity.
This occurs if the tuple being deleted is referenced by foreign keys from
other tuples in the database.
To specify deletion, a condition on the attributes of the relation selects
the tuple (or tuples) to be deleted
27. 5.3.4 The Transaction Concept
A database application program running against a relational
database typically executes one or more transactions.
A transaction is an executing program that includes some
database operations, such as reading from the database, or
applying insertions, deletions, or updates to the database.
28. Chapter 8(8.1 to 8.5)
The basic set of operations for the formal relational model is the
relational algebra. These operations enable a user to specify basic
retrieval requests as relational algebra expressions.
8.1 Unary Relational Operations: SELECT and PROJECT
31. For example, to select the tuples for all employees who either work in
department 4 and make over $25,000 per year, or work in department
5 and make over $30,000, we can specify the following SELECT
operation:
σ(Dno=4 AND Salary>25000) OR (Dno=5 AND Salary>30000)(EMPLOYEE)
34. Results of a sequence of operations.
(a) πFname, Lname, Salary
(σDno=5(EMPLOYEE)).
35. To rename the attributes in a relation, we simply list the new attribute
names in parentheses, as in the following example:
TEMP ← σDno=5(EMPLOYEE)
R(First_name, Last_name, Salary) ← πFname, Lname, Salary(TEMP)
These two operations are illustrated in Figure 8.2(b).
37. 8.2 Relational Algebra Operations from Set Theory
8.2.1 The UNION, INTERSECTION, and MINUS
Operations