Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
The document discusses data modeling, which involves creating a conceptual model of the data required for an information system. There are three types of data models - conceptual, logical, and physical. A conceptual data model describes what the system contains, a logical model describes how the system will be implemented regardless of the database, and a physical model describes the implementation using a specific database. Common elements of a data model include entities, attributes, and relationships. Data modeling is used to standardize and communicate an organization's data requirements and establish business rules.
This document provides an overview of multinomial logistic regression. It discusses how multinomial logistic regression is used when the dependent variable has more than two nominal categories. An example is presented where voting behavior is predicted based on age, gender, economic beliefs, and religious beliefs, with the dependent variable having four categories for different candidates. The document walks through setting up and interpreting the results of a multinomial logistic regression analysis in SPSS for this example. Key results shown include the regression coefficients, odds ratios, goodness of fit statistics, and classification accuracy for each category of the dependent variable.
What is Data ?
What is Information?
Data Models, Schema and Instances
Components of Database System
What is DBMS ?
Database Languages
Applications of DBMS
Introduction to Databases
Fundamentals of Data Modeling and Database Design
Database Normalization
Types of keys in database management system
Distributed Database
Normalization is a process used to organize data in a database. It involves breaking tables into smaller, more manageable pieces to reduce data redundancy and improve data integrity. There are several normal forms including 1NF, 2NF, 3NF, BCNF, 4NF and 5NF. The document provides examples of tables and how they can be decomposed into different normal forms to eliminate anomalies and redundancy through the creation of additional tables and establishing primary keys.
The document discusses database management systems and data modeling. It begins by defining key terms like data, databases, database management systems, and data models. It then provides a brief history of database development from the 1960s to the 1980s. The rest of the document discusses database concepts in more detail, including components of a DBMS, types of database users, database administration responsibilities, data modeling techniques, and the evolution of different data models.
The document discusses Gestalt theory and its principles of visual perception that are relevant for advertising, including similarity, proximity, closure, and continuity. It provides examples of how each principle is applied in logos, advertisements, and web designs. For similarity, items that are similar in size, shape, or color tend to be grouped together. Proximity refers to objects closer together being perceived as more related. Continuity means following the smoothest line through a group of objects. And closure is the tendency to see complete images even when not all details are visible.
The document discusses the classification and organization of hotel functional areas and departments. It outlines revenue-generating departments like front office, food and beverage, versus support departments like housekeeping and accounting. Front-of-house areas involve guest interaction while back-of-house areas have less guest contact. The major hotel divisions and the roles of the rooms division manager and front office and housekeeping departments are described in detail.
The document provides an overview of databases and their advantages over traditional file systems. It discusses key database concepts like data hierarchy, entities and attributes, database models, and components. The main points are:
- Databases organize related data centrally for efficient data sharing and management, avoiding data duplication found in file systems.
- Key concepts include data hierarchy, database components, architecture with three logical levels, and entity-attribute modeling.
- Popular database models include hierarchical, network, and relational models, with relational being most common today.
- Database languages like DDL and DML manipulate and query the database, while the data dictionary documents the stored data.
A data model is a set of concepts that define the structure of data in a database. The three main types of data models are the hierarchical model, network model, and relational model. The hierarchical model uses a tree structure with parent-child relationships, while the network model allows many-to-many relationships but is more complex. The relational model - which underlies most modern databases - uses tables with rows and columns to represent data, and relationships are represented by values in columns.
The document provides an overview of databases and database design. It defines what a database is, what databases do, and the components of database systems and applications. It discusses the database design process, including identifying fields, tables, keys, and relationships between tables. The document also covers database modeling techniques, normalization to eliminate redundant or inefficient data storage, and functional dependencies as constraints on attribute values.
An Introduction to Architecture of Object Oriented Database Management System and how it differs from RDBMS means Relational Database Management System
The document discusses dimensional modeling and data warehousing. It describes how dimensional models are designed for understandability and ease of reporting rather than updates. Key aspects include facts and dimensions, with facts being numeric measures and dimensions providing context. Slowly changing dimensions are also covered, with types 1-3 handling changes to dimension attribute values over time.
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.
This document provides an overview of data modeling concepts. It discusses the importance of data modeling, the basic building blocks of data models including entities, attributes, and relationships. It also covers different types of data models such as conceptual, logical, and physical models. The document discusses relational and non-relational data models as well as emerging models like object-oriented, XML, and big data models. Business rules and their role in database design are also summarized.
The document introduces databases and database management systems (DBMS). It discusses that a DBMS is software that allows users to create, access, and manage data and databases. A DBMS is made up of four main components: users, a database, database applications, and the DBMS itself. The DBMS controls access to the database and enforces rules like security and data integrity. It also discusses some advantages of using a DBMS like improved data sharing and consistency.
The document discusses different database models including hierarchical, network, relational, entity-relationship, object-oriented, object-relational, and semi-structured models. It provides details on the characteristics, structures, advantages and disadvantages of each model. It also includes examples and diagrams to illustrate concepts like hierarchical structure, network structure, relational schema, entity relationship diagrams, object oriented diagrams, and XML schema. The document appears to be teaching materials for a database management course that provides an overview of various database models.
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.
This document provides an overview of data warehousing. It defines data warehousing as collecting data from multiple sources into a central repository for analysis and decision making. The document outlines the history of data warehousing and describes its key characteristics like being subject-oriented, integrated, and time-variant. It also discusses the architecture of a data warehouse including sources, transformation, storage, and reporting layers. The document compares data warehousing to traditional DBMS and explains how data warehouses are better suited for analysis versus transaction processing.
This document provides an overview of data modeling, including definitions of key concepts like data models and data modeling. It describes the evolution of popular data models from hierarchical to network to relational to entity-relationship to object-oriented models. For each model, it outlines the basic concepts, advantages, and disadvantages. The document emphasizes that newer data models aimed to address shortcomings of previous approaches and capture real-world data and relationships.
Data Warehouse – Introduction, characteristics, architecture, scheme and modelling, Differences between operational database systems and data warehouse.
The document compares file systems and database management systems (DBMS) for storing a company's 500GB of employee, department, product, and sales data. It notes several drawbacks of using a file system, including data redundancy, integrity issues, restricted concurrent access, and lack of flexibility. It then outlines key advantages of using a DBMS instead, such as data sharing, enforcement of security and integrity, reduction of redundancy, and support for concurrent access and crash recovery.
This document provides an overview of database management systems and related concepts. It discusses data hierarchy, traditional file processing, the database approach to data management, features and capabilities of database management systems, database schemas, components of database management systems, common data models including hierarchical, network, and relational models, and the process of data normalization.
This document provides an introduction to database concepts. It discusses the advantages of a database system compared to file processing, including reduced data redundancy, controlled inconsistency, shared data, standardized data, secured data, and integrated data. It also describes three levels of abstraction in a database - the physical level, conceptual level, and external or view level. Additionally, it covers database models including the relational, network, and hierarchical models as well as key database concepts such as primary keys, foreign keys, candidate keys, and alternate keys.
A star schema is a data warehouse design that represents multidimensional data with one or more fact tables referencing any number of dimension tables. It consists of a central fact table surrounded by dimension tables that describe the facts. To design a star schema, business processes are identified, measures or facts are selected, dimensions for the facts are determined, dimension columns are listed, and the lowest level of summary in the fact table is defined. Star schemas have advantages like simpler queries, simplified business reporting, query performance gains, and fast aggregations. The ERDPlus tool can be used to implement star schemas.
This document discusses different data models used in database management systems including record-based, relational, network, hierarchical, and entity-relationship models. It provides details on each model such as how data is organized. A record-based model uses fixed-length records and fields. The relational model organizes data into tables with rows and columns. The network model links entities through multiple paths in a graph structure. The hierarchical model arranges data in a tree structure. Finally, the entity-relationship model views the real world as entities and relationships between entities.
This document discusses the object oriented data model (OODM). It defines the OODM and describes how it accommodates relationships like aggregation, generalization, and particularization. The OODM provides four types of data operations: defining schemas, creating databases, retrieving objects, and expanding objects. Key features of the OODM include object identity, abstraction, encapsulation, data hiding, inheritance, and classes. The document concludes that a prototype of the OODM has been implemented to model application domains and that menus can be created, accessed, and updated like data from the database schema in the OODM.
This document defines database and DBMS, describes their advantages over file-based systems like data independence and integrity. It explains database system components and architecture including physical and logical data models. Key aspects covered are data definition language to create schemas, data manipulation language to query data, and transaction management to handle concurrent access and recovery. It also provides a brief history of database systems and discusses database users and the critical role of database administrators.
The document discusses different data models including hierarchical, network, and relational models. The hierarchical model uses a tree structure with parent-child relationships, while the network model allows a child to have multiple parents. The relational model uses tables with rows and columns to represent relationships between data through the use of primary and foreign keys. Each model has advantages like ease of use, but also disadvantages such as complexity or inability to represent certain relationships. The relational model is currently the most widely used.
The document provides an overview of databases and their advantages over traditional file systems. It discusses key database concepts like data hierarchy, entities and attributes, database models, and components. The main points are:
- Databases organize related data centrally for efficient data sharing and management, avoiding data duplication found in file systems.
- Key concepts include data hierarchy, database components, architecture with three logical levels, and entity-attribute modeling.
- Popular database models include hierarchical, network, and relational models, with relational being most common today.
- Database languages like DDL and DML manipulate and query the database, while the data dictionary documents the stored data.
A data model is a set of concepts that define the structure of data in a database. The three main types of data models are the hierarchical model, network model, and relational model. The hierarchical model uses a tree structure with parent-child relationships, while the network model allows many-to-many relationships but is more complex. The relational model - which underlies most modern databases - uses tables with rows and columns to represent data, and relationships are represented by values in columns.
The document provides an overview of databases and database design. It defines what a database is, what databases do, and the components of database systems and applications. It discusses the database design process, including identifying fields, tables, keys, and relationships between tables. The document also covers database modeling techniques, normalization to eliminate redundant or inefficient data storage, and functional dependencies as constraints on attribute values.
An Introduction to Architecture of Object Oriented Database Management System and how it differs from RDBMS means Relational Database Management System
The document discusses dimensional modeling and data warehousing. It describes how dimensional models are designed for understandability and ease of reporting rather than updates. Key aspects include facts and dimensions, with facts being numeric measures and dimensions providing context. Slowly changing dimensions are also covered, with types 1-3 handling changes to dimension attribute values over time.
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.
This document provides an overview of data modeling concepts. It discusses the importance of data modeling, the basic building blocks of data models including entities, attributes, and relationships. It also covers different types of data models such as conceptual, logical, and physical models. The document discusses relational and non-relational data models as well as emerging models like object-oriented, XML, and big data models. Business rules and their role in database design are also summarized.
The document introduces databases and database management systems (DBMS). It discusses that a DBMS is software that allows users to create, access, and manage data and databases. A DBMS is made up of four main components: users, a database, database applications, and the DBMS itself. The DBMS controls access to the database and enforces rules like security and data integrity. It also discusses some advantages of using a DBMS like improved data sharing and consistency.
The document discusses different database models including hierarchical, network, relational, entity-relationship, object-oriented, object-relational, and semi-structured models. It provides details on the characteristics, structures, advantages and disadvantages of each model. It also includes examples and diagrams to illustrate concepts like hierarchical structure, network structure, relational schema, entity relationship diagrams, object oriented diagrams, and XML schema. The document appears to be teaching materials for a database management course that provides an overview of various database models.
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.
This document provides an overview of data warehousing. It defines data warehousing as collecting data from multiple sources into a central repository for analysis and decision making. The document outlines the history of data warehousing and describes its key characteristics like being subject-oriented, integrated, and time-variant. It also discusses the architecture of a data warehouse including sources, transformation, storage, and reporting layers. The document compares data warehousing to traditional DBMS and explains how data warehouses are better suited for analysis versus transaction processing.
This document provides an overview of data modeling, including definitions of key concepts like data models and data modeling. It describes the evolution of popular data models from hierarchical to network to relational to entity-relationship to object-oriented models. For each model, it outlines the basic concepts, advantages, and disadvantages. The document emphasizes that newer data models aimed to address shortcomings of previous approaches and capture real-world data and relationships.
Data Warehouse – Introduction, characteristics, architecture, scheme and modelling, Differences between operational database systems and data warehouse.
The document compares file systems and database management systems (DBMS) for storing a company's 500GB of employee, department, product, and sales data. It notes several drawbacks of using a file system, including data redundancy, integrity issues, restricted concurrent access, and lack of flexibility. It then outlines key advantages of using a DBMS instead, such as data sharing, enforcement of security and integrity, reduction of redundancy, and support for concurrent access and crash recovery.
This document provides an overview of database management systems and related concepts. It discusses data hierarchy, traditional file processing, the database approach to data management, features and capabilities of database management systems, database schemas, components of database management systems, common data models including hierarchical, network, and relational models, and the process of data normalization.
This document provides an introduction to database concepts. It discusses the advantages of a database system compared to file processing, including reduced data redundancy, controlled inconsistency, shared data, standardized data, secured data, and integrated data. It also describes three levels of abstraction in a database - the physical level, conceptual level, and external or view level. Additionally, it covers database models including the relational, network, and hierarchical models as well as key database concepts such as primary keys, foreign keys, candidate keys, and alternate keys.
A star schema is a data warehouse design that represents multidimensional data with one or more fact tables referencing any number of dimension tables. It consists of a central fact table surrounded by dimension tables that describe the facts. To design a star schema, business processes are identified, measures or facts are selected, dimensions for the facts are determined, dimension columns are listed, and the lowest level of summary in the fact table is defined. Star schemas have advantages like simpler queries, simplified business reporting, query performance gains, and fast aggregations. The ERDPlus tool can be used to implement star schemas.
This document discusses different data models used in database management systems including record-based, relational, network, hierarchical, and entity-relationship models. It provides details on each model such as how data is organized. A record-based model uses fixed-length records and fields. The relational model organizes data into tables with rows and columns. The network model links entities through multiple paths in a graph structure. The hierarchical model arranges data in a tree structure. Finally, the entity-relationship model views the real world as entities and relationships between entities.
This document discusses the object oriented data model (OODM). It defines the OODM and describes how it accommodates relationships like aggregation, generalization, and particularization. The OODM provides four types of data operations: defining schemas, creating databases, retrieving objects, and expanding objects. Key features of the OODM include object identity, abstraction, encapsulation, data hiding, inheritance, and classes. The document concludes that a prototype of the OODM has been implemented to model application domains and that menus can be created, accessed, and updated like data from the database schema in the OODM.
This document defines database and DBMS, describes their advantages over file-based systems like data independence and integrity. It explains database system components and architecture including physical and logical data models. Key aspects covered are data definition language to create schemas, data manipulation language to query data, and transaction management to handle concurrent access and recovery. It also provides a brief history of database systems and discusses database users and the critical role of database administrators.
The document discusses different data models including hierarchical, network, and relational models. The hierarchical model uses a tree structure with parent-child relationships, while the network model allows a child to have multiple parents. The relational model uses tables with rows and columns to represent relationships between data through the use of primary and foreign keys. Each model has advantages like ease of use, but also disadvantages such as complexity or inability to represent certain relationships. The relational model is currently the most widely used.
The document discusses several database management system (DBMS) models:
- Relational, network, hierarchical, object-oriented, and object-relational models are described as the main DBMS models.
- Key aspects of each model are covered, including data structure, relationships, querying capabilities, and advantages/disadvantages.
- Examples are provided for the relational model in the form of a sample table and for the hierarchical model in terms of its data structure and relationships.
- Overall the document provides a high-level overview and comparison of the main DBMS models.
https://www.learntek.org/blog/types-of-databases/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
0001 introduction to database management systemJugdambay S
This document provides an introduction to database management systems and relational database management systems from Algae Services. It defines what a database and DBMS are, describes common database models like hierarchical, network and relational, and discusses key aspects of RDBMS like data representation in tables and relationships between tables. The document also provides a brief history of database development and examples of DBMS and RDBMS systems.
Chapter-2 Database System Concepts and ArchitectureKunal Anand
This document provides an overview of database management systems concepts and architecture. It discusses different data models including hierarchical, network, relational, entity-relationship, object-oriented, and object-relational models. It also describes the 3-schema architecture with external, conceptual, and internal schemas and explains components of a DBMS including users, storage and query managers. Finally, it covers database languages like DDL, DML, and interfaces like menu-based, form-based and graphical user interfaces.
The DBMS manages the database, providing an interface between users and applications and the underlying data. It handles data storage and retrieval, concurrency control, security, and other database management functions. Popular DBMS types include relational, hierarchical, network, object-oriented, and NoSQL systems. The relational model, implemented in systems like Oracle and SQL Server, remains dominant despite challenges from newer technologies.
1) The document discusses different database models including hierarchical, network, and relational models. The relational model organizes data into tables and allows relationships between tables.
2) It provides examples of one-to-one, one-to-many, and many-to-many relationships.
3) The relational database management system (RDBMS) is introduced, with Oracle given as an example RDBMS. RDBMSs must satisfy E.F. Codd's 12 rules to be considered fully relational.
Database Models, Client-Server Architecture, Distributed Database and Classif...Rubal Sagwal
Introduction to Data Models
-Hierarchical Model
-Network Model
-Relational Model
-Client/Server Architecture
Introduction to Distributed Database
Classification of DBMS
The document provides an overview of database management systems (DBMS). It discusses the need for DBMS, different database architectures including centralized, client-server and distributed. It also covers data models, ER diagrams, relational models, and SQL. Key advantages of DBMS over file systems include reducing data redundancy, improving data integrity and security, and enabling concurrent access.
1. The document discusses different types of database management systems and data models including DBMS, RDBMS, file systems, and manual systems.
2. It provides brief definitions and examples of each type as well as their advantages and disadvantages.
3. The key database models covered are hierarchical, network, relational, and object-oriented models, with descriptions of their characteristics and how they have evolved over time.
RELATIONAL MODEL OF DATABASES AND OTHER CONCEPTS OF DATABASESEdwinJacob5
The document discusses different database models:
- The relational model was proposed by E.F. Codd and organizes data into tables with rows and columns. Popular relational DBMS include Oracle, SQL Server, and Access.
- The hierarchical model uses a tree structure where each item has a single predecessor and subordinates. It is natural for applications with parent-child relationships.
- The network model is a generalization of the hierarchical model, allowing many-to-many relationships through multiple parent segments connected through graphs.
- Other models discussed include the AI frame model which uses slots to flexibly arrange related information similarly to object-oriented representations.
This document discusses different types of databases: hierarchical, entity-relationship, relational, object-oriented, and network. Hierarchical databases organize data in a tree structure and were the first model created by IBM in 1960. Entity-relationship databases were developed in 1976 and represent things of interest through entities and relationships. Relational databases were first described by Edgar Codd in 1969 and manage data using a structure consistent with predicate logic. Object-oriented databases store information as objects and are used for multimedia or CAD. Network databases form a flexible graph structure and allowed multiple parent-child relationships but became obsolete due to relational databases.
The document discusses the architecture and components of database management systems (DBMS). It describes how DBMS packages have evolved from monolithic to modular client-server systems. It also discusses the three schema architecture comprising the internal, conceptual, and external schemas which enables data independence. The key components of a DBMS include the data definition language, data manipulation language, and various interfaces. DBMSs can be classified based on their data model, number of users, distribution, and purpose.
The document discusses different data models used in database management systems. It describes hierarchical, network, and relational data models. The hierarchical model uses a parent-child structure and pointers to link data. The network model also uses pointers but allows a many-to-many relationship between data unlike the hierarchical one-to-many structure. The relational model stores data in tables and allows flexible querying of relationships between different tables.
The document discusses the three levels of abstraction in a database management system: the internal, conceptual, and external levels. The internal level describes the physical representation and storage of data. The conceptual level defines the logical structure and relationships of data in the database. The external level describes different views of the data that are relevant to users but hides implementation details.
The document provides an overview of open source operating systems and concepts. It defines key terms like software, source code, open source, and free software. It discusses the ideals of open source like sharing goals, work, and results. It provides examples of popular open source software like Linux, Apache, and explains open source licenses and definitions. It also summarizes the history of Linux and compares Linux to Windows.
A database administrator (DBA) is responsible for the design, operation, and management of an organization's database. A DBA requires technical skills to understand hardware and software issues, management skills to plan and coordinate tasks, and diplomatic skills to communicate with users and determine data needs. Key responsibilities of a DBA include planning the database, developing and maintaining the operational database, and ensuring optimal database performance.
The document discusses traditional file systems and database management systems (DBMS). It provides an overview of traditional file systems, including their advantages and limitations. It then discusses DBMS, including its components, advantages like reduced data redundancy and improved data integrity, and limitations such as increased complexity. The document uses examples to illustrate key differences between traditional file systems and DBMS.
This document defines key database concepts such as data, information, tables, fields, records, files, databases, database management systems (DBMS), and database systems. It provides examples and descriptions of each concept. Data is defined as raw unorganized facts, while information is organized data used for decision making. Tables contain records made of individual fields of data. A file is a collection of records, a database contains logically organized data, and a DBMS manages the database and enables users to create and manipulate the stored data.
This paper proposes a shoulder inverse kinematics (IK) technique. Shoulder complex is comprised of the sternum, clavicle, ribs, scapula, humerus, and four joints.
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.
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.
Sorting Order and Stability in Sorting.
Concept of Internal and External Sorting.
Bubble Sort,
Insertion Sort,
Selection Sort,
Quick Sort and
Merge Sort,
Radix Sort, and
Shell Sort,
External Sorting, Time complexity analysis of Sorting Algorithms.
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.
Value Stream Mapping Worskshops for Intelligent Continuous SecurityMarc Hornbeek
This presentation provides detailed guidance and tools for conducting Current State and Future State Value Stream Mapping workshops for Intelligent Continuous Security.
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.
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
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
2. DATA MODELS INTRODUCTION
• A Data Model is a set of concepts that can be used
to describe the structure of data in a database.
• A database model shows the logical structure of a
database, including the relationships and constraints
that determine how data can be stored and
accessed.
• Data Models are used to support the development of
information systems by providing the definition and
format of data to be involved in future systems.
• Data model also gives idea about possible alternatives
to achieve targeted solution.
8/20/2018 2DBMS
3. Types of Data Models
• Hierarchical Model
• Network Model
• Relational Model
8/20/2018 3DBMS
4. Hierarchical Model
• This model is developed by IBM and
North American Rockwell Known as
Information Management System.
• This is the oldest and simplest DBMS
model.
• The model is sorted hierarchical ,either in
top down or bottom up approach of
designing.
• This model uses pointers to navigate
between stored data.
8/20/2018 4DBMS
6. Business Rule
• One parent node can have many child
nodes ,but one child cannot have
more than one parent.
• Relationship is one to many.
8/20/2018 6DBMS
7. Advantages of Hierarchical Model
1. Conceptual simplicity
• Relationship between various level
is logically very simple. Hence
database structure becomes
easier to view.
2. Database Security
• Security is given by DBMS system
itself.
8/20/2018 7DBMS
8. 3. Simple creation ,Updation and Access
• This model is simple to construct
with help of pointers .
• Easy to understand .
• Easy to delete and add records in the
database using pointers .
• This is faster and easy data retrival
through higher level records in tree
structure.
8/20/2018 8DBMS
9. 4. Database Integrity
• There is parent child association
between different levels of
records in files.
• Child record is attached with the
parent record which maintains the
integrity
8/20/2018 9DBMS
10. 5. Efficiency
• This model having good
performance when database
contains large amount of data in
which one record has many
related records like a class
contains many students studying
in it.
8/20/2018 10DBMS
11. Disadvantages of Hierarchical Model
1. Complex Implementation
• Programmers and designers need to
have knowledge of physical data storage
which may be complex.
2. Difficult to manage
• Any change in a location of data needs
change in all application programs that
accesses changes data.
• Data access is restricted by pointer path.
8/20/2018 11DBMS
12. 3. Limitations in implementation
• It is difficult to implement many to
many relationship.
• Query optimization is not possible
or possible up to certain extent.
8/20/2018 12DBMS
13. Network Model
• This model is similar to Hierarchical
model, this model also uses pointers
toward data but there is no need of
parent to child association .
• It uses graph data structure.
• A child can have more than one parent.
• It has one to many or many to many
relationship.
8/20/2018 13DBMS
14. • A relationship between any two record
types is called as a set.
• Data in network model are
represented by collection of records
and relationships among data are
represented by links, which can be
viewed as pointers.
• The records in the database are
organized as collection of arbitrary
groups.
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16. Advantages of Network Model
1. Simple design
• The network model is simple and
easy to design and understand.
2. Ability to handle many types of
relationship
• The network model can handle the
one to many or many to many or
other relationships.
• Hence network model manages
multiuser environment.
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17. 3. Ease of data access
• In a network model, an application can
access a root(parent) record and all the
member records within a SET (child).
• Provide very efficient and high speed
retrieval.
4. Data Integrity
• In a network model, no member can exist
without a parent entity.
• A user must first define the root record and
then the child record.
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18. 5. Data Independence
• In network model, application
programs work independently of the
data.
• Any changes made in the data do not
affect the application programs.
• In a network model ,administrators
offer data creation by DDL and DML.
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19. Disadvantages of Network Model
1. System Complexity
• In a network model ,data are accessed
one record at a time.
• This can increase the complexity of
system for accessing multiple records at
a time.
2. Lack of structural independence
• Any changes made to the database
structure require the application
programs to be modified before it can
access data.
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20. Relational Model
• The Relational Model is first proposed by E.F
Codd.
• This model uses collection of tables to represent
relationships amongst the data.
• In this model ,each database item is viewed as a
record with attributes. A set of records with
similar attributes is called a TABLE. Each table
contains a record of a particular type.
• The database uses Relational model called as
RDBMS .
• A Relational database is a collection of 2-D tables
which consist of rows and columns.
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21. • Relational model is the most popular model and the
most extensively used model.
• In this model the data can be stored in the “tables”
and this storing is called as “relation”, the relations
can be normalized and the normalized relation values
are called atomic values.
• Each row in a relation contains unique value and it is
called as “tuple”, each column contains value from
same domain and it is called as “attribute”.
• Most of the popular commercial DBMS products like
Oracle, Sybase , MySQL, are based on relational
model.
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22. • A particular attribute or combination of
attributes is chosen as a primary key that can be
referred to in other tables, when it’s called a
foreign key.
• The model also accounts for the types of
relationships between those tables, including
one-to-one, one-to-many, and many-to-many
relationships.
• Within the database, tables can be normalized
that make the database flexible, adaptable, and
scalable. When normalized, each piece of data is
atomic, or broken into the smallest useful pieces.
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24. ADVANTAGES OF RELATIONAL MODEL
1. Relational Algebra
• A relational database supports
relational algebra and also relational
operations of the set theory like
union , intersection ,difference ,
cartisen product , relational database
also support select , project , join
and division operations.
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25. 2. Dynamic Views
• In a RDBMS , a view is not a part of
the physical schema , it is always
dynamic.
3. Structured query language (SQL)
• For data access in RDBMS we have
query language SQL .Most of the
database vendors support the SQL
standards.
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26. 4. Excellent data security
• Relational databases support the
concept of user rights, every user is
assigned with some database
permission called as user rights.
• Relational databases are scalable and
provide good support for the
implementation of distributed systems
and other advanced database
systems.
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