The document discusses constraints in the relational model of databases. It covers domain constraints, which require that the value of each attribute in a tuple must be within the defined domain for that attribute. Key constraints are also discussed, which ensure that tuples within a relation are unique. Key constraints can be expressed in data definition language by specifying that no two tuples can have the same combination of values for a subset of attributes called a super key. A super key is a minimal set of attributes where no duplicate values are allowed.
The document is a course syllabus for an Electronics and Communication Engineering program. It outlines the topics, units, and content covered in the course "Electronic Devices and Circuits". The 5 units cover semiconductor physics, junction diodes, transistors, JFETs and MOSFETs, and small signal amplifiers. Key concepts and circuits discussed include diode and transistor characteristics, amplifier configurations, biasing techniques, and frequency response. The syllabus provides textbook and reference materials and allocates contact hours over 40 weeks to the various units.
The document provides details about courses offered in the third semester of B.Tech Information Technology curriculum at Anna University of Technology Chennai for students admitted from 2010-2011 onwards. It lists 6 theory courses and 3 practical labs covering areas like transforms, partial differential equations, object oriented programming, digital principles and systems design, data structures and algorithms, principles of communication, and environmental science. For each theory course, it provides the course title, code, hours, and brief objectives and content outlines. It also lists textbooks and references for further study.
The document discusses domain specific languages (DSLs), including what they are, different types of DSLs, an example DSL for configuring a framework, and how DSLs relate to lambda calculus and modeling. It also covers benefits and drawbacks of DSLs and technologies for developing them like the XMF family and language factories.
The document summarizes recursive definitions and algorithms. It provides examples of recursively defined sequences like the Fibonacci sequence. It also describes recursively defined functions, including the Ackermann function. Algorithms for sorting and searching like selection sort and binary search are presented recursively. The benefits of recursive algorithms are their concise definition and mapping to inherent recursive structures.
The document discusses various topics related to analyzing algorithms, including:
i. Analysis of running time and using recurrence equations to predict how long recursive algorithms take on different input sizes.
ii. Iteration, induction, and recursion as fundamental concepts in data structures and algorithms. Recursive programs can sometimes be simpler than iterative programs.
iii. Proving properties of programs formally or informally, such as proving statements are true for each iteration of a loop or recursive call of a function. This is often done using induction.
The document is a lecture on the SQL language given at Trinity College. It introduces SQL as the standard language for relational databases and describes it as declarative, meaning the user specifies the desired results without specifying how to retrieve them. It then discusses some basic SQL statements like CREATE TABLE to define the structure of tables and import data types for attributes.
This document provides an overview of basic SQL concepts including attribute ambiguity, aliasing, tuple variables, missing WHERE clauses, selecting all attributes with *, tables as sets, and set operations. It discusses how SQL allows duplicate tuples, qualifying attribute names, and retrieving distinct vs all values. Examples are provided for common SQL queries. The goal is to help students understand how to create and manipulate relational databases using SQL.
The document discusses the relational model of data, which was proposed by Edgar F. Codd in the 1970s. It presents the key concepts of the relational model including relation schemes, relation instances, keys, foreign keys, and referential integrity constraints. It also introduces relational algebra operations such as select, project, join, and set operations that allow querying of relational databases and provides examples to illustrate how they work. Finally, it discusses how relational algebra provides the foundation for query optimization and execution in relational database management systems.
The document is a course syllabus for an Electronics and Communication Engineering program. It outlines the topics, units, and content covered in the course "Electronic Devices and Circuits". The 5 units cover semiconductor physics, junction diodes, transistors, JFETs and MOSFETs, and small signal amplifiers. Key concepts and circuits discussed include diode and transistor characteristics, amplifier configurations, biasing techniques, and frequency response. The syllabus provides textbook and reference materials and allocates contact hours over 40 weeks to the various units.
The document provides details about courses offered in the third semester of B.Tech Information Technology curriculum at Anna University of Technology Chennai for students admitted from 2010-2011 onwards. It lists 6 theory courses and 3 practical labs covering areas like transforms, partial differential equations, object oriented programming, digital principles and systems design, data structures and algorithms, principles of communication, and environmental science. For each theory course, it provides the course title, code, hours, and brief objectives and content outlines. It also lists textbooks and references for further study.
The document discusses domain specific languages (DSLs), including what they are, different types of DSLs, an example DSL for configuring a framework, and how DSLs relate to lambda calculus and modeling. It also covers benefits and drawbacks of DSLs and technologies for developing them like the XMF family and language factories.
The document summarizes recursive definitions and algorithms. It provides examples of recursively defined sequences like the Fibonacci sequence. It also describes recursively defined functions, including the Ackermann function. Algorithms for sorting and searching like selection sort and binary search are presented recursively. The benefits of recursive algorithms are their concise definition and mapping to inherent recursive structures.
The document discusses various topics related to analyzing algorithms, including:
i. Analysis of running time and using recurrence equations to predict how long recursive algorithms take on different input sizes.
ii. Iteration, induction, and recursion as fundamental concepts in data structures and algorithms. Recursive programs can sometimes be simpler than iterative programs.
iii. Proving properties of programs formally or informally, such as proving statements are true for each iteration of a loop or recursive call of a function. This is often done using induction.
The document is a lecture on the SQL language given at Trinity College. It introduces SQL as the standard language for relational databases and describes it as declarative, meaning the user specifies the desired results without specifying how to retrieve them. It then discusses some basic SQL statements like CREATE TABLE to define the structure of tables and import data types for attributes.
This document provides an overview of basic SQL concepts including attribute ambiguity, aliasing, tuple variables, missing WHERE clauses, selecting all attributes with *, tables as sets, and set operations. It discusses how SQL allows duplicate tuples, qualifying attribute names, and retrieving distinct vs all values. Examples are provided for common SQL queries. The goal is to help students understand how to create and manipulate relational databases using SQL.
The document discusses the relational model of data, which was proposed by Edgar F. Codd in the 1970s. It presents the key concepts of the relational model including relation schemes, relation instances, keys, foreign keys, and referential integrity constraints. It also introduces relational algebra operations such as select, project, join, and set operations that allow querying of relational databases and provides examples to illustrate how they work. Finally, it discusses how relational algebra provides the foundation for query optimization and execution in relational database management systems.
This document discusses supervised learning techniques for text classification. It describes how supervised learning involves assigning objects like documents to predefined categories or classes based on labeled examples. For text classification, some key challenges include dealing with large feature spaces, data scarcity issues, and hierarchical relationships between classes. The document outlines several classification techniques used for text like nearest neighbor classifiers, Bayesian classifiers, support vector machines, and rule induction. It also discusses evaluating classifier accuracy and issues like feature selection.
The document provides the B.Tech. CS syllabus as submitted to the 15th Academic Council. It includes 6 subjects:
1. Electronic Devices & Circuits
2. Data Structures & Algorithms
3. Digital Electronics
4. Linux and Shell Programming
5. Object Oriented Programming
6. Advanced Engineering Mathematics
For each subject, it lists the units of study, topics covered in each unit, recommended textbooks, class/branch, schedule, and exam details. The syllabus covers core concepts in electronics, programming, data structures, operating systems, and advanced mathematics for computer science students.
The document discusses relational algebra and relational database schemas. It defines key concepts such as relation schemas, relational database schemas, and instances of schemas. Examples of banking and university schemas are provided. Relational algebra is introduced as a procedural language for querying relational databases using operations like select, project, join etc. Finally, the document discusses the difference between declarative and procedural query languages and provides examples.
The Data Distribution Service (DDS) is a standard for ubiquitous, interoperable, secure, platform independent, and real-time data sharing across network connected devices. DDS is today used in a large class of applications, such as, Power Generation, Large Scale SCADA, Air Traffic Control and Management, Smart Cities, Smart Grids, Vehicles, Medical Devices, Simulation, Aerospace, Defense and Financial Trading.
Differently from traditional message-centric technologies, DDS is data-centric – the accent is on seamless (user-defined) data sharing as opposed to message delivery. Therefore, when embracing DDS and data-centricity, data modeling becomes a key step in the design of a distributed system.
This webcast will (1) explain the role and scope of data modeling in DDS, (2) introduce the techniques at the foundation of effective and extensible Data Models, and (3) summarize the most common DDS Data Modeling Idioms.
This document summarizes a seminar presentation on the graph database Neo4j. It introduces trends in big data like increasing data size and connectedness. It also discusses NoSQL databases and describes different types including column, document, key-value, and graph databases. The document focuses on graph databases, provides examples of graph-structured data, and gives an overview of the graph database Neo4j, its data model, query language Cypher, and pros and cons.
Web search engines index documents and respond to keyword queries by returning a ranked list of relevant documents. Early search engines like Archie allowed searching by title across FTP sites. Modern search engines preprocess documents by removing tags and stopwords, stemming words, and building inverted indexes to map terms to documents for fast retrieval. They evaluate search results using metrics like precision and recall compared to human judgments of relevance.
This document discusses the relationship between ontology-based data access (OBDA) and relational mapping techniques. It describes how OBDA uses mappings to specify the relationship between data in a source and the vocabulary of an ontology, addressing the abstraction gap. Query answering in OBDA works by rewriting queries over the ontology into queries over data sources using the mappings. The document also discusses how object-relational mapping techniques address similar issues to OBDA in synchronizing object-oriented and relational representations.
Smart Metrics for High Performance Material Designaimsnist
This document discusses smart metrics for high-performance material design using density functional theory (DFT), classical force fields (FF), and machine learning (ML). It provides an overview of the JARVIS database and tools containing over 35,000 materials and classical properties calculated using DFT, FF, and ML methods. Metrics discussed include formation energy, exfoliation energy, elastic constants, surface energy, vacancy energy, grain boundary energy, bandgaps, and other electronic and optical properties important for applications like solar cells. ML models are developed to predict these properties with mean absolute errors within chemical accuracy compared to DFT benchmarks.
Smart Metrics for High Performance Material DesignKAMAL CHOUDHARY
The document discusses smart metrics for high-performance material design using density functional theory (DFT), classical force fields (FF), and machine learning (ML). It outlines the development of the JARVIS database and tools containing over 35,000 materials with over 1 million computed properties using various computational methods. Metrics discussed include formation energy, exfoliation energy, elastic constants, surface energy, vacancy formation energy, grain boundary energy, bandgaps, dielectric functions, and other electronic, magnetic, thermal, and mechanical properties. ML models are developed to predict these properties with mean absolute errors often better than DFT. The database aims to accelerate materials discovery and design using high-throughput computations and data-driven approaches.
This document provides an overview of the relational data model and relational database constraints. It defines key concepts like relations, tuples, attributes, domains and schemas. It describes the different integrity constraints for relational databases including key, entity and referential integrity constraints. It also discusses update operations and how to deal with constraint violations that may occur.
This document provides information about the LEET entrance exam for undergraduate education technology programs. It outlines the eligibility criteria, which requires a minimum of 60% marks in a three-year diploma program in fields like computer engineering or electronics. The exam format is described, with 100 multiple choice questions in sections on mathematics, physics, chemistry, computer awareness, and logical reasoning. Detailed syllabi are provided for the topics covered in each section. The coaching institute, Competition Gurukul, is described as providing qualified faculty and a focus on concepts, weaknesses, and mock tests to help students succeed on the LEET exam with minimal effort.
This document discusses supervised learning techniques for text classification. It describes how supervised learning involves assigning objects like documents to predefined categories or classes based on labeled examples. For text classification, some key challenges include dealing with large feature spaces, data scarcity issues, and hierarchical relationships between classes. The document outlines several classification techniques used for text like nearest neighbor classifiers, Bayesian classifiers, support vector machines, and rule induction. It also discusses evaluating classifier accuracy and issues like feature selection.
The document provides the B.Tech. CS syllabus as submitted to the 15th Academic Council. It includes 6 subjects:
1. Electronic Devices & Circuits
2. Data Structures & Algorithms
3. Digital Electronics
4. Linux and Shell Programming
5. Object Oriented Programming
6. Advanced Engineering Mathematics
For each subject, it lists the units of study, topics covered in each unit, recommended textbooks, class/branch, schedule, and exam details. The syllabus covers core concepts in electronics, programming, data structures, operating systems, and advanced mathematics for computer science students.
The document discusses relational algebra and relational database schemas. It defines key concepts such as relation schemas, relational database schemas, and instances of schemas. Examples of banking and university schemas are provided. Relational algebra is introduced as a procedural language for querying relational databases using operations like select, project, join etc. Finally, the document discusses the difference between declarative and procedural query languages and provides examples.
The Data Distribution Service (DDS) is a standard for ubiquitous, interoperable, secure, platform independent, and real-time data sharing across network connected devices. DDS is today used in a large class of applications, such as, Power Generation, Large Scale SCADA, Air Traffic Control and Management, Smart Cities, Smart Grids, Vehicles, Medical Devices, Simulation, Aerospace, Defense and Financial Trading.
Differently from traditional message-centric technologies, DDS is data-centric – the accent is on seamless (user-defined) data sharing as opposed to message delivery. Therefore, when embracing DDS and data-centricity, data modeling becomes a key step in the design of a distributed system.
This webcast will (1) explain the role and scope of data modeling in DDS, (2) introduce the techniques at the foundation of effective and extensible Data Models, and (3) summarize the most common DDS Data Modeling Idioms.
This document summarizes a seminar presentation on the graph database Neo4j. It introduces trends in big data like increasing data size and connectedness. It also discusses NoSQL databases and describes different types including column, document, key-value, and graph databases. The document focuses on graph databases, provides examples of graph-structured data, and gives an overview of the graph database Neo4j, its data model, query language Cypher, and pros and cons.
Web search engines index documents and respond to keyword queries by returning a ranked list of relevant documents. Early search engines like Archie allowed searching by title across FTP sites. Modern search engines preprocess documents by removing tags and stopwords, stemming words, and building inverted indexes to map terms to documents for fast retrieval. They evaluate search results using metrics like precision and recall compared to human judgments of relevance.
This document discusses the relationship between ontology-based data access (OBDA) and relational mapping techniques. It describes how OBDA uses mappings to specify the relationship between data in a source and the vocabulary of an ontology, addressing the abstraction gap. Query answering in OBDA works by rewriting queries over the ontology into queries over data sources using the mappings. The document also discusses how object-relational mapping techniques address similar issues to OBDA in synchronizing object-oriented and relational representations.
Smart Metrics for High Performance Material Designaimsnist
This document discusses smart metrics for high-performance material design using density functional theory (DFT), classical force fields (FF), and machine learning (ML). It provides an overview of the JARVIS database and tools containing over 35,000 materials and classical properties calculated using DFT, FF, and ML methods. Metrics discussed include formation energy, exfoliation energy, elastic constants, surface energy, vacancy energy, grain boundary energy, bandgaps, and other electronic and optical properties important for applications like solar cells. ML models are developed to predict these properties with mean absolute errors within chemical accuracy compared to DFT benchmarks.
Smart Metrics for High Performance Material DesignKAMAL CHOUDHARY
The document discusses smart metrics for high-performance material design using density functional theory (DFT), classical force fields (FF), and machine learning (ML). It outlines the development of the JARVIS database and tools containing over 35,000 materials with over 1 million computed properties using various computational methods. Metrics discussed include formation energy, exfoliation energy, elastic constants, surface energy, vacancy formation energy, grain boundary energy, bandgaps, dielectric functions, and other electronic, magnetic, thermal, and mechanical properties. ML models are developed to predict these properties with mean absolute errors often better than DFT. The database aims to accelerate materials discovery and design using high-throughput computations and data-driven approaches.
This document provides an overview of the relational data model and relational database constraints. It defines key concepts like relations, tuples, attributes, domains and schemas. It describes the different integrity constraints for relational databases including key, entity and referential integrity constraints. It also discusses update operations and how to deal with constraint violations that may occur.
This document provides information about the LEET entrance exam for undergraduate education technology programs. It outlines the eligibility criteria, which requires a minimum of 60% marks in a three-year diploma program in fields like computer engineering or electronics. The exam format is described, with 100 multiple choice questions in sections on mathematics, physics, chemistry, computer awareness, and logical reasoning. Detailed syllabi are provided for the topics covered in each section. The coaching institute, Competition Gurukul, is described as providing qualified faculty and a focus on concepts, weaknesses, and mock tests to help students succeed on the LEET exam with minimal effort.
1. Trinity College
The Relational Model and
Relational Constraints
Timothy Richards
Trinity College, Hartford CT • Department of Computer Science • CPSC 372
2. Last Time
• Relational Model Concepts
• A collection of relations
• A table of values
• Each row represents a collection of related data values -
corresponds to a real-world entity
• Table/Column names interpret meaning
• Table represents facts
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 2
3. Last Time
• Domains
• Each value corresponds to a domain
• Domains are associated with a datatype and a format
• Relation Schema
• Describe the facts
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 3
4. Last Time
• Domains
• Each value corresponds to a domain
• Domains are associated with a datatype and a format
• Relation Schema
• Describe the facts
R(A1, A2, ..., An)
Each attribute Ai is the name of a role
played by some domain D in the relation schema R
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 4
5. Last Time
R = Student(name, ssn, homephone, address, age, gpa)
R(A1, A2, ..., An)
Each attribute Ai is the name of a role
played by some domain D in the relation schema R
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 5
6. Last Time
Arity/Degree: deg(R) = 6
R = Student(name, ssn, homephone, address, age, gpa)
R(A1, A2, ..., An)
Each attribute Ai is the name of a role
played by some domain D in the relation schema R
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 6
7. Last Time
A relation instance r of R(A1, A2, ..., An)
is a set of n-tuples, r = {t1, t2, ..., tm,}, where
each n-tuple ti is an ordered list of
n values ti = <v1, v2, ..., vn>.
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 7
8. Last Time
Important Constraint
Each value vi, 1 <= i <= n, in dom(Ai)
A relation instance r of R(A1, A2, ..., An)
is a set of n-tuples, r = {t1, t2, ..., tm,}, where
each n-tuple ti is an ordered list of
n values ti = <v1, v2, ..., vn>.
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 8
9. Last Time
Relational databases are based on relations
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 9
10. Last Time
Relational databases are based on relations
What characteristics make a relation
different from a file or table?
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 10
11. Last Time
Relational databases are based on relations
What characteristics make a relation
different from a file or table?
Tuples have no ordering Tuples are unique
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 11
12. Last Time
Relational databases are based on relations
What characteristics make a relation
different from a file or table?
Tuples have no ordering Tuples are unique
Not necessarily true at physical level!
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 12
13. Last Time
Constraints are Important
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 13
14. Last Time
Constraints are Important
1. model-based
2. schema-based
3. application-based
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 14
15. Last Time
Constraints are Important
uniqueness,
ordering
1. model-based
2. schema-based
3. application-based
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 15
16. Last Time
Constraints are Important
uniqueness,
ordering
1. model-based
Constraints we
2. schema-based
can express in a
3. application-based
relation schema
using DDL
(SQL)
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 16
17. Last Time
Constraints are Important
uniqueness,
ordering
1. model-based
Constraints we
2. schema-based
can express in a
3. application-based
relation schema
using DDL
(SQL) The meaning and behavior
or attributes
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 17
18. Last Time
Constraints are Important
uniqueness,
ordering
1. model-based
Constraints we
2. schema-based
can express in a
3. application-based
relation schema
using DDL
(SQL) The meaning and behavior • Difficult to express and
enforce in data model.
or attributes • Checked in application
programs
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 18
19. Domain Constraints
The value of each attribute Ai
for a particular tuple
is constrained to dom(Ai)
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 19
20. Domain Constraints
The value of each attribute Ai
for a particular tuple
is constrained to dom(Ai)
DDL can express this using types!
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 20
21. Domain Constraints
The value of each attribute Ai
for a particular tuple
is constrained to dom(Ai)
DDL can express this using types! money
integers reals characters boolean date time
short float strings time stamp
regular double fixed length
long variable length
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 21
22. Key Constraints
Tuples in a relation must be unique
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 22
23. Key Constraints
Tuples in a relation must be unique
How to express in DDL?
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 23
24. Key Constraints
Tuples in a relation must be unique
How to express in DDL?
For all i, j : ti[SK] != tj[SK] : i != j
Subset of attributes SK of a relation schema R
such that, no two tuples in r(R) should have
the same combination of values for SK.
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 24
25. Key Constraints
Tuples in a relation must be unique
How to express in DDL?
For all i, j : ti[SK] != tj[SK] : i != j
Subset of attributes SK of a relation schema R
such that, no two tuples in r(R) should have
the same combination of values for SK.
Super Key
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 25
26. Super Keys
Properties of SK:
• Distinct type can’t have duplicate values for SK.
• It is a minimal super key if we can’t remove attribute
ai in SK that breaks the first property.
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 26
27. Super Keys
Properties of SK:
• Distinct type can’t have duplicate values for SK.
• It is a minimal super key if we can’t remove attribute
ai in SK that breaks the first property.
A relation schema may have more than one key
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 27
28. Super Keys
Properties of SK:
• Distinct type can’t have duplicate values for SK.
• It is a minimal super key if we can’t remove attribute
ai in SK that breaks the first property.
A relation schema may have more than one key
candidate key
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 28
29. Super Keys
Properties of SK:
• Distinct type can’t have duplicate values for SK.
• It is a minimal super key if we can’t remove attribute
ai in SK that breaks the first property.
A relation schema may have more than one key
candidate key
The candidate key we pick, is called the primary key
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 29
30. Foreign Keys
Foreign Keys:
Values in one relations that reference
existing primary keys in other relations.
Trinity College, Hartford CT • Department of Computer Science • CPSC 372
31. Foreign Keys
Foreign Keys:
Values in one relations that reference
existing primary keys in other relations.
Also a constraint!
Trinity College, Hartford CT • Department of Computer Science • CPSC 372
32. non-Null Constraints
non-NULL is a constraint
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 32
33. non-Null Constraints
non-NULL is a constraint
Certain attribute values are
required to not be null.
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 33
34. non-Null Constraints
non-NULL is a constraint
Certain attribute values are
required to not be null.
Primary Keys can’t be null!
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 34
35. A Relational Databases Is...
• A relation database schema S
• is a set of relation schemas: S = {R , R , ..., R }
1 2 m
• A set of integrity constraints IC
• A relational database state
• DB of S, that are a set of relation states:
DB = {r1, r2, ..., rn}
• such that for all i, r satisfies IC
i
Trinity College, Hartford CT • Department of Computer Science • CPSC 372 35