The document provides an overview of entity-relationship (E-R) modeling concepts including:
- Entity sets represent collections of real-world entities that share common properties
- Relationship sets define associations between entity sets
- Attributes provide additional information about entities and relationships
- Keys uniquely identify entities and relationships
- Cardinalities constrain how entities can participate in relationships
- E-R diagrams visually depict entity sets, attributes, relationships and constraints.
This document provides an overview of entity-relationship modeling as a first step for designing a relational database. It describes how to model entities, attributes, relationships, and participation constraints. Key aspects covered include using boxes to represent entity types, diamonds for relationship types, and labeling relationships with degrees. The document also discusses handling multi-valued attributes and deciding whether to model concepts as attributes or entity types.
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
Functional dependencies in Database Management SystemKevin Jadiya
Slides attached here describes mainly Functional dependencies in database management system, how to find closure set of functional dependencies and in last how decomposition is done in any database tables
The document summarizes some of the key potential problems with distributed database management systems (DDBMS), including:
1) Distributed database design issues around how to partition and replicate the database across sites.
2) Distributed directory management challenges in maintaining consistency across global or local directories.
3) Distributed query processing difficulties in determining optimal strategies for executing queries across network locations.
4) Distributed concurrency control complications in synchronizing access to multiple copies of the database across sites while maintaining consistency.
The document discusses databases and database management systems (DBMS). It provides an example of a UNIVERSITY database to illustrate how data is structured and related. Key advantages of the database approach include controlling redundancy, restricting unauthorized access, and enforcing integrity constraints. The history of database applications is reviewed, from early hierarchical and network systems to current technologies like XML, no-sql, and using databases on the web.
The document provides an overview of entity-relationship (E-R) modeling concepts including:
- Entity sets represent collections of real-world entities that share common properties
- Relationship sets define associations between entity sets
- Attributes provide additional information about entities and relationships
- Keys uniquely identify entities and relationships
- Cardinalities constrain how entities can participate in relationships
- E-R diagrams visually depict entity sets, attributes, relationships and constraints.
This document provides an overview of entity-relationship modeling as a first step for designing a relational database. It describes how to model entities, attributes, relationships, and participation constraints. Key aspects covered include using boxes to represent entity types, diamonds for relationship types, and labeling relationships with degrees. The document also discusses handling multi-valued attributes and deciding whether to model concepts as attributes or entity types.
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
Functional dependencies in Database Management SystemKevin Jadiya
Slides attached here describes mainly Functional dependencies in database management system, how to find closure set of functional dependencies and in last how decomposition is done in any database tables
The document summarizes some of the key potential problems with distributed database management systems (DDBMS), including:
1) Distributed database design issues around how to partition and replicate the database across sites.
2) Distributed directory management challenges in maintaining consistency across global or local directories.
3) Distributed query processing difficulties in determining optimal strategies for executing queries across network locations.
4) Distributed concurrency control complications in synchronizing access to multiple copies of the database across sites while maintaining consistency.
The document discusses databases and database management systems (DBMS). It provides an example of a UNIVERSITY database to illustrate how data is structured and related. Key advantages of the database approach include controlling redundancy, restricting unauthorized access, and enforcing integrity constraints. The history of database applications is reviewed, from early hierarchical and network systems to current technologies like XML, no-sql, and using databases on the web.
The document provides an introduction to database management systems (DBMS) and database models. It defines key terms like data, database, DBMS, file system vs DBMS. It describes the evolution of DBMS from 1960 onwards and different database models like hierarchical, network and relational models. It also discusses the roles of different people who work with databases like database designers, administrators, application programmers and end users.
The document discusses deductive databases and how they differ from conventional databases. Deductive databases contain facts and rules that allow implicit facts to be deduced from the stored information. This reduces the amount of storage needed compared to explicitly storing all facts. Deductive databases use logic programming through languages like Datalog to specify rules that define virtual relations. The rules allow new facts to be inferred through an inference engine even if they are not explicitly represented.
Fundamentals of Database Systems questions and answers with explanation for fresher's and experienced for interview, competitive examination and entrance test.
The Dempster-Shafer Theory was developed by Arthur Dempster in 1967 and Glenn Shafer in 1976 as an alternative to Bayesian probability. It allows one to combine evidence from different sources and obtain a degree of belief (or probability) for some event. The theory uses belief functions and plausibility functions to represent degrees of belief for various hypotheses given certain evidence. It was developed to describe ignorance and consider all possible outcomes, unlike Bayesian probability which only considers single evidence. An example is given of using the theory to determine the murderer in a room with 4 people where the lights went out.
This document summarizes a presentation on Big Data analytics using R. It introduces R as a programming language for statistics, mathematics, and data science. It is open source and has an active user community. The presentation then discusses Revolution R Enterprise, a commercial product that builds upon R to enable high performance analytics on big data across multiple platforms and data sources through parallelization, distributed computing, and integration tools. It aims to allow writing analytics code once that can be deployed anywhere.
Object Relational Database Management System(ORDBMS)Rabin BK
The document discusses Object Relational Database Management Systems (ORDBMS). It defines an ORDBMS as a system that attempts to extend relational database systems with functionality to support a broader class of applications by providing a bridge between relational and object-oriented paradigms. This allows objects, classes and inheritance in database schemas and query languages. The document outlines some advantages of ORDBMS like reusability and preserving relational application knowledge, but also disadvantages like increased complexity. It also describes common OR operations like create, retrieve, update and delete objects, as well as Object-Relational Mapping (ORM) which converts data between incompatible type systems.
The document describes a simple code generator that generates target code for a sequence of three-address statements. It tracks register availability using register descriptors and variable locations using address descriptors. For each statement, it determines the locations of operands, copies them to a register if needed, performs the operation, updates the register and address descriptors, and stores values before procedure calls or basic block boundaries. It uses a getreg function to determine register allocation. Conditional statements are handled using compare and jump instructions and condition codes.
This document provides an overview of database system concepts and architecture. It discusses different data models including conceptual, physical and implementation models. It also covers database languages, interfaces, utilities and centralized versus distributed (client-server) architectures. Specifically, it describes hierarchical and network data models, the three schema architecture, data independence, DBMS languages like DDL and DML, and different DBMS classifications including relational, object-oriented and distributed systems.
This document is from a textbook on database systems. It introduces fundamental concepts such as what a database is, the role of database management systems, and typical database functionality including defining schemas, loading data, querying, and concurrency control. It also discusses different types of database users and the advantages of the database approach such as data sharing and integrity enforcement. Examples of entity-relationship diagrams and database relations are provided to illustrate conceptual data modeling.
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.
This document provides an overview of database management systems (DBMS). It discusses the history and components of DBMS, including how they were developed to address limitations of earlier file-based data management systems by providing data independence, efficient access, integrity, security, concurrent access and recovery from crashes. The document also covers DBMS concepts such as data definition and manipulation languages, database administration, types of users and databases, and advantages and disadvantages of DBMS.
In DBMS (DataBase Management System), the relation algebra is important term to further understand the queries in SQL (Structured Query Language) database system. In it just give up the overview of operators in DBMS two of one method relational algebra used and another name is relational calculus.
This document discusses various strategies for register allocation and assignment in compiler design. It notes that assigning values to specific registers simplifies compiler design but can result in inefficient register usage. Global register allocation aims to assign frequently used values to registers for the duration of a single block. Usage counts provide an estimate of how many loads/stores could be saved by assigning a value to a register. Graph coloring is presented as a technique where an interference graph is constructed and coloring aims to assign registers efficiently despite interference between values.
The document discusses entity-relationship (ER) modeling concepts including entities, attributes, relationships, and ER diagrams. It provides an example database for a company (COMPANY) that tracks departments, projects, employees, and employee dependents. Key concepts covered include entity types, relationship types, attributes, keys, weak entities, roles, recursive relationships, and higher order relationships. The example ER diagram for the COMPANY database includes entity types for EMPLOYEE, DEPARTMENT, PROJECT, and DEPENDENT connected by relationship types like WORKS_FOR, MANAGES, WORKS_ON, and DEPENDENTS_OF.
1) This document discusses semantic networks, which are a knowledge representation technique used in artificial intelligence. Semantic networks represent knowledge through nodes and links, where nodes represent concepts or objects, and links represent relationships between the nodes.
2) As an example, a simple semantic network is presented representing facts about a cat named Jerry - that Jerry is a cat, a mammal, owned by Jay, white in color, and likes cheese.
3) The document outlines different types of semantic networks including definitional, assertional, implicational, and learning networks. It also discusses advantages such as being a natural representation of knowledge, and disadvantages including lack of quantifiers and lack of intelligence.
Codd's 12 rules are a set of rules proposed by Edgar Codd to define what is required for a database management system to be considered relational. The rules include that all data must be represented in tables and columns, all data must be logically addressable, null values must be supported systematically, the database structure must be accessible through queries, and the system must support set-based operations like inserts, updates and deletes. The rules also require physical and logical independence between the application, data and constraints.
1. What two conditions must be met before an entity can be classif.docxjackiewalcutt
1. What two conditions must be met before an entity can be classified as a weak entity? Give an example of a weak entity.
The Two conditions that must be met to be classified as a weak entity are under :
1. The entity must be associated with another entity ,called identifying or owner or parent entity. That it is existence dependent on parent entity.
2. The entity must inherit at least part of its primary key from its parent entity.Only than it would be a valid and strong primary key.
CRS_NAME
CRS_CODE
CONNECT
CLASSNAME
CLASS_SECTION
Class
Course
For example, the (strong) relationship depicted in the above Figure shows a weak CLASS entity:
1. CLASS is clearly existence-dependent on COURSE. (You can’t have a database class unless a database course exists.)
2. The CLASS entity’s PK is defined through the combination of CLASS_SECTION and CRS_CODE. The CRS_CODE attribute is also the PK of COURSE.
The conditions which define a weak entity are similar to the strong relationship between an entity and its parent. In short, you can say, the existence of a weak entity produces a strong relationship. And if the entity is strong, its relationship to the other entity is weak.
Whether or not an entity to be weak it is usually dependent on the database designer’s decisions. For instance, if the database designer had decided to use a single-attribute as shown in the text’s Figure below., the CLASS entity would be strong. (The CLASS entity’s PK is CLASS_CODE, which is not derived from the COURSE entity.) In this case, the relationship between COURSE and CLASS is weak. However, regardless of how the designer classifies the relationship – weak or strong – CLASS is always existence-dependent on COURSE.
CONNECT
CLASS_CODE
Class
CRS_NAM E
Course
CRS_CODE
cr
2. What is a strong (or identifying) relationship, and how is it depicted in a Crow’s Foot ERD?
A strong relationship exists / occurs when an entity is existence-dependent on another entity and inherits at least part of its primary key from that entity. The Visio Professional software shows the strong relationship as a solid line. In other words, a strong relationship exists when a weak entity is related to its parent entity. As discussed in the above question Class , the weak entity is related to strong entity Course with the help of a relationship called CONNECT which is a showing /identifying a strong relationship. As shown in the above figure a strong relationship is represented by two decision boxes… One inside another embedded together represent this.
3. Given the business rule “an employee may have many degrees,” discuss its effect on attributes, entities, and relationships. (Hint: Remember what a multivalued attribute is and how it might be implemented.)
Suppose that an employee has the following degrees: BBA, Ph.D , and MBA. These degrees could be stored in a single string as a multivalued attribute named EMP_DEGREE in an EMPLOYEE table such as the one shown next:
EMP_NUM
EMP_LNAME ...
SECTION D2)Display the item number and total cost for each order l.docxkenjordan97598
SECTION D
2)Display the item number and total cost for each order line (total cost = no of items X item cost). Name the calculated column TOTAL COST.
Answer:
SELECT item_number, no_of_items * item_cost “TOTAL COST”
FROM ORDER_LINE
4)Display the order number and client number from the ORDER table. Output the result in the format. Client <clientno> ordered <orderno>
Answer:
SELECT ‘Client ‘+ clientno+’ordered ‘+ orderno AS result
FROM ORDER
6)Display the client name and order date for all orders using the traditional method.
Answer:
SELECT name, order_date
FROM
CLIENT c INNER JOIN ORDER o
ON (c.clientno = o.clientno);
7)Repeat query (7) but also display all clients who have never ordered anything.
Answer:
SELECT name, order_date
FROM
CLIENT c LEFT OUTER JOIN ORDER o
ON (c.clientno = o.clientno);
8) Display the client name and order date for all orders using the natural join keywords.
SELECT name, order_date
FROM
CLIENT NATURAL JOIN ORDER;
9) Display the client name and order date for all orders using the JOIN . . . USING method.
SELECT name, order_date
FROM CLIENT c JOIN ORDER o
USING (clientno);
10) Display the client number, order date and shipping date for all orders where the shipping date is between three and six months after the order date.
SELECT clientno, order_date, shipping_date
FROM
CLIENT c,
ORDER o,
ORDER_LINE ol
WHERE c.clientno = o.clientno
AND o.orderno = ol.orderno
AND shipping_date BETWEEN ADD_MONTHS(shipping_date,3) AND ADD_MONTHS(shipping_date,6);
16) Display the order number, order line number and the shipping date. If the shipping date is null, display the string <not shipped yet>.
SELECT orderno, order_line_number, NVL(shipping_date,’<not shipped yet>’)
FROM ORDER_LINE
18)Display the clientno and total value for all orders placed by that client. Output the result in the following format: Client <clientno> has placed orders to the value of <total value>
SELECT ‘Client ‘+clientno+’ has placed order to the value of ‘+ SUM(no_of_items*item_cost)
FROM ORDER_LINE
GROUP BY clientno
19) Display all clients whose name begins with the letter J or contains the letter M anywhere or contains E as the third letter.
SELECT *
FROM CLIENT
WHERE UPPER(name) LIKE ‘J%’
OR upper(name) LIKE ‘%M%’
OR
Upper(name) LIKE ‘??E%’
20)Using a set operator, display the client number of all clients who have never placed an order.
Answer:
SELECT clientno
FROM CLIENT
MINUS
SELECT clientno
FROM ORDER
21)Using a set operator, display the client number of all clients who have ever placed an order and whose name does not contain the string Sm.
SELECT clientno
FROM CLIENT
WHERE INSTR(name,’Sm’) = 0
INTERSECT
SELECT clientno
FROM
ORDER
23)Display the client name for all clients who have placed an order where any order line has more than 3 items. Do not use a table join anywhere in your query.
SELECT name
FROM CLIENT c,
ORDER o,
ORDER_LINE ol
WHERE c.clientno = o.clientno
AND o.orderno = ol.orderno
AND ol.no_of_items> 3
26)Display the earli.
The document provides an introduction to database management systems (DBMS) and database models. It defines key terms like data, database, DBMS, file system vs DBMS. It describes the evolution of DBMS from 1960 onwards and different database models like hierarchical, network and relational models. It also discusses the roles of different people who work with databases like database designers, administrators, application programmers and end users.
The document discusses deductive databases and how they differ from conventional databases. Deductive databases contain facts and rules that allow implicit facts to be deduced from the stored information. This reduces the amount of storage needed compared to explicitly storing all facts. Deductive databases use logic programming through languages like Datalog to specify rules that define virtual relations. The rules allow new facts to be inferred through an inference engine even if they are not explicitly represented.
Fundamentals of Database Systems questions and answers with explanation for fresher's and experienced for interview, competitive examination and entrance test.
The Dempster-Shafer Theory was developed by Arthur Dempster in 1967 and Glenn Shafer in 1976 as an alternative to Bayesian probability. It allows one to combine evidence from different sources and obtain a degree of belief (or probability) for some event. The theory uses belief functions and plausibility functions to represent degrees of belief for various hypotheses given certain evidence. It was developed to describe ignorance and consider all possible outcomes, unlike Bayesian probability which only considers single evidence. An example is given of using the theory to determine the murderer in a room with 4 people where the lights went out.
This document summarizes a presentation on Big Data analytics using R. It introduces R as a programming language for statistics, mathematics, and data science. It is open source and has an active user community. The presentation then discusses Revolution R Enterprise, a commercial product that builds upon R to enable high performance analytics on big data across multiple platforms and data sources through parallelization, distributed computing, and integration tools. It aims to allow writing analytics code once that can be deployed anywhere.
Object Relational Database Management System(ORDBMS)Rabin BK
The document discusses Object Relational Database Management Systems (ORDBMS). It defines an ORDBMS as a system that attempts to extend relational database systems with functionality to support a broader class of applications by providing a bridge between relational and object-oriented paradigms. This allows objects, classes and inheritance in database schemas and query languages. The document outlines some advantages of ORDBMS like reusability and preserving relational application knowledge, but also disadvantages like increased complexity. It also describes common OR operations like create, retrieve, update and delete objects, as well as Object-Relational Mapping (ORM) which converts data between incompatible type systems.
The document describes a simple code generator that generates target code for a sequence of three-address statements. It tracks register availability using register descriptors and variable locations using address descriptors. For each statement, it determines the locations of operands, copies them to a register if needed, performs the operation, updates the register and address descriptors, and stores values before procedure calls or basic block boundaries. It uses a getreg function to determine register allocation. Conditional statements are handled using compare and jump instructions and condition codes.
This document provides an overview of database system concepts and architecture. It discusses different data models including conceptual, physical and implementation models. It also covers database languages, interfaces, utilities and centralized versus distributed (client-server) architectures. Specifically, it describes hierarchical and network data models, the three schema architecture, data independence, DBMS languages like DDL and DML, and different DBMS classifications including relational, object-oriented and distributed systems.
This document is from a textbook on database systems. It introduces fundamental concepts such as what a database is, the role of database management systems, and typical database functionality including defining schemas, loading data, querying, and concurrency control. It also discusses different types of database users and the advantages of the database approach such as data sharing and integrity enforcement. Examples of entity-relationship diagrams and database relations are provided to illustrate conceptual data modeling.
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.
This document provides an overview of database management systems (DBMS). It discusses the history and components of DBMS, including how they were developed to address limitations of earlier file-based data management systems by providing data independence, efficient access, integrity, security, concurrent access and recovery from crashes. The document also covers DBMS concepts such as data definition and manipulation languages, database administration, types of users and databases, and advantages and disadvantages of DBMS.
In DBMS (DataBase Management System), the relation algebra is important term to further understand the queries in SQL (Structured Query Language) database system. In it just give up the overview of operators in DBMS two of one method relational algebra used and another name is relational calculus.
This document discusses various strategies for register allocation and assignment in compiler design. It notes that assigning values to specific registers simplifies compiler design but can result in inefficient register usage. Global register allocation aims to assign frequently used values to registers for the duration of a single block. Usage counts provide an estimate of how many loads/stores could be saved by assigning a value to a register. Graph coloring is presented as a technique where an interference graph is constructed and coloring aims to assign registers efficiently despite interference between values.
The document discusses entity-relationship (ER) modeling concepts including entities, attributes, relationships, and ER diagrams. It provides an example database for a company (COMPANY) that tracks departments, projects, employees, and employee dependents. Key concepts covered include entity types, relationship types, attributes, keys, weak entities, roles, recursive relationships, and higher order relationships. The example ER diagram for the COMPANY database includes entity types for EMPLOYEE, DEPARTMENT, PROJECT, and DEPENDENT connected by relationship types like WORKS_FOR, MANAGES, WORKS_ON, and DEPENDENTS_OF.
1) This document discusses semantic networks, which are a knowledge representation technique used in artificial intelligence. Semantic networks represent knowledge through nodes and links, where nodes represent concepts or objects, and links represent relationships between the nodes.
2) As an example, a simple semantic network is presented representing facts about a cat named Jerry - that Jerry is a cat, a mammal, owned by Jay, white in color, and likes cheese.
3) The document outlines different types of semantic networks including definitional, assertional, implicational, and learning networks. It also discusses advantages such as being a natural representation of knowledge, and disadvantages including lack of quantifiers and lack of intelligence.
Codd's 12 rules are a set of rules proposed by Edgar Codd to define what is required for a database management system to be considered relational. The rules include that all data must be represented in tables and columns, all data must be logically addressable, null values must be supported systematically, the database structure must be accessible through queries, and the system must support set-based operations like inserts, updates and deletes. The rules also require physical and logical independence between the application, data and constraints.
1. What two conditions must be met before an entity can be classif.docxjackiewalcutt
1. What two conditions must be met before an entity can be classified as a weak entity? Give an example of a weak entity.
The Two conditions that must be met to be classified as a weak entity are under :
1. The entity must be associated with another entity ,called identifying or owner or parent entity. That it is existence dependent on parent entity.
2. The entity must inherit at least part of its primary key from its parent entity.Only than it would be a valid and strong primary key.
CRS_NAME
CRS_CODE
CONNECT
CLASSNAME
CLASS_SECTION
Class
Course
For example, the (strong) relationship depicted in the above Figure shows a weak CLASS entity:
1. CLASS is clearly existence-dependent on COURSE. (You can’t have a database class unless a database course exists.)
2. The CLASS entity’s PK is defined through the combination of CLASS_SECTION and CRS_CODE. The CRS_CODE attribute is also the PK of COURSE.
The conditions which define a weak entity are similar to the strong relationship between an entity and its parent. In short, you can say, the existence of a weak entity produces a strong relationship. And if the entity is strong, its relationship to the other entity is weak.
Whether or not an entity to be weak it is usually dependent on the database designer’s decisions. For instance, if the database designer had decided to use a single-attribute as shown in the text’s Figure below., the CLASS entity would be strong. (The CLASS entity’s PK is CLASS_CODE, which is not derived from the COURSE entity.) In this case, the relationship between COURSE and CLASS is weak. However, regardless of how the designer classifies the relationship – weak or strong – CLASS is always existence-dependent on COURSE.
CONNECT
CLASS_CODE
Class
CRS_NAM E
Course
CRS_CODE
cr
2. What is a strong (or identifying) relationship, and how is it depicted in a Crow’s Foot ERD?
A strong relationship exists / occurs when an entity is existence-dependent on another entity and inherits at least part of its primary key from that entity. The Visio Professional software shows the strong relationship as a solid line. In other words, a strong relationship exists when a weak entity is related to its parent entity. As discussed in the above question Class , the weak entity is related to strong entity Course with the help of a relationship called CONNECT which is a showing /identifying a strong relationship. As shown in the above figure a strong relationship is represented by two decision boxes… One inside another embedded together represent this.
3. Given the business rule “an employee may have many degrees,” discuss its effect on attributes, entities, and relationships. (Hint: Remember what a multivalued attribute is and how it might be implemented.)
Suppose that an employee has the following degrees: BBA, Ph.D , and MBA. These degrees could be stored in a single string as a multivalued attribute named EMP_DEGREE in an EMPLOYEE table such as the one shown next:
EMP_NUM
EMP_LNAME ...
SECTION D2)Display the item number and total cost for each order l.docxkenjordan97598
SECTION D
2)Display the item number and total cost for each order line (total cost = no of items X item cost). Name the calculated column TOTAL COST.
Answer:
SELECT item_number, no_of_items * item_cost “TOTAL COST”
FROM ORDER_LINE
4)Display the order number and client number from the ORDER table. Output the result in the format. Client <clientno> ordered <orderno>
Answer:
SELECT ‘Client ‘+ clientno+’ordered ‘+ orderno AS result
FROM ORDER
6)Display the client name and order date for all orders using the traditional method.
Answer:
SELECT name, order_date
FROM
CLIENT c INNER JOIN ORDER o
ON (c.clientno = o.clientno);
7)Repeat query (7) but also display all clients who have never ordered anything.
Answer:
SELECT name, order_date
FROM
CLIENT c LEFT OUTER JOIN ORDER o
ON (c.clientno = o.clientno);
8) Display the client name and order date for all orders using the natural join keywords.
SELECT name, order_date
FROM
CLIENT NATURAL JOIN ORDER;
9) Display the client name and order date for all orders using the JOIN . . . USING method.
SELECT name, order_date
FROM CLIENT c JOIN ORDER o
USING (clientno);
10) Display the client number, order date and shipping date for all orders where the shipping date is between three and six months after the order date.
SELECT clientno, order_date, shipping_date
FROM
CLIENT c,
ORDER o,
ORDER_LINE ol
WHERE c.clientno = o.clientno
AND o.orderno = ol.orderno
AND shipping_date BETWEEN ADD_MONTHS(shipping_date,3) AND ADD_MONTHS(shipping_date,6);
16) Display the order number, order line number and the shipping date. If the shipping date is null, display the string <not shipped yet>.
SELECT orderno, order_line_number, NVL(shipping_date,’<not shipped yet>’)
FROM ORDER_LINE
18)Display the clientno and total value for all orders placed by that client. Output the result in the following format: Client <clientno> has placed orders to the value of <total value>
SELECT ‘Client ‘+clientno+’ has placed order to the value of ‘+ SUM(no_of_items*item_cost)
FROM ORDER_LINE
GROUP BY clientno
19) Display all clients whose name begins with the letter J or contains the letter M anywhere or contains E as the third letter.
SELECT *
FROM CLIENT
WHERE UPPER(name) LIKE ‘J%’
OR upper(name) LIKE ‘%M%’
OR
Upper(name) LIKE ‘??E%’
20)Using a set operator, display the client number of all clients who have never placed an order.
Answer:
SELECT clientno
FROM CLIENT
MINUS
SELECT clientno
FROM ORDER
21)Using a set operator, display the client number of all clients who have ever placed an order and whose name does not contain the string Sm.
SELECT clientno
FROM CLIENT
WHERE INSTR(name,’Sm’) = 0
INTERSECT
SELECT clientno
FROM
ORDER
23)Display the client name for all clients who have placed an order where any order line has more than 3 items. Do not use a table join anywhere in your query.
SELECT name
FROM CLIENT c,
ORDER o,
ORDER_LINE ol
WHERE c.clientno = o.clientno
AND o.orderno = ol.orderno
AND ol.no_of_items> 3
26)Display the earli.
This document discusses scalar and correlated subqueries in SQL. It defines scalar subqueries as returning a single column from one row, and can be used in certain clauses like SELECT and WHERE. Correlated subqueries are executed once for each row of the outer query and reference columns from the outer query. Examples are given of using scalar and correlated subqueries in SELECT statements. The document also covers using the EXISTS operator and WITH clause with subqueries.
The document discusses database design and normalization. It begins by describing different design alternatives such as using larger or smaller schemas. It then covers first normal form (1NF), which requires attributes to be atomic and domains to be indivisible. Second normal form (2NF) and third normal form (3NF) are introduced to further reduce anomalies. The document also discusses functional dependencies, normal forms like Boyce-Codd normal form (BCNF), decomposition using functional dependencies, and closure of attribute sets. Overall, the document provides an overview of relational database design principles and normalization techniques.
The document discusses SQL commands for schema definition, constraints, and queries. It covers using CREATE TABLE to define tables and columns, including data types and constraints like PRIMARY KEY and FOREIGN KEY. ALTER TABLE is used to modify existing tables. DROP TABLE removes tables. Queries use SELECT to retrieve data that matches conditions in the WHERE clause. Joins and aliases are discussed. Additional data types like DATE and TIMESTAMP are introduced in later SQL standards.
Building data fusion surrogate models for spacecraft aerodynamic problems wit...Shinwoo Jang
This document discusses building surrogate models to approximate aerodynamic data from spacecraft. The data comes from both high-fidelity wind tunnel tests and lower-fidelity computational fluid dynamics simulations. It presents three approaches to combine this multi-fidelity data based on tensor product approximations: 1) a "merged solution" that fits models based on data type and weights points, 2) a "fused solution" that estimates high-fidelity values using a bias model between low and high-fidelity data, and 3) a "sequential solution" that first fits a low-fidelity model and then corrects it using high-fidelity data residuals. The goal is to generate accurate and consistent surrogate models over an entire flight envelope from
This document discusses various techniques for manipulating data in R, including sorting, subsetting, ordering, reshaping between wide and long formats using the reshape2 package, and using plyr for efficient splitting and combining of large datasets. Specific functions and examples covered include sort(), order(), cut(), melt(), dcast(), and plyr functions. The goal is to demonstrate common ways to manipulate and rearrange data for further processing and analysis in R.
Abstract Data Types (a) Explain briefly what is meant by the ter.pdfkarymadelaneyrenne19
Abstract Data Types
(a) Explain briefly what is meant by the term abstract data type (ADT). Give two
reasons why use of ADTs is good programming practice.
(b) Write out a signature, or interface, that defines the operations of a stack ADT.
(c) Consider a string of characters of the form
... (.( ... ).) ...
where ... indicates an arbitrary sequence of characters (except for parentheses),
(.( indicates an arbitrary number (one or more) of opening parentheses, and
similarly ).) indicates an arbitrary number of closing parentheses.
Using only the stack abstraction operations defined above, write pseudocode for
an algorithm that determines, using a stack, whether or not the number of closing
parentheses is the same as the number of opening parentheses.
You may assume the existence of a function read(str,ch) that reads the next character
of string str into ch.
You may also assume that you can invoke a function reportFail, that will cause
termination with failure, and similarly, reportSuccess causes termination with a
success indication.
Further, you may also assume that you can call a function newStack(S) to create
a new empty stack S, and eos(str) that returns false when you reach the end of
the string.
Solution
(a) Explain briefly what is meant by the term abstract data type (ADT). Give two
reasons why use of ADTs is good programming practice.
A data type is a collection of values and a set of operations on those values. That collection and
these operations form a mathematical construct that may be implemented with the use of a
particular hardware or software data structure. The term abstract data type (ADT) refers to the
basic mathematical concept that defines the data type. We have discussed four different
implementations of the list data structure.
In case of implementation of the list with the use of an array, the size of the array gives difficulty
if increased.
To avoid this, we allocate memory dynamically for nodes before connecting these nodes with the
help of pointers.
For this purpose, we made a singly linked list and connected it with the next pointer to make a
chain.
Moving forward is easy but going back is a difficult task.
To overcome this problem, we made a doubly linked list using prev andnext pointers. With the
help of these pointers, we can move forward and backward very easily. Now we face another
problem that the prev pointer of first node and the next pointer of the last node are NULL.
Therefore, we have to be careful in case of NULL pointers. To remove the NULL pointers, we
made the circular link list by connecting the first and last node.
The program employing the list data structure is not concerned with its implementation.
We do not care how the list is being implemented whether through an array, singly linked list,
doubly linked list or circular linked list. It has been witnessed that in these four implementations
of the list, the interface remained the same i.e. it implements the same methods like add, get,
next, start a.
Chapter 55.1 Describe the circumstances in which you would c.docxDinahShipman862
Chapter 5
5.1 Describe
the circumstances in which you would choose to use embedded
SQL
rather than
SQL
alone or only a general-purpose programming language.
Answer:
5.2
Write a Java function using
JDBC
metadata features that takes a
ResultSet
as an input parameter, and prints out the result in tabular form, with appropriate names as column headings.
Answer:
5.3
Write a Java function using
JDBC
metadata features that prints a list of all relations in the database, displaying for each relation the names and types of its attributes.
Answer:
5.4
Show how to enforce the constraint
“
an instructor cannot teach in two different classrooms in a semester in the same time slot.
”
using a trigger (remember that the constraint can be violated by changes to the
teaches
relation as well as to the
section
relation).
Answer:
5.5
Write triggers to enforce the referential integrity constraint from
section
to
time
slot
, on updates to
section
, and
time
in Figure 5.8 do not cover the
update
operation.
slot
. Note that the ones we wrote
5.6
To maintain the
tot cred
attribute of the
student
relation, carry out the fol-
lowing:
a. Modify the trigger on updates of
takes
, to handle all updates that can
affect the value of
tot
b. Write a trigger to handle inserts to the
takes
relation.
cred
.
c. Under what assumptions is it reasonable not to create triggers on the
course
relation?
Answer:
5.7
Consider the bank database of Figure 5.25. Let us define a view
branch cust
as follows:
create view
branch cust
as
select
branch name, customer name
from
depositor, account
where
depositor.account number
=
account.account number
Answer:
5.8
Consider the bank database of Figure 5.25. Write an
SQL
trigger to carry out the following action: On
delete
of an account, for each owner of the account, check if the owner has any remaining accounts, and if she does not, delete her from the
depositor
relation.
Answer:
5.9
Show how to express
group by cube
(
a
,
b
,
c
,
d
) using
rollup
; your answer should have only one
group by
clause.
Answer:
5.10
Given a relation
S
(
student
,
subject
,
marks
), write a query to find the top
n
students by total marks, by using ranking.
Answer:
5.11
Consider the
sales
relation from Section 5.6.Write an
SQL
query to compute the cube operation on the relation, giving the relation in Figure 5.21. Do not use the
cube
construct.
Answer:
5.12
Consider the following relations for a company database:
•
emp
(
ename
,
dname
,
salary
)
•
mgr
(
ename
,
mname
) and the Java code in Figure 5.26, which uses the JDBC API. Assume that the userid, password, machine name, etc. are all okay. Describe in concise
English what the Java program does. (That is, produce an English sentence like “It finds the manager of the toy department,” not a line-by-line description of what each Java statement does.)
Answer:
5.13
Suppose you were asked to define a class
MetaDisp.
This document provides an overview of SQL concepts including joined relations, views, transactions, integrity constraints, data types, schemas, and authorization. It discusses join operations and examples, different types of joins like outer joins, and view definitions, updates, and expansions. Transactions, integrity constraints like not null, unique, primary key, foreign key and check constraints are explained. Common SQL data types, indexes, user-defined types, and large object types are covered. The document concludes with an explanation of authorization specification and revocation in SQL using the grant and revoke statements and roles.
1) Chapter 6 discusses database integrity and security. Integrity constraints such as domain constraints, referential integrity constraints, assertions, and triggers help maintain data consistency and integrity.
2) Authorization is an important aspect of database security. Authorization in SQL allows the specification of read, write, update, delete, and other permissions on relations, views, and the database schema for individual users or roles.
3) Views can be used to simplify data access and enhance security by restricting what data a user can see based on the view definition and their underlying permissions. Triggers allow actions to be automatically executed in response to data modifications but have performance overhead so should be used judiciously.
This document discusses relational database design and informal guidelines for designing good relational schemas. It covers four main guidelines: 1) ensuring attribute semantics are clear, 2) reducing redundant information and update anomalies, 3) reducing null values, and 4) avoiding spurious tuples. It also discusses functional dependencies, which specify constraints on how attributes relate to each other and can be used to measure schema quality. Functional dependencies must hold for all possible instances of a relation.
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This document discusses exercises related to entity relationship modeling. It includes ER diagrams for examples like a university registration system, car insurance claims, and sports team statistics. It also provides answers to various questions about identifying optimal ER diagrams, handling weak entities, and merging databases from two different banks. The key topics covered are ER modeling, identifying relationships, handling attributes and weak entities, and resolving conflicts when merging related databases.
Introducing Data Redaction - an enabler to data security in EDB Postgres Adva...EDB
With the rapid growth in digitalization, coupled with the current pandemic situation globally, many organizations and businesses are forced to operate remotely and online, more than they would prefer. At such times, how do corporations and businesses ensure data security, especially the secure management of personal information?
There are many techniques used to secure information, such as authentication, authorization, access control, virtual database, and encryption. In this webinar, we focus on Data Redaction - a technique that limits sensitive data exposure in EDB Postgres Advanced Server (EPAS).
This webinar covers:
- What is EDB Data Redaction
- How to limit sensitive data exposure in EPAS
- Provision for Oracle compatibility in EPAS
- Demo
Semantic Management of Deduplicate tuples in the Relational DatabasesIRJET Journal
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c language faq's contains the frequently asked questions in c language and useful for all c programmers. This is an pdf document of Sterve_summit who is one of the best writer for c language
This document provides information about an assignment for the subject RDBMS and MySQL for the 3rd semester BSc IT program. It includes 6 questions related to MySQL database features, data types, insert statements, joins, subqueries, and mysqld security options. Students are instructed to send their semester and specialization details to a email ID or call a phone number to get fully solved assignments.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
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An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.