The Relational Data Model and Relational Database Constraints Ch5 (Navathe 4t...Raj vardhan
The Relational Data Model and Relational Database Constraints
Ch5 (Navathe 4th edition)/ Ch7 (Navathe 3rd edition)
Example of STUDENT Relation(figure 5.1)
A database management system (DBMS) is system software for creating and managing databases. The DBMS provides users and programmers with a systematic way to create, retrieve, update and manage data.
A DBMS makes it possible for end users to create, read, update and delete data in a database. The DBMS essentially serves as an interface between the database and end users or application programs, ensuring that data is consistently organized and remains easily accessible.Read more.........
Purpose of the data base system, data abstraction, data model, data independence, data definition
language, data manipulation language, data base manager, data base administrator, data base users,
overall structure.
ER Models, entities, mapping constrains, keys, E-R diagram, reduction E-R diagrams to tables,
generatio, aggregation, design of an E-R data base scheme.
Oracle RDBMS, architecture, kernel, system global area (SGA), data base writer, log writer, process
monitor, archiver, database files, control files, redo log files, oracle utilities.
SQL: commands and data types, data definition language commands, data manipulation commands,
data query language commands, transaction language control commands, data control language
commands.
Joins, equi-joins, non-equi-joins, self joins, other joins, aggregate functions, math functions, string
functions, group by clause, data function and concepts of null values, sub-querries, views.
PL/SQL, basics of pl/sql, data types, control structures, database access with PL/SQL, data base
connections, transaction management, data base locking, cursor management.
The Relational Data Model and Relational Database Constraints Ch5 (Navathe 4t...Raj vardhan
The Relational Data Model and Relational Database Constraints
Ch5 (Navathe 4th edition)/ Ch7 (Navathe 3rd edition)
Example of STUDENT Relation(figure 5.1)
A database management system (DBMS) is system software for creating and managing databases. The DBMS provides users and programmers with a systematic way to create, retrieve, update and manage data.
A DBMS makes it possible for end users to create, read, update and delete data in a database. The DBMS essentially serves as an interface between the database and end users or application programs, ensuring that data is consistently organized and remains easily accessible.Read more.........
Purpose of the data base system, data abstraction, data model, data independence, data definition
language, data manipulation language, data base manager, data base administrator, data base users,
overall structure.
ER Models, entities, mapping constrains, keys, E-R diagram, reduction E-R diagrams to tables,
generatio, aggregation, design of an E-R data base scheme.
Oracle RDBMS, architecture, kernel, system global area (SGA), data base writer, log writer, process
monitor, archiver, database files, control files, redo log files, oracle utilities.
SQL: commands and data types, data definition language commands, data manipulation commands,
data query language commands, transaction language control commands, data control language
commands.
Joins, equi-joins, non-equi-joins, self joins, other joins, aggregate functions, math functions, string
functions, group by clause, data function and concepts of null values, sub-querries, views.
PL/SQL, basics of pl/sql, data types, control structures, database access with PL/SQL, data base
connections, transaction management, data base locking, cursor management.
purpose of database systems, components of dbms, applications of
dbms, three tier dbms architecture, data independence, database schema, instance, data modeling,
entity relationship model, relational model
Guidelines for ER to Relational Mapping.
Mapping rules/ guidelines for mapping various ER constructs to Relational model with appropriate examples
Relational Query Languages Formal Query Languages
Introduction to Relational Algebra
Relational operators
Set operators
Join operators
Aggregate functions.
Grouping operator
Relational Calculus concepts
Relational algebra queries for data retrieval with sample relational schemas. relational algebra operations.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
3. Normalization: The biggest problem needed to be solved in database is data
redundancy. Normalization is a process which can remove or reduce
redundancy of a database.
Why data redundancy is the problem?
Because it causes:
1. Insert Anomaly
2. Update Anomaly
3. Delete Anomaly
Steps of Normalization
First Normal Form (1NF)
Second Normal Form (2NF)
Third Normal Form (3NF)
4. Table Student:
Student_ID Name Subject PostCode City
10617 Rinku DBMS,AOL 1212 Lalbagh
10571 Susmita DBMS,NETWORKI
NG
1204 Sahabagh
10741 Shakir DBMS,C 1309 Mirpur01
10546 Rod DBMS 1407 Mirpur14
5. 1st Normal Form:
1.Each attribute name must be unique.
2. Each attribute value must be single.
3. Each row must be unique.
4. There is no repeating groups.
Student_ID Name Subject PostCode City
10617 Rinku DBMS 1212 Lalbagh
10617 Rinku AOL 1212 Lalbagh
10571 Susmita DBMS 1204 Shahabagh
10571 Susmita NETWORKING 1204 Shahabagh
10741 Shakir DBMS 1309 Mirpur01
10741 Shakir C 1309 Mirpur01
10546 Rod DBMS 1407 Mirpur14
6. 2ND Normal Form:
1. A table is already in 1NF.
2. All nonkey attributes are fully dependent on the primary key. All partial dependencies are
removed to place in another table.
Table: Student_Info Table:Student_Sub
Student_ID Name PostCode City
10617 Rinku 1212 Lalbagh
10571 Susmita 1204 Shahabagh
10741 Shakir 1309 Mirpur01
10546 Rod 1407 Mirpur14
Student_ID Subject
10617 DBMS
10617 AOL
10571 DBMS
10571 NETWORKIN
G
10741 DBMS
10741 C
10546 DBMS
7. 3rd Normal Form:
1. A table is already in 2NF.
2.Nonprimary key attributes do not depend on other nonprimary key attributes (i.e. no transitive
dependencies) All transitive dependencies are removed to place in another table.
Table: Student_Info Table:Student_city
Table:
Student_Sub
Student_Id Name PostCode
10617 Rinku 1212
10571 Susmita 1204
10741 Shakir 1309
10546 Rod 1407
PostCode City
1212 Lalbagh
1204 Sahabagh
1309 Mirpur01
1407 Mirpur14
Student_ID Subject
10617 DBMS
10617 AOL
10571 DBMS
10571 NETWORK
ING
10741 DBMS
10741 C
10546 DBMS
8.
9. Functional Dependency: Functional dependency is a relationship that
exists when one attribute uniquely determines another attributes. If R
is a relation with attributes X and Y, a functional dependency between
the attributes is represented as X Y, which specifies Y is functionally
dependent on X.
For Example :- Suppose we have a student table with attributes:
Stu_ID, Stu_Name, Stu_Age. Here Stu_ID attribute uniquely identifies
the Stu_Name attribute of student table because ID we can tell the
student name associated with it.
Full Functional Depenedency:A Functional Dependency X Y is a full
functional dependency if removal of any attributes A from X means that
the dependency does not hold any more.
11. Partial Functional Dependency: Partial Dependency occurs when a non-prime attribute
is functionally dependent on part of a candidate key.
The 2nd Normal Form (2NF) eliminates the Partial Dependency.
Here Roll_No , Project_No are the primary attributes.
Roll_No Student_Name
Project_No Project_Name
Roll_No Project_No Student_Name Project_Name
01 20 Rinku SRT
02 34 Susmita BGY
03 21 Shakir XYZ
12. Transitive Functional Dependency:A functional dependency is said to be
transitive if it is indirectly formed by two functional dependencies.
X->Z is a transitive dependency if the following three functional dependencies hold true:
• X->Y
• Y does not -> X
• Y->Z
A transitive dependency can only occur in a relation of three of more attributes. This
dependency helps us normalizing the database in 3NF (3rd Normal Form).
Example:
Book Author Author_Age
Sherlcok Holmes Conan Doyle 66
Harry Potter J.K.Rowling 49
Dying Of the Light George R.R Martin 68
13. {Book}->{Author} (if we know the book, we knows the author name)
{Author} does not->{Book}
{Author} ->{Author_age}
Therefore as per the rule of transitive dependency.
{Book}->{Author_age} should hold, that makes sense because if we
know the book name we can know the author’s age.
14.
15. KEY:A key part of a relational database and a vital part of the structure of a table.
They ensure each record within a table can be uniquely identified by one or a
combination of fields within the table. They help enforce integrity and help identify
the relationship between tables.
Super key: A super key is a set of one or more attributes (columns), which can
uniquely identify a row in a table.
Example:
The Car relation schema:
CAR(State, Reg, SerialNo, Make, Model, Year)
Here Super key is {SrialNo, Make}
A candidate key is a super key but vice versa is not true.
16. Primary Key: A primary key is used as a unique identifier to quickly parse
data within the database and find the relation between different tables. A
relational database cannot have more than one primary key.
Example:
17. Candidate key: A candidate key is a column, or set of columns, in a table that
can uniquely identify any database record without referring to any other data.
Each table may have one or more candidate keys, but one candidate key is
unique, and it is called the primary key.
Example:
18. Foreign Key: A foreign key is a column or group of columns in a relational
database table that provides a link between data in two tables. It acts as a
cross-reference between tables because it references the primary key of another
table, thereby establishing a link between them.
Example:
19.
20. DDL:
DDL stands for Data Definition Language.
DDL statements are used to build and modify the structure of tables and other objects
in the database
DDL is a sub-language of SQL used to create and manipulate objects in a database
Advantages of DDL :
Shared data
Data independence
Improved integrity
Multiple user
Improved security
Efficient data access
21. DML:
DML stands for Data Manipulation Language.
A DML is a family of syntax elements similar to a computer programming language
used for
selecting,
inserting,
deleting and
updating data in a database.
Advantages of DML:
The DML statements can modify the data stored in a database.
User can specify what data is needed.
DML tends to have many different flavors and capabilities between database vendors.
It provides efficient human interaction with the system.
22. Disadvantages of DML:
DML cannot be used to change the database structure.
Tables or columns cannot be created or deleted using DML.
View the data without storing the data into the object.
Restrict the view of a table i.e. Can hide some of columns in the tables.
23.
24. Generalization: In generalization, a number of entities are brought together
into one generalized entity based on their similar characteristics.
Example:
25. Specialization: A group of entities is divided into sub-groups based on their
characteristics. Specialization is the just opposite of generalization.
Example: