MN405 Database and Information Management - Free Samples

The purpose of this assignment is to develop skills in managing data in databases and to gain understanding of data model development and implementation using a commercially available database management system development tool.

On completion of this assignment students will be able to:

a.Model organisational information requirements using conceptual data modelling techniques.

b. Convert the conceptual data models into relational data model and verify their structural characteristics with normalisation techniques.

Insert into Person values ("2", "Henry", “Ford”, “126 kingstone street”, “Perth”, “4000001”, “32872789”);

Insert into Person values ("3", "Peter", “Cat”, “183 round street”, “Canberra”, “5000007”, “42873789”);

Insert into Book values ("1", “elephant Story”,"57", “1990”, “123”, “60”, “1”);

Insert into Book values ("2", “toy story”,"7", “2000”, “126”, “70”, “2”);

Insert into Book values ("3",”last story”, "9", “1954”, “129 ”, “90”, “3”);

Insert into Borrow values ("1", "1", “5/11/2015”, “5/17/2015”, “1”);

Insert into Borrow values ("2", "2", “6/11/2015”, “6/16/2015”, “2”);

Insert into Borrow values ("3", "3", “7/1/2015”, “7/9/2015”, “3”);

SQL Queries

1. SELECT Book.ISBN, Book.title, Book.VolNumber, Book.NoOfCopies

FROM Publisher INNER JOIN (Person INNER JOIN (Book INNER JOIN Borrow ON Book.ISBN = Borrow.IBSN) ON Person.PersonID = Borrow.personID) ON Publisher.publisherID = Book.PublisherID;

1. SELECT Count(Book.Year_Published) AS CountOfYear_Published

FROM Book;

1. SELECT Price from Book WHERE Price > (SELECT AVG(price) FROM Book);

1. SELECT Book.*

FROM Publisher INNER JOIN Book ON Publisher.publisherID = Book.PublisherID

WHERE (((Publisher.publisherName)="ACER Press") AND ((Book.Year_Published)=2017));

Create Table Borrower As

1. SELECT Person.*, Borrow.borrowdate, Borrow.returendate

FROM Publisher INNER JOIN (Person INNER JOIN (Book INNER JOIN Borrow ON Book.ISBN = Borrow.IBSN) ON Person.PersonID = Borrow.personID) ON Publisher.publisherID = Book.PublisherID;

1. SELECT Person.*, Borrow.borrowdate, Borrow.returendate

Into Borrower

FROM Publisher INNER JOIN (Person INNER JOIN (Book INNER JOIN Borrow ON Book.ISBN = Borrow.IBSN) ON Person.PersonID = Borrow.personID) ON Publisher.publisherID = Book.PublisherID;

Author (Name: text, Address: text, URL: link)

Publisher (Name: text, Address: text, phone: Number, URL: link)

Customer (email: link, Name: text, Address: text, phone: Number)

Book (ISBN: Number, title: text, year: Number, price: Number)

Shopping basket (basketID: Number)

Warehouse (code: Number, address: Text, phone: Number)

1. The address field and the url field are the ones that are the multivalued attribute in the system.

Address: the address is composed of the street name, city and post code

URL: the url is composed of A scheme, host and a path

Business rule: an author writes a number of books.

Business rule: A publisher would publish a number of books.

Business rule: A single book would be consisting of a number of copies in the warehouse.

Hadoop is an open source tool from ASF. Open source means its codes are easily available and its framework is written in Java. It is used for distributed storage and processing of dataset of Big Data

Capabilities and limitations

  Hadoop is very popular because within an hour, an engineer can download, install, and issue a simple query.  It’s also an open source project, so there are no software costs, which makes it a very attractive alternative to Oracle and Teradata. However, the main limitation of Hadoop is that, for harder analytics problems, Hadoop quickly falls flat and requires the user to directly develop Map/Reduce code directly.  

Conclusion

For conclusion it can be said that the Hadoop is a technology that is very popular in today’s world. The use of the tool is generally done to manage large sets of data. Although the tool has some drawbacks the main objectives of the tool is served in most of the real life cases.

MapReduce is the programming model design of Hadoop. Its provide batch processing. Its work is for processing large volumes of data in parallel by dividing the work into a set of independent tasks.

The Map Reduce method provides easy methods to handle large amounts of data. They distributes the results of the different nodes and reduce the results obtained from the nodes. However, MapReduce programming model restricts all the logic to mappers and reducers and programmers end up writing lot of boiler plate code rather than the actual data processing logic. With intermediate results from mapper being written to disk, it results in lot of disk I/O and hence making iterative algorithms hard to implement.

Conclusion

For conclusion it can be said that the techniques is very useful in management of large amounts of data, however it has some drawbacks but are negligible in respect to its uses.

Bibliography

[1] John Walker, S. Big data: A revolution that will transform how we live, work, and think.

[2] Hashem, I.A.T., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A. and Khan, S.U., 2015. The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, pp.98-115, 2014.

[3] Wu, X., Zhu, X., Wu, G.Q. and Ding, W. Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), pp.97-107, 2014.

[4] Mathew, P.A., Dunn, L.N., Sohn, M.D., Mercado, A., Custudio, C. and Walter, T. Big-data for building energy performance: Lessons from assembling a very large national database of building energy use. Applied Energy, 140, pp.85-93, 2015.

[5] Giese, M., Soylu, A., Vega-Gorgojo, G., Waaler, A., Haase, P., Jiménez-Ruiz, E., Lanti, D., Rezk, M., Xiao, G., Özçep, Ö. and Rosati, R. Optique: Zooming in on big data. Computer, 48(3), pp.60-67, 2015.