## Questions:

1.  Consider a disk with the following characteristics (these are not parameters of any particular disk unit): block size B=512 bytes, interblock gap size G=128 bytes, number of blocks per track=20, number of tracks per surface=400. A disk pack consists of 15 double-sided disks.

i. What is the total capacity of a track and what is its useful capacity (excluding interblock gaps)?

ii. How many cylinders are there?

iii. What are the total capacity and the useful capacity of a cylinder?

iv. What are the total capacity and the useful capacity of a disk pack?

2.  After a transaction is submitted to a DBMS for execution it may fail. There are several possible reasons for a transaction to fail in the middle of execution. What are some of more common types of failures among all possible ones? Name and briefly explain at least 3 types.

3.  what are some of the issues that are addressed in the area of database security? Give one example for each issue?

4. Describe some of the advantages of distributed databases and name the additional functions that that the Distributed Database Management System software must be able to provide in order to achieve the above advantages.

5. Define data mining and describe some of its applications?

1. i. total capacity of a track = (interblock gap size + block size) * total number of block in a track = (128 + 512) * 20 = 12800 bytes

Usable capacity of a track (excluding interblock gaps) = size of a block * number of blocks in a track = 512 * 20 = 10240 bytes

ii. Number of cylinders and equal to number of tracks = 400

iii. Every cylinder has 2 tracks on each of its disk (one each side). There are a total of 15 disks in a cylinder and the total capacity of a cylinder is 12800 bytes.

Therefore, total capacity of a cylinder = 12800 * 15 * 2 = 384,000 bytes

Similarly, the useful capacity of a track is 10240 ergo useful capacity of a cylinder = 10240 * 15 * 2 = 307,200 bytes

iv. One cylinder has total capacity of 384,000 bytes and there are 400 cylinders. Hence, Total capacity of a disk pack = 384,000 * 400 = 153,600,000 bytes = 153.6 MB

Similarly

One cylinder has useful capacity of 307,200 bytes and there are a total of 400 cylinders. Hence, total useful capacity of a disk pack = 307,200 * 400 = 122,880,000 bytes = 122.88 MB

2. Failure in a transaction may occur in middle of a DBMS transaction for several possible reasons. Some common reasons are listed below-

1. System Crash: In middle of a transaction it is possible that the hardware, network, or software parts of the system crashes (Ramez, 1994). In such a case the transaction fails in middle of execution. For example- main memory failure.

2. Exception case identified by the transaction: A transaction may face exception case in middle of the execution and fail to execute. For example an ATM transaction may fail due to insufficient balance in the account due to a simultaneous transaction occurring different method of withdrawal.

3. Disk failure: This is a common reason of failure for DBMS transactions in middle of execution. A particular block of data may result in a failure due to a malfunctioning read/write function in disk (Watson, 2008).

3. Issues in the area of database security refers to the commonly addressed issues while designing a database security module. Following are the major issues associated with database security.

1. Loss of integrity: Integrity of a Database is a measure of assuring that the information stored in the database is not vulnerable to modification from unauthorized access (Elmasri and Navathe, 2014).

2. Loss of availability: This is a common denial of services kind of issue in a database where a user may not be able to access the information for which the user is authenticated to.

3. Loss of confidentiality: This threat refers to the disclosure of information stored in a database to users who do not possess right authentication.

4. Control measures: Control flow is a major issue in database security and defines the level of security in the database access module. Data encryption, interface control and access control are addressed in control measures (Burtescu, 2009).

4. A distributed database system provides some advantages over the conventional centralized database systems. The advantages are listed below:

- Access from remote location: A distributed database allows better sharing of information from remote locations. For example- in a distributed database, employee at one brand of a company can access the files of other branch offices for assistance (Ray, 2009).

- Faster access: data in a distributed system is stored in numerous locations. Each database location has some part of the database. If the information a user is looking for is stored locally then the database access is much faster than a centralized database.

- Faster query resolution: in a distributed system, all the queries are not for the same database hence it is possible to create a parallel query system. This arrangement increases the speed of query resolution.

- Higher availability: In the conventional centralized database in which if the database is offline, the whole network remains idle. This issue is very well addressed in a distributed system as even if one database is offline, all the remaining databases remain active and provide query resolution (Heathcote, 2003).

- Reduced cost: For large companies and enterprises it is cheaper to add more computer systems to manage several databases in distributed manner than to have one mainframe to handle a centralized database.

For a distributed system to provide all the advantages listed below it is essential that the database is modified and accessed by multiple locations. For example- a large corporation that functions with multiple branches in different cities and each branch office has its own database while requiring access to information from other branches as well.

5. Data mining (or Knowledge discovery) is the technique of analyzing a large chunk of data to gather meaningful information from it. The process finds pattern and relationship in the data set and then validates the patterns by implementing the patterns in real world data set (Han, Kamber and Pei, 2011). Some practical applications of data mining are listed below:

- In healthcare industry: Healthcare services collect a huge amount of data and analyzing this data with conventional methods is not practical. Data mining technique provides tools and means of analyzing healthcare characteristics to assess health related risks (Suh, 2012).

- In banking: The banking industry is adapting quickly to data mining practices. Such practices are utilized to identify fraudulent activities from customer accounts.

- Retail industry: retail companies make use of data mining procedures to find out the shopping trends of customers and develop marketing strategy based on the findings.

- Intrusion detection: Data mining techniques finds pattern in a large chunk of data. By comparing the past patterns of a user, such techniques can evaluate if a person is intruder or the legitimate user.

## References:

Ramez, E. (1994). Fundamentals Of Database Systems. Pearson Education India, p.618.

Elmasri, R. and Navathe, S. (2014). Fundamentals of database systems. Harlow: Pearson.

Ray, C. (2009). Distributed database systems. Delhi: Pearson.

Heathcote, P. (2003). 'A' level ICT. Ipswich: Payne-Gallway Publishers.

Burtescu, E. (2009). Database Security - Attacks and Control Methods. Journal of Applies Quantitative Methods, 4(4), pp.452-454.

Watson, R. (2008). Data Management, Databases And Organizations. 3rd ed. John Wiley & Sons, p.512.

Han, J., Kamber, M. and Pei, J. (2011). Data Mining. Burlington: Elsevier Science.

Suh, S. (2012). Practical applications of data mining. Sudbury, Mass.: Jones & Bartlett Learning, p.20.
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