BUS105 Business Information Systems

1.Define data mining and describe three applications in your own words.

2.List the five major elements of data mining. Provide an example of each element, for example, element 1 is extracting, transforming, and loading transaction data onto the data warehouse system. Data on mobile phone sales can be extracted, transformed into categories, e.g. brand, size, etc., and placed onto a database or warehouse.

3. Using the articles you have read, write a paragraph in your own words on five issues (problems) with data mining.

Answers:

The process of gathering, penetrating through, and examining abundant data in the database, for determining the data communication and patterns is termed as Data Mining (Bharati & Ramageri, 2010). Data Mining is extensively utilized in many areas.

Analysis of Financial Data

The financial information contained in banks and financial industry is normally stable and of greater aspect that leads to precise analysis of data and data mining. Some cases are:

• Data Warehouse Construction and Design
• Prognosis of Loan Payment

2. Retail Industry

Data mining has its wide application in Retail Industry since it gathers abundant information from on services, sales, product consumption, transportation of goods, and purchasing history of customers (Sharma, Kaur & Manju, 2013). Examples are:

• Customer Detention
• Effectiveness analysis on purchase operations

3. Telecommunication Industry

Being used in Telecommunication Industry, Data mining identifies the telecommunication devices, predict fraudulent movements, etc. Some examples are:

• Telecommunication Data Analysis
• Multidimensional syndication and analysis of sequential device patterns

Artificial Intelligence plays a major role in data mining since it implements manual activity to statistical issues (Sonawane & Nandwalkar, 2015). Data mining contains five major elements:

1. The data are extracted, transformed, and loaded on to the data warehouse system.
2. The data are stored and administered in multidimensional database environment.
3. The access to data is provided to the business inquisitors and information technology specialists.
4. The data are analyzed by means of application software.
5. The data can be visualized in an appropriate format like table or graphs.

Example:

The National Cricket Association is employing a data mining application. The score and success prediction in the cricket match is provided by the data mining application. The examples for five data mining elements in the specified data mining application are given below:

1. The records for the cricket game are extracted, transformed into categories like game statistics, images, player movements, current run rate, etc.
2. The cricket game records are managed and stored in a multidimensional database environment.
3. The data access is provided only to the experts for giving precise prediction in the cricket match.
4. The cricket records are combined and analyzed by means of advanced pioneer software.
5. The derived record patterns are visualized in the interactive formats like graph, table, or charts (Gheorghe & Petre, 2014). Thereby, the score and success estimation in the cricket match is efficiently offered by the data mining application by analysing the data from different angles and dimensions.

Data mining is not a simple task since the integrated algorithms are very difficult and the information is not present in single place alone (Goebel & Gruenwald, 1999).

There are 20,224 accounting jobs are available today.

1. The “Any Classification” menu is clicked.

Based on the findings, Accounting jobs are comparatively higher than any other jobs. More number of skilled people is employed in accounting jobs since every business needs to maintain the financial records (Ganga, 2014). Other notable jobs further down the list are Sales, Administration, and Information Technology, show casing the evolving importance of commercial business data (Kumar & Swaroop, 2013).

1. The number of jobs found in the related pay category is summarized in the table below:

There are 11,291 jobs are available in the salary range of $50k to $80k. Hence more number of jobs is available from $50k to $80k. Next higher in the list is $40k to $60k pay range. So, the jobs for $40k to $60k are comparatively higher than the $30k to $40k pay range category. Next higher in list in $100k to $200k in which more number of accounting jobs are available in this price category. The lower salary range i.e. from $30k to $40k is mostly provided to storekeepers, insurance agents, and receptionists.

1. The table illustrating the number of jobs available in certain locations is provided below:

While comparing the jobs available in two states, the career especially accounting jobs are more in South Australia when compared to New Zealand. The Adelaide city contains more number of jobs than any other locations in South Australia. The Auckland city contains massive amount of jobs when compared to any other cities in New Zealand.

The financial accountant salary is very high when compared to other account related jobs in Australia. More graduate accountants can transition to other job if they have more than 20 years of experience. An additional expertise of financial modelling is required for the graduate accountants for procuring high payment for this job.

The salary of chartered accountant job is very higher than any other account related jobs in New Zealand. The persons having 5-9 years of experience in this career is recommended for the accounting graduates in the related field.

The location, job experience, and the job related expertise decides the payment and other benefits for the graduates.

Reference

Balasubramanian, M & Selvarani, M, 2013, Churn Prediction In Mobile Telecommunication System Using Data Mining Techniques, International Journal of Scientific and Research Publications, vol. 4, no. 4, pp. 15-25.

Bharati, M & Ramageri, R, 2010, Data Mining Techniques and Applications, Indian Journal of Computer Science and Engineering, Vol. 1, no. 4, pp. 301-305.

Ganga, S, 2014, A SURVEY ON DISTRIBUTED DATA MINING AND ITS TRENDS, International Journal of Research in Engineering & Technology, vol. 2, no. 3, pp. 107-120.

Gheorghe, M & Petre, R, 2014, The importance of normalization methods for mining medical data, International journal of computers & technology, Vol.14, No.8, pp.22-30.

Goebel, M & Gruenwald, L, 1999, A Survey of Data Mining and Knowledge Discovery Software Tools, SIGKDD Explorations, Vol.1, no.1, pp. 45-50.

Kumar, D & Swaroop, V, 2013, Data Security and Privacy in Data Mining: Research Issues & Preparation, International Journal of Computer Trends and Technology, vol.1, no.2, pp. 2231-2803.

Sonawane, N & Nandwalkar, B, 2015, Time Efficient Sentinel Data Mining using GPU, International Journal of Engineering Research and Technology, vol. 4, no. 2, pp. 7-15.

Sharma, B, Kaur, D & Manju, D, 2013, A Review on Data Mining: Its Challenges, Issues and Applications, International Journal of Current Engineering and Technology, vol. 3, no. 2.

Vaughan, L & Chen, Y, 2015, Data mining from web search queries: A comparison of google trends and baidu index, Journal of the Association for Information Science and Technology, vol. 66, no. 1, pp. 13-22.

Vikas, G, 2013, A survey on Data Mining: Tools, Techniques, Applications, Trends and Issues, International Journal of Scientific & Engineering Research, vol. 4, no. 3.