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University of

Assignment Report

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Table of Contents

1. Business Intelligence

1.1. Statement  …………………………………………………………………… 3

1.2. Data mining   …………………………….…………………………………… 3

1.3. Challenges .……………………………………………………………………. 4

1.4. Benefits     ……………………………………………………………………… 5

1.5. Applications   ………………………………………………………………… 5

2. Functions Triggers and Active Rules in Oracle

2.1. Table   ………………………………………………………………………… 6

2.2. Procedures ……………….……………………………………………….. 7

2.3. Sales Report ………….…………………………………………………… 11

2.4. Active Rules Using Triggers ……………………………………….. 12

3. References ………………………………………………………………………. 14

Business Intelligence

1.1 Statement

“We live in a world where vast amounts of data are collected daily. Analyzing such data is an important need.”

2 [Han, Kamber& Pei]

In the light of the above statement, research the field of data analysis and modern database technologies that support business intelligence and write a report which critically evaluates the benefits and issues of adopting any one modern data analysis technology. For example Big Data, Data Warehouse, Data Mining.

1.2 Data Mining

Data mining is also called knowledge discovery, data mining is the process which is used for analyzing data from different source and get useful information. Data mining analyze data from different perspectives and summarized it in to useful data, which can be used to cuts costs, revenue or both. Data mining software’s are one of analytical tools for analyze data. It allow user to analyze data from different source, different dimensions or angles.[1] Data mining is primarily used today by companies with a strong consumer focus - financial, retail, marketing organizations, and communication. It set up these companies to determine relationships among \"internal\" factors such as price, product positioning, or staff skills, and \"exterior\" aspect such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate earnings. Certainly, it set up them to \"drill down\" into summary information to view detail transactional data.

With data mining, a retailer could use point-of-sale records of customer purchases to send intend promotions based on an individual\'s purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments.

1.3 Challenges

There are lots of areas where data mining is applied for data analysis but unfortunately there are some challenges one has to face, when handling steam data there are some additional demands on association rule mining. They are among other caused by a time critical response demand or a high arriving rate of new items. Nowadays association rule mining is applied more and more. It can be used to figure out dependencies between items and to look for linkages between them. Basis is frequent item set mining which has a vast amount of implementation forms stated in [3]. This paper concentrates on its adoption for association rule mining. The number of application areas is large and the cardinality of datasets such an algorithm has to handle can get huge. Therefore the algorithms have to be quite efficient without computational overhead.

During the process of software engineering several phases are passed [4]. Especially the requirement analysis and the following design phase are important if aiming at creating an efficient and effective algorithm, because one has to know the general conditions. There are some such challenges that one has to face when applying stream mining. In [5] Yu and Chi define three of them as ”key challenges” are shown which Challenge represents the requirement that should be fulfilled to improve the efficiency of the algorithm and Description summarises the reasons for the requirement named in Challenge

1. Single Access to data

It is very expensive to access data more than one time. Therefore it is necessary to just access the data once for detecting frequent item sets.

2. Handel inbounded data

The arriving data is unbounded. In contrast the storage and calculating capacity are limited. Hence it is inevitable to get used to work on limited resources.

3. Real-time response

Stream mining algorithms are time-critical and require a short response time. Hence the speed of the association rule algorithm has to be higher than the incoming rate of the data.

4. Data drifting

It is possible that the data distribution evolves over time. Stream mining algorithms have to be able to handle such events.

5. Incremental Processing

Arriving data that evolves may also cause evolving analysis results. Therefore the mining process has to be an incremental one.

1.4 Benefits

Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among \"internal\" factors such as price, product positioning, or staff skills, and \"external\" factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to \"drill down\" into summary information to view detail transactional data, with data mining, a retailer could use point-of-sale records of customer purchases to send planed promotions based on an individual\'s purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments.

Data mining consists of five major elements:

• Load transaction, Extract, and transform data onto the data warehouse system.

• Manage and Store the data in a multidimensional database system.

• Provide data access to business analysts and information technology professionals.

• Analyze the data by application software.

• Present the data in a useful format, such as a graph or table.

1.5 Applications

• Blockbuster Entertainment mines its video rental history database to recommend rentals to individual customers. American Express can advocate products to its cardholders based on analysis of their monthly spending.

• Wal-Mart is pioneering massive data mining to transform its supplier relationships. Wal-Mart captures point-of-sale transactions from over 2,900 stores in 6 countries and continuously transmits this data to its massive 7.5 terabyte Tera data warehouse. Wal-Mart allows more than 3,000 suppliers, to access data on their products and performs data analyses. These suppliers use this data to identify customer buying patterns at the store display level. They use this information to manage local store inventory and identify new merchandising opportunities. In 1995, Wal-Mart computers processed over 1 million complex data queries.

• The National Basketball Association (NBA) is exploring a data mining application that can be used in conjunction with image recordings of basketball games. The Advanced Scout software analyzes the movements of players to help coaches strategies and orchestrate plays. For e.g. an analysis of the play-by-play sheet of the game played between the New York Knicks and the Cleveland Cavaliers on January 7, 1995 announce that when Mark Price played the Guard position, John Williams attempted four jump shots and made each one! Advanced Scout not only finds this pattern, but explains that it is exotic because it differs appreciably from the average shooting percentage of 48% for the Cavaliers during that game.

• By using the NBA universal clock, a coach can automatically bring up the video clips showing each of the jump shots attempted by Williams with Price on the floor, without needing to comb through hours of video footage. Those clips show a very successful pick-and-roll play in which Price draws the Knick\'s defense and then finds Williams for an open jump shot.

Procedure, functions Triggers and Active Rules in Oracle

2.1 Table

   

2.2 Procedures

Execution

When order Return Executed and an order number passed to the trigger

Order cannot be cancelled

Task 2b

Monthly Report Generation

 

Calling Monthly Report

Task 2C

Active Rule Using Triggers

 

Change Log Records

Trigger that records the changes made in order table to log record the changes.

Stock Management

Trigger For View and Update Stock

References

1. Jason frand. Information about Data mining  http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/datamining.htm

2. Yonatan Aumann and Yehuda Lindell. A statistical theory for quantitative association rules. Fifth ACM SIGKDD international conference on Knowledge discovery and data mining, 1999

3. R. Agrawal and R. Srikant. Fast Algorithms for Mining Association Rules. Proc. of the 20th Int’l Conf. on Very Large Data Bases (VLDB). Santiago, Chile. September 12-15, 1994.

4. B.W. Boehm. A Spiral Model of Software Development and Enhancement. IEEE Computer, Vol. 21, No. 5, p.61-72. May, 1988.

5. P. S. Yu and Y. Chi. Association Rule Mining on Streams. Encyclopedia of Database Systems, p.136-139. Springer US. 2009

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