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CSIS-114: Final Exam Review The final exam will include 75 questions with the same general format of the first two exams. It will include mostly T/F, multiple-choice, and fill-ins. About 7-9 questions will have open-ended short paragraph answers (2-4 sentence responses). 25 questions will be taken from Exam 1 25 questions will be taken from Exam 2 8 questions will pertain to student presentations Customer Relationship Management Role of Social Networking System in Marketing Basics of Accounting Systems Google Docs (cloud computing concept) vs. Microsoft Office (installed software) Women in IT Artificial Intelligence in Business It is recommended that you view and read your classmates websites (see the links on the Schedule) 8 questions will come directly from the Chapter 8 and Chapter 9 lectures. It is recommended that you read Chapter 8 and 9 and review the Power Point presentations (see the links on the Schedule) 9 questions will come directly from the Market Basket, SCM, Fund Trading and Pivot chart labs. Be prepared to compare and contrast these labs with previous labs, namely the WageMart and Excel lab. (see the lab review sheet) Lab Review SCM Lab (RFID + XML) RFID - a great example of a hardware technology that gives a company a strategic advantage What is an RFID chip? What is the chip’s role in Supply Chain Management Hints: o RFID allows items to be uniquely identified (ID number) o Superior alternative to Bar Code stickers XML - a great example of information/software technology What is an XML document? How can XML be used with RFID chips? What is the document’s role in Supply Chain Management Tags add context to data: <context> Data </context> XML can encode everything from a Purchase Order to Web Page XML is standardized and robust; What does this mean? Market Basket Analysis - example of Data Mining Support: Probability (P) that an item is in someone’s checkout basket A,B,E C,D A,B,F C,D,G A,B E,F,G A,B,F,G E,F A,D,F E,G P(A) = 5/10 = 50% P(AB) = 4/10 = 40% P(C) = 2/10 = 20% P(CD) = 2/10 = 20% Confidence X Y = P(XY)/P(X) : If item X is purchased, what is the probability that item Y is also purchased Confidence B A Confidence C D = P(AB)/P(A) = 40%/50% = 80% = P(CD)/P(C) = 20%/20% = 100% Quality X Y = Confidence X Y * P(YX) : High quality association rules Quality A B Quality C D = 80% * 40% = 100% * 20% = 32% = 20% Apriori Algorithm: Enables the calculation of high quality, confident association rules given billions of transactions and thousands of items Without the Apriori Algorithm, the calculation would take too long (millions of years). How it works: By setting minimum support level, the algorithm can prune low confidence pairs (2-itemsets) to compute 3-itemsets. Then, the pruned 3-itemsets can compute 4-itemsets. The algorithm is guaranteed to return all the itemsets above the minimum support level. When you get to 5-, 6-, or 7-itemsets, the pruning reduces the number of possible sets from trillions to a few thousand or hundred, which can help humans discover very complex, high quality association rules. Complex Rule Example ADGMS CLPT, i.e., 5 items (A,D,G,M, and S) imply with great confidence that 4 items (C, L, P, and T) are purchased. Pivot Chart Lab - Great example of Online Analytical Processing Slice/view data by dimension Drill down to smaller and smaller data sets Allows lots of data to be summarized and analyzed by human interaction Online means Real-time and Interactive, not on the web Artificial Intelligence for Business We used OLAP to confirm patterns, we encoded the patterns as IF statements to predict future cases. The spreadsheet can automate the human decision making process on a large scale, faster than a human. Such a system enables timely, accurate predictions without a human decision-maker (Business Intelligence System) OLAP (Pivot Chart Lab) vs. Data Mining (Market Basket Lab) OLAP – a hypothesis, human intelligence, and interactive analysis tools drive the pattern finding process Example: A marketer thinks that geography plays a role in sales; a Pivot chart shows that Southern stores have better sales. The hypothesis can come before the data and the data helps to confirmed the hypothesis. Data Mining – An algorithm (Apriori, pattern matching, etc.) drives the pattern finding process. After patterns are found human intelligence can derive a hypothesis. Example: The Apriori algorithm discovers a high quality association rule (Beer Diapers). Later, Marketers try to unravel the reason why. The data analysis must come before the hypothesis because the data is to big for humans to analyze. You see this in Market Basket, Genomic, and Network analysis. Fund Trading Lab (Decision Support Automation) Another Example of Business intelligence: Using a Database to compute the optimal sequence of trades. At first we use a graph and human intuition to make the trades We do better if we use a query to calculate and sort all possible transactions We use Database tools to pick the best one’s that don’t overlap Contrast to Wagemart (Decision Support Summarizing) In Wagemart, we start with tons of data (individual salaries, availability) and reduce it to simple info (total cost, average rating) to help make a decision. In Fund Trading, we start with less data and compute every possible transaction Both system help model scenarios to compute the outcome of decisions, but one is structured (one scenario to optimize) and the other unstructured (many different scenarios to consider). o Wagemart was very unstructured, many different ways to cut costs. o Fund Trading was more structured, i.e., you can only buy and sell and you just have to decide the optimal time and funds to buy/sell.