Data Mining - Concepts and Techniques.

According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

MKT 6009 Marketing Internship (0 semester credit hours) Student gains experience and improves skills through appropriate developmental work assignments in a real business environment. Student must identify and submit specific business learning objectives at the beginning of the semester. The student must demonstrate exposure to the managerial perspective via involvement or observation. At semester end, student prepares an oral or poster presentation, or a written paper reflecting on the work experience. Student performance is evaluated by the work supervisor. Pass/Fail only. Prerequisites: (MAS 6102 or MBA major) and department consent required. (0-0) S MKT 6244 Digital Marketing Strategy (2 semester credit hours) Executive Education Course. The course explores three distinct areas within marketing and sales namely, digital marketing, traditional sales prospecting, and executive sales organization and strategy. The continuing convergence of the digital marketing and sales funnels has created a strategic continuum from digital lead generation to digital sales. The course identifies the current composition of this digital continuum while providing opportunities to evaluate sales and marketing digital strategies. Prerequisites: MKT 6301 and instructor consent required. (2-0) Y MKT 6301 (SYSM 6318) Marketing Management (3 semester credit hours) Overview of marketing management methods, principles and concepts including product, pricing, promotion and distribution decisions as well as segmentation, targeting and positioning. (3-0) S MKT 6309 Marketing Data Analysis and Research (3 semester credit hours) Methods employed in market research and data analysis to understand consumer behavior, customer journeys, and markets so as to enable better decision-making. Topics include understanding different sources of data, survey design, experiments, and sampling plans. The course will cover the techniques used for market sizing estimation and forecasting. In addition, the course will cover the foundational concepts and techniques used in data visualization and \"story-telling\" for clients and management. Corequisites: MKT 6301 and OPRE 6301. (3-0) Y MKT 6310 Consumer Behavior (3 semester credit hours) An exposition of the theoretical perspectives of consumer behavior along with practical marketing implication. Study of psychological, sociological and behavioral findings and frameworks with reference to consumer decision-making. Topics will include the consumer decision-making model, individual determinants of consumer behavior and environmental influences on consumer behavior and their impact on marketing. Prerequisite: MKT 6301. (3-0) Y MKT 6321 Interactive and Digital Marketing (3 semester credit hours) Introduction to the theory and practice of interactive and digital marketing. Topics covered include: online-market research, consumer behavior, conversion metrics, and segmentation considerations; ecommerce, search and display advertising, audiences, search engine marketing, email, mobile, video, social networks, and the Internet of Things. (3-0) T MKT 6322 Internet Business Models (3 semester credit hours) Topics to be covered are: consumer behavior on the Internet, advertising on the Internet, competitive strategies, market research using the Internet, brand management, managing distribution and supply chains, pricing strategies, electronic payment systems, and developing virtual organizations. Further, students learn auction theory, web content design, and clickstream analysis. Prerequisite: MKT 6301. (3-0) Y MKT 6323 Database Marketing (3 semester credit hours) Techniques to analyze, interpret, and utilize marketing databases of customers to identify a firm's best customers, understanding their needs, and targeting communications and promotions to retain such customers. Topics

Marketing for management.

Under the umbrella of buzzwords such as “Business Activity Monitoring” (BAM) and “Business Process Intelligence” (BPI) both academic (e.g., EMiT, Little Thumb, InWoLvE, Process Miner, and MinSoN) and commercial tools (e.g., ARIS PPM, HP BPI, and ILOG JViews) have been developed. The goal of these tools is to extract knowledge from event logs (e.g., transaction logs in an ERP system or audit trails in a WFM system), i.e., to do process mining. Unfortunately, tools use different formats for reading/storing log files and present their results in different ways. This makes it difficult to use different tools on the same data set and to compare the mining results. Furthermore, some of these tools implement concepts that can be very useful in the other tools but it is often difficult to combine tools. As a result, researchers working on new process mining techniques are forced to build a mining infrastructure from scratch or test their techniques in an isolated way, disconnected from any practical applications. To overcome these kind of problems, we have developed the ProM framework, i.e., an “pluggable” environment for process mining. The framework is flexible with respect to the input and output format, and is also open enough to allow for the easy reuse of code during the implementation of new process mining ideas. This paper introduces the ProM framework and gives an overview of the plug-ins that have been developed.

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The prom framework: a new era in process mining tool support

Exploring and identifying structure is even more important for multivariate data than univariate data, given the difficulties in graphically presenting multivariate data and the comparative lack of parametric models to represent it. Unfortunately, such exploration is also inherently more difficult.

Multivariate Density Estimation

A method and apparatus for end-to-end retail store one-stop site configuration integrates multiple data sources, identifying key customers, forecasting merchandise demand. Site configuration is formulated as a mathematical optimization problem with both in-store and external data as input to the problem whose solution provides proper suggestions for retail store transformation.

Method and apparatus for end-to-end retail store site optimization

A method for predicting sales for a new store in a certain geographical area is disclosed, the method comprising geographic and non-geographic information and customer segmentation in the area to estimate sales and optionally the impact on existing competitor stores.

Sales predication for a new store based on on-site market survey data and high resolution geographical information

In an era of globalization, it is almost impossible for a company to stay competitive with its supply chain locating in a single country ( A. Sabbaghi & N. Sabbaghi, 2004 , Global supply-chain strategy and global competitiveness. International Business & Economics Research Journal, 3 (7), 63–76). On the other hand, increasing global sourcing and selling brings substantial challenges on the material flow, information flow, and financial flow along lengthened supply chains. Many researches in the past decades have illustrated the benefits of supply chain visibility by streamlining information flow ( Eppen, 1979 , Effects of centralization on expected costs in a multilocation newsboy problem. Management Science, 25 (5), 498–501; Lee & Whang, 2000 , Information sharing in a supply chain. International Journal of Manufacturing Technology and Management, 1 (1), 79–93; Vickery, Jayaram, Droge, & Calantone, 2003 , The effects of an integrative supply chain strategy on customer service and financial performance: An analysis of direct versus indirect relationships. Journal of Operations Management, 21 (5), 523–539; Zhang & Zipkin, 1990 , A queueing model to analyze the value of centralized inventory information. Operations Research, 38 (2), 296–307). It is well known that an informational centralized supply chain outperforms its decentralized counterpart through reducing bullwhip effect (Chen, Drezner, Ryan, & Simchi-Levi, 2000, Qualifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information. Management Science, 46 (3), 436–443; Lee, Padmanabhan, & Whang, 1997 , Information distortion in a supply chain: The bullwhip effect. Management Science, 34 (4), 546–558).

Supply Chain Finance: Concept and Modeling

This paper describes a new approach to emotion recognition based on pressure sensor keyboards. The pressure sensor keyboard is a new product that occurs in the market recently, which produces a pressure sequence when keystroke occurs. The analysis of the pressure sequence should be a novel research area. It has been used for identity verification in our previous research. In this paper, we use the pressure sequence for emotion recognition. Three methods (global features of pressure sequences, dynamic time warping and traditional keystroke dynamics) are proposed for the emotion recognition task; then we combined the three methods together using a classifier fusion technique. Several experiments were performed on a database containing 3000 samples (from 50 individuals, including six emotions: neutral, anger, fear, happiness, sadness and surprise) and the best result were achieved utilizing all the method, obtaining an overall accuracy of 93.4%. Our technique of emotion recognition has been used for intelligent game controlling and several other applications.

Emotion recognition based on pressure sensor keyboards

Random forest is an excellent ensemble learning method, which is composed of multiple decision trees grown on random input samples and splitting nodes on a random subset of features. Due to its good classification and generalization ability, random forest has achieved success in various domains. However, random forest will generate many noisy trees when it learns from the data set that has high dimension with many noise features. These noisy trees will affect the classification accuracy, and even make a wrong decision for new instances. In this paper, we present a new approach to solve this problem through weighting the trees according to their classification ability, which is named Trees Weighting Random Forest (TWRF). Here, Out-Of-Bag, which is the training data subset generated by Bagging and not involved in building decision tree, is used to evaluate the tree. For simplicity, we choose the accuracy as the index that notes tree’s classification ability and set it as the tree’s weight. Experiments show that TWRF has better performance than the original random forest and other traditional methods, such as C45, Naive Bayes and so on.

Jin Dong

Papers

Method and apparatus for end-to-end retail store site optimization

Sales predication for a new store based on on-site market survey data and high resolution geographical information

Supply Chain Finance: Concept and Modeling

Emotion recognition based on pressure sensor keyboards

Trees Weighting Random Forest Method for Classifying High-Dimensional Noisy Data