Management And Accounting Web

Data Mining and Big Data Bibliography

Provided by James R. Martin, Ph.D., CMA
Professor Emeritus, University of South Florida

Data Mining Main Page | Quantitative Methods Main Page

Aldhizer, G. R. III. 2017. Visual and text analytics: The next step in forensic auditing and accounting. The CPA Journal (June): 30-33.

Alles, M. and G. L. Gray. 2016. Incorporating big data in audits: Identifying inhibitors and a research agenda to address those inhibitors. International Journal of Accounting Information Systems (22): 44-59.

Alles, M., G. Brennan, A. Kogan and M. A. Vasarhelyi. 2006. Continuous monitoring of business process controls: A pilot implementation of a continuous auditing system at Siemens. International Journal of Accounting Information Systems 7(2): 137-161.

Amani, F. A. and A. M. Fadlalla. 2017. Data mining applications in accounting: A review of the literature and organizing framework. International Journal of Accounting Information Systems (24): 32-58.

Anders, S. B. 2017. Audit data analytics resources. The CPA Journal (June): 72-73.

Appelbaum, D. 2016. Securing big data provenance for auditors: The big data provenance black box as reliable evidence. Journal of Emerging Technologies in Accounting (13): 17-36.

Appelbaum, D., A. Kogan and M. A. Vasarhelyi. 2017. An introduction to data analysis for auditors and accountants. The CPA Journal (February): 32-37. (Summary).

Appelbaum, D., A. Kogan and M. A. Vasarhelyi. 2017. Big data and analytics in the modern audit engagement: Research needs. Auditing: A Journal of Practice & Theory 36(4): 1-27.

Appelbaum, D., A. Kogan, M. Vasarhelyi and Z. Yan. 2017. Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems (25): 29-44. (Summary).

Barton, D. and D. Court. 2012. Making advanced analytics work for you: A Practical guide to capitalizing on big data. Harvard Business Review (October): 78-83. (Choose the right data, Build models that predict and optimize business outcomes, and Transform your company's capabilities).

Basu, A., 2013. Executive edge: Five pillars of prescriptive analytics success. Analytics Magazine. (March/April): 8-12. (Hybrid data, integrated predictions and prescriptions, prescriptions and side effects, adaptive algorithms, and feedback mechanism).

Berinato, S. 2014. With big data comes big responsibility. Harvard Business Review (November): 100-104.

Berry, M. J. A. and G. S. Linoff. 2004. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. Wiley Computer Publishing.

Borthick A. F., G. P. Schneider and T. R. Viscelli. 2017. Analyzing data for decision making: Integrating spreadsheet modeling and database querying. Issues in Accounting Education (February): 59-66.

Bramer, M. 2007. Principles of Data Mining (Undergraduate Topics in Computer Science. Springer.

Brown-Liburd, H., H. Issa and D. Lombardi. 2015. Behavioral implications of big data's impact on audit judgment and decision making and future research directions. Accounting Horizons (June): 451-468.

Calderon, T. G., J. J. Cheh and I. Kim. 2003. How large corporations use data mining to create value. Management Accounting Quarterly (Winter): 1-11.

Cao, M., R. Chychyla and T. Stewart. 2015. Big data analytics in financial statement audits. Accounting Horizons (June): 423-429.

Chae, B. and D. L. Olson. 2013. Business analytics for supply chain: A dynamic-capabilities framework. International Journal of Information Technology & Decision Making 12 (01): 9-26.

Chae, B. K., C. Yang, D. Olson, and C. Sheu. 2014. The impact of advanced analytics and data accuracy on operational performance: A contingent resource based theory (RBT) perspective. Decision Support Systems (59): 119–126.

Chai, S. and W. Shih. 2017. Why big data isn't enough. MIT Sloan Management Review (Winter): 57-61.

Chaudhuri, S., U. Dayal, and V. Narasayya. 2011. An overview of business intelligence technology. Communications of the ACM 54(8), 88-98.

Chugh, R. and S. Grandhi. 2013. Why business intelligence? Significance of business intelligence tools and integrating BI governance with corporate governance. International Journal of E-Entrepreneurship and Innovation (IJEEI) 4 (2), 1-14.

Churyk, N. T., D. Janvrin and M. W. Watson. 2017. Special issue on Big Data. Journal of Accounting Education (38): 1-2.

Cokins, G. 2013. Top 7 trends in management accounting. Strategic Finance (December): 20-29.

Collins, J. C. 2017. Data mining your general ledger with Excel. Journal of Accountancy (January): 27-32.

Cosic, R., G. Shanks and S. Maynard. 2012. Towards a business analytics capability maturity model. Location, location, location. Proceedings of the 23rd Australasian Conference on Information Systems: 1-11.

Davenport, T., 2014. Big Data at Work: Dispelling the myths, Uncovering the Opportunities. Harvard Business Review Press.

Davenport, T. H. 2006. Competing on analytics. Harvard Business Review (January): 98-107. ("Some companies have built their very businesses on their ability to collect, analyze, and act on data. Every company can learn from what these firms do." Some applications include: 1) Simulating and optimizing supply chain flows, reducing inventory and stock-outs, 2) Identifying customers with the greatest profit potential, 3) Identifying the price that will maximize yield or profit, 4) Selecting the best employees for tasks or jobs, 5) Detecting and minimizing quality problems, 6) Proving a better understanding of the drivers of financial performance including nonfinancial factors, 7) Improving quality, efficacy and safety of products and services).

Davenport, T. H. 2013. Analytics 3.0. Harvard Business Review (December): 64-72.

Davenport, T. H. 2014. What businesses can learn from sports analytics. MIT Sloan Management Review (Summer): 10-13.

Davenport, T. H. and J. G. Harris. 2007. Competing on Analytics: The New Science of Winning. Harvard Business School Press.

Davenport, T. H., J. G. Harris and R. Morison. 2010. Analytics at Work: Smarter Decisions, Better Results. Harvard Business Press.

Davenport, T. H. and S. Kudyba. 2016. Designing and developing analytics-based data products. MIT Sloan Management Review (Fall): 82-89.

Davenport, T. H., J. G. Harris and Robert Morison. 2010. Analytics at Work: Smarter Decisions, Better Results. Harvard Business Press.

Davenport, T. H., P. Barth and R. Bean. 2012. How 'big data' is different. MIT Sloan Management Review (Fall): 43-46.

Debreceny, R. and G. L. Gray. 2004. Grab your picks and shovels! There's gold in your data. Strategic Finance (January): 24-28.

Dilla, W., D. J. Janvrin and R. Raschke. 2010. Interactive data visualization: New directions for accounting information systems research. Journal of Information Systems (Fall): 1-37.

Dilla, W. N. and R. L. Raschke. 2015. Data visualization for fraud detection: Practice implications and a call for future research. International Journal of Accounting Information Systems (16): 1-22.

Duman, E. and M. H. Ozcelik. 2011. Detecting credit card fraud by genetic algorithm and scatter search. Expert Systems with Applications 38 (10): 13057-13063.

Dunn, C. L., G. J. Gerard and S. V. Grabski. 2017. The combined effects of user schemas and degree of cognitive fit on data retrieval performance. International Journal of Accounting Information Systems (26): 46-67.

Enget, K., G. D. Soucedo and N. S. Wright. 2017. Mystery, Inc.: A Big Data case. Journal of Accounting Education (38): 9-22.

Fay, R. and E. M. Negangard. 2017. Manual journal entry testing: Data analytics and the risk of fraud. Journal of Accounting Education (38): 37-49.

Fischetti, T. 2015. Data Analysis with R. Packt Publishing.

Fisher, I. E., M. R. Garnsey, S. Goel and K. Tam. 2010. The role of text analytics and information retrieval in the accounting domain. Journal of Emerging Technologies in Accounting (7): 1-24.

Fitzgerald, M. 2015. General Mills builds up big data to answer big questions. MIT Sloan Management Review (Summer): 34.

Fitzgerald, M. 2016. Better data brings a renewal at the Bank of England. MIT Sloan Management Review (Summer): 3-13.

Fitzgerald, M. 2016. Building a better car company with analytics. MIT Sloan Management Review (Summer): 40-44.

Fitzgerald, M. 2016. Data-driven city management. MIT Sloan Management Review (Summer): 3-10.

Fitzgerald, M. 2016. General Motors relies on IoT to keep its customers safe and secure. MIT Sloan Management Review (Summer): 86-91.

Fogarity, D. and P. C. Bell. 2014. Should you outsource analytics? MIT Sloan Management Review (Winter): 41-45.

Foreman, J. W. 2013. Data Smart: Using Data Science to Transform Information into Insight. Wiley.

Gray, G. L. and R. S. Debreceny. 2014. A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits. International Journal of Accounting Information Systems 15(4): 357-380.

Gregg, A. 2017. Start-ups embrace cryptocurrency to raise needed capital: 'Initial coin offerings' let companies raise money without ceding control. The Washington Post (December 4): A13. (Note).

Griffin, P. A. and A. M. Wright. 2015. Commentaries on big data's importance for accounting and auditing. Accounting Horizons (June): 377-379.

Grus, J. 2015. Data Science from Scratch: First Principles with Python. O'Reily Media.

Hagel, J. 2013. Why accountants should own big data. Journal of Accountancy (November): 20-21. (Business intelligence).

Han, J. and M. Kamber. 2006. Data Mining Concepts and Techniques. Morgan Kaufmann Publishers.

Harvard Business Review. 2017. How companies really use big data. Harvard Business Review (September/October): 26.

Harvard Business Review. 2017. How data science is disrupting the job market. Harvard Business Review (September/October): 24.

Hastie, T., R. Tibshirani and J. Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, second edition. Springer.

Hayashi, A. M. 2014. Thriving in a big data world. MIT Sloan Management Review (Winter): 35-39.

Hey, T. 2010. The next scientific revolution. Harvard Business Review (November): 56-63.

Hoffman, R. 2016. Using artificial intelligence to set information free. MIT Sloan Management Review (Fall): 1-15.

Hogarth, R. M. and E. Soyer. 2015. Using simulated experience to make sense of big data. MIT Sloan Management Review (Winter): 49-54.

Holsapple, C., A. Lee-Post and R. Pakath. 2014. A unified foundation for business analytics. Decision Support. Systems. 64, 130-141.

Holt, M., B. Lang and S. G. Sutton. 2017. Potential employees' ethical perceptions of active monitoring: The dark side of data analytics. Journal of Information Systems (Summer): 107-124.

Holton, C., 2009. Identifying disgruntled employee systems fraud risk through text mining: A simple solution for a multi-billion dollar problem. Decision Support Systems 46(4): 853-864.

Hua, Z., Y. Wang, X. Xu, B. Zhang and L. Liang. 2007. Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Systems with Applications 33(2): 434-440.

Janert, P. K. 2010. Data Analysis with Open Source Tools. O'Reilly Media.

Jans, M., M. Alles and M. Vasarhelyi. 2013. The case for process mining in auditing: Sources of value added and areas of application. International Journal of Accounting Information Systems 14(1): 1-20.

Janvrin, D. J. and M. W. Watson. 2017. "Big data": A new twist to accounting. Journal of Accounting Education (38): 3-8.

Jernigan, S., S. Ransbotham and D. Kiron. 2016. Data sharing and analytics drive success with IoT. MIT Sloan Management Review (Fall): 1-17.

Journal of Accountancy. 2016. 6 ethical questions about big data. Journal of Accountancy (October): 24.

Kabacoff, R. 2015. R in Action: Data Analysis and Graphics with R. Manning Publications.

Kane, G. C. 2015. How digital transformation is making health care safer, faster and cheaper. MIT Sloan Management Review (Fall): 41-47.

Kane, G. C. 2017. Big data and IT talent drive improved patient outcomes at Schumacher Clinical Partners. MIT Sloan Management Review (Fall): 96.

Khalifa, M. and I. Zabani. 2016. Utilizing health analytics in improving the performance of healthcare services: A case study on a tertiary care hospital. Journal of Infection and Public Health (November-December): 757-765.

Kim, R., J. Gangolly and P. Elsas. 2017. A framework for analytics and simulation of accounting information systems: A Petri net modeling primer. International Journal of Accounting Information Systems (27): 30-54.

Kirkos, E., C. Spathis and Y. Manolopoulos. 2007. Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications 32 (4), 995-1003.

Koch, R. 2015. Big data or big empathy? Strategic Finance (December): 62-63.

Kohavi, R., L. Mason, R. Parekh and Z. Zheng. 2004. Lessons and challenges from mining retail e-commerce data. Machine Learning 57 (1-2): 83-113.

Kokina, J., D. Pachamanova and A. Corbett. 2017. The role of data visualization and analytics in performance management: Guiding entrepreneurial growth decisions. Journal of Accounting Education (38): 50-62. (A case that addresses the growing need for accountants to develop compentency in predictive analytics).

Kovalerchuk, B., E. Vityaev and R. Holtfreter. 2007. Correlation of complex evidence in forensic accounting using data mining. Journal of Forensic Accounting 8(1-2): 53-88.

Krahel, J. P. and W. R. Titera. 2015. Consequences of big data and formalization on accounting and auditing standards. Accounting Horizons (June): 409-422.

Kwon, O., N. Lee and B. Shin. 2014. Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management. 34 (3), 387-394.

Larose, D. T. 2004. Discovering Knowledge in Data: An Introduction to Data Mining. Wiley-Interscience.

Laursen, G. H. N. and J. Thorlund. 2010. Business Analytics for Managers: Taking Business Intelligence Beyond Reporting. Wiley.

Li, H., J. Dai, T. Gershberg and M. A. Vasarhelyi. 2018. Understanding usage and value of audit analytics for internal auditors: An organizational approach. International Journal of Accounting Information Systems (28): 59-76.

Lin, P. P. 2014. What CPAs need to know about big data. The CPA Journal (November): 50-55.

Liu, B. 2007 and 2010. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer.

Liu, Y. and K. C. Moffitt. 2016. Text mining to uncover the intensity of SEC comment letters and its association with the probability of 10-K restatement. Journal of Emerging Technologies in Accounting (13): 85-94.

Loveman, G. 2003. Diamonds in the data mine. Harvard Business Review (May): 109-123.

Markov, Z. and D. T. Larose. 2007. Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage. Wiley-Interscience.

Martin, J. R. Not dated. What is data mining? Management And Accounting Web.

Matignon, R. 2007. Data Mining Using SAS Enterprise Miner. Wiley-Interscience.

May, T. 2009. The New Know: Innovation Powered by Analytics. Wiley.

McAfee, A. and E. Brynjolfsson. 2012. Big data: The management revolution: Exploiting vast new flows of information can radically improve your company's performance. But first you'll have to change your decision-making culture. Harvard Business Review (October): 60-68.

McCue, C. 2007. Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis. Butterworth-Heinemann.

McKinney, E. Jr., C. J. Yoos II and K. Snead. 2017. The need for 'skeptical' accountants in the era of Big Data. Journal of Accounting Education (38): 63-80.

Milton, M. 2009. Head First Data Analysis: A Learner's Guide to Big Numbers, Statistics, and Good Decisions. O'Reilly Media.

MIT Sloan Management Review. 2017. Lessons from becoming a data-driven organization. MIT Sloan Management Review (Winter): 3-13.

Moffit, K. C. and M. A. Vasarhelyi. 2013. Editorial. AIS in a age of big data. Journal of Information Systems (Fall): 1-19.

Nichols, W. 2013. Advertising analytics 2.0: Marketers now have and unprecedented ability to fine-tune their allocation decisions while making course corrections in real time. Harvard Business Review (March): 60-68.

Nielsen, S. 2015. The impact of business analytics on management accounting.

Framework on Business Analytics 

Nielsen, S., E. H. Nielsen, A. Jacobsen and L. Bjern Pedersen. 2014. Management accounting and business analytics: An example of system dynamics modelling's use in the design of a balanced scorecard. Danish Journal of Management & Business 78(3-4): 31-44.

Nisbet, R., J. Eder IV and G. Miner. 2009. Handbook of Statistical Analysis and Data Mining Applications. Academic Press.

Ott, R. L and M. T. Longnecker. 2015. An Introduciton to Statistical Methods and Data Analysis. 7th Edition. Brooks Cole.

Padmanabhan, B. and A. Tuzhilin. 2002. Knowledge refinement based on the discovery of unexpected patterns in data mining. Decision Support Systems 33(3): 309-321.

Padmanabhan, B. and A. Tuzhilin. 2003. On the use of optimization for data mining: Theoretical interactions and eCRM opportunities. Management Science (October): 1327-1343.

Peters, M. D., B. Wieder, S. G. Sutton and J. Wakefield. 2016. Business intelligence systems use in performance capabilities: Implications for enhanced competitive advantage. International Journal of Accounting Information Systems (21): 1-17.

Porter, M. E. and J. E. Heppelmann. 2017. Why every organization needs an augmented reality strategy. Harvard Business Review (November/December): 46-57. (Augmented reality or AR "transforms volumes of data and analytics into images or animations that are overlaid on the real world." ..."By superimposing digital information directly on real objects or environments, AR allows people to process the physical and digital simultaneously, eliminating the need to mentally bridge the two. That improves our ability to rapidly and accurately absorb information, make decisions, and execute required tasks quickly and efficiently."..."Every company needs an implementation road map that lays out how the organization will start to capture the benefits of AR in its business while building the capabilities needed to expand its use."... "It will profoundly change training and skill development, allowing people to perform sophisticated work without protracted and expensive conventional instruction - a model that is inaccessible to so many today. AR, then, enables people to better tap into the digital revolution and all it has to offer.").

Prokesch, S. 2017. Reinventing talent management: How GE uses analytics to guide a more digital, farflung workforce. Harvard Business Review (September/October): 54-55.

Provost, F. and T. Dawcett. 2013. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking.  O'Reily Media.

Rajaraman, A., J. Leskovec and J. D. Ullman. 2012. Mining of Massive Datasets.

Ransbotham, S. and D. Kiron. 2017. Analytics as a source of business innovation. MIT Sloan Management Review (Spring): 1-16.

Ransbotham, S. 2017. The subtle sources of sampling bias hiding in your data. MIT Sloan Management Review (Fall): 20-22.

Ransbotham, S., D. Kiron, P. Gerbert and M. Reeves. 2017. Reshaping business with artificial intelligence. MIT Sloan Management Review (Fall): 1-17.

Redman, T. C. 2008. Data Driven: Profit from Your Most Important Business Asset. Harvard Business School Press.

Riggins, F. J. and B. K. Klamm. 2017. Data governance case at KrauseMcMahon LLP in an ear of self-service BI and Big Data. Journal of Accounting Education (38): 23-36.

Roberts-Witt, S. L. 2001. Gold diggers: Let customers and partners mine your data using new e-business intelligence tools. It could turn into a gold rush. PC Magazine (February, 20): ibiz 6-ibiz 10.

Roberts-Witt, S. L. 2002. Data mining: What lies beneath? Finding patterns in customer behavior can deliver profitable insights into your business. PC Magazine (November, 19): iBiz 1-6. (Note).

Rosenbaum, D. 2012. Digging out from big data: Unstructured data is piling up in corporate computers, making compliance and other tasks more difficult. CFO (July/August): 32-33.

Ross, J. W., C. M. Beath and A. Quaadgras. 2013. You may not need big data after all. Harvard Business Review (December): 90-98.

Schneider, G. P., J. Dai, D. J. Janvrin, K. Ajayi and R. L. Raschke. 2015. Infer, predict, and assure: Accounting opportunities in data analytics. Accounting Horizons (September): 719-742.

Schymik, G., K. Corral, D. Schuff and R. St. Louis. 2015. The benefits and costs of using metadata to improve enterprise document searches. Decision Sciences 46(6): 1049-1075.

Scott, J. 2015. Optimizing big data. Strategic Finance (November): 12.

Seow, P., G. Pan and T. Suwardy. 2016. Data mining journal entries for fraud detection: A replication of Debreceny and Gray's (2010) techniques. Journal of Forensic & Investigative Accounting 8(3): 501-514.

Sharda, R., D. Delen and E. Turban. 2017. Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition. Pearson. (Contents: Chapter 1: An Overview of Business Intelligence, Analytics, and Data Science. Chapter 2: Descriptive Analytics I: Nature of Data, Statistical Modeling, and Visualization. Chapter 3: Descriptive Analytics II: Business Intelligence and Data Warehousing. Chapter 4: Predictive Analytics I: Data Mining Process, Methods, and Algorithms. Chapter 5: Predictive Analytics II: Text, Web, and Social Media Analytics. Chapter 6: Prescriptive Analytics: Optimization and Simulation. Chapter 7: Big Data Concepts and Tools. Chapter 8: Future Trends, Privacy and Managerial Considerations in Analytics).

Shirata, C. Y. and M. Sakagami. 2008. An analysis of the “going concern assumption”: Text mining from Japanese financial reports. Journal of Emerging Technologies in Accounting (5): 1-16.

Shirata, C. Y., H. Takeuchi, S. Ogino and H. Watanabe. 2011. Extracting key phrases as predictors of corporate bankruptcy: Empirical analysis of annual reports by text mining. Journal of Emerging Technologies in Accounting (8): 31-44.

Short, J. E. and S. Todd. 2017. What's your data worth? MIT Sloan Management Review (Spring): 17-19.

Silvi, R., K. Moeller and M. Schlacfke. 2010. Performance management analytics - The next extension in managerial accounting.

Skiena, S. S. 2017. The Data Science Design Manual. Springer. (Contents: Chapter 1: What is Data Science? Chapter 2: Mathematical Preliminaries. Chapter 3: Data Munging. Chapter 4: Scores and Ranking. Chapter 5: Statistical Analysis. Chapter 6: Visualizing Data. Chapter 7: Mathematical Models. Chapter 8: Linear Algebra. Chapter 9: Linear and Logistic Regression. Chapter 10: Distance and Network Methods. Chapter 11: Machine Learning. Chapter 12: Big Data: Achieving Scale. Chapter 13: Coda.)

Sledgianowski, D., M. Gomaa and C. Tan. 2017. Toward integration of Big Data, technology and information systems competencies into the accounting curriculum. Journal of Accounting Education (38): 81-93.

Tan, P., M. Steinbach and V. Kumar. 2005. Introduction to Data Mining. Addison Wesley.

Tang, J. and K. E. Karim. 2017. Big data in business analytics: Implications for the audit profession. The CPA Journal (June): 34-39.

Torgo, L. 2010. Data Mining with R: Learning with Case Studies. Chapman and Hall/CRC.

Tschakert, N., J. Kokina, S. Kozlowski and M. Vasarhelyi. 2017. How business schools can integrate data analytics into the accounting curriculum. The CPA Journal (September): 10-12. (Summary).

Tsiptsis, K. and A. Chorianopoulos. 2010. Data Mining Techniques in CRM: Inside Customer Segmentation. Wiley.

Vasarhelyi, M. A., A. Kogan and B. M. Tuttle. 2015. Big data in accounting: An overview. Accounting Horizons (June): 381-396.

Vasarhelyi, M. A., M. G. Alles and A. Kogan. 2004. Principles of analytic monitoring for continuous assurance. Journal of Emerging Technologies in Accounting (1): 1-21.

Vercellis, C. 2009. Business Intelligence: Data Mining and Optimization for Decision Making. Wiley.

Walkowiak, S. 2016. Big Data Analytics with R: Utilize R. to uncover patterns in your Big Data. Packt Publishing.

Wang, J. and J. G. S. Yang. 2009. Data mining techniques for auditing attest function and fraud detection. Journal of Forensic & Investigative Accounting 1(1): 1-24.

Warren, J. D. Jr., K. C. Moffitt and P. Byrnes. 2015. How big data will change accounting. Accounting Horizons (June): 397-407.

Werner, M. 2017. Financial process mining - Accounting data structure dependent control flow inference. International Journal of Accounting Information Systems (25): 57-80.

Williams, S. 2011. 5 Barriers to BI success and how to overcome them. Strategic Finance (July): 26-33. (Note).

Winston, W. 2016. Microsoft Excel Data Analysis and Business Modeling, 5th Edition. Microsoft Press.

Witten, I. H. and E. Frank. 1999. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufman.

Witten, I. H. and E. Frank. 2005. Data Mining: Practical Machine Learning Tools and Techniques, 2nd Edition. Morgan Kaufman.

Wixom, B. H. and J. W. Ross. 2017. How to monetize your data. MIT Sloan Management Review (Spring): 10-13.

Yoon, K., L. Hoogduin and L. Zhang. 2015. Big data as complementary audit evidence. Accounting Horizons (June): 431-438.

Zhang, J., X. Yang and D. Appelbaum. 2015. Toward effective big data analysis in continuous auditing. Accounting Horizons (June): 469-476.

Zheng, Z., B. Padmanabhan and S. Kimbrough. 2003. On the existence and significance of data preprocessing biases in web usage mining. INFORMS Journal on Computing 15(2): 148-170.

Zumel, N., J. Mount and J. Porzak. 2014. Practical Data Science with R. Manning.