A Three-phased Online Association Rule Mining Approach for Diverse Mining Requests

In the past, most incremental mining and online mining algorithms considered finding the set of association rules or patterns consistent with the entire set of data inserted so far. Users can not easily obtain the results from their only interested portion of data. For providing ad-hoc, query-driven and online mining supports, we first propose a relation called multidimensional pattern relation to structurally and systematically store the context information and the mining information for later analysis. Each tuple in the relation comes from an inserted dataset in the database. This concept is similar to the construction of a data warehouse for OLAP. However, unlike the summarized information of fact attributes in a data warehouse, the mined patterns in the multidimensional pattern relation can not be directly aggregated to satisfy users’ mining requests. We then develop an online mining approach called Three-phased Online Association Rule Mining (TOARM) based on the proposed multidimensional pattern relation to support online generation of association rules under multidimensional considerations. Experiments for both homogeneous and heterogeneous datasets are made, with results showing the effectiveness of the proposed approach

Pattern Mining and Sense-Making Support for Enhancing the User Experience

While data mining techniques such as frequent itemset and sequence mining are well established as powerful pattern discovery tools in domains from science, medicine to business, a detriment is the lack of support for interactive exploration of high numbers of patterns generated with diverse parameter settings and the relationships among the mined patterns. To enhance the user experience, real-time query turnaround times and improved support for interactive mining are desired. There is also an increasing interest in applying data mining solutions for mobile data. Patterns mined over mobile data may enable context-aware applications ranging from automating frequently repeated tasks to providing personalized recommendations. Overall, this dissertation addresses three problems that limit the utility of data mining, namely, (a.) lack of interactive exploration tools for mined patterns, (b.) insufficient support for mining localized patterns, and (c.) high computational mining requirements prohibiting mining of patterns on smaller compute units such as a smartphone. This dissertation develops interactive frameworks for the guided exploration of mined patterns and their relationships. Contributions include the PARAS pre- processing and indexing framework; enabling analysts to gain key insights into rule relationships in a parameter space view due to the compact storage of rules that enables query-time reconstruction of complete rulesets. Contributions also include the visual rule exploration framework FIRE that presents an interactive dual view of the parameter space and the rule space, that together enable enhanced sense-making of rule relationships. This dissertation also supports the online mining of localized association rules computed on data subsets by selectively deploying alternative execution strategies that leverage multidimensional itemset-based data partitioning index. Finally, we designed OLAPH, an on-device context-aware service that learns phone usage patterns over mobile context data such as app usage, location, call and SMS logs to provide device intelligence. Concepts introduced for modeling mobile data as sequences include compressing context logs to intervaled context events, adding generalized time features, and identifying meaningful sequences via filter expressions

Building a Business Data Analytics Graduate Certificate

In this paper we present the evolution of the Business Data Analytics Graduate Certificate (BDA Certificate) at our institution, Loyola University Chicago. This certificate is a successful and expanding program that attracts a diverse group of dynamic professionals from local, national, and international populations. The program evolution described in this paper involves multiple revisions of the curriculum, additions, and subtractions of individual courses, expansions of delivery methods, and program name changes. The core principles of acknowledging the centrality of data, mandating the modeling-based course sequencing, and recognizing the proper role of software tools, are outlined and recognized as the foundation of the program’s success

A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces

Mutemi, A., & Bacao, F. (2023). A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces. Scientific Reports, 13(1), 1-16. [12499]. https://doi.org/10.1038/s41598-023-38304-5Organized retail crime (ORC) is a significant issue for retailers, marketplace platforms, and consumers. Its prevalence and influence have increased fast in lockstep with the expansion of online commerce, digital devices, and communication platforms. Today, it is a costly affair, wreaking havoc on enterprises’ overall revenues and continually jeopardizing community security. These negative consequences are set to rocket to unprecedented heights as more people and devices connect to the Internet. Detecting and responding to these terrible acts as early as possible is critical for protecting consumers and businesses while also keeping an eye on rising patterns and fraud. The issue of detecting fraud in general has been studied widely, especially in financial services, but studies focusing on organized retail crimes are extremely rare in literature. To contribute to the knowledge base in this area, we present a scalable machine learning strategy for detecting and isolating ORC listings on a prominent marketplace platform by merchants committing organized retail crimes or fraud. We employ a supervised learning approach to classify postings as fraudulent or real based on past data from buyer and seller behaviors and transactions on the platform. The proposed framework combines bespoke data preprocessing procedures, feature selection methods, and state-of-the-art class asymmetry resolution techniques to search for aligned classification algorithms capable of discriminating between fraudulent and legitimate listings in this context. Our best detection model obtains a recall score of 0.97 on the holdout set and 0.94 on the out-of-sample testing data set. We achieve these results based on a select set of 45 features out of 58.publishersversionpublishe

The Bush Legacy: An Assault on Public Protections

This report shows that attacks on a variety of common-sense regulations over the past eight years have taken a great toll on the United States. Though not intended to serve as a comprehensive record of every anti-regulatory effort by the Bush administration, this report uses clear examples to document a wide range of activity, much of which occurred behind the scenes, away from the eyes of all but the most observant members of the press and the public. The storytelling style of the report, crafted by freelance writer and author Osha Gray Davidson, helps readers begin to understand how much damage has been done under the watch of George W. Bush and his vice president, Richard B. Cheney

Conflict Minerals Legislation: The SEC’s New Role as Diplomatic and Humanitarian Watchdog

Buried in the voluminous Dodd-Frank Wall Street Reform and Consumer Protection Act is an oft-overlooked provision requiring corporate disclosure of the use of “conflict minerals” in products manufactured by issuing corporations. This Article scrutinizes the legislative history and lobbying efforts behind the conflict minerals provision to establish that, unlike the majority of the bill, its goals are moral and political, rather than financial. Analyzing the history of disclosure requirements, the Article suggests that the presence of conflict minerals in an issuer’s product is not inherently material information and that the Dodd-Frank provision statutorily renders nonmaterial information material. The provision, therefore, forces the SEC to expand beyond its congressional mandate of protecting investors and ensuring capital formation by requiring issuers to engage in additional nonfinancial disclosures in order to meet the provision’s humanitarian and diplomatic aims. Further, the Article posits that the conflict minerals provision is a wholly ineffective means to accomplish its stated humanitarian goals and likely will cause more harm than good in the Democratic Republic of the Congo. In conclusion, this Article proposes that a more efficient regulatory model for conflict minerals is the Clean Diamond Trade Act and the Kimberly Process Certification Scheme

Is a knowledge based value network an effective model for implementing e-government?

Is a knowledge based value network an effective model for implementing e-government? E-Government is a vision of how public sector organisations will govern, serve citizens, and interact with business partners, their employees, and other Government organisations. The “e” in e-Government represents a move to fully integrated, secure, on-demand accessible electronic Government that will: • improve integrated service delivery • provide universal citizen access • begin to enhance traditional Government structures and processes • support new Government products and services by relying on the emergence and convergence of new technologies • improve effectiveness Electronic commerce (e-commerce) has fundamentally changed the way business is being conducted and Government is rushing to catch up

Web observations: analysing Web data through automated data extraction

In this thesis, a generic architecture for Web observations is introduced. Beginning with fundamental data aspects and technologies for building Web observations, requirements and architectural designs are outlined. Because Web observations are basic tools to collect information from any Web resource, legal perspectives are discussed in order to give an understanding of recent regulations, e.g. General Data Protection Regulation (GDPR). The general idea of Web observatories, its concepts, and experiments are presented to identify the best solution for Web data collections and based thereon, visualisation from any kind of Web resource. With the help of several Web observation scenarios, data sets were collected, analysed and eventually published in a machine-readable or visual form for users to be interpreted. The main research goal was to create a Web observation based on an architecture that is able to collect information from any given Web resource to make sense of a broad amount of yet untapped information sources. To find this generally applicable architectural structure, several research projects with different designs have been conducted. Eventually, the container based building block architecture emerged from these initial designs as the most flexible architectural structure. Thanks to these considerations and architectural designs, a flexible and easily adaptable architecture was created that is able to collect data from all kinds of Web resources. Thanks to such broad Web data collections, users can get a more comprehensible understanding and insight of real-life problems, the efficiency and profitability of services as well as gaining valuable information on the changes of a Web resource