| Assoc Prof. Tan Swee ChuanSingapore University of Social Sciences, SingaporeResearch Area: Business Analytics; Decision Tree; Anomaly Detection; Ensemble Learning; Clustering. Title of Keynote Speech: From Chaos to Clarity: Applying Analytics with BDA-MAP Abstract: This talk presents the Business-Data-Analytics (BDA) Mind-Mapping approach for Analytics Projects (MAP) management. BDA-MAP offers a flexible methodology to managing analytics projects by focusing on aligning business objective, data requirements, and application of appropriate analytics techniques. Unlike traditional sequential, process-oriented methodologies such as CRISP-DM or SEMMA, BDA-MAP is agile, and allows a quick response to issues encountered during the planning and execution of real-world analytics projects. The methodology allows analysts and end-users to collectively refine their understanding of a project at hand and enhance it iteratively. The talk illustrates the effectiveness of the BDA-MAP approach through real-world case studies in visual analytics, customer segmentation, and predictive analytics, demonstrating how this methodology can uncover actionable insights that drive improved business performance. In the digital era, adopting an agile and flexible analytics approach like BDA-MAP is essential for organizational success. If time permits, this talk will also cover how this framework has been used to design an undergraduate degree programme in Business Analytics |
Assoc.Prof. Chuan LuoSichuan University, ChinaResearch Area: Research on data mining and knowledge discovery, granular computing and rough set, incremental learning and parallel computing Title of Keynote Speech: Distributed Feature Selection for Scalable Dimensionality Reduction Abstract: The emerging Big Dimensionality presents an immediate challenge pertaining to the scalability issue in the data analytics and computational intelligence communities. Feature selection, as a type of dimension reduction technique, has been proven to be effective and efficient in handling high dimensional data. However, the appearance of large data explosion leads to the existing serial computing feature selection algorithms are extremely time-consuming due to the limited computational and storage resources. As a practical pathway to pursue the challenge of explosive growth and aggregation of data, parallelization of algorithms by exploiting high performance computing resources in a distributed computing environment have increasingly gained strengths in facilitating large-scale data analysis. This talk will introduce our recent research works targeting scalable feature selection from multiple perspectives: Spark rough hypercuboid approach for scalable feature selection, Large-scale meta-heuristic feature selection based on BPSO assisted rough hypercuboid approach, and RHDOFS: a distributed online algorithm towards scalable streaming feature selection. |
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 | Assoc Prof. Muhammad Faizal A. GhaniUniversity of Malaya, Malaysia Research Area: Business Analytics; Decision Tree; Anomaly Detection; Ensemble Learn Title of Keynote Speech: The Development of a Best Practices Profile for Successful Malaysian Entrepreneurs Abstract: One of the contributing factors to business success is having individuals known as entrepreneurs, rather than merely traders. Entrepreneurs run businesses with a focus on profit as well as social welfare, whereas traders are more concerned with personal profits. Furthermore, the best practices of entrepreneurs vary based on location. What works well in developed countries may not necessarily succeed in Malaysia. Nevertheless, local entrepreneurs still look up to models of successful entrepreneurs in developed nations, such as Steven Paul Jobs, the founder of Apple, because Malaysia has not yet had a model or profile that fits local needs and capabilities. Therefore, this study aims to develop a best practices profile for successful Malaysian entrepreneurs based on the consensus of expert panel in the field of entrepreneurship. To develop this profile, data were collected using a multi-method design that applies the Design and Development Research (DDR) approach. DDR involves three phases as follows: 1. **Needs Analysis Phase**: This phase aims to identify the necessity for developing a best practices profile for successful Malaysian entrepreneurs. Three individuals, including entrepreneurs and academicians, were interviewed using qualitative methods, and the data were analyzed thematically. This phase identified issues in business operations that necessitate such a profile development, including financial challenges, lack of vision for market dominance, and negative behaviors while conducting business. 2. **Design and Development Phase**: This phase involves two rounds of sessions to create the best practices profile for successful Malaysian entrepreneurs. In the first round, three experts with over five years of business experience were interviewed to develop the profile’s content, covering domains and items. The experts included an academician from a local educational institution, an entrepreneur with over 10 years of experience, and another with less than 10 years of experience. Thematic analysis revealed six main domains and 60 items, including creativity, innovation, seizing opportunities, foresight, faith and piety, resilience, risk-taking, and business knowledge. The second round focused on expert review to refine the profile content. 3. **Evaluation Phase**: Four experts, including two academics and two entrepreneurs with less than 10 years of experience, assessed the developed profile based on clarity, accuracy, and language. The findings indicate that the profile scored over 90 percent in these aspects, demonstrating a high level of clarity, accuracy, and language quality. The study's findings can guide local entrepreneurs to achieve greater success by applying this best practices profile tailored to their needs and capabilities, following the "All Size Fits All" approach. |
