https://scholarworks.unist.ac.kr/handle/201301/48 2026-04-21T15:11:48Z https://scholarworks.unist.ac.kr/handle/201301/90975 Title: Business Value of Process Mining Author(s): HIJRIANI, ASTRIA Abstract: Process mining (PM) has evolved from a set of algorithms for discovering, checking, and enhancing processes from event data into a broader organizational capability for continuous improvement and value creation. Yet organizations and researchers repeatedly report a gap between insights and impact: while PM often uncovers bottlenecks, deviations, and compliance issues, it remains unclear when, how, and for whom these analytics translate into tangible business value. This thesis addresses the general research question: How can we improve the current understanding of process mining value? The work integrates cross-domain business-value concepts, systematically reviews the PM value literature, theorizes about negative contingencies that hinder value realization, and designs a holistic maturity model for PM. The thesis develops a cross-domain conceptualization of business value that can be systematically applied to process mining. Drawing on systematic literature reviews in information systems (IS), business intelligence (BI), and business process management (BPM), it synthesizes a multidimensional, multilevel view of business value across operational, financial, strategic, and stakeholder outcomes. Using the Context– Intervention–Mechanism–Outcome (CIMO) lens, the thesis distinguishes contextual conditions, interventions, value-creation mechanisms, and outcomes. It proposes an integrative business value framework suitable for analyzing digital initiatives such as This framework is then applied to the process mining literature. A systematic review of PM studies that explicitly discuss value shows that existing research concentrates on process-level operational gains and transparency, with limited attention to strategic or ecosystem-level outcomes and few explicit accounts of mechanisms linking PM to broader business value. The mapping reveals notable gaps around AI-enabled features, value inhibitors, cross-process stakeholder perspectives, and intermediate outcomes. To explain why PM initiatives often fall short of their promised benefits, the thesis theorizes and empirically identifies negative contingencies that undermine value realization. Based on qualitative analysis of multiple PM projects and narratives, it develops a typology of nine recurring contingencies across data, tooling, organizational, and ecosystem dimensions, showing how PM interventions can fail and how value-creation mechanisms are weakened or blocked. Actively managing these contingencies is argued to be essential for closing the PM value gap. The thesis also designs and evaluates a Holistic Process Mining Maturity Model (HP3M) using a design science research approach. HP3M specifies maturity levels and dimensions that characterize the evolution of PM from isolated experiments to an embedded, value-oriented capability. Multi-method evaluation and an industry case study illustrate how HP3M can be used to diagnose PM maturity, identify capability gaps, align PM ambition with organizational context, and relate maturity to value realization. Conceptually, the thesis sharpens the foundations of business value for process mining and embeds PM within a mechanism-oriented, context-sensitive framework. Methodologically, it demonstrates a CIMO-based synthesis across domains, a structured protocol for reviewing PM value studies, a qualitative and sentiment-aware approach to identifying negative contingencies, and a design science process for developing a maturity model. Practically, it offers a structured vocabulary for scoping PM value, a map of current value patterns and gaps, a contingency-based diagnostic, and a holistic maturity model to guide organizations from ad hoc PM projects toward a mature, value-driven PM capability. Major: Department of Industrial Engineering 2026-01-31T15:00:00Z https://scholarworks.unist.ac.kr/handle/201301/90974 Title: Masked Time Series Pre-training Models Tailored to the Unique Characteristics of Time Series Data Author(s): Seo, Hyunwoo Abstract: Masked pre-training has been widely demonstrated as an effective method for learning useful representations on vast amounts of unlabeled data. Following its success in natural language processing and computer vision, masked time series pre-training has been proposed to extend this approach to time series analysis. However, while existing methods have primarily focused on adapting approaches from other fields, they have often overlooked the inherent characteristics of time series data, which are significantly different from other data types. Specifically, time series data structurally contains: (a) cross-time dependencies; (b) cross-channel dependencies; and (c) noise and variability, which during data collection hinders the accurate learning of the structural characteristics. This thesis addresses these challenges by proposing masked time series pre-training models tailored to these unique characteristics of time series data. To capture cross-time dependency within time series, I propose ST-MTM, a masked time series pre-training models with seasonal-trend decomposition. By incorporating decomposition architecture in both masking and representation learning methods, ST-MTM effectively learns the representations of time series components by disentangling the distinct temporal variations from each component. Extensive evaluations on real-world benchmarks demonstrate ST-MTM’s superior performance in time series forecasting, where capturing intricate temporal dependency is crucial. To capture the cross-channel de- pendency in multivariate time series, I propose ShuffleMTM, a simple yet innovative masked time series pre-training model that captures cross-channel dependency through shuffled series. Specifically, ShuffleMTM adaptively incorporates the dependent structure from cross-channel patches through the patch shuffling methods and dependency bridge layer, thereby achieving the learning of both cross-channel and cross-time dependencies. Through the rigorous experiments on time series forecasting and classification, ShuffleMTM performs on par or better than state-of-the-art baselines and effectively captures both dependencies in multivariate time series. In addition, ShuffleMTM’s pre-training architecture improves the downstream performance of the time series foundation model, highlighting its potential to strengthen the cross-channel modeling capacity of large-scale, pre-trained time series models. Lastly, to address the inherent noise and variability arising from time series data collection, I develop a noise-variability-robust pre-training framework and introduce NERVE, a biosignal time series foundation model. As biosignals naturally contain considerable environmental noise and variability, this framework effectively enhances resilience to these factors, improving generalization performance across diverse downstream tasks. The masked time series pre-training models developed in this thesis tackle a broad spectrum of challenges comprehensively, encompassing both intrinsic structural characteristics and the exogenous factors that hinder their effective learning. The broad scope of this thesis, which extends from single-dataset learning to large-scale pre-training for enhanced generalization performance, offers compelling evidence of the effectiveness and fundamental contribution of masked time series pre-training for various time series problems. Major: Department of Industrial Engineering 2026-01-31T15:00:00Z https://scholarworks.unist.ac.kr/handle/201301/90973 Title: A Study on the Application of Multi-modal Learning for Real-World Challenges using Prototype Author(s): Sohn, Wonho Abstract: Modern information systems increasingly capture heterogeneous signals in multiple modalities, including images, text, audio/video, and structured logs, creating strong incentives for multi-modal learning. By integrating complementary information and enforcing cross-modal consistency, multi-modal models can learn unified representation implying complementary information, thereby improving accuracy, robustness, and generalization. However, deploying these methods beyond curated benchmarks introduces additional real-world requirements that are not addressed by predictive performance alone. Real-world application must remain feasible under continuously growing dataset, remain resilient to incomplete, noisy, or partially missing inputs, and provide checkable rationales for decisions. This dissertation advances a unified perspective that treats these two objectives: (i) strengthening multi-modal representations to improve downstream performance via heterogeneous integration, and (ii) introducing prototypes as a complementary design principle to mitigate real-world constraints. In the first study on fashion e-commerce, we propose MDL-FR, an end-to-end framework that integrates visual and textual data and learns style prototypes that capture high-level structure, enabling style-aware outfit generation beyond compatibility-only recommendation. In the second study on single-cell multi-omics integration, we propose CPG-AE, which replaces dense cell–cell interactions with a sparse cell–prototype graph and combines prototype-mediated message passing with multi-modal fusion autoencoder to learn coherent joint embeddings. Across both domains, experimental results show that multi-modal architectures improve task performance, while learned prototypes provide compact anchors that enhance scalability, robustness to incomplete data, and evidence-oriented validation beyond aggregate metrics. Major: Department of Industrial Engineering 2026-01-31T15:00:00Z https://scholarworks.unist.ac.kr/handle/201301/90972 Title: Combinatorial Optimization through Bayesian Optimization for Parameterization and Decomposition-Based Methods Author(s): Jo, Sugyeong Abstract: Real-world decision-making problems are increasingly complex due to combinatorial structures, un- certain environments, and conflicting multi-objective goals. Operations research (OR) provides a power- ful set of mathematical tools to structure and solve such problems, but traditional OR approaches often rely on expert-driven formulations, fixed parameters, and predefined search strategies. In large-scale combinatorial optimization (CO) problems, where the solution space is vast and uncertainty is inherent, computational complexity quickly becomes prohibitive, making conventional methods impractical for many real-world applications. Recent advances in machine learning (ML) offer promising directions for addressing these challenges. ML can learn complex problem structures directly from data, approximate expensive computations, and adapt to different problem distributions through simulation and experience. By learning policies tailored to specific problem instances, ML enables scalable and adaptive decision- making in dynamic environments. This thesis proposes an integrated methodological framework that combines ML-based modules with mathematical optimization to robustly and effectively solve various CO problems arising in real- world industrial contexts. The proposed framework links quantitative forecasting, qualitative evaluation, and structured optimization models into a cohesive system, designed to be domain-agnostic and broadly applicable without requiring intensive expert intervention. In particular, the framework leverages the efficiency of exact solvers from the OR domain and the flexibility of ML techniques to address large- scale decision-making problems through gradient-free optimization. This thesis is structured as follows: Chapter 1 introduces the motivation and background for integrating machine learning (ML) tech- niques with operations research (OR) to address complex combinatorial optimization (CO) problems in real-world decision-making. It discusses the background and necessity of the research, highlighting the increasing complexity of decision problems due to structural constraints and uncertainty. The chapter also presents the potential of ML to complement traditional OR methods by learning from data and adapting to changing environments. It further emphasizes the need for a unified ML-OR framework that combines the strengths of both fields. The overarching goal of this thesis is to develop and evaluate new integrated methods that address the limitations of conventional approaches and enable scalable, adaptive solutions to large-scale CO problems without relying heavily on domain-specific expertise. Chapter 2 describes the theoretical background and related literature on integrating ML with OR for solving large-scale CO problems. The chapter reviews key developments in BO, hyperparameter tuning, solver configuration, and learning-based algorithm design. The challenges and limitations of traditional OR methods for dealing with real-world CO problems are discussed, highlighting their dependence on expert knowledge, manual parameter calibration, and limited adaptability to dynamic problem distribu- tions. These limitations emphasize the need for new data-driven approaches that can effectively handle the complexity and variability of decision-making in uncertain environments. Overall, the goal of this thesis is to advance the integration of ML and OR by proposing scalable, adaptive, and generalizable optimization frameworks that are applicable across diverse industrial domains. Chapter 3 focuses primarily on the problem of industrial steam procurement, where decision-makers must balance cost efficiency, supplier quality, and carbon emissions. A key challenge in this case study lies in handling the discontinuities and uncertainties introduced by block-rate pricing policies applied to steam usage. This study provides a novel integrated decision-making method using a BO-based ensem- ble forecasting model and a hybrid multi-criteria decision-making framework. A multi-objective linear programming model is then developed to optimize trade-offs among cost, qualitative supplier perfor- mance, and environmental impact. The proposed framework is validated through a case study of a major manufacturing firm in Ulsan, Republic of Korea. Chapter 4 addresses the location-allocation problem of EV charging stations (EVCSs) on directed highway networks, considering path-based demands and flexible station capacities. The proposed model jointly determines the locations of EVCSs and the number of chargers at each site, relaxing the common assumption of fixed charger quantities. Since the number of feasible configurations grows exponen- tially with the number of candidate sites and charger options per site, this study adopts a modified BO algorithm using the expected improvement criterion to guide the search efficiently. The BO approach balances demand coverage with model uncertainty to focus on promising combinations. Case studies on the Pennsylvania and South Dakota highway networks demonstrate that flexible capacity planning outperforms uniform allocation strategies in terms of cost, coverage, and computational efficiency. Chapter 5 presents a multi-period optimization framework for the urban-scale deployment of electric vehicle charging stations (EVCSs) under demand uncertainty and budget constraints. To estimate charg- ing needs in areas without existing infrastructure, the study introduces the latent charging demand rate (LCDR) and latent charging demand (LCD) using a survival analysis approach. A large-scale mixed- integer programming model is formulated to determine annual installation and expansion decisions over a multi-year horizon. To efficiently solve this high-dimensional problem, a hybrid metaheuristic com- bining large neighborhood search (LNS) and BO is developed. A five-year case study in Seoul, Republic of Korea, demonstrates the effectiveness of phased investment strategies that adapt to spatiotemporal demand patterns. Chapter 6 presents a summary of the key findings and conclusions of this study. The major con- tributions are highlighted, and the implications of the results for future research are discussed. It is demonstrated that the proposed ML-OR integrated framework can effectively solve large-scale combina- torial optimization problems under uncertainty, offering practical value for real-world decision-making. Possible extensions include online decision-making, reinforcement learning integration, and real-time infrastructure planning in dynamic environments. Major: Department of Industrial Engineering 2026-01-31T15:00:00Z