DeepSI: Interactive Deep Learning for Semantic Interaction

In this paper, we design novel interactive deep learning methods to improve semantic interactions in visual analytics applications. The ability of semantic interaction to infer analysts' precise intents during sensemaking is dependent on the quality of the underlying data representation. We propose the $\text{DeepSI}_{\text{finetune}}$ framework that integrates deep learning into the human-in-the-loop interactive sensemaking pipeline, with two important properties. First, deep learning extracts meaningful representations from raw data, which improves semantic interaction inference. Second, semantic interactions are exploited to fine-tune the deep learning representations, which then further improves semantic interaction inference. This feedback loop between human interaction and deep learning enables efficient learning of user- and task-specific representations. To evaluate the advantage of embedding the deep learning within the semantic interaction loop, we compare $\text{DeepSI}_{\text{finetune}}$ against a state-of-the-art but more basic use of deep learning as only a feature extractor pre-processed outside of the interactive loop. Results of two complementary studies, a human-centered qualitative case study and an algorithm-centered simulation-based quantitative experiment, show that $\text{DeepSI}_{\text{finetune}}$ more accurately captures users' complex mental models with fewer interactions

The bridge of dreams::Towards a method for operational performance alignment in IT-enabled service supply chains

Concerns on performance alignment, especially on business-IT alignment, have been around for three decades. It is still considered to be one of the most important driving forces for business success, as well as one of the top concerns of many practitioners and organizational researchers. It is also found to be a major issue in two thirds of digital transformation projects. Many attempts from researchers in diverse disciplines have been made to tackle this issue. Unfortunately, they have been working separately and the research appears in various forms and names. This dissertation presents a piece of interdisciplinary research that focuses on identifying operational performance alignment issues, discovering and assessing their root causes with attention to the dynamics in operating IT-enabled service supply chain (SSC). It makes a modest contribution by providing a communication-centred instrument which can modularize complex SSC in terms of a hierarchically-structured set of services and analyze the performance causality between them. With a special focus on the impact of IT, it makes it possible to monitor and tune various performance issues in SSC. This research intends to provide a solution-oriented common ground where multiple service research streams can meet together. Following the framework proposed in this research, services, at different tiers of an SSC, are modelled with a balanced perspective on both business, technical service components and KPIs. It allows a holistic picture of service performances and interactions throughout the entire supply chain to be viewed through a different research lens and permits the causal impact of technology, business strategy, and service operations on supply chain performance to be unveiled

Designing a Conversation Mining System for Customer Service Chatbots

As chatbots are gaining popularity in customer service, it is critically important for companies to continuously analyze and improve their chatbots’ performance. However, current analysis approaches are often limited to the question-answer level or produce highly aggregated metrics (e.g., conversations per day) instead of leveraging the full potential of the large volume of conversation data to provide actionable insights for chatbot developers and chatbot managers. To address this challenge, we developed a novel chatbot analytics approach — conversation mining — based on concepts and methods from process mining. We instantiated our approach in a conversation mining system that can be used to visually analyze customer-chatbot conversations at the process level. The results of four focus group evaluations suggest that conversation mining can help chatbot developers and chatbot managers to extract useful insights for improving customer service chatbots. Our research contributes to research and practice with novel design knowledge for conversation mining systems

Designing a Conversation Mining System for Customer Service Chatbots

As chatbots are gaining popularity in customer service, it becomes increasingly important for companies to continuously analyze and improve their chatbots’ performance. However, current analysis ap-proaches are often limited to the level of question-answer pairs or produce highly aggregated metrics (e.g., average intent scores, conversations per day) rather than leveraging the full potential of the large volume of conversation data to extract actionable insights for chatbot developers and chatbot operators (e.g., customer service managers). To address this challenge, we developed a novel chatbot analytics approach — conversation mining — based on concepts and methods from process mining. We instanti-ated our approach in a conversation mining system that can be used to visually analyze customer-chatbot conversations at the process level. The findings of four focus group evaluations show that our system can help chatbot developers and operators identify starting points for chatbot improvement. Our re-search contributes novel design knowledge for conversation mining systems

Enhancing the Capabilities of Fluid Bed Granulation through Process Automation and Digitalisation

This paper describes a PAT-enabled, digitalised, and automated fluid bed granulation system. A multichannel Near-Infrared (NIR) spectrophotometer and a direct imaging particle size and shape analyser in constant dialogue with the SmartX no-code/low-code platform provide a ground-breaking process automation toolset now located at the Bernal Institute in the University of Limerick. Two sets of results are presented for this study, from two iterations of the Advance Dynamic Process Control (ADPC) controller application. The results demonstrate the direct measurement and control of the product’s critical quality attributes through digitality enabled feedback control of processing setpoints and parameters. The platform controlled the particle size more tightly compared to non-automated control and a more accurate measurement-driven process endpoint for moisture content was achieved. Implementing a digitally enabled control approach can significantly reduce batch to batch variation and greatly improve process performance and product consistency

Visual interaction with dimensionality reduction: a structured literature analysis

Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data. For DR techniques to be useful in exploratory data analysis, they need to be adapted to human needs and domain-specific problems, ideally, interactively, and on-the-fly. Many visual analytics systems have already demonstrated the benefits of tightly integrating DR with interactive visualizations. Nevertheless, a general, structured understanding of this integration is missing. To address this, we systematically studied the visual analytics and visualization literature to investigate how analysts interact with automatic DR techniques. The results reveal seven common interaction scenarios that are amenable to interactive control such as specifying algorithmic constraints, selecting relevant features, or choosing among several DR algorithms. We investigate specific implementations of visual analysis systems integrating DR, and analyze ways that other machine learning methods have been combined with DR. Summarizing the results in a “human in the loop” process model provides a general lens for the evaluation of visual interactive DR systems. We apply the proposed model to study and classify several systems previously described in the literature, and to derive future research opportunities

On Intelligence Augmentation and Visual Analytics to Enhance Clinical Decision Support Systems

Human-in-the-loop intelligence augmentation (IA) methods combined with visual analytics (VA) have the potential to provide additional functional capability and cognitively driven interpretability to Decision Support Systems (DSS) for health risk assessment and patient-clinician shared decision making. This paper presents some key ideas underlying the synthesis of IA with VA (IA/VA) and the challenges in the design, implementation, and use of IA/VA-enabled clinical decision support systems (CDSS) in the practice of medicine through data driven analytical models. An illustrative IA/VA solution provides a visualization of the distribution of health risk, and the impact of various parameters on the assessment, at the population and individual levels. It also allows the clinician to ask “what-if” questions using interactive visualizations that change actionable risk factors of the patient and visually assess their impact. This approach holds promise in enhancing decision support systems design, deployment and use outside the medical sphere as well

Visual interaction with dimensionality reduction: a structured literature analysis

Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data. For DR techniques to be useful in exploratory data analysis, they need to be adapted to human needs and domain-specific problems, ideally, interactively, and on-the-fly. Many visual analytics systems have already demonstrated the benefits of tightly integrating DR with interactive visualizations. Nevertheless, a general, structured understanding of this integration is missing. To address this, we systematically studied the visual analytics and visualization literature to investigate how analysts interact with automatic DR techniques. The results reveal seven common interaction scenarios that are amenable to interactive control such as specifying algorithmic constraints, selecting relevant features, or choosing among several DR algorithms. We investigate specific implementations of visual analysis systems integrating DR, and analyze ways that other machine learning methods have been combined with DR. Summarizing the results in a “human in the loop” process model provides a general lens for the evaluation of visual interactive DR systems. We apply the proposed model to study and classify several systems previously described in the literature, and to derive future research opportunities