The surge in the application of big data and predictive analytics in fields of crisis management, such as pandemics and epidemics, highlights the vital need for advanced research in these areas, particularly in the wake of the COVID-19 pandemic. Traditional methods, which typically rely on historical data to forecast future trends, fall short in addressing the complex and ever-changing nature of challenges like pandemics and public health crises. This inadequacy is further underscored by the pandemic's significant impact on various sectors, notably healthcare, government, and the hotel industry. Current models often overlook key factors such as static spatial elements, socioeconomic conditions, and the wealth of data available from social media, which are crucial for a comprehensive understanding and effective response to these multifaceted crises. This thesis employs spatial forecasting and predictive analytics to address crisis management in several distinct but interrelated contexts: the COVID-19 pandemic, the opioid crisis, and the impact of the pandemic on the hotel industry. The first part of the study focuses on using big data analytics to explore the relationship between socioeconomic factors and the spread of COVID-19 at the zip code level, aiming to predict high-risk areas for infection. The second part delves into the opioid crisis, utilizing semi-supervised deep learning techniques to monitor and categorize drug-related discussions on Reddit. The third part concentrates on developing spatial forecasting and providing explanations of the rising epidemic of drug overdose fatalities. The fourth part of the study extends to the realm of the hotel industry, aiming to optimize customer experience by analyzing online reviews and employing a localized Large Language Model to generate future customer trends and scenarios. Across these studies, the thesis aims to provide actionable insights and comprehensive solutions for effectively managing these major crises. For the first work, the majority of current research in pandemic modeling primarily relies on historical data to predict dynamic trends such as COVID-19. This work makes the following contributions in spatial COVID-19 pandemic forecasting: 1) the development of a unique model solely employing a wide range of socioeconomic indicators to forecast areas most susceptible to COVID-19, using detailed static spatial analysis, 2) identification of the most and least influential socioeconomic variables affecting COVID-19 transmission within communities, 3) construction of a comprehensive dataset that merges state-level COVID-19 statistics with corresponding socioeconomic attributes, organized by zip code. For the second work, we make the following contributions in detecting drug Abuse crisis via social media: 1) enhancing the Dynamic Query Expansion (DQE) algorithm to dynamically detect and extract evolving drug names in Reddit comments, utilizing a list curated from government and healthcare agencies, 2) constructing a textual Graph Convolutional Network combined with word embeddings to achieve fine-grained drug abuse classification in Reddit comments, identifying seven specific drug classes for the first time, 3) conducting extensive experiments to validate the framework, outperforming six baseline models in drug abuse classification and demonstrating effectiveness across multiple types of embeddings. The third study focuses on developing spatial forecasting and providing explanations of the escalating epidemic of drug overdose fatalities. Current research in this field has shown a deficiency in comprehensive explanations of the crisis, spatial analyses, and predictions of high-risk zones for drug overdoses. Addressing these gaps, this study contributes in several key areas: 1) Establishing a framework for spatially forecasting drug overdose fatalities predominantly affecting U.S. counties, 2) Proposing solutions for dealing with scarce and heterogeneous data sets, 3) Developing an algorithm that offers clear and actionable insights into the crisis, and 4) Conducting extensive experiments to validate the effectiveness of our proposed framework. In the fourth study, we address the profound impact of the pandemic on the hotel industry, focusing on the optimization of customer experience. Traditional methodologies in this realm have predominantly relied on survey data and limited segments of social media analytics. Those methods are informative but fall short of providing a full picture due to their inability to include diverse perspectives and broader customer feedback. Our study aims to make the following contributions: 1) the development of an integrated platform that distinguishes and extracts positive and negative Memorable Experiences (MEs) from online customer reviews within the hotel industry, 2) The incorporation of an advanced analytical module that performs temporal trend analysis of MEs, utilizing sophisticated data mining algorithms to dissect customer feedback on a monthly and yearly scale, 3) the implementation of an advanced tool that generates prospective and unexplored Memorable Experiences (MEs) by utilizing a localized Large Language Model (LLM) with keywords extracted from authentic customer experiences to aid hotel management in preparing for future customer trends and scenarios. Building on the integrated predictive modeling approaches developed in the earlier parts of this dissertation, this final section explores the significant impacts of the COVID-19 pandemic on the airline industry. The pandemic has precipitated substantial financial losses and operational disruptions, necessitating innovative crisis management strategies within this sector. This study introduces a novel analytical framework, EAGLE (Enhancing Airline Groundtruth Labels and Review rating prediction), which utilizes Large Language Models (LLMs) to improve the accuracy and objectivity of customer sentiment analysis in strategic airline route planning. EAGLE leverages LLMs for zero-shot pseudo-labeling and zero-shot text classification, to enhance the processing of customer reviews without the biases of manual labeling. This approach streamlines data analysis, and refines decision-making processes which allows airlines to align route expansions with nuanced customer preferences and sentiments effectively. The comprehensive application of LLMs in this context underscores the potential of predictive analytics to transform traditional crisis management strategies by providing deeper, more actionable insights. / Doctor of Philosophy / In today's digital age, where vast amounts of data are generated every second, understanding and managing crises like pandemics or economic disruptions has become increasingly crucial. This dissertation explores the use of advanced predictive modeling and analytics to manage various crises, significantly enhancing how predictions and responses to these challenges are developed. The first part of the research uses data analysis to identify areas at higher risk during the COVID-19 pandemic, focusing on how different socioeconomic factors can affect virus spread at a local level. This approach moves beyond traditional methods that rely on past data, providing a more dynamic way to forecast and manage public health crises. The study then examines the opioid crisis by analyzing social media platforms like Reddit. Here, a method was developed to automatically detect and categorize discussions about drug abuse. This technique aids in understanding how drug-related conversations evolve online, providing insights that could guide public health responses and policy-making. In the hospitality sector, customer reviews were analyzed to improve service quality in hotels. By using advanced data analysis tools, key trends in customer experiences were identified, which can help businesses adapt and refine their services in real-time, enhancing guest satisfaction. Finally, the study extends to the airline industry, where a model was developed that uses customer feedback to improve airline services and route planning. This part of the research shows how sophisticated analytics can help airlines better understand and meet traveler needs, especially during disruptions like the pandemic. Overall, the dissertation provides methods to better manage crises and illustrates the vast potential of predictive analytics in making informed decisions that can significantly mitigate the impacts of future crises. This research is vital for anyone—from government officials to business leaders—looking to harness the power of data for crisis management and decision-making.

Yes / The prevalence of big data is starting to spread across the public and private sectors however, an impediment to its widespread adoption orientates around a lack of appropriate big data analytics (BDA) and resulting skills to exploit the full potential of big data availability. In this paper, we propose a novel BDA to contribute towards this void, using a fuzzy cognitive map (FCM) approach that will enhance decision-making thus prioritising IT service procurement in the public sector. This is achieved through the development of decision models that capture the strengths of both data analytics and the established intuitive qualitative approach. By taking advantages of both data analytics and FCM, the proposed approach captures the strength of data-driven decision-making and intuitive model-driven decision modelling. This approach is then validated through a decision-making case regarding IT service procurement in public sector, which is the fundamental step of IT infrastructure supply for publics in a regional government in the Russia federation. The analysis result for the given decision-making problem is then evaluated by decision makers and e-government expertise to confirm the applicability of the proposed BDA. In doing so, demonstrating the value of this approach in contributing towards robust public decision-making regarding IT service procurement. / EU FP7 project Policy Compass (Project No. 612133)

Yes / The emerging information revolution makes it necessary to manage vast amounts of unstructured data rapidly. As the world is increasingly populated by IoT devices and sensors that can sense their surroundings and communicate with each other, a digital environment has been created with vast volumes of volatile and diverse data. Traditional AI and machine learning techniques designed for deterministic situations are not suitable for such environments. With a large number of parameters required by each device in this digital environment, it is desirable that the AI is able to be adaptive and self-build (i.e. self-structure, self-configure, self-learn), rather than be structurally and parameter-wise pre-defined. This study explores the benefits of self-building AI and machine learning with unsupervised learning for empowering big data analytics for smart city environments. By using the growing self-organizing map, a new suite of self-building AI is proposed. The self-building AI overcomes the limitations of traditional AI and enables data processing in dynamic smart city environments. With cloud computing platforms, the selfbuilding AI can integrate the data analytics applications that currently work in silos. The new paradigm of the self-building AI and its value are demonstrated using the IoT, video surveillance, and action recognition applications. / Supported by the Data to Decisions Cooperative Research Centre (D2D CRC) as part of their analytics and decision support program and a La Trobe University Postgraduate Research Scholarship.

Yes / With the emergence of information and communication technologies, organizations worldwide have been putting in meaningful efforts towards developing and gaining business insights by combining technology capability, management capability and personnel capability to explore data potential, which is known as big data analytics (BDA) capability. In this context, variables such as sensing capability—which is related to the organization’s ability to explore the market and develop opportunities—and analytics culture—which refers to the organization’s practices and behavior patterns of its analytical principles—play a fundamental role in BDA initiatives. However, there is a considerable literature gap concerning the effects of BDA-enabled sensing capability and analytics culture on organizational outcomes (i.e., customer linking capability, financial performance, market performance, and strategic business value) and on how important the organization’s analytics culture is as a mediator in the relationship between BDA-enabled sensing capability and organizational outcomes. Therefore, this study aims to investigate these relationships. And to attain this goal, we developed a conceptual model supported by dynamics capabilities, BDA, and analytics culture. We then validated our model by applying partial least squares structural equation modeling. The findings showed not only the positive effect of the BDA-enabled sensing capability and analytics culture on organizational outcomes but also the mediation effect of the analytics culture. Such results bring valuable theoretical implications and contributions to managers and practitioners.

Yes / There are various kinds of applications of BDA in the firms. Not many studies are there which deal with the impact of BDA towards issues like forecasting, decision-making, as well as performance of the firms simultaneously. So, there exists a gap in the research. In such a background, this study aims at examining the impacts of BDA on the process of decision-making, forecasting, as well as firm performance. Using resource-based view (RBV) as well as dynamic capability view (DCV) and related research studies, a research model was proposed conceptually. This conceptual model was validated taking help of PLS-SEM approach considering 366 respondents from Indian firms. This study has highlighted that smart decision making and accurate forecasting process can be achieved by using BDA. This research has demonstrated that there is a considerable influence of adoption of BDA on decision making process, forecasting process, as well as overall firm performance. However, the present study suffers from the fact that the study results depend on the cross-sectional data which could invite defects of causality and endogeneity bias. The present research work also found that there is no impact of different control variables on the firm's performance.

No / This case study is based on a research study that examined the relationship between big data analytics capability and market performance. The study investigated the intervening role of disruptive business models and the contingency role of competitive intensity on the relationship between big data analytics capability and market performance using both qualitative and quantitative methods. This case-study will focus on the qualitative and quantitative methods utilised including NVivo and IBM SPSS to conduct qualitative analysis and quantitative analysis. You will learn the factors to consider when conducting a mixed-methods study and develop the ability to apply similar analytical techniques to your research context.

Yes / In today’s rapidly changing business landscape, organizations increasingly invest in different technologies to enhance their innovation capabilities. Among the technological investment, a notable development is the applications of big data analytics (BDA), which plays a pivotal role in supporting firms’ decision-making processes. Big data technologies are important factors that could help both exploratory and exploitative innovation, which could affect the efforts to combat climate change and ease the shift to green energy. However, studies that comprehensively examine BDA’s impact on innovation capability and technological cycle remain scarce. This study therefore investigates the impact of BDA on innovation capability, technological cycle, and firm performance. It develops a conceptual model, validated using CB-SEM, through responses from 356 firms. It is found that both innovation capability and firm performance are significantly influenced by big data technology. This study highlights that BDA helps to address the pressing challenges of climate change mitigation and the transition to cleaner and more sustainable energy sources. However, our results are based on managerial perceptions in a single country. To enhance generalizability, future studies could employ a more objective approach and explore different contexts. Multidimensional constructs, moderating factors, and rival models could also be considered in future studies.

The forecasting of time series data is an integral component for management, planning, and decision making in many domains. The prediction of electricity demand and supply in the energy domain or sales figures in market research are just two of the many application scenarios that require thorough predictions. Many of these domains have in common that they are influenced by the Big Data trend which also affects the time series forecasting. Data sets consist of thousands of temporal fine grained time series and have to be predicted in reasonable time. The time series may suffer from noisy behavior and missing values which makes modeling these time series especially hard, nonetheless accurate predictions are required. Furthermore, data sets from different domains exhibit various characteristics. Therefore, forecast techniques have to be flexible and adaptable to these characteristics. Long-established forecast techniques like ARIMA and Exponential Smoothing do not fulfill these new requirements. Most of the traditional models only represent one individual time series. This makes the prediction of thousands of time series very time consuming, as an equally large number of models has to be created. Furthermore, these models do not incorporate additional data sources and are, therefore, not capable of compensating missing measurements or noisy behavior of individual time series. In this thesis, we introduce CSAR (Cross-Sectional AutoRegression Model), a new forecast technique which is designed to address the new requirements on forecasting large-scale time series data. It is based on the novel concept of cross-sectional forecasting that assumes that time series from the same domain follow a similar behavior and represents many time series with one common model. CSAR combines this new approach with the modeling concept of ARIMA to make the model adaptable to the various properties of data sets from different domains. Furthermore, we introduce auto.CSAR, that helps to configure the model and to choose the right model components for a specific data set and forecast task. With CSAR, we present a new forecast technique that is suited for the prediction of large-scale time series data. By representing many time series with one model, large data sets can be predicted in short time. Furthermore, using data from many time series in one model helps to compensate missing values and noisy behavior of individual series. The evaluation on three real world data sets shows that CSAR outperforms long-established forecast techniques in accuracy and execution time. Finally, with auto.CSAR, we create a way to apply CSAR to new data sets without requiring the user to have extensive knowledge about our new forecast technique and its configuration.

Background: Nowadays an ever-increasing amount of data is generated which is why companies face the challenge of extracting valuable information from these data streams. An enhanced Information Utilisation carriers the opportunity for improved decision-making. This could address challenges that come along with delayed trucks in inbound logistics and associated warehouse resource planning. Purpose: This study aims to deepen the understanding of Big Data Analytics capabilities that foster Information Integration and decision support to facilitate Information Utilisation. We apply this to the context of warehouse resource replanning in inbound logistics in case of unexpected short-term deviations. Method: We conducted a qualitative research study, comprising a Ground Theory approach in combination with an abductive reasoning. Derived from a literature review we adapted a framework and proposed an own conceptual framework after conducting and analysing 14 semi-structured interviews with inbound logistics practitioners and experts. Conclusion: We identified four interconnected capabilities that facilitate Information Utilisation. Data Generation Capabilities and Data Integration & Management Capabilities contribute to improved Information Integration, establishing a base for subsequent data analytics. Consequently, Data Analytics Capabilities and Data Interpretation Capabilities lead to enhanced decision support, facilitating Information Utilisation.

Data collection and analysis approaches have changed drastically in the past few years. The reason behind adopting different approach is improved data availability and continuous change in analysis requirements. Data have been always there, but data management is vital nowadays due to rapid generation and availability of various formats. Big data has opened the possibility of dealing with potentially infinite amounts of data with numerous formats in a short time. The data analytics is becoming complex due to data characteristics, sophisticated tools and technologies, changing business needs, varied interests among stakeholders, and lack of a standardized process. DataOps is an emerging approach advocated by data practitioners to cater to the challenges in data analytics projects. Data analytics projects differ from software engineering in many aspects. DevOps is proven to be an efficient and practical approach to deliver the project in the Software Industry. However, DataOps is still in its infancy, being recognized as an independent and essential task data analytics. In this thesis paper, we uncover DataOps as a methodology to implement data pipelines by conducting a systematic search of research papers. As a result, we define DataOps outlining ambiguities and challenges. We also explore the coverage of DataOps to different stages of the data lifecycle. We created comparison matrixes of different tools and technologies categorizing them in different functional groups to demonstrate their usage in data lifecycle management. We followed DataOps implementation guidelines to implement data pipeline using Apache Airflow as workflow orchestrator inside Docker and compared with simple manual execution of a data analytics project. As per evaluation, the data pipeline with DataOps provided automation in task execution, orchestration in execution environment, testing and monitoring, communication and collaboration, and reduced end-to-end product delivery cycle time along with the reduction in pipeline execution time. / Datainsamling och analysmetoder har förändrats drastiskt under de senaste åren. Anledningen till ett annat tillvägagångssätt är förbättrad datatillgänglighet och kontinuerlig förändring av analyskraven. Data har alltid funnits, men datahantering är viktig idag på grund av snabb generering och tillgänglighet av olika format. Big data har öppnat möjligheten att hantera potentiellt oändliga mängder data med många format på kort tid. Dataanalysen blir komplex på grund av dataegenskaper, sofistikerade verktyg och teknologier, förändrade affärsbehov, olika intressen bland intressenter och brist på en standardiserad process. DataOps är en framväxande strategi som förespråkas av datautövare för att tillgodose utmaningarna i dataanalysprojekt. Dataanalysprojekt skiljer sig från programvaruteknik i många aspekter. DevOps har visat sig vara ett effektivt och praktiskt tillvägagångssätt för att leverera projektet i mjukvaruindustrin. DataOps är dock fortfarande i sin linda och erkänns som en oberoende och viktig uppgiftsanalys. I detta examensarbete avslöjar vi DataOps som en metod för att implementera datarörledningar genom att göra en systematisk sökning av forskningspapper. Som ett resultat definierar vi DataOps som beskriver tvetydigheter och utmaningar. Vi undersöker också täckningen av DataOps till olika stadier av datalivscykeln. Vi skapade jämförelsesmatriser med olika verktyg och teknologier som kategoriserade dem i olika funktionella grupper för att visa hur de används i datalivscykelhantering. Vi följde riktlinjerna för implementering av DataOps för att implementera datapipeline med Apache Airflow som arbetsflödesorkestrator i Docker och jämfört med enkel manuell körning av ett dataanalysprojekt. Enligt utvärderingen tillhandahöll datapipelinen med DataOps automatisering i uppgiftskörning, orkestrering i exekveringsmiljö, testning och övervakning, kommunikation och samarbete, och minskad leveranscykeltid från slut till produkt tillsammans med minskningen av tid för rörledningskörning.