Yes / This study looks at the relationship between big data analytics capability and market performance and how this relationship can be facilitated by adopting disruptive business models in competitive environments.

The hospitality industry confronts continuous challenges from external environments, such as the COVID pandemic, the proliferation of short-term rentals, and the disruptive innovations of Generative AI. For businesses, understanding these external conditions and adapting strategies accordingly is crucial yet challenging, especially considering environmental uncertainties. Therefore, this dissertation investigates the effectiveness of different strategies in navigating market, competitive, and technological uncertainties, through a big-data analytical approach. It incorporates three studies, each focusing on one specific strategy and its varying outcomes under environmental changes. These studies employ machine learning algorithms to quantify strategies and utilize econometric models to infer the causal relationships between strategies and their outcomes. The first study examines how standardization affects short-term rental unit survival across two market conditions: pre-COVID growth and during-COVID decline. The results indicate that the risks arising from standardization are heightened under market decline. In addition, the effectiveness of standardization varies with design attributes to which the strategy is applied. Standardizing functional design boosts unit survival in the growing market but leads to a higher failure rate during the decline. Aesthetic standardization, on the other hand, negatively impacts survival in both conditions, with a stronger effect in the declining market. The second study identifies the impacts of differentiation on unit performance in the short-term rental context in two competitive environments: local versus city-level. The findings suggest that the effectiveness of differentiation increases with competitive pressure. At the local level where firms face localized competition, differentiation enhances unit performance. Conversely, in city-level environments where direct competition diminishes, it yields negative outcomes. Moreover, competition intensity, as reflected by the number of competitors and the degree of market concentration, is found to amplify the benefits of and mitigate the drawbacks of differentiation. The third study explores if adopting Generative AI to hotel online review response can improve customer feedback, under varying technological settings. It finds that simulated AI adoption improves customer perceptions when Generative AI models operate at high temperatures, while models with low temperatures lead to negative outcomes. The findings further underscore the importance of task-technology fit, revealing that Generative AI's effectiveness varies with review valence. Specifically, high-temperature settings for positive reviews generate significant benefits, whereas low-temperature settings lead to adverse effects. Conversely, for negative reviews, AI adoption demonstrates more stable outcomes across temperature settings, indicating balanced benefits of both low and high temperatures. In short, this dissertation identifies that the effectiveness of standardization, differentiation, and AI adoption strategies is contingent on environmental conditions. It underscores the importance of strategic adaptation in navigating contemporary challenges. / Doctor of Philosophy / It is difficult to operate hospitality businesses because this industry faces constant challenges from ever-changing external conditions, including the COVID pandemic, the rise of short-term rental platforms, and the breakthroughs in technology like Generative AI. It is important but challenging for hotels and short-term rentals to understand these conditions and plan their operations accordingly. Thus, this dissertation aims to help business operators to understand how to deal with different external changes. It carries on a series of studies based on big data, using various analytical tools. This dissertation is composed of three studies. The first one finds that, generally, it is risker for short-term rental hosts to make one property similar to his/her other properties when the whole market declines. There are differences identified between functionality and aesthetics. Keeping the functionalities, such as WIFI and coffeemaker, consistent among multiple properties will make the property more likely to survive when the market grows but it increases the likelihood of failure when the market demand decreases. When deciding property aesthetics, like color or layout, it is risky to have properties similar to each other, no matter if the market demand grows or drops. The second study concludes that short-term rental hosts should decide the product design relative to their competitors from different scopes of areas. They are suggested to make their properties' interior design style different from their nearby competitors to gain high revenues, especially when there are more neighboring supplies managed by a large number of hosts. On the contrary, it is more beneficial to follow the general trend of properties located in the same city when deciding one property's aesthetic style. The third study guides hotels to apply Generative AI like ChatGPT to generate response to customer online reviews. It found that, to reply to online reviews with four- or five-star ratings, hotels should not use the default GPT model to increase the quality of customer communication. Instead, they need to use the professional OpenAI API and set the parameter called temperature to 2. However, when hotels reply to online reviews with lower star ratings, like one or two, there is no big difference between low and high temperatures (0 to 2). They can simply use the default model. In general, there are no one-size-for-all solutions to deal with external challenges. Hospitality operators are highly recommended to adjust their operations to fit different conditions.

In B2B organizations, the adoption of digital marketing strategies has increased, leading to the collection of large amounts of data, big data. This has enabled the use of big data analytics, BDA, to uncover valuable insights for digital marketing purpose. Yet, there is limited research on how the B2B organizations integrate and utilize BDA in their digital marketing strategies, especially in the international context. This study aimed to address this research gap by examining how international B2B organizations integrate and utilize BDA in their digital marketing strategy, employing a dynamic capabilities perspective. The methodology of qualitative case study was applied, focusing on two established Swedish B2B organizations with an international presence. Empirical data was collected through semi-structured interviews and complemented with document analysis. Through abductive approach and hermeneutic interpretation, the findings show that despite the need for internal structural improvements, international B2B organizations are actively integrating BDA into their digital marketing strategies. By developing new routines and skills, these organizations can navigate the challenges posed by BDA while harnessing its benefits. Additionally, a framework comprising 10 practices in which international B2B organizations leverage BDA is proposed.

This study aspired to gain a more a nuanced understanding of the emerging analytics technologies and the vital capabilities that ultimately drive evidence-based decision making. Big data technology is widely discussed by varying groups in society and believed to revolutionize corporate decision making. In spite of big data's promising possibilities only a trivial fraction of firms deploying big data analytics (BDA) have gained significant benefits from their initiatives. Trying to explain this inability we leaned back on prior IT literature suggesting that IT resources can only be successfully deployed when combined with organizational capabilities. We identified key theoretical components at an organizational, relational, and human level. The data collection included 20 interviews with decision makers and data scientist from four analytical leaders. Early on we distinguished the companies into two categories based on their empirical characteristics. The terms “Born analyticals” and “Big intelligent firms” were coined. The analysis concluded that social, non-technical elements play a crucial role in building BDA abilities. These capabilities differ among companies but can still enable BDA in different ways, indicating that organizations´ history and context seem to influence how firms deploy capabilities. Some capabilities have proven to be more important than others. The individual mindset towards data is seemingly the most determining capability in building BDA ability. Varying mindsets foster different BDA-environments in which other capabilities behave accordingly. Born analyticals seemed to display an environment benefitting evidence based decisions.

Background: Big data analytics (BDA) is important to reduce healthcare costs. However, there are many challenges. The study objective was high performance establishment of interactive BDA platform of hospital system. Methods: A Hadoop/MapReduce framework formed the BDA platform with HBase (NoSQL database) using hospital-specific metadata and file ingestion. Query performance tested with Apache tools in Hadoop’s ecosystem. Results: At optimized iteration, Hadoop distributed file system (HDFS) ingestion required three seconds but HBase required four to twelve hours to complete the Reducer of MapReduce. HBase bulkloads took a week for one billion (10TB) and over two months for three billion (30TB). Simple and complex query results showed about two seconds for one and three billion, respectively. Interpretations: BDA platform of HBase distributed by Hadoop successfully under high performance at large volumes representing the Province’s entire data. Inconsistencies of MapReduce limited operational efficiencies. Importance of the Hadoop/MapReduce on representation of health informatics is further discussed. / Graduate / 0566 / 0769 / 0984 / dillon.chrimes@viha.ca

The goal of this thesis was to investigate the factors that influence the adoption of big data analytics by public sector employees based on the adapted Unified Theory of Acceptance and Use of Technology (UTAUT) model. A mixed method of survey and interviews were used to collect data from employees of a Canadian provincial government ministry. The results show that performance expectancy and facilitating conditions have significant positive effects on the adoption intention of big data analytics, while effort expectancy has a significant negative effect on the adoption intention of big data analytics. The result shows that social influence does not have a significant effect on adoption intention. In terms of moderating variables, the results show that gender moderates the effects of effort expectancy, social influence and facilitating condition; data experience moderates the effects of performance expectancy, effort expectancy and facilitating condition; and leadership moderates the effect of social influence. The moderation effects of age on performance expectancy, effort expectancy is significant for only employees in the 40 to 49 age group while the moderation effects of age on social influence is significant for employees that are 40 years and more. Based on the results, implications for public sector organizations planning to implement big data analytics were discussed and suggestions for further research were made. This research contributes to existing studies on the user adoption of big data analytics.

Big data is currently a buzzword in both academia and industry, with the term being used todescribe a broad domain of concepts, ranging from extracting data from outside sources, storingand managing it, to processing such data with analytical techniques and tools.This thesis work thus aims to provide a review of current big data analytics concepts in an attemptto highlight big data analytics’ importance to decision making.Due to the rapid increase in interest in big data and its importance to academia, industry, andsociety, solutions to handling data and extracting knowledge from datasets need to be developedand provided with some urgency to allow decision makers to gain valuable insights from the variedand rapidly changing data they now have access to. Many companies are using big data analyticsto analyse the massive quantities of data they have, with the results influencing their decisionmaking. Many studies have shown the benefits of using big data in various sectors, and in thisthesis work, various big data analytical techniques and tools are discussed to allow analysis of theapplication of big data analytics in several different domains.

<p>The topic of the research in this dissertation is the application of machine<br />learning in solving problems characteristic to biosystems, with special<br />emphasis on agriculture. Firstly, an innovative regression algorithm based on<br />big data was presented, that was used for yield prediction. The predictions<br />were then used as an input for the improved portfolio optimisation algorithm,<br />so that appropriate soybean varieties could be selected for fields with<br />distinctive parameters. Lastly, a multi-objective optimisation problem was set<br />up and solved using a novel method for categorical evolutionary algorithm<br />based on NSGA-III.</p> / <p>Предмет истраживања докторске дисертације је примена машинског учења у решавању проблема карактеристичних за биосистемe са нагласком на пољопривреду. Најпре је представљен иновативни алгоритам за регресију који је примењен на великој количини података како би се са предиковали приноси. На основу предикција одабране су одговарајуће сорте соје за њиве са одређеним карактеристикама унапређеним алгоритмом оптимизације портфолија. Напослетку је постављен оптимизациони проблем одређивања сетвене структуре са вишеструким функцијама циља који је решен иновативном методом, категоричким еволутивним алгоритмом заснованом на NSGA-III алгоритму.</p> / <p>Predmet istraživanja doktorske disertacije je primena mašinskog učenja u rešavanju problema karakterističnih za biosisteme sa naglaskom na poljoprivredu. Najpre je predstavljen inovativni algoritam za regresiju koji je primenjen na velikoj količini podataka kako bi se sa predikovali prinosi. Na osnovu predikcija odabrane su odgovarajuće sorte soje za njive sa određenim karakteristikama unapređenim algoritmom optimizacije portfolija. Naposletku je postavljen optimizacioni problem određivanja setvene strukture sa višestrukim funkcijama cilja koji je rešen inovativnom metodom, kategoričkim evolutivnim algoritmom zasnovanom na NSGA-III algoritmu.</p>

Το Υπολογιστικό Νέφος (Cloud Computing) χαρακτηρίζεται ως το νέο μοντέλο ανάπτυξης λογισμικού και παροχής υπηρεσιών στον τομέα των Τεχνολογιών Πληροφορικής και Επικοινωνιών. Τα κύρια χαρακτηριστικά του είναι η κατά απαίτηση διάθεση υπολογιστικών πόρων, η απομακρυσμένη πρόσβαση σε αυτούς μέσω διαδικτύου και η ευελιξία των παρεχόμενων υπηρεσιών. Η ευελιξία επιτρέπει την αναβάθμιση ή υποβάθμιση των υπολογιστικών πόρων σύμφωνα με τις απαιτήσεις του τελικού χρήστη. Επιπλέον, η συνεχής αύξηση του μεγέθους της παραγόμενης από διάφορες πηγές πληροφορίας (διαδίκτυο, επιστημονικά πειράματα) έχει δημιουργήσει μία τεράστια ποσότητα πολύπλοκων και διάχυτων ψηφιακών δεδομένων . Η απόσπαση χρήσιμης γνώσης από μεγάλου όγκου ψηφιακά δεδομένα απαιτεί έξυπνες και ευκόλως επεκτάσιμες υπηρεσίες ανάλυσης, εργαλεία προγραμματισμού και εφαρμογές. Επομένως, η δυνατότητα της ελαστικότητας και της επεκτασιμότητας έχει κάνει το Υ-πολογιστικό Νέφος να είναι μια αναδυόμενη τεχνολογία αναφορικά με τις αναλύσεις μεγάλου όγκου δεδομένων οι οποίες απαιτούν παραλληλισμό, πολύπλοκες ροές ανάλυσης και υψηλό υπολογιστικό φόρτο εργασίας. Για την καλύτερη δυνατή διαχείριση πολύπλοκων αναλύσεων και ενορχήστρωση των απαιτούμενων διαδικασιών, είναι απαραίτητη η ένθεση ροών εργασιών. Μια ροή εργασίας είναι ένα οργανωμένο σύνολο ενεργειών που πρέπει να πραγματοποιηθούν για να επιτευχθεί μια εμπορική ή ερευνητική διεργασία, καθώς και οι μεταξύ τους εξαρτήσεις αφού κάθε ενέργεια αποτελείται από ορισμένα βήματα που πρέπει να εκτελεστούν σε συγκεκριμένη σειρά. Στην παρούσα μεταπτυχιακή διπλωματική εργασία δημιουργήθηκε ένα σύστημα για τη δυναμική διαχείριση των προσφερόμενων πόρων σε μια υποδομή Υπολογιστικού Νέφους και την εκτέλεση κατανεμημένων υλοποιήσεων υπολογιστικής ανάλυσης δεδομένων. Συγκεκριμένα, η εφαρμογή, αφού λάβει από το χρήστη τα δεδομένα εισόδου για την έναρξη μιας νέας διαδικασίας ανάλυσης, εξετάζει τα δεδομένα των επιστημονικών προβλημάτων καθώς και την πολυπλοκότητά τους και παρέχει δυναμικά και αυτόματα τους αντίστοιχους υπολογιστικούς πόρους για την εκτέλεση της αντίστοιχης λειτουργίας ανάλυσής τους. Επίσης, επιτρέπει την καταγραφή της ανάλυσης και αναθέτει τον συντονισμό της διαδικασίας σε αντίστοιχες ροές εργασιών ώστε να διευκολυνθεί η ενορχήστρωση των παρεχόμενων πόρων και η παρακολούθηση της εκτέλεσης της υπολογιστικής διαδικασίας. Η συγκεκριμένη μεταπτυχιακή εργασία, με τη χρήση τόσο των παρεχόμενων υπηρεσιών μιας υποδομής Υπολογιστικού Νέφους όσο και των δυνατοτήτων που παρέχουν οι ροές εργασιών στην διαχείριση των εργασιών, έχει σαν αποτέλεσμα να απλουστεύει την πρόσβαση, τον έλεγχο, την οργάνωση και την εκτέλεση πολύπλοκων και παράλληλων υλοποιήσεων ανάλυσης δεδομένων από την στιγμή εισαγωγής των δεδομένων από το χρήστη έως τον υπολογισμό του τελικού αποτελέσματος. Πιο αναλυτικά η διπλωματική εργασία επικεντρώθηκε στη πρόταση μιας ολοκληρωμένης λύσης για: 1. τη παροχή μιας εφαρμογής στην οποία ο χρήστης θα έχει τη δυνατότητα να εισάγεται και να ξεκινά μια σύνθετη ανάλυση δεδομένων, 2. τη δημιουργία της κατάλληλης υποδομής για τη δυναμική διάθεση πόρων από μια cloud υποδομή ανάλογα με τις ανάγκες του εκάστοτε προβλήματος και 3. την αυτοματοποιημένη εκτέλεση και συντονισμό της διαδικασίας της ανάλυσης με χρήση ροών εργασιών. Για την επικύρωση και αξιολόγηση της εφαρμογής, αναπτύχθηκε η πλατφόρμα IRaaS η οποία παρέχει στους χρήστες του τη δυνατότητα επίλυσης προβλημάτων πολλαπλών πεδίων / πολλαπλών φυσικών. Η πλατφόρμα IRaaS βασίστηκε πάνω στην προαναφερόμενη εφαρμογή για τη δυναμική ανάθεση υπολογιστικών πόρων και συντονισμός εκτέλεσης πολύπλοκων διαδικασιών ανάλυσης δεδομένων. Εκτελώντας μια σειρά αναλύσεων παρατηρήθηκε ότι η συγκεκριμένη εφαρμογή παρέχει καλύτερους χρόνους εκτέλεσης, μικρότερη δέσμευση υπολογιστικών πόρων και κατά συνέπεια μικρότερο κόστος για τις αναλύσεις. Η εγκατάσταση της πλατφόρμας IRaaS για την εκτέλεση των πειραμάτων έγινε στην υποδομή Υπολογιστικού Νέφους του εργαστηρίου Αναγνώρισης Προτύπων. Η υποδομή βασίστηκε στα λογισμικά XenServer και Cloudstack, τα οποία εγκαταστάθηκαν και παραμετροποιήθηκαν στα πλαίσια της παρούσας εργασίας. / Cloud Computing is the new software development and service providing model in the area of Information and Communication Technologies. The main aspects of Cloud Computing are the on-demand allocation of computational resources, the remote access to the latter via the Internet and the elasticity of the provided services. Elasticity provides the capability to scale the computational resources depending on the computational needs. The continuous proliferation of data warehouses, webpages, audio and video streams, tweets, and blogs is generating a massive amount of complex and pervasive digital data. Extracting useful knowledge from huge digital datasets requires smart and scalable analytics services, programming tools, and applications. Due to the aspects of elasticity and scalability, Cloud Computing has become an emerging technology regarding to big data analysis, which demands parallelization, complex workflow analysis and massive computational workload. In this respect, workflows have an important role in managing complex flows and orchestrating the required processes. A workflow is an orchestrated set of activities that are necessary in order to complete a commercial or scientific task, as well as any dependencies between these tasks, since each one of them can be further decomposed into finer tasks that need to be executed in a predefined order. In this thesis, a system is presented that dynamically allocates the available resources provided by a cloud infrastructure and orchestrates the execution of complex and distrib-uted data analysis on these allocated resources. In particular, the system calculates the required computational resources (memory and CPU) based on the size of the input data and on the available resources of the cloud infrastructure, concluding to allocate dynamically the most suitable resources. . Moreover, the application offers the ability to coordinate the distributed analysis process utilising workflows for the orchestration and monitoring of the different tasks of the computational flow execution. Taking advantage of the services provided by a cloud infrastructure as well as the functionality of workflows in task management, this thesis has resulted in simplifying access, control, coordination and execution of complex and parallel data analysis implementations from the moment that a user enters a set of input data to the computation of the final result. In this context, this thesis focuses on a comprehensive and integrated solution that: 1. provides an application, through which the user is able to log in and start a complex data analysis, 2. offers the necessary infrastructure for dynamically allocating the cloud resources of, based on the needs of the particular problem, and 3. executes and coordinates the analysis process automatically by leveraging workflows. In order to validate and evaluate the application, the IRaaS platform was developed, offering the ability of solving multi-domain/multi-physics problems. The IRaaS platform is based on the aforementioned system in order to enable the dynamic allocation of computational resources and to coordinate the execution of complex data analysis processes. By executing a series of experiments with different input data, we observed that the presented application resulted in improved execution times, better allocation of computational resources and, thus, lower cost. In order to perform experiments, the IRaaS platform was set up on the cloud infrastructure of Pattern Recognition laboratory. In the context of this thesis, a new infrastructure has been installed and parameterized based on XenServer as virtualization hypervisor and CloudStack platform for the creation of a private cloud infrastructure.

The sheer increase in the volume of data over the last decade has triggered research in cluster computing frameworks that enable web enterprises to extract big insights from big data. While Apache Spark defines the state of the art in big data analytics platforms for (i) exploiting data-flow and in-memory computing and (ii) for exhibiting superior scale-out performance on the commodity machines, little effort has been devoted to understanding the performance of in-memory data analytics with Spark on modern scale-up servers. This thesis characterizes the performance of in-memory data analytics with Spark on scale-up servers.Through empirical evaluation of representative benchmark workloads on a dual socket server, we have found that in-memory data analytics with Spark exhibit poor multi-core scalability beyond 12 cores due to thread level load imbalance and work-time inflation (the additional CPU time spent by threads in a multi-threaded computation beyond the CPU time required to perform the same work in a sequential computation). We have also found that workloads are bound by the latency of frequent data accesses to the memory. By enlarging input data size, application performance degrades significantly due to the substantial increase in wait time during I/O operations and garbage collection, despite 10% better instruction retirement rate (due to lower L1cache misses and higher core utilization).For data accesses, we have found that simultaneous multi-threading is effective in hiding the data latencies. We have also observed that (i) data locality on NUMA nodes can improve the performance by 10% on average,(ii) disabling next-line L1-D prefetchers can reduce the execution time by upto14%. For garbage collection impact, we match memory behavior with the garbage collector to improve the performance of applications between 1.6xto 3x and recommend using multiple small Spark executors that can provide up to 36% reduction in execution time over single large executor. Based on the characteristics of workloads, the thesis envisions near-memory and near storage hardware acceleration to improve the single-node performance of scale-out frameworks like Apache Spark. Using modeling techniques, it estimates the speed-up of 4x for Apache Spark on scale-up servers augmented with near-data accelerators. / <p>QC 20171121</p>