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>

Over the past years, the value of data and DDDM have increased significantly as technological advancements have made it possible to store and analyze large amounts of data at a reasonable cost. This has resulted in completely new business models that has disrupt whole industries. DDDM allows businesses to rely their decisions on data, as opposed to on gut feeling. Up until this point, literature is eligible to provide a general view of what are the major challenges corporations encounter when implementing a DDDM strategy. However, as the field is still rather new, the challenges identified are yet very general and many corporations, especially B2B MNCs selling consumer goods, seem to struggle with this implementation. Hence, a single-case study on such a corporation, named Alpha, was carried out with the purpose to explore what are their major challenges in this process. Semi-structured interviews revealed evidence of four major findings, whereas, execution and organizational culture were supported in existing literature, however, two additional findings associated with organizational structure and consumer behavior data were discovered in the case of Alpha. Based on this, the conclusions drawn were that B2B MNCs selling consumer goods encounter the challenges of identifying local markets as frontrunners for strategies such as the one to become more data-driven, as well as the need to find a way to retrieve consumer behavior data. With these two main challenges identified, it can provide a starting point for managers when implementing DDDM strategies in B2B MNCs selling consumer goods in the future.

Continues field monitoring and searching sensor data remains an imminent element emphasizes the influence of the Internet of Things (IoT). Most of the existing systems are concede spatial coordinates or semantic keywords to retrieve the entail data, which are not comprehensive constraints because of sensor cohesion, unique localization haphazardness. To address this issue, we propose deep learning inspired sensor-rank consolidation (DLi-SRC) system that enables 3-set of algorithms. First, sensor cohesion algorithm based on Lyapunov approach to accelerate sensor stability. Second, sensor unique localization algorithm based on rank-inferior measurement index to avoid redundancy data and data loss. Third, a heuristic directive algorithm to improve entail data search efficiency, which returns appropriate ranked sensor results as per searching specifications. We examined thorough simulations to describe the DLi-SRC effectiveness. The outcomes reveal that our approach has significant performance gain, such as search efficiency, service quality, sensor existence rate enhancement by 91%, and sensor energy gain by 49% than benchmark standard approaches.

The advance of the Internet has resulted in an immensely interconnected world, which produces a tremendous amount of data. It has come to change our daily lives and behaviours tremendously. The trend is especially seen in the field of e-commerce where the customers have started to require more and more from the product and service providers. Moreover, with the rising competition, the companies have to adopt new ways of doing things to keep their position on the market as well as keeping and attracting new customers. One important factor for this is excelling customer service. Today, companies adopt technologies like BDA and AI to enhance and provide excellent customer service. This study aims to investigate how two Swedish cooperations extract value from their customer services with the help of BDA and AI. This study also strives to create an understanding of the expectations, requirements and implications of the technologies from the participants' perspectives that in this case are the employees of these mentioned businesses. Moreover, many fail to see the true potential that the technologies can bring and especially in the field of customer service. This study helps to address these challenges and by pinpointing the ’value- factors’ that companies participating in this study extracts, it might encourage the implementation of digital technologies in the customer service with no regard to the size of the company. This thesis was conducted with a qualitative approach and with semi-structured interviews and systematic observations with two Swedish companies acting on the Chinese market. The findings from the interviews, conducted with these selected companies, present that the companies actively use BDA and AI in their customer service. Moreover, several value-factors are pinpointed in the different stages of customer service. The most reoccurring themes are: ”proactive support”, ”relationship establishment”, ”identifying attitudes and behaviours” and ”real-time support”. Moreover, as for the value-creating factors before and after the actual interaction the reoccurring themes are ”competitive advantage”, ”high-impact customer insights”, ”classification”, ”practicality”, as well as ”reflection and development”. This essay provides knowledge that can help companies to further their understanding of how important customer service along with BDA and AI is and how they can support competitive advantage as well as customer loyalty. Since the thesis only focused on the investigation of Swedish organizations on the Shanghainese market, it would be of interest to continue further research on Swedish companies as China is seen to be in the forefront when it comes to utilizing these technologies.

Despite the increased cost of data breaches due to advanced, persistent threats from malicious sources, the adoption of big data security analytics among U.S. small businesses has been slow. Anchored in a diffusion of innovation theory, the purpose of this correlational study was to examine ways to increase the adoption of big data security analytics among small businesses in the United States by examining the relationship between small business leaders' perceptions of big data security analytics and their adoption. The research questions were developed to determine how to increase the adoption of big data security analytics, which can be measured as a function of the user's perceived attributes of innovation represented by the independent variables: relative advantage, compatibility, complexity, observability, and trialability. The study included a cross-sectional survey distributed online to a convenience sample of 165 small businesses. Pearson correlations and multiple linear regression were used to statistically understand relationships between variables. There were no significant positive correlations between relative advantage, compatibility, and the dependent variable adoption; however, there were significant negative correlations between complexity, trialability, and the adoption. There was also a significant positive correlation between observability and the adoption. The implications for positive social change include an increase in knowledge, skill sets, and jobs for employees and increased confidentiality, integrity, and availability of systems and data for small businesses. Social benefits include improved decision making for small businesses and increased secure transactions between systems by detecting and eliminating advanced, persistent threats.