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.

Affärsanalys är en ökande trend som många organisationer idag använder på grund av potentialen att fastställa värdefulla insikter, ökad lönsamhet och förbättrad operativ effektivitet. Något som visat sig vara problematiskt då det önskade resultatet inte alltid är en självklarhet. Syftet med studien är att undersöka hur modeföretag kan använda datadriven affärsanalys för att generera positiva insikter genom värdeskapande mekanismer. Utifrån semistrukturerade intervjuer med anställda på ett modeföretag har vi, med utgångspunkt i tidigare forskning, kartlagt hur datadriven affärsanalys brukas för att skapa värde genom att applicera en processmodell på verksamheten. Empirin resulterade i tre värdefulla insikter (1) Det studerade företaget använder affärsanalys för ökad lönsamhet (2) Företagets data tillgångar är tillräckliga för att utvinna värdefulla insikter (3) Vidare såg vi att företaget arbetar med influencers vilket är en ny affärsanalys-funktion som inte definierats i tidigare forskning. / Business analysis is an increasingly popular trend that many organisations use because of its potential to establish valuable insights, increased profitability and improved operational efficiency. Something that has proved to be rather problematic as the desired results rarely is a certainty. The purpose of the study is to examine how fashion retailers can use business analytics to generate positive insights through value-creating mechanisms by applying a process model. Based on semi-structured interviews with the employees of a fashion company and a starting point in previous research, we have mapped how business analysis can be used to obtain value. The empirical study resulted in three valuable insights (1) The examined organisation uses business analysis to increase profitability. (2) The data assets of the organisation are enough to acquire valuable insights. (3) Further we discovered that the organisation uses influencers as a valuable asset and can be categorised as a business analysis capability, previously undefined in preceding research.

For the second time, the Data Science Challenge took place as part of the 18th symposium “Database Systems for Business, Technology and Web” (BTW) of the Gesellschaft für Informatik (GI). The Challenge was organized by the University of Rostock and sponsored by IBM and SAP. This year, the integration, analysis and visualization around the topic of particulate matter pollution was the focus of the challenge. After a preselection round, the accepted participants had one month to adapt their developed approach to a substantiated problem, the real challenge. The final presentation took place at BTW 2019 in front of the prize jury and the attending audience. In this article, we give a brief overview of the schedule and the organization of the Data Science Challenge. In addition, the problem to be solved and its solution will be presented by the participants.

Data processing is generally defined as the collection and transformation of data to extract meaningful information. Data processing involves a multitude of processes such as validation, sorting summarization, aggregation to name a few. Many analytics engines exit today for largescale data processing, namely Apache Spark, Apache Flink and Apache Beam. Each one of these engines have their own advantages and drawbacks. In this thesis report, we used all three of these engines to process data from the Carbon Monoxide Daily Summary Dataset to determine the emission levels per area and unit of time. Then, we compared the performance of these 3 engines using different metrics. The results showed that Apache Beam, while offered greater convenience when writing programs, was slower than Apache Flink and Apache Spark. Spark Runner in Beam was the fastest runner and Apache Spark was the fastest data processing framework overall. / Databehandling definieras generellt som insamling och omvandling av data för att extrahera meningsfull information. Databehandling involverar en mängd processer som validering, sorteringssammanfattning, aggregering för att nämna några. Många analysmotorer lämnar idag för storskalig databehandling, nämligen Apache Spark, Apache Flink och Apache Beam. Var och en av dessa motorer har sina egna fördelar och nackdelar. I den här avhandlingsrapporten använde vi alla dessa tre motorer för att bearbeta data från kolmonoxidens dagliga sammanfattningsdataset för att bestämma utsläppsnivåerna per område och tidsenhet. Sedan jämförde vi prestandan hos dessa 3 motorer med olika mått. Resultaten visade att Apache Beam, även om det erbjuds större bekvämlighet när man skriver program, var långsammare än Apache Flink och Apache Spark. Spark Runner in Beam var den snabbaste löparen och Apache Spark var den snabbaste databehandlingsramen totalt.

Within the architecture, engineering, and construction (AEC) domain, simulation modeling is mainly used to facilitate decision-making by enabling the assessment of different operational plans and resource arrangements, that are otherwise difficult (if not impossible), expensive, or time consuming to be evaluated in real world settings. The accuracy of such models directly affects their reliability to serve as a basis for important decisions such as project completion time estimation and resource allocation. Compared to other industries, this is particularly important in construction and infrastructure projects due to the high resource costs and the societal impacts of these projects. Discrete event simulation (DES) is a decision making tool that can benefit the process of design, control, and management of construction operations. Despite recent advancements, most DES models used in construction are created during the early planning and design stage when the lack of factual information from the project prohibits the use of realistic data in simulation modeling. The resulting models, therefore, are often built using rigid (subjective) assumptions and design parameters (e.g. precedence logic, activity durations). In all such cases and in the absence of an inclusive methodology to incorporate real field data as the project evolves, modelers rely on information from previous projects (a.k.a. secondary data), expert judgments, and subjective assumptions to generate simulations to predict future performance. These and similar shortcomings have to a large extent limited the use of traditional DES tools to preliminary studies and long-term planning of construction projects. In the realm of the business process management, process mining as a relatively new research domain seeks to automatically discover a process model by observing activity records and extracting information about processes. The research presented in this Ph.D. Dissertation was in part inspired by the prospect of construction process mining using sensory data collected from field agents. This enabled the extraction of operational knowledge necessary to generate and maintain the fidelity of simulation models. A preliminary study was conducted to demonstrate the feasibility and applicability of data-driven knowledge-based simulation modeling with focus on data collection using wireless sensor network (WSN) and rule-based taxonomy of activities. The resulting knowledge-based simulation models performed very well in properly predicting key performance measures of real construction systems. Next, a pervasive mobile data collection and mining technique was adopted and an activity recognition framework for construction equipment and worker tasks was developed. Data was collected using smartphone accelerometers and gyroscopes from construction entities to generate significant statistical time- and frequency-domain features. The extracted features served as the input of different types of machine learning algorithms that were applied to various construction activities. The trained predictive algorithms were then used to extract activity durations and calculate probability distributions to be fused into corresponding DES models. Results indicated that the generated data-driven knowledge-based simulation models outperform static models created based upon engineering assumptions and estimations with regard to compatibility of performance measure outputs to reality.

The term Big Data has been gaining importance both at the academic and at the business level. Information technology plays a critical role in shipping since there is a high demand for fast transfer and communication between the parts of a shipping contract. The development of Automatic Identification System (AIS) is intended to improve maritime safety by tracking the vessels and exchange inter-ship information.  This master’s thesis purpose was to a) investigate in which business decisions the Automatic Identification System helps the shipowners and operators (i.e., users), b) find the benefits and perils arisen from its use, and c) investigate the possible improvements based on the users’ perceptions. This master’s thesis is a qualitative study using the interpretivism paradigm. Data were collected through semi-structured interviews. A total of 6 people participated with the following criteria: a) position on technical department or DPA or shipowner, b) participating on business decisions, c) shipping company owns a fleet, and d) deals with AIS data. The Thematic Analysis led to twenty-six codes, twelve categories and five concepts. Empirical findings showed that AIS data mostly contributes to make strategic business decisions. Participants are interested in using AIS data to measure the efficiency of their fleet and ports, to estimate the fuel consumption, to reduce their costs, to protect the environment and people’s health, to analyze the trade market, to predict the time of arrival, the optimal route and speed, to maintain highest security levels and to reduce the inaccuracies due to manual input of some AIS attributes. Finally, participants mentioned some AIS challenges including technological improvement (e.g., transponders, antennas) as well as the operation of autonomous vessels.  Finally, this master’s thesis contributes using the prescriptive and descriptive theories and help stakeholders to find new decisions while researchers and developers to advance their products.

[ES] La investigación llevada a cabo en esta tesis doctoral tiene como objetivo general analizar la importancia del poder transformador de la analítica de big data, a través de las capacidades analíticas de big data en el ecosistema español de las pequeñas y medianas empresas. El contexto de la transformación digital ha remodelado la forma de hacer negocios en las organizaciones debido a la complejidad e incertidumbre del entorno, el surgimiento de empresas nativas digitales, la introducción de nuevas tecnologías e industria 4.0 y el aumento de la competitividad de los mercados. Si bien la implantación de la tecnología e infraestructuras digitales ha sido un tema estudiado en la literatura académica en los últimos años, se divisan retos importantes a nivel humano debido a la modificación del contexto laboral, el liderazgo y las habilidades y competencias necesarias para competir de forma exitosa actualmente. Las personas, y no la tecnología, son el centro de la transformación digital y todo cambio organizativo derivado debe priorizarlas. Muchas de las empresas que invierten en tecnologías como el big data son incapaces de extraer el valor que éste puede ofrecer a través de la analítica de datos, y por tanto, no lo utilizan para tomar decisiones de valor para la organización que lleven a un incremento del desempeño. Tienen poco desarrolladas las capacidades analíticas de big data, necesarias para aprovechar la transformación digital y promover una implementación efectiva de las nuevas tecnologías. De esta problemática derivamos la importancia de conocer cuáles son los antecedentes de las capacidades analíticas de big data y su efecto en ellas, con el objetivo de conseguir un verdadero impacto en el desempeño organizativo. Por una parte, la cultura organizativa se ha identificado como una de las barreras al cambio o un factor impulsor que permite efectuar una transformación digital efectiva. Para ello es necesario implantar nuevas formas de trabajar y adquirir habilidades y conocimientos adecuados que permitan tomar decisiones en base al análisis de los datos. Es por tanto fundamental, que la cultura organizativa promueva e incentive la promoción de capacidades analíticas de big data y la transformación digital. Por otra parte, se destaca el papel del CEO de la organización, y de su visión estratégica orientada al dato para incentivar, liderar y motivar el cambio hacia una transformación digital. El rol del directivo es crucial para motivar un cambio cultural que permita ver la transformación digital y las capacidades analíticas de big data como instrumentos para lograr una mejora de la competitividad, desempeño, creación de valor y aumento de la reputación y satisfacción de las personas. Por tanto, el CEO debe tener un compromiso con la transformación digital tangible y visión estratégica orientada a los datos para tomar decisiones y planificar la estrategia a seguir por toda la organización. Entre las conclusiones del estudio se destaca en primer lugar la relación positiva y significativa de las capacidades analíticas de big data con la transformación digital y el desempeño organizativo a través de la innovación. Además, se pone en valor la importancia de la cultura organizativa y de la orientación al dato, así como de un nivel adecuado de madurez digital, como antecedentes de las capacidades analíticas de big data. Finalmente se analizan los diversos arquetipos culturales para destacar que una cultura digital, jerárquica o adhocrática favorecen la creación de capacidades analíticas y por tanto incrementan el proceso de transformación digital. A partir de las conclusiones se deriva la necesidad de inversión en formación para las personas en capacidades digitales y analíticas y el rol clave del directivo para conseguir una transformación digital exitosa y aprovechar la inversión tecnológica realizada. Por último, se destaca la importancia del diagnóstico cultural y elaboración de un plan de cambio cultural. / [CA] La investigació duta a terme en aquesta tesi doctoral té com a objectiu general analitzar la importància del poder transformador de l'analítica de big data, a través de les capacitats analítiques de big data en l'ecosistema espanyol de les petites i mitjanes empreses. El context de la transformació digital ha remodelat la manera de fer negocis en les organitzacions a causa de la complexitat i incertesa de l'entorn, el sorgiment d'empreses natives digitals, la introducció de noves tecnologies i indústria 4.0 i l'augment de la competitivitat dels mercats. Tot i que la implantació de la tecnologia i infraestructures digitals ha sigut un tema estudiat en la literatura acadèmica en els últims anys, s'albiren reptes importants a nivell humà a causa de la modificació del context laboral, el lideratge i les habilitats necessàries per a competir de manera exitosa actualment. Les persones, i no la tecnologia, són el centre de la transformació digital i tot canvi organitzatiu derivat ha de prioritzar-les. Moltes de les empreses que inverteixen en tecnologies com el big data són incapaços d'extraure el valor que aquest pot oferir a través de l'analítica de dades, i per tant, no l'utilitzen per a prendre decisions de valor per a l'organització que porten a una millora dels resultats. La raó és que tenen poc desenvolupades les capacitats analítiques de big data, necessàries per a aprofitar la transformació digital i promoure una implementació efectiva de les noves tecnologies. D'aquesta problemàtica derivem la importància de conéixer quins són els antecedents de les capacitats analítiques de big data i el seu efecte en elles, amb l'objectiu d'aconseguir un vertader impacte en la millora dels resultats. D'una banda, la cultura organitzativa s'ha identificat com una de les barreres al canvi per a efectuar una transformació digital efectiva. Per aquesta raó cal implantar noves maneres de treballar i adquirir habilitats i coneixements adequats que permeten prendre decisions sobre la base de l'anàlisi de les dades. És per tant fonamental, que la cultura organitzativa promoga i incentive la promoció de capacitats analítiques de big data i la transformació digital. D'altra banda, es destaca el paper del CEO de l'organització, i de la seua visió estratègica orientada a les dades per a incentivar, liderar i motivar el canvi cap a una transformació digital. El paper directiu és crucial per a motivar un canvi cultural que permeta veure la transformació digital i les capacitats analítiques de big data com a instruments per a aconseguir una millora de la competitivitat, acompliment, creació de valor i augment de la reputació i satisfacció de les persones. Per tant, el CEO ha de tindre un compromís amb la transformació digital tangible i una visió orientada a les dades per a prendre decisions i planificar l'estratègia a seguir per tota l'organització. Entre les conclusions de l'estudi es destaca en primer lloc la relació positiva i significativa de les capacitats analítiques de big data amb la transformació digital i la millora dels resultats a través de la innovació. A més, es posa en valor la importància de la cultura organitzativa i de l'orientació a la dada, així com d'un nivell adequat de maduresa digital, com a antecedents de les capacitats analítiques de big data. Finalment s'analitzen els diversos arquetips culturals i es destaca que una cultura digital, jeràrquica o adhocrática afavoreixen la creació de capacitats analítiques i per tant incrementen l'éxit del procés de transformació digital. A partir de les conclusions es deriven algunes implicacions pràctiques com la necessitat d'inversió en formació per a les persones en competències i capacitats digitals i analítiques, el rol clau del directiu per a aconseguir una transformació digital exitosa i aprofitar la inversió tecnològica. Finalment es destaca la importància del diagnòstic cultural i elaboració d'un pla de canvi cultural alineat amb els objectius envers la transformació digital. / [EN] The general objective of the research carried out in this doctoral thesis is to analyse the importance of the transformative power of big data analytics through big data analytical capabilities in the Spanish context of small and medium-sized enterprises. The context of digital transformation has reshaped the way of doing business in organisations due to the complexity and uncertainty of the environment, the emergence of digital native companies, the introduction of new technologies and the increased competitiveness of markets. Whilst the implementation of technology and digital infrastructures has been covered in the academic literature in recent years, there are significant challenges at the human level due to the changing context of work, leadership and the skills needed to compete successfully today. People, not technology, are at the heart of digital transformation and any resulting organisational change must priorise them. Many companies that invest in technologies such as big data are unable to extract the value that big data can offer through data analytics, and therefore do not use it to make valuable decisions for the organisation that lead to increased performance. They have underdeveloped big data analytical capabilities, which are necessary to take advantage of digital transformation and promote the effective implementation of new technologies. From this problem the importance of knowing the background of big data analytical capabilities and their effect on them is derived, in order to achieve a real impact on organisational performance. On the one hand, organisational culture has been identified as a barrier or booster of change for an effective digital transformation. This requires the implementation of new ways of working and the acquisition of appropriate skills and knowledge to enable data-driven decision making. It is therefore essential that the organisational culture promotes and encourages the promotion of big data analytical capabilities and digital transformation. On the other hand, the role of the CEO of the organisation, and his or her data-driven strategic vision to incentivise, lead and motivate change towards digital transformation is highlighted. The role of the top management is crucial to motivate a cultural change that allows to see digital transformation and big data analytics capabilities as instruments to achieve superior outcomes (i.e., improved competitiveness, performance, value creation and increased reputation and people satisfaction). Therefore, the CEO must have a strong commitment to digital transformation and a data-driven orientation to make decisions and settle the strategy for the entire organisation. Among the conclusions of the study, the positive and significant relationship of big data analytics capabilities with digital transformation and organisational performance through innovation are highlighted. This thesis points out the importance of organisational culture and data orientation, as well as an appropriate level of digital maturity, as antecedents to big data analytics capabilities. Finally, the various cultural archetypes are analysed to highlight that a digital, hierarchical or adhocratic culture favours the creation of analytical capabilities and therefore enhances the digital transformation process. From the conclusions, some practical implications are derived, such as the need to invest in training people in digital and analytical skills and capabilities, the key role of the manager in achieving a successful digital transformation and leveraging technological investment. Finally, the importance of cultural diagnosis and the development of a cultural change plan aligned with the strategic objectives for digital transformation is highlighted, and practical recommendations are settled. / Tesis elaborada gracias al apoyo de las Ayudas de Formación del Profesorado Universitario (FPU) otorgadas por el Ministerio de Educación, Cultura y Universidades, del Gobierno de España, y a la Cátedra de Empresa y Humanismo de la Universitat de València. / Orero Blat, M. (2023). Enhancing Big Data Analytics Capabilities: The Influence of Organisational Culture and Data-Driven Orientation [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/191788

<p>  </p> <p>Advances in technology have led to the availability of data from different platforms such as the web and social media platforms. Much of this data can be represented in the form of a network consisting of a set of nodes connected by edges. The nodes represent the items in the networks while the edges represent the interactions between the nodes. Community detection methods have been used extensively in analyzing these networks. However, community detection in evolving networks has been a significant challenge because of the frequent changes to the networks and the need for real-time analysis. Using Static community detection methods for analyzing dynamic networks will not be appropriate because static methods do not retain a network’s history and cannot provide real-time information about the communities in the network.</p> <p>Existing incremental methods treat changes to the network as a sequence of edge additions and/or removals; however, in many real-world networks, changes occur when a node is added with all its edges connecting simultaneously. </p> <p>For efficient processing of such large networks in a timely manner, there is a need for an adaptive analytical method that can process large networks without recomputing the entire network after its evolution and treat all the edges involved with a node equally. </p> <p>We proposed a node-centric community detection method that incrementally updates the community structure in the network using the already known structure of the network to avoid recomputing the entire network from the scratch and consequently achieve a high-quality community structure. The results from our experiments suggest that our approach is efficient for incremental community detection of node-centric evolving networks. </p>