With the rapid growth of crime data, overwhelming amounts of electronic evidence need to be stored and shared with the relevant agencies. Without addressing this challenge, the sharing of crime data and electronic evidence will be highly inefficient, and the resource requirements for this task will continue to increase. Relational database solutions face size limitations in storing larger amounts of crime data where each instance has unique attributes with unstructured nature. In this thesis, the Electronic Evidence Locker (EEL) was proposed and developed to address such problems. The EEL was built using a NoSQL database and a C# website for querying stored data. Baseline results were collected to measure the growth of required machine resources (in memory and time) using various test cases and larger datasets. The results showed that search time is more impacted by the search direction in the data than the addition of the query search conditions.

This diploma thesis deals with the phenomenon of recent years called Big Data. Big Data are unstructured data of large volume which cannot be managed and processed by commonly used software tools. The analytical part deals with the concept of Big Data and analyses the possibilities of using this concept in the in-surance sector. The practical part presents specific methods and approaches for the use of big data analysis, specifically in increasing the competitiveness of the insurance company and in detecting insurance frauds. Most space is devoted to data mining methods in modelling the task of detecting insurance frauds. This di-ploma thesis builds on and extends the bachelor thesis of the author titled "Mod-ern technology of data analysis and its use in detection of insurance frauds".

Background: Big data analytics has been referred to as a hype the past decade, making manyorganizations adopt data-driven processes to stay competitive in their industries. Many of theorganizations adopting big data analytics use agile methodologies where the most importantoutcome is to maximize business value. Multiple scholars argue that big data analytics lead toincreased business value, however, there is a theoretical gap within the literature about how agileorganizations can capture this business value in a practically relevant way. Purpose: Building on a combined definition that capturing business value means being able todefine-, communicate- and measure it, the purpose of this thesis is to explore how agileorganizations capture business value from big data analytics, as well as find out what aspects ofvalue are relevant when defining it. Method: This study follows an abductive research approach by having a foundation in theorythrough the use of a qualitative research design. A single case study of Nike Inc. was conducted togenerate the primary data for this thesis where nine participants from different domains within theorganization were interviewed and the results were analysed with a thematic content analysis. Findings: The findings indicate that, in order for agile organizations to capture business valuegenerated from big data analytics, they need to (1) define the value through a synthezised valuemap, (2) establish a common language with the help of a business translator and agile methods,and (3), measure the business value before-, during- and after the development by usingindividually idenified KPIs derived from the business value definition.

The Internet is a major communication tool that handles massive information exchanges, sees a rapidly increasing usage, and offers an increasingly wide variety of services.   In addition to these trends, the services themselves have highly varying quality of service (QoS), requirements and the network providers must take into account the frequent releases of new network standards like 5G. This has resulted in a significant need for new theoretical models that can capture different network traffic characteristics. Such models are important both for understanding the existing traffic in networks, and to generate better synthetic traffic workloads that can be used to evaluate future generations of network solutions using realistic workload patterns under a broad range of assumptions and based on how the popularity of existing and future application classes may change over time. To better meet these changes, new flexible methods are required. In this thesis, a new framework aimed towards analyzing large quantities of traffic data is developed and used to discover key characteristics of application behavior for IP network traffic. Traffic models are created by breaking down IP log traffic data into different abstraction layers with descriptive values. The aggregated statistics are then clustered using the K-means algorithm, which results in groups with closely related behaviors. Lastly, the model is evaluated with cluster analysis and three different machine learning algorithms to classify the network behavior of traffic flows. From the analysis framework a set of observed traffic models with distinct behaviors are derived that may be used as building blocks for traffic simulations in the future. Based on the framework we have seen that machine learning achieve high performance on the classification of network traffic, with a Multilayer Perceptron getting the best results. Furthermore, the study has produced a set of ten traffic models that have been demonstrated to be able to reconstruct traffic for various network entities. / <p>Due to COVID-19 the presentation was performed over ZOOM.</p>

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / Resumen. Grandes vendedores tecnológicos ponen sus esfuerzos en crear nuevas tecnologías y plataformas para solucionar los problemas que poseen los principales sectores de la industria. En los últimos años, el turismo está desarrollando una mayor tendencia al crecimiento, aunque carece de tecnologías que hayan sido integradas para la explotación de la información de gran volumen que esta genera. Con el análisis de las herramientas de IBM y Oracle, se ha llegado a proponer una arquitectura que sea capaz de considerar las condiciones y particularidades propias del sector para la toma de decisiones en tiempo real. La plataforma propuesta tiene la finalidad de hacer uso de los procesos de negocio involucrados en el sector turismo, y tomar diversas fuentes de información especializadas en brindar información al turista y a los negocios. La arquitectura está conformada por tres capas. La primera describe la extracción y carga de datos de las diversas fuentes de información estructurada, no estructurada y sistemas de negocio. El procesamiento de los datos, segunda capa, permite realizar una limpieza y análisis de datos utilizando herramientas como MapReduce y tecnologías de stream computing para el procesamiento en tiempo real. Y la última capa, Entrega y Visualización, permite identificar la información relevante que son presentadas en diversas interfaces como web o plataformas móviles. Con esta propuesta se busca lograr la obtención de resultados en tiempo real sobre las necesidades del sector turismo. / Instytut Biologii Medycznej Polskiej Akademii Nauk

Humanity, since its inception, has been interested in the materialization of knowledge. Various ancient cultures generated a lot of information through their writing systems. The beginning of the increase of information could date back to 1880 when a census performed in the United States of America took 8 years to be tabulated. In the 1930s the demographic growth exacerbated this increase of data. Already in 1940, libraries had collected a large amount of writing and it is in this decade when scientists begin to use the term “information explosion”. The term first appears in the Lawton (Oklahoma) Constitution newspaper in 1941. Currently, it can be said that we live in the age of big data. Exabytes of data are generated every day; therefore, the term big data has become one of the most important concepts in information systems. Big data refer to large amounts of data on a large scale that exceeds the capacity of conventional software to be captured, processed, and stored in a reasonable time. As a general criterion, most experts consider big data to be the largest volume of data, the variety of formats and sources from which it comes, the immense speed at which it is generated, the veracity of its content, and the value of the information extracted/processed. Faced with this reality, several questions arise: How to manipulate this large amount of data? How to obtain important results to gain knowledge from this data? Therefore, the need to create a connecting bridge between big data and wisdom is evident. People, machines, applications, and other elements that make up a complex and constantly evolving ecosystem are involved in this process. Each project presents different peculiarities in the development of an framework based on big data. This, in turn, makes the landscape more complex for the designer since multiple options can be selected for the same purpose. In this work, we focus on an framework for processing mobile network performance management data. In mobile networks, one of the fundamental areas is planning and optimization. This area analyzes the key performance indicators to evaluate the behavior of the network. These indicators are calculated from the raw data sent by the different network elements. The network administration teams, which receive these raw data and process them, use systems that are no longer adequate enough due to the great growth of networks and the emergence of new technologies such as 5G and 6G that also include equipment from the Internet of things. For the aforementioned reasons, we propose in this work a big data framework for processing mobile network performance management data. We have tested our proposal using performance files from real networks. All the processing carried out on the raw data with XML format is detailed and the solution is evaluated in the ingestion and reporting components. This study can help telecommunications vendors to have a reference big data framework to face the current and future challenges in the performance management in mobile networks. For instance, to reduce the processing time data for decisions in many of the activities involved in the daily operation and future network planning.

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.

Le développement des mégadonnées et des technologies de l'Intelligence Artificielle a donné naissance à une économie numérique contrôlée par les géants du web (GAFAM). Cette économie témoigne d’une certaine inégalité quant à l'accès et à la gestion des mégadonnées et des technologies de l'Intelligence Artificielle. La présente étude vise à explorer l'inégalité entre les grandes organisations et les petites et moyennes entreprises (PME) au sujet de l'accès et de l'utilisation des mégadonnées et des technologies de l'IA. Pour ce, il s'agit de répondre à la question suivante : « Comment les équipes de communication dans les PME, au Canada, envisagent l'usage et l'importance des mégadonnées et des technologies de l'IA pour leur travail ? » Le cadre théorique mobilisé dans ce travail de recherche est, d’un côté, la sociologie des usages qui aidera à comprendre et à analyser les usages des mégadonnées et des technologies de l'IA par les équipes de communication des PME ; d'un autre côté, l'approche narrative qui permettra de décrire les contextes de pratiques de ces usages. Nous avons eu recours à une méthode mixte. La méthode quantitative, via un questionnaire en ligne, a permis d'identifier la place qu'occupent ces technologies actuellement dans le travail régulier des professionnels de la communication des PME ainsi que les défis qu'ils affrontent pour la mise en place et l'utilisation de ces technologies. La méthode qualitative, via des entrevues semi-dirigées, a servi à mieux comprendre les contextes de pratiques où ces technologies sont utilisées ou pourraient être utilisées. Les résultats ont suggéré qu'il existe un écart entre les PME et les grandes organisations par rapport à l'exploitation et à l'utilisation de ces technologies. Cet écart est dû avant tout à certains défis tels que le manque de connaissances et d'expertise et le manque d'intérêt envers ces technologies. Cette inégalité pourrait être mitigée en mettant en place un plan de formation des gestionnaires afin de garantir des changements au niveau de la culture organisationnelle. Les résultats ont fait émerger l'importance de l'intervention humaine sans laquelle les idées générées par les mégadonnées et les technologies de l'IA risquent d'être biaisées. Ainsi, compte tenu des limites de cette étude exploratoire, elle a permis d'avancer les connaissances en faisant émerger quelques pistes de recherches futures en ce qui concerne les mégadonnées et les technologies de l'IA et leur importance pour les activités de communication dans les PME.

Indiana University-Purdue University Indianapolis (IUPUI) / Data and information have increased exponentially in recent years. The promising era of big data is advancing many new practices. One of the emerging big data applications is healthcare. Large quantities of data with varying complexities have been leading to a great need in smart and secure big data systems. Mobile edge, more specifically the smart phone, is a natural source of big data and is ubiquitous in our daily lives. Smartphones offer a variety of sensors, which make them a very valuable source of data that can be used for analysis. Since this data is coming directly from personal phones, that means the generated data is sensitive and must be handled in a smart and secure way. In addition to generating data, it is also important to interact with the big data. Therefore, it is critical to create edge systems that enable users to access their data and ensure that these applications are smart and secure. As the first major contribution of this thesis, we have implemented a mobile edge system, called s2Edge. This edge system leverages Amazon Web Service (AWS) security features and is backed by an AWS cloud system. The implemented mobile application securely logs in, signs up, and signs out users, as well as connects users to the vast amounts of data they generate. With a high interactive capability, the system allows users (like patients) to retrieve and view their data and records, as well as communicate with the cloud users (like physicians). The resulting mobile edge system is promising and is expected to demonstrate the potential of smart and secure big data interaction. The smart and secure transmission and management of the big data on the cloud is essential for healthcare big data, including both patient information and patient measurements. The second major contribution of this thesis is to demonstrate a novel big data cloud system, s2Cloud, which can help enhance healthcare systems to better monitor patients and give doctors critical insights into their patients' health. s2Cloud achieves big data security through secure sign up and log in for the doctors, as well as data transmission protection. The system allows the doctors to manage both patients and their records effectively. The doctors can add and edit the patient and record information through the interactive website. Furthermore, the system supports both real-time and historical modes for big data management. Therefore, the patient measurement information can, not only be visualized and demonstrated in real-time, but also be retrieved for further analysis. The smart website also allows doctors and patients to interact with each other effectively through instantaneous chat. Overall, the proposed s2Cloud system, empowered by smart secure design innovations, has demonstrated the feasibility and potential for healthcare big data applications. This study will further broadly benefit and advance other smart home and world big data applications. / 2023-06-01

The latest trends in highway pavement management aim at implementing a rational, data-driven procedure to allocate resources for pavement maintenance and rehabilitation. To this end, decision-making is based on network-wide surface condition and structural capacity data – preferably collected in a non-destructive manner such as a deflection testing device. This more holistic approach was proven to be more cost-effective than the current state of the art, in which the pavement manager grounds their maintenance and rehabilitation-related decision making on surface distress measurements. However, pavement practitioners still rely mostly on surface distress because traditional deflection measuring devices are not practical for network-level data collection. Traffic-speed deflection devices, among which the Traffic Speed Deflectometer [TSD], allow measuring pavement surface deflections at travel speeds as high as 95 km/h [60 miles per hour], and reporting the said measurements with a spatial resolution as dense as 5cm [2 inches] between consecutive measurements. Since their inception in the early 2000s, and mostly over the past 15 years, numerous research efforts and trial tests focused on the interpretation of the deflection data collected by the TSD, its validity as a field testing device, and its comparability against the staple pavement deflection testing device – the Falling Weight Deflectometer [FWD]. The research efforts have concluded that although different in nature than the FWD, the TSD does furnish valid deflection measurements, from which the pavement structural health can be assessed. Most published TSD-related literature focused on TSD surveys of flexible pavement networks and the estimation of structural health indicators for hot-mix asphalt pavement structures from the resulting data – a sensible approach given that the majority of the US paved road pavement network is asphalt. Meanwhile, concrete and composite pavements (a minority of the US pavement network that yet accounts for nearly half of the US Interstate System) have been mostly neglected in TSD-related research, even though the TSD has been deemed a suitable device for sourcing deflection data from which to infer the structural health of the pavement slabs and the load-carrying joints. Thus, this Dissertation's main objective is to fulfill this gap in knowledge, providing the pavement manager/practitioner with a streamlined, comprehensive interpretation procedure to turn dense TSD deflection measurements collected at a jointed pavement network into characterization parameters and structural health metrics for both the concrete slab system, the sub-grade material, and the load-carrying joints. The proposed TSD data analysis procedure spans over two stages: Data extraction and interpretation. The Data Extraction Stage applies a Lasso-based regularization scheme [Basis Pursuit coupled with Reweighted L1 Minimization] to simultaneously remove the white noise from the TSD deflection measurements and extract the deflection response generated as the TSD travels over the pavement's transverse joints. The examples presented demonstrate that this technique can actually pinpoint the location of structurally weak spots within the pavement network from the network-wide TSD measurements, such as deteriorated transverse joints or segments with early stages of fatigue damage, worthy of further investigation and/or structural overhaul. Meanwhile, the Interpretation Stage implements a linear-elastic jointed-slab-on-ground mathematical model to back-calculate the concrete pavement's and subgrade's stiffness and the transverse joints' load transfer efficiency index [LTE] from the denoised TSD measurements. In this Dissertation, the performance of this back-calculation technique is analyzed with actual TSD data collected at a 5-cm resolution at the MnROAD test track, for which material properties results and FWD-based deflection test results at select transverse joints are available. However, during an early exploratory analysis of the available 5-cm data, a discrepancy between the reported deflection slope and velocity data and simulated measurements was found: The simulated deflection slopes mismatch the observations for measurements collected nearby the transverse joints whereas the measured and simulated deflection velocities are in agreement. Such a finding prompted a revision of the well-known direct relationship between TSD-based deflection velocity and slope data, concluding that it only holds on very specific cases, and that a jointed pavement is a case in which deflection velocity and slope do not correlate directly. As a consequence, the back-calculation approach to the pavement properties and the joints' LTE index was implemented with the TSD's deflection velocity data as input. Validation results of the back-calculation tool using TSD data from the MnROAD low volume road showed a reasonable agreement with the comparison data available while at the same time providing an LTE estimate for all the transverse joints (including those for which FWD-based deflection data is unavailable), suggesting that the proposed data analysis technique is practical for corridor-wide screening. In summary, this Dissertation presents a streamlined TSD data extraction and interpretation technique that can (1) highlight the location of structurally deficient joints within a jointed pavement corridor worthy of further investigation with an FWD and/or localized repair, thus optimizing the time the FWD spends on the road; and 2) reasonably estimate the structural parameters of a concrete pavement structure, its sub-grade, and the transverse joints, thus providing valuable data both for inventory-keeping and rehabilitation management. / Doctor of Philosophy / When allocating funds for network-wide pavement maintenance, such as the State or Country level, the engineer relies on as much pavement condition data as possible to optimally assign the most suitable maintenance or rehabilitation treatment to each pavement segment. Currently, practitioners rely mostly on surface condition data to decide on how to maintain their roads, as this data can be collected fast and easily with automated vehicle-mounted equipment and analyzed by computer software. However, managerial decisions based solely on surface condition data do not optimally make use of the Agency resources, for they do not precisely account for the pavements' structural capacity when assigning maintenance solutions. As such, the manager may allocate a surface treatment on a structurally weak segment with a poor surface which will be prone to an early failure (thus wasting the investment) or, conversely, reconstruct a deteriorated yet strong segment that could be fixed with a surface treatment. The reason for such a sub-optimal managerial practice has been the lack of a commercially-available pavement testing device capable of producing structural health data at a similar rate as the existing surface scanning equipment – pavement engineers could only appeal to crawling-speed or stop-and-go deflection devices to gather such data, which are fit for project-level applications but totally unsuitable for routine network-wide surveying. Yet, this trend reverted in the early 2000s with the launch of the Traffic Speed Deflectometer [TSD], a device capable of getting dense pavement deflection measurements (spaced as close as 5cm [2 inches] between each other) while traveling at speeds higher than 50 mph. Following the device's release, numerous research activities studied its feasibility as a network-wide routine data collection device and developed analysis schemes to interpret the collected measurements into pavement structural condition information. This research effort is still ongoing, the Transportation Pooled Fund [TPF] Project 5(385) is aimed in that direction, and set the goal of furnishing standards on the acquisition, storage, and interpretation of TSD data for pavement management. This being said, data collection and analysis protocols should be drafted to interpret the data gathered by the TSD on flexible and rigid pavements. Concerning TSD-based evaluation of flexible asphalt pavements, abundant published literature discussing exists; whereas TSD surveying of concrete and composite (concrete + asphalt) pavements has been off the center of attention, partly because these pavements constitute only a minority of the US paved highway network – even though they account for roughly half of the Interstate system. Yet, the TSD has been found suitable to provide valuable structural health information concerning both the pavement slabs and the load-bearing joints, the weakest element of such structures. With this in mind, this Dissertation research is aimed at bridging this existing gap in knowledge: a streamlined analysis methodology is proposed to process the TSD deflection data collected while surveying a jointed rigid pavement and derive important structural health metrics for the manager to drive their decision-making. Broadly speaking, this analysis methodology is constituted by two main elements: • The Data Extraction stage, in which the TSD deflection data is mined to both clear it from measurement noise and extract meaningful features, such as the pulse responses generated as the TSD travels over the pavement joints. • The Interpretation stage, which is more pavement engineering-related. Herein, the filtered TSD measurements are utilized to fit a pavement response model so that the pavement structural parameters (its stiffness, the strength of the sub-grade soil, and the joints' structural health) can be inferred. This Dissertation spans both the mathematical grounds for these analysis techniques, validation tests on computer-generated data, and experiments done with actual TSD data to test their applicability. The ultimate intention is for these techniques to eventually be adopted in practice as routine analysis of the TSD data for a more rational and resource-wise pavement management.