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.

Yes / As big data (BD) and data analytics having gain significance the industry expects helping executives will eventually move towards evidence based decision making. The hope is to achieve more sustainable competitive advantage for their organisations. A key question is whether executives make decisions by intuition. This leads to another question whether big data would ever substitute human intuition. In this research, the ‘mind-set’ of executives about application and limitations of big data be investigated by taking into account their decision making behaviour. The aim is to look deeply into how BD technologies facilitate greater intuitiveness in executives, and consequently lead to faster and sustainable business growth.

No / With a cornucopia of literature on Big Data and Data Analytics it has become a recent buzzword. The literature is full of hymns of praise for big data, and its potential applications. However, some of the latest published material exposes the challenges involved in implementing Big Data (BD) approach, where the uncertainty surrounding its applications is rendering it ineffective. The paper looks at the mind-sets and perspective of executives and their plans for using Big Data for decision making. Our data collection involved interviewing senior executives from a number of world class organisations in order to determine their understanding of big data, its limitations and applications. By using the information gathered by this is used to analyse how well executives understand big data and how well organisations are ready to use it effectively for decision making. The aim is to provide a realistic outlook on the usefulness of this technology and help organisations to make suitable and realistic decisions on its investment. Professionals and academics are becoming increasingly interested in the field of big data (BD) and data analytics. Companies invest heavily into acquiring data, and analysing it. More recently the focus has switched towards data available through the internet which appears to have brought about new data collection opportunities. As the smartphone market developed further, data sources extended to include those from mobile and sensor networks. Consequently, organisations started using the data and analysing it. Thus, the field of business intelligence emerged, which deals with gathering data, and analysing it to gain insights and use them to make decisions (Chen, et al., 2012). BD is seem to have a huge immense potential to provide powerful information businesses. Accenture claims (2015) that organisations are extremely satisfied with their BD projects concerned with enhancing their customer reach. Davenport (2006) has presented applications in which companies are using the power of data analytics to consistently predict behaviours and develop applications that enable them to unearth important yet difficult to see customer preferences, and evolve rapidly to generate revenues.

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