Building Information Modeling (BIM), used by many for building design and construction, and Geographic Information GIS System (GIS), used for city planning, contain large spatial and attribute data which could be used for Lean and green city planning and development. However, there exist a systematic gap and interoperability challenge between BIM and GIS that creates a disjointed workflow between city planning data in GIS and building data in BIM. This hinders the seamless analysis of data between BIM and GIS for lean and green developments. This study targets the creation of a system which integrates BIM and GIS system data. The methods involve the establishment of a novel Environmental Information Modeling (EIM) framework to bridge the gap using Microsoft Visual C#. The application of this framework shows the potential of this concept. The research results provide an opportunity for more analysis for lean and green construction planning, development and management.

Title: No thanks to unnecessary advertising! -A study on targeted marketing via Big Data in a consumer perspective Authors: Ricky Carlsson and Alexander Vilhelmsson Supervisor: Anders Parment Key words: Targeted marketing, Big Data, Customer segmentation, Buying process, Integrity concern, Customer relationship management, Marketing communication, Strategic management, Big Data management, Online Behavioural Targeting Introduction: In a world that is globalizing and where digital development is advancing, companies have had to adapt. In recent times with the increasingly more digital world, technology has become an increasingly more relevant factor, not least in marketing. A digital method that has emerged is Big Data, whichmakes it possible forcompanies tocollect large amounts of information about consumers. By analysing the information extracted from Big Data, it is easier to find and understand consumers' needs and what motivates their buying process. It is important that companies analyse the information correctly so that they do not run the risk of creating negative effects from targeted marketing via Big Data. Purpose: To investigate Swedish consumers' attitudestowards targeted marketing via Big Data and to find out how companies that sell goods and services to consumers can improve their use of Big Data in targeted marketing from a consumer perspective. Method: The study is a cross-sectional study of a qualitative and quantitative nature. The qualitative empirical data consists of 11 semi-structured interviews with students in Sweden. The quantitative empirical data consists of 203 survey answers collected from consumers around Sweden. The study is based on an abductive approach and has a hermeneutic approach. Conclusion: The result of the study shows that there are both opportunities and challenges for companies when using Big Data in targeted marketing. Targeted marketing with the help of Big Data that is performed correctly should only have a positive impact on the targeted marketing and something that creates value for both the consumers and the companies, but this is not the case today. The population of the study perceives that marketing often does not match their needs; this shows that companies must become better at analysing the data. If the data extracted from Big Data is analysed in a better way, the segmentation of consumers will also be better. / Titel: Nej tack till onödig reklam! - En studie om riktad marknadsföring via Big Data i ett konsumentperspektiv. Författare: Ricky Carlsson och Alexander Vilhelmsson Handledare: Anders Parment Bakgrund: I en värld som globaliseras och där den digitala utvecklingen går framåt har företag varit tvungna att anpassa sig. På senare tid i takt med den ständigt mer digitaliserade världen har teknologi blivit en alltmer relevant faktor, inte minst inom marknadsföring. En digital metod som har vuxit fram är Big Data genom vilken företag har möjlighet att samla in stora mängder information om konsumenter. Genom att analysera informationen som utvinns från Big Data går det att lättare finna och förstå konsumenters behov och vad som motiverar deras köpprocess. Det är viktigt att företag analyserar informationen på rätt sätt för att inte löpa risken att skapa negativa effekter av den riktade marknadsföringen via Big Data. Syfte: Att undersöka svenska konsumenters attityder till riktad marknadsföring via Big Data samt ta reda på hur företag som säljer varor eller tjänster till konsumenter kan förbättra användningen av Big Data inom riktad marknadsföring utifrån ett konsumentperspektiv. Metod: Studien är en tvärsnittsstudie av kvalitativ och kvantitativ karaktär. Den kvalitativa empirin består av 11 semi-strukturerade intervjuer med studenter i Sverige. Den kvantitativa empirin består av 203 insamlade enkätsvar från konsumenter runt om i Sverige. Studien grundas i en abduktiv ansats och har ett hermeneutiskt synsätt. Slutsatser: Resultatet i studien visar på att det finns möjligheter och utmaningar för företag vid användning av Big Data inom riktad marknadsföring. En riktad marknadsföring medhjälp av Big Data som utförs på rätt sätt borde enbart ha en positiv påverkan på den riktade marknadsföringen och något som skapar värde för konsumenter och företag, men så är inte fallet idag. Då studiens population uppfattar att den riktade marknadsföringen ofta inte matchar deras behov bör företag bli bättre på att analysera data. Om data som utvinns från Big Data analyseras på ett bättre sätt kommer även segmenteringen av konsumenter att bli bättre.

The amount of data that has been measured, transmitted/received, and stored in the recent years has dramatically increased. So, today, we are in the world of big data. Fortunately, in many applications, we can take advantages of possible structures and patterns in the data to overcome the curse of dimensionality. The most well known structures include sparsity, low-rankness, block sparsity. This includes a wide range of applications such as machine learning, medical imaging, signal processing, social networks and computer vision. This also led to a specific interest in recovering signals from noisy compressed measurements (Compressed Sensing (CS) problem). Such problems are generally ill-posed unless the signal is structured. The structure can be captured by a regularizer function. This gives rise to a potential interest in regularized inverse problems, where the process of reconstructing the structured signal can be modeled as a regularized problem. This thesis particularly focuses on finding the optimal regularization parameter for such problems, such as ridge regression, LASSO, square-root LASSO and low-rank Generalized LASSO. Our goal is to optimally tune the regularizer to minimize the mean-squared error (MSE) of the solution when the noise variance or structure parameters are unknown. The analysis is based on the framework of the Convex Gaussian Min-max Theorem (CGMT) that has been used recently to precisely predict performance errors.

As information and communication technology develops swiftly, amount of information produced by various sources grows as well. Sorting and obtaining knowledge from this data requires significant effort which is not ensured easily by a human, meaning machine processing is taking place. Acquiring emotion from text data is an interesting area of research and it’s going through considerable expansion while being used widely. Purpose of this thesis is to create a system for positive and negative emotion detection from text along with evaluation of its performance. System was created with Java programming language and it allows training with use of large amount of data (known as Big Data), exploiting Spark library. Thesis describes structure and handling text from database used as source of input data. Classificator model was created with use of Support Vector Machines and optimized by the n-grams method.

In retail, the explosion of data sources and data has provided incentive to invest in information systems (IS), which enable leaders to understand the market and make timely decisions to improve performance. Given that users’ perceptions of IS affects their use of IS, understanding the factors influencing user acceptance is critical to acquiring an effective business intelligence system (BIS) for an organization. Grounded in the technology acceptance model theory, the purpose of this correlational study was to examine the relationship between perceived usefulness (PU), perceived ease of use (PEOU), and user acceptance of business intelligence systems (BIS) in retail organizations. A 9-question survey was used to collect data from end-users of BIS in strategic managerial positions from retail organizations in the eastern United States who reported using BIS within the past 5 years. A total of 106 complete survey responses were collected and analyzed using multiple linear regression and Pearson’s product-moment correlation. The results of the multiple linear regression indicated the model’s ability to predict user acceptance, F(2,103) = 21.903, p < .000, R2 = 0.298. In addition, PU was a statistically significant predictor of user acceptance (t = -3.947, p = .000), which decreased with time as shown by the results from Pearson’s product-moment correlation, r = -.540, n = 106, p < .01. The implications of this study for positive social change include the potential for business leaders to leverage BIS in addressing the underlying causes of social and economic challenges in the communities they serve.

New knowledge and insights are generated when big data is collected and processed. Traditionally, business generated data internally from operations and transactions across the value chain such as sales, customer service visits, orders, interaction with supplier as well as data gathered from research, surveys or other sources externally. Today, with improved software and connectivity, the products become smarter which makes it easier to collect and generate large amount of real-time data.  The fast growing volumes and varieties of big data bring many challenges for companies on how to store, manage, utilize and create value from these data.     This thesis represents a case study of a large heat pump manufacturer, Bosch Thermoteknik AB, situated in Tranås, Sweden. Bosch Thermoteknik AB has started to collect data in real time from several heat pumps connected to the internet. These data are currently used during development phase of the products and occasionally to support installers during maintenance services. The company understands the potential benefits resulting from big data and would like to further deepen their knowledge on how to utilize big data to create value. One of the company’s goals is to identify how big data can reduce maintenance costs and improve maintenance approaches.  The purpose of this study is to provide knowledge on how to obtain insights and create value by collecting and analyzing big data from smart connected products. A focus point will be on improving maintenance approaches and reducing maintenance costs. This study shows that if companies create capabilities to perform data analytics, insights obtained from big data analytics could be used to create business value targeting many areas such as: customer experience, product and service innovation, organization performance improvement as well as improving business image and reputation. Creating capabilities requires deploying many resources other than big data, including a technology infrastructure, integrating and storing a vast amount of data, implementing data-driven culture and having talented employees with business, technical and analytics knowledge and skills. Insights obtained through analytics of big data could provide a better understanding of problems, identifying the root causes and reacting faster to problems. Additionally, failures could be prevented and predicted in the future. This could result in the overall improvement of maintenance approaches, products and services.

The 2018 Swedish general election saw the largest collective polling error so far in the twenty-first century. As in most other advanced democracies Swedish pollsters have faced extensive challenges in the form of declining response rates. To deal with this problem a new method based on search query data is proposed. This thesis predicts the Swedish general election using Google Trends data by introducing three models based on the assumption, that during the pre-election period actual voters of one party are searching for that party on Google. The results indicate that a model that exploits information about searches close to the election is in general a good predictor. However, I argue that this has more to do with the underlying weight this model is based on and little to do with Google Trends data. However, more analysis needs to be done before any direct conclusion, about the use of search query data in election prediction, can be drawn.

Comparing to traditional cartography, big data geographic information processing is not a simple task at all, it requires special methods and methods. When existing geovisualization systems face millions of data, the zoom function and the dynamical data adding function usually cannot be satisfied at the same time. This research classify the existing methods of geovisualization, then analyze its functions and bottlenecks, analyze its applicability in the big data environment, and proposes a method that combines spatial data structure and iterative calculation on demand. It also proves that this method can effectively balance the performance of scaling and new data, and it is significantly better than the existing library in the time consumption of new data and scaling<br>

Thesis (MTech (Business Information Systems))--Cape Peninsula University of Technology, 2019 / The internet, mobile communication, social media, and other digital services have integrated so much into our daily lives and businesses alike. Companies facing digital transformation experience this as exceptionally challenging. While there are several studies that state the importance of digital transformation and how it influences current and future businesses, there is little academic literature available on factors that affect the success or failure of digital transformation in companies. It is unclear what factors affect digital transformation in an established telecommunications company. The aim of this study was therefore to explore the factors that affect digital transformation in a telecommunications company in South Africa, and to what extent. One primary research question was posed, namely: “What factors affect digital transformation in a telecommunications company in South Africa?” To answer the question, a study was conducted at a telecommunications company in South Africa. The researcher adopted a subjective ontological and interpretivist epistemological stance, as the data collected from the participants’ perspective were interpreted to make claims about the truth, and because there are many ways of looking at the phenomena. An inductive approach was selected to enable the researcher to gain in-depth insight into the views and perspective of factors that influence digital transformation in the specific company. The explorative research strategy was used to gain an understanding of the underlying views, reasons, opinions, and thoughts of the 15 participants by means of semi-structured interviews. The participants were made aware that they do not have to answer any question if they are uncomfortable, and they could withdraw their answers at any time. The data collected were transcribed, summarised, and categorised to provide a clear understanding of the data. For this study, 36 findings were identified. From this research, it was inter alia concluded that successful digital transformation of companies depends on how Management drives digital transformation, and the benefits of new digital technologies should be carefully considered when planning to implement digital transformation.

This diploma thesis deals with the topic of preserving cultural heritage in the context of digital humanities. The topic of this work will be the presentation of modern tools and technologies aimed at preserving cultural memory in Europe. The aim of this work will be to map the benefits and potential risks of digital infrastructures CESSDA, ARIADNE PLUS and DARIAH-EU for the preservation of cultural heritage. The work will be divided into three parts - the theoretical part will briefly introduce the topic of digital humanities and their studies, the concept of big data and the transformation of digital archives in the context of digital humanities. The analytical part will focus on mapping the current situation of cultural heritage preservation in Europe. The last practical part will offer an analysis of the advantages and disadvantages of digital infrastructures ARIADNE PLUS, CESSDA and DARIAH-EU both for the preservation of cultural heritage and for research in the social sciences and humanities.