Likt flera branscher utvecklas revisionsbranschen under tidens gång. Nya arbetssätt och verktyg introduceras för att utveckla revisionsutförandet. Ett av dessa verktyg som har implementerats av många revisionsbolag är Big data analys. Forskningsområdet kring Big data analys användande i revisionsutförandet är i ett inledande stadie, och resultaten är skilda. Tidigare forskning är dock ense om att Big data analys har en påverkan på revisionsutförandet och att revisorns kompetenser spelar en avgörande roll om användandet av Big data analys blir framgångsrikt. Till följd av detta var studiens syfte att undersöka vilken påverkan användningen av Big data analys har på revisionsutförandet samt vilka kompetenser som behövs vid användandet av Big data analys. Utifrån studiens syfte formuleradestre forskningsfrågor som behandlade Big data analys påverkan på revisionskvaliteten, revisionsutförandets legitimitet samt vilka kompetenser som behövs för att uppnå komfort och upprätthålla professionell skepticism i revisionsutförandet.För att uppnå studiens syfte har insamlingen av empiri grundat sig i en kvalitativ metod. Tio respondenter deltog i studien, varav sju var auktoriserade revisorer och resterande tre var revisorsassistenter.De slutsatser som studien generat är att revisorns kompetenser ansågs väsentliga för hur välfungerande användandet Big data analys var. Dock ansåg inte respondenterna att det var några nya kompetenser som behövdes vid användandet av Big data analys. Utan det var alltjämt en god förståelse för revision och redovisning som behövdes. Vidare ansågs Big data analys bidra till mer komfort i revisionsutförandet. Samtidigt som den professionella skepticismen alltjämt genomsyrade allt i revisions arbetssätt, och då även Big data analyser. Däremot påvisades det att användandet av Big data analys kunde hjälpa revisorer finna fler revisionsbevis, vilket hjälpte revisorn att vara professionellt skeptisk gentemot det reviderade bolaget. Big data analys förtjänster att testa en större population, jämfört med traditionella stickprov, ansågs öka den övergripande revisionskvaliteten. Detta medförde även att legitimiteten påverkades positivt hos de bolag som hade större kunder med mer transaktioner. Medan revisionsbolaget som jobbade mot kunder med mindre verksamheter inte upplevde samma positiva inverkan på deras legitimitet vid användningen av Big data analys i revisionsutförandet. / Like several industries, the auditing industry is evolving over time. New methods and tools are being introduced to enhance the auditing process. One of these methods that has been implemented by many audit firms is Big Data analysis. The research field on the use of Big Data analysis in auditing is in its early stages, and the results are mixed. However, previous research agreed that Big Data analysis has an impact on the auditing process, and the auditor's competencies play a crucial role in the successful implementation of Big Data analysis.As a result, the aim of this study was to investigate the impact of using Big Data analysis on the auditing process and identify the competencies required for its effective use. Based on the study's objective, three research questions were formulated, addressing the impact of Big Data analysis on audit quality, the legitimacy of the auditing process, and the competencies needed to achieve comfort and maintain professional skepticism in the audit process when using Big Data analysis. To achieve the study's objective, empirical data was collected using a qualitative methodology. Ten respondents participated in the study, including seven certified auditors and three audit assistants. The study's conclusions indicate that the auditor's competencies were considered essential for the successful implementation of Big Data analysis. However, the respondents did not believe that any new competencies were required specifically for conducting Big Data analysis reviews. Instead, a solid understanding of auditing and accounting was deemed necessary. Furthermore, Big Data analysis was seen to contribute to greater comfort in the audit process. At the same time, professional skepticism continued to permeate the entire auditing approach, including Big Data analysis. However, it was demonstrated that the use of Big Data analysis could help auditors find more audit evidence, aiding the auditor in maintaining professional skepticism toward the audited company. The advantages of Big Data analysis in testing a larger population, compared to traditional sampling, were seen to enhance overall audit quality. This also had a positive impact on the legitimacy of companies with larger clients and more transactions. Conversely, audit firms working with smaller businesses did not experience the same positive impact on their legitimacy when using Big Data analysis in the audit process.

作業基礎成本制度（Activity-Based Costing, ABC）為現行管理會計制度中，為較多企業所採用之制度，吳安妮教授在經過多年理論與實務之研究後，將ABC制度IT系統商品化，並與許多不同的制度整合為一體，命名為「作業價值管理系統（Activitiy Value Management System,AVMS）」，藉由作業價值管理能提供管理階層正確、即時、攸關之資訊，並協助其做出較適當之管理決策。 　　大數據（Big Data）被許多產業所使用，藉由歷史資料與未來預測，開創了新市場與新商業模式，而大數據也結合了許多制度，例如工業4.0、物聯網等，但卻沒有與管理會計相結合之研究，本研究藉由作業價值管理與產能管理之結合進行大數據之分析，初步的將管理會計與大數據結合，並同時協助個案公司發現有關產能管理之問題，並改善之。 　　本研究使用個案研究法，個案公司為一民防工程與地下空間設計之公司，藉由該個案公司所處產業之產業資料、未來趨勢、競爭者資料、作業價值管理資料等，分析找出個案公司之產能管理問題，並協助個案公司解決所發現之問題以提升管理之效率。 / Activity-Based Costing (ABC) is a well-known management accounting method and used by many companies. After professor An Wu’s 30-years research, she put Activity-Based Costing into IT system and named it Activity Value Management System (AVMS). This system provides correct and immediate information for company’s manager which can help them making a good decision. 　　Big data Analysis is used by many industry for creating new markets and new business models. Big Data Analysis combined with many systems such as Industry 4.0 and Internet of Things (IOT), but there aren’t any integration with management accounting. In this thesis we will Integrate Activity Value Management and Capacity Management with Big Data Analysis. By doing so, this can help the company reviving and solving the problem of Capacity Management. 　　The thesis is a case study with a China Basement designing company. Using the industry information, future trends, Competitor information and AVM data we can not only figure out the problem of Capacity Management that the company is facing but also help the company solving them.

The arrival of the Big Data era has become a major topic of discussion in many sectors because of the premises of big data utilizations and its impact on decision-making. It is an interdisciplinary issue that has captured the attention of scholars and created new research opportunities in information science, business, heath care, and many others fields. The problem is the Big Data is not well defined, so that there exists confusion in IT what jobs and skill sets are required in big data area. The problem stems from the newness of the Big Data profession. Because many aspects of the area are unknown, organizations do not yet possess the IT, human, and business resources necessary to cope with and benefit from big data. These organizations include health care, enterprise, logistics, universities, weather forecasting, oil companies, e-business, recruiting agencies etc., and are challenged to deal with high volume, high variety, and high velocity big data to facilitate better decision- making. This research proposes a new way to look at Big Data and Big Data analysis. It helps and meets the theoretical and methodological foundations of Big Data and addresses an increasing demand for more powerful Big Data analysis from the academic researches prospective. Essay 1 provides a strategic overview of the untapped potential of social media Big Data in the business world and describes its challenges and opportunities for aspiring business organizations. It also aims to offer fresh recommendations on how companies can exploit social media data analysis to make better business decisions—decisions that embrace the relevant social qualities of its customers and their related ecosystem. The goal of this research is to provide insights for businesses to make better, more informed decisions based on effective social media data analysis. Essay 2 provides a better understanding of the influence of social media during the 2016 American presidential election and develops a model to examine individuals' attitudes toward participating in social media (SM) discussions that might influence their decision in choosing between the two presidential election candidates, Donald Trump and Hilary Clinton. The goal of this research is to provide a theoretical foundation that supports the influence of social media on individual's decisions. Essay 3 defines the major job descriptions for careers in the new Big Data profession. It to describe the Big Data professional profile as reflected by the demand side, and explains the differences and commonalities between company-posted job requirements for data analytics, business analytics, and data scientists jobs. The main aim for this work is to clarify of the skill requirements for Big Data professionals for the joint benefit of the job market where they will be employed and of academia, where such professionals will be prepared in data science programs, to aid in the entire process of preparing and recruiting for Big Data positions.

Throughout the world, there is a great demand to map out the increasing environmental changes and life habitats on Earth. The vast majority of Earth Observations today, are collected using satellites. The Global Watch Center (GWC) initiative was started with the purpose of producing a global situational awareness of the premises for all life on Earth. By collecting, studying and analyzing vast amounts of data in an automatic, scalable and transparent way, the GWC aims are to work towards reaching the United Nations (UN) Sustainable Development Goals (SDG). The GWC vision is to make use of qualified accessible data together with leading organizations in order to lay the foundation of the important decisions that have the biggest potential to make an actual difference for the common awaited future. As a show-case for the initiative, the UN strategic department has recommended a specific use-case, involving mapping large accumulation of waste in areas greatly affected, which they believe will profit the initiative very much. This Master Thesis aim is, in an automatic and scalable way, to detect and classify dumpsites in Kampala, the capital of Uganda, by using available satellite imagery. The hopes are that showing technical feasibility and presenting interesting remarks will aid in spurring further interest in coming closer to a realization of the initiative. The technical approach is to use a lightweight version of Automatic Target Recognition. This is conventionally used in military applications but is here used, to detect and classify features of large accumulations of solid-waste by using techniques from the field of Image Analysis and Data Mining. Choice of data source, this study's area of interest as well as choice of methodology for Feature Extraction and choice of the Machine Learning algorithm Support Vector Machine will all be described and implemented. With a classification precision of 95 percent will technical results be presented, with the ambition to promote further work and contribute to the GWC initiative with valuable information for later realization.

Cyber-Physical System (CPS) or Digital-Twin approach are becoming popular in industry 4.0 revolution. CPS not only allow to view the online status of equipment, but also allow to predict the health of tool. Based on the real time sensor data, it aims to detect anomalies in the industrial operation and prefigure future failure, which lead it towards smart maintenance. CPS can contribute to sustainable environment as well as sustainable production, due to its real-time analysis on production. In this thesis, we analyzed the behavior of a tool of Ringvalsverk 4, at Ovako with its twin model (known as Digital-Twin) over a series of data. Initially, the data contained unwanted signals which is then cleaned in the data processing phase, and only before production signal is used to identify the tool’s model. Matlab’s system identification toolbox is used for identifying the system model, the identified model is also validated and analyzed in term of stability, which is then used in CPS. The Digital-Twin model is then used and its output being analyzed together with tool’s output to detect when its start deviate from normal behavior.