Business Intelligence and Process Management M.Sc.
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What does the end consumers think about our recently launched smartphone and how does this translate to future sales prospects? What are the implications of economic sanctions on our business? Economists and business analysts are facing these questions in their daily work. Sentiment analysis allows answering such questions as it enables corporations to evaluate web content on a large scale. Exploring the possibility of inte-grating global sentiments into the sales forecast is the aim of this research paper. Twitter has been chosen as the sole source for data retrieval due to its unique property of being a social network-, news broadcasting- and advertising-platform. The analysis reveals the opportunities and limits of sentiment analysis and its integration capability. The results show that a causal relationship among the public's opinion and sales development is present. Sanctions are reflected in the voice of the public with immediate effect but have moderate impact on sales in the short-term. In the midterm, such political and economic measures could impact the sentiments and – among other micro-economic factors –translate into decreasing sales. Ultimately, the sentiment analysis is suitable for a con-tinuous monitoring procedure and thus optimizes the comprehensiveness of convention-al forecasting methods.
Artificial intelligence (AI) is quickly moving from a differentiator to a necessity for many organisations with $37 billion estimated to be invested worldwide in its im-plementation in 2019. Nevertheless, this new technology is not always well under-stood by the businesses and its specific characteristics further complicate the adop-tion. Research has shown that merely half of all efforts to introduce new technology in organisations fully meet their set objectives. The application of organisational change management (OCM) methods is believed to increase the chances of success for such large and costly undertakings. This study aims to review general and tech-nology-specific OCM approaches and examine their relevance in the context of AI transformations. By contrasting existing work and practices on AI adoption against the OCM approaches used for previous technology implementations, the change management characteristics specific to AI are explored, as well as the suitability of the best-regarded established OCM methods is evaluated.
Based on a review of the literature on organisational change management, technol-ogy adoption and artificial intelligence, six semi-structured expert interviews were carried out. These conversations gathered qualitative data with the goal to enrich the existing knowledge on the topic, as well as to bridge some gaps with regards to AI’s practical adoption. Analysis of the responses, continuously compared to exist-ing research, showed that a few factors and AI traits require adjustments to the conventional OCM methods. Ethics, ambiguity, complexity, process and policy changes, speed, ubiquitous potential, as well as fear and anxiety highly influence AI transformations opposed to other technological adoptions. These might not re-quire revolutionary new OCM approaches but would imply focusing on developing organisational culture and ensuring an experimental and iterative change process. Kotter’s 8-step process and Prosci’s ADKAR model were found most appropriate in the face of organisational AI adoption. Further research is needed to validate these findings and examine them in specific contexts.
In this thesis, we focus on predicting diagnosis from drug treatment.We investigate if such a
task can benefit from recent advances in Natural Language Processing (NLP), transfer learning techniques, and in particular the new state-of-the-art transformer-based BERT models.
The paper shows a comparison of traditional machine learning methods, deep learning methods and pre-trained BERT models for predicting diagnosis. During the evaluation step, we
found simple traditional models and deep learning models to be more effective on the task
in question. The paper further discussed why pre-trained BERT models failed to deliver
results.
This thesis analyzes whether financial news articles can predict the stock prices of companies and the S&P 500 Index. The selected companies for the analysis are Facebook Inc., Apple Inc., Microsoft Corp., Google (Alphabet Inc.), and Amazon.com Inc. This thesis evaluates the predictive power of financial news by comparing the sentiment of financial news articles published on a business day to
the corresponding closing value of stock prices and the S&P 500 Index. For the analysis, financial news from various resources is used for a time period between January 2018 and May 2018. The sentiment of the news articles is analyzed using a lexicon-based approach. Then, regression models are used to predict the stock market. The models used for forecasting the stock prices and the S&P 500 Index
are Auto-Regressive Integrated Moving Average (ARIMA), Support Vector
Regression (SVR), and Linear Regression (LR). The results of the research in overall indicate that ARIMA performs better for predicting the stock prices and the S&P 500 Index.
In this research paper, the main focus was to concerns the role of Business Intelligence (BI) in decision-making processes in organizations. The biggest goal of this thesis is to explore how decision-makers use and deploy this BI output to structure collective vision and achieve organizational choices. Business intelligence
can be described not as a process or a product or even a framework, but as a new strategy in organizational architecture based on velocity in data analysis in order to make correct strategic choices in company with maximum performance in a minimum quantity of time. After a study evaluating a conceptual model of the impact of business intelligence on strategic choices was constructed together with interviews among decision-makers. In order to evaluate the model, few questions
were raised and later solved during the studies. The research concentrated on knowing how the information supplied by the business intelligence system were used by the staff in the organization. The project focused on conducting in-depth interviews in a diverse organization with managers with six different backgrounds.
Each of the interviewers make decisions based on the output of BI.
In recent years, music genre classification has been studied widely within the musical information retrieval community to detect music genre (e.g., pop, rap) automatically. The existing methods, reported in the literature, usually extract features from the melodic content or lyrics of the song and address this classification as a multi-class problem. This thesis presents a comprehensive investigation of the prediction of the music genre solely from an examination of the lyrical content of the songs. The lyrics were thoroughly analyzed to obtain the features as inputs to the various machine learning algorithms and the features were represented using tf-idf values. In order to perform the algorithms, a dataset with a total of 12,000 songs for 12 genres was created by crawling the music websites.
Furthermore, this study considers the genre classification as a multi-label task in which a song can belong to more than one genre as it is encountered. Therefore, multi-label approaches were examined in-depth and applied along with the popular classifiers. The experiments in this thesis show that binary relevance method conducted with logistic regression model outperforms among all for the lyric-based multi-label genre classification.
Search Engine Opt imisation, also known as SEO, is one of the online marketing channels t hat, when it is s et u p s uitably, it could continue to pay dividends over time without investment. Recently, SEO teams of some companies keep investigating historical data to predict the future trends of revenue associated with the number of clicks, number of imp ressions, and number of sear ches which is prop osed to help with companies’ quality planning and campaign investment. Some of the challenges experienced by SEO analysts when attempting to forecast the revenue is that there is currently no way to standardize or forecast customers’ behaviour, which means the trends could be different every day, month, and year. In this research, SEO traffic data from one of the online travel a gencies are collected for the purpose of data exploration, analysis and forecasting which are expected to bring business values and give some be neficialinsights. Moreover, different time-series forecasting models are selected to conduct experiments seeking the best fit model for SEO data; Autoregressive Integrated Moving Average (ARIMA) model is initially performed, followed by Long-Short Term Memory (LSTM) of Recurrent Neural Networks (RNNs). As a result, it is proved that ARIMA is yet a classical statistics model but powerful enough for such small-size data, albeit the data is non-stationary and has too much white noise. Me anwhile, the LSTM is a deep learning tool which could deal with different types of data, but still need to be applied with a larger size of data to prove its competence.
Process models are often used as a Knowledge Management method, because these are able to store, visualize, and distribute knowledge within an organization. These knowledge-intensive processes can have a flexible, unstructured form, which is often hard to represent within a process model. The already available approaches of process-oriented Knowledge Management methods, which are considered for Business Process Management, are conducted manually and can therefore become time-consuming and labor-intensive. In addition, in times of Big Data, it is even more challenging to consider all possible cases. Due to the possibility of automatically generating process models through Process Mining, there is a huge potential for creating these processes with real information from event data. This study identifies the possibilities of generating knowledge-intensive process models through Process Mining. At the same time, it addresses the issue of the lack of representation of discovered flexible process models. Although few articles already appeal to the challenge of generating knowledge-intensive processes through Process Mining, the current main focus lies within the operational support in the information systems. The research question of this study is whether actual Process Mining approaches are able to generate process models that are as informative as the models created by processoriented Knowledge Management methods in their current state. To answer the research question, a comprehensive literature review as well as expert interviews have been conducted. Both approaches are part of the design science research methodology, which has been followed during the whole study. The theoretical results have been compared to the practical insights gained from the interviews. As a result, an informal, textual, best practice specification for mining knowledge-intensive process models within a Process Mining project has been developed. This should be considered for implementing an Enterprise Knowledge Medium into the already available IT infrastructure, so that process knowledge can be effectively saved and leveraged through Process Mining.
This thesis contributes to the growing interest of mobile and gait-based authentication. A realworld authentication system needs to ensure performance stability, even if the walking situation of phones holder changes. In this paper we analyze different circumstances of an environmental nature, such as phone placement (pocket position and orientation), clothing (trousers’ type, trouser width, trouser pocket’s distance to the user’s hipbone, shoe and bag), surrounding(location, surface) and walking style (walking speed, direction, group walk). All these labels were recorded by the theme ”A Walk Through Berlin”. 24 participants were equipped with a Samsung Galaxy S7 smartphone and collected their walking behavior at up to three locations in Berlin, Germany in at least two day-independent workshop sessions. Besides data collection, this research addresses limitations in regard to a one-class classification problem. An application should process data directly on the device itself and should not share these sensitive data streams with external parties or servers. We execute feature preprocessing and extraction in sliding
windows and use a one-class Support Vector Machine for user classification. After best feature evaluation, we gain an Equal Error Rate of 21% for model’s total performance, where train and validation set are enrolled on different days.
As an innovative tool, gamification has gained considerable importance in recent years to navigate and find many creative solutions to the problems and challenges faced by existing companies.
In this study, a game toolkit designed by using gamification elements that can potentially serve as a guide to creative ideation is introduced and applied in a data-driven industry. The aim is to
analyze the effects of the specifically designed game toolkit and the applied game elements on creative ideation and the attributes of users in general. The toolkit is explored using insights obtained from the analyses and observation of the behaviors of 16 players during the actual experiments. Furthermore, interviews and online surveys are used to conduct the quantitative and qualitative analyses in order to enhance the game within the scope of Action Design Research.
The toolkit proposed by the researcher is assumed to contribute to the existing literature. It can provide guidance and give valuable suggestions to possible future studies about the creation and
execution of a game toolkit in a field focused on data-driven innovations. Moreover, the results specifically indicate that the game toolkit directly drives creative idea generation while promoting engagement, motivation, and enjoyment as well as altering mindsets and thinking patterns.