Business Intelligence and Analytics have changed the business needs, but the market requires a more data-driven decision-making environment. Self-Service Business Intelligence initiatives are currently providing more competitive advantages. The role of the users and freedom of access is one of the essential advantages that SSBI holds. Despite this fact, there is still needed analysis on how business can gain more value from SSBI, based on the technological, operational and organizational aspects. The work in this thesis serves to analysis on the SSBI requirements that bring value to business. The paper is organized starting from building knowledge on the existing literature and exploring the domain. Data will be collected by interviewing experts within the BI, SSBI and IT fields. The main findings of the study show that on the technological aspect, data is more governed and its quality is improved by implementing SSBI. Visualization is one of the features of SSBI that boosts quality and governance. On the digital capability aspect, the end-users need training and there is found a rate of impact of SSBI on the main departments in an organization. It is discussed how SSBI implementation affects the companies that do not have BI solution. The final conclusions show that in order for SSBI to be successful, a solid BI environment is necessary. This research will provide future suggestions related to the topic and the results will serve both, the companies that have implemented SSBI and the ones that want to see it as a perspective in the future.

abstract: The volume of available spatial data has increased tremendously. Such data includes but is not limited to: weather maps, socioeconomic data, vegetation indices, geotagged social media, and more. These applications need a powerful data management platform to support scalable and interactive analytics on big spatial data. Even though existing single-node spatial database systems (DBMSs) provide support for spatial data, they suffer from performance issues when dealing with big spatial data. Challenges to building large-scale spatial data systems are as follows: (1) System Scalability: The massive-scale of available spatial data hinders making sense of it using traditional spatial database management systems. Moreover, large-scale spatial data, besides its tremendous storage footprint, may be extremely difficult to manage and maintain due to the heterogeneous shapes, skewed data distribution and complex spatial relationship. (2) Fast analytics: When the user runs spatial data analytics applications using graphical analytics tools, she does not tolerate delays introduced by the underlying spatial database system. Instead, the user needs to see useful information quickly. In this dissertation, I focus on designing efficient data systems and data indexing mechanisms to bolster scalable and interactive analytics on large-scale geospatial data. I first propose a cluster computing system GeoSpark which extends the core engine of Apache Spark and Spark SQL to support spatial data types, indexes, and geometrical operations at scale. In order to reduce the indexing overhead, I propose Hippo, a fast, yet scalable, sparse database indexing approach. In contrast to existing tree index structures, Hippo stores disk page ranges (each works as a pointer of one or many pages) instead of tuple pointers in the indexed table to reduce the storage space occupied by the index. Moreover, I present Tabula, a middleware framework that sits between a SQL data system and a spatial visualization dashboard to make the user experience with the dashboard more seamless and interactive. Tabula adopts a materialized sampling cube approach, which pre-materializes samples, not for the entire table as in the SampleFirst approach, but for the results of potentially unforeseen queries (represented by an OLAP cube cell). / Dissertation/Thesis / Doctoral Dissertation Computer Science 2020

The Facebook - Cambridge Analytica scandal came to light in 2018 revealing the problematic surveillance practices, and violations of privacy the companies allowed. The EU has introduced a privacy legislation, GDPR, that came into effect in 2018 shortly after the scandal erupted. Privacy is a key problem with modern technologies, as companies are trying to gain all possible data on individuals. The purpose of this thesis is to explore the surveillance-privacy nexus in the EU. This thesis asked the research question of: How has surveillance, through emerging technologies, affected the EU's ability to protect the right to privacy? To analyse this research question, this thesis used case study and post-structuralist discourse analysis on the recordings of Alexander Nix, CEO of Cambridge Analytica, at a marking festival, and of Mark Zuckerberg at the European Parliament. To analyse the recordings, biopower and panopticon were used as core theoretical tools. Through utilization of the methods and the theoretical tools, the findings of this thesis point to the conclusion that the EU’s ability to protect privacy from surveillance practices was not affected by the modern surveillance technology, and therefore the protection against exploitation of privacy remains low.

This thesis analyzes the end-user quality of service (QoS) in cellular mobile networks using device-side measurements. Quality of service in a wireless network is a significant factor in determining a user's satisfaction. Customers' perception of poor QoS is one of the core sources of customer churn for telecommunications companies. A core focus of this work is on assessing how user density impacts QoS within cellular networks. Kernel density estimation is used to produce user density estimates for high, medium, and low density areas. The QoS distributions are then compared across these areas. The k-sample Anderson-Darling test is used to determine the degree to which user densities vary over time. In general, it is shown that users in higher density areas tend to experience overall lower QoS levels than those in lower density areas, even though these higher density areas service more subscribers. The conducted analyses highlight the value of mobile device-side QoS measurements in augmenting traditional network-side QoS measurements. / Graduate

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Big data is an enabling technology in digital transformation. It perfectly complements ordinary linear models and generalized linear models, as training well-performed ordinary linear models and generalized linear models require huge amounts of data. With the help of big data, ordinary and generalized linear models can be well-trained and thus offer better services to human beings. However, there are still many challenges to address for training ordinary linear models and generalized linear models in big data. One of the most prominent challenges is the computational challenges. Computational challenges refer to the memory inflation and training inefficiency issues occurred when processing data and training models. Hundreds of algorithms were proposed by the experts to alleviate/overcome the memory inflation issues. However, the solutions obtained are locally optimal solutions. Additionally, most of the proposed algorithms require loading the dataset to RAM many times when updating the model parameters. If multiple model hyper-parameters needed to be computed and compared, e.g. ridge regression, parallel computing techniques are applied in practice. Thus, multiple learning with sufficient statistics arrays are proposed to tackle the memory inflation and training inefficiency issues.

<div>Equipment health is the root of productivity and profitability in a company; through the use of machine learning and advancements in computing power, a maintenance strategy known as Predictive Maintenance (PdM) has emerged. The predictive maintenance approach utilizes performance and condition data to forecast necessary machine repairs. Predicting maintenance needs reduces the likelihood of operational errors, aids in the avoidance of production failures, and allows for preplanned outages. The PdM strategy is based on machine-specific data, which proves to be a valuable tool. The machine data provides quantitative proof of operation patterns and production while offering machine health insights that may otherwise go unnoticed.</div><div><br> </div><div>Purdue University's Wade Utility Plant is responsible for providing reliable utility services for the campus community. The Wade Utility Plant has invested in an equipment monitoring system for a thirty-megawatt turbine generator. The equipment monitoring system records operational and performance data as the turbine generator supplies campus with electricity and high-pressure steam. Unplanned and surprise maintenance needs in the turbine generator hinder utility production and lessen the dependability of the system.</div><div><br> </div> The work of this study leverages the turbine generator data the Wade Utility Plant records and stores, to justify equipment care and provide early error detection at an in-house level. The research collects and aggregates operational, monitoring and performance-based data for the turbine generator in Microsoft Excel, creating a dashboard which visually displays and statistically monitors variables for discrepancies. The dashboard records ninety days of data, tracked hourly, determining averages, extrema, and alerting the user as data approaches recommended warning levels. Microsoft Excel offers a low-cost and accessible platform for data collection and analysis providing an adaptable and comprehensible collection of data from a turbine generator. The dashboard offers visual trends, simple statistics, and status updates using 90 days of user selected data. This dashboard offers the ability to forecast maintenance needs, plan work outages, and adjust operations while continuing to provide reliable services that meet Purdue University's utility demands. <br>

Automated video interviews (AVIs) use machine learning algorithms to predict interviewee personality traits and social skills, and they are increasingly being used in industry. The present study examines the possibility of expanding the scope and utility of these approaches by developing and testing AVIs that score ability from interviewee verbal, paraverbal, and nonverbal behavior in video interviews. To advance our understanding of whether AVI ability assessments are useful, I develop AVIs that predict ability (GMA, verbal ability, and interviewer-rated intellect) and investigate their reliability (i.e., inter-algorithm reliability, internal consistency across interview questions, and test retest reliability). Then, I investigate the convergent and discriminant-related validity evidence as well as potential ethnic and gender bias of such predictions. Finally, based on the Brunswik lens model, I compare how ability test scores, AVI ability assessments, and interviewer ratings of ability relate to interviewee behavior. By exploring how ability relates to behavior and how ability ratings from both AVIs and interviewers relate to behavior, the study advances our understanding of how ability affects interview performance and the cues that interviewers use to judge ability.

This diploma thesis deals with creating platform which serves for easy manipulation with large data set. There are numerous technical knowledge described in this thesis to understand web development. Later there are proposed approaches of how to make as easy as possible for user to define and work with large data sets. Platform is written and created in a way, that it is easy to extend eny part of it.

The aim of this thesis is to show how to design and implement an application for network traces analysis using Apache Spark distributed system. Implementation can be divided into three parts - loading data from a distributed HDFS storage, supported network protocols analysis and distributed data processing. As a data visualization tool is used web-based notebook Apache Zeppelin. The resulting application is able to analyze individual packets as well as the entire flows. It supports JSON and pcap as input data formats. The goal of the application is to allow Big Data processing. The greatest impact on its performance has the input data format and allocation of the available cores.