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Dissertations / Theses on the topic 'Data Analytics in Litigation'

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Published: 7 June 2025
Last updated: 16 July 2025

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1

Tata, Maitreyi. "Data analytics on Yelp data set." Kansas State University, 2017. http://hdl.handle.net/2097/38237.

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Master of Science<br>Department of Computing and Information Sciences<br>William H. Hsu<br>In this report, I describe a query-driven system which helps in deciding which restaurant to invest in or which area is good to open a new restaurant in a specific place. Analysis is performed on already existing businesses in every state. This is based on certain factors such as the average star rating, the total number of reviews associated with a specific restaurant, the price range of the restaurant etc. The results will give an idea of successful restaurants in a city, which helps you decide where
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Le, Quoc Do. "Approximate Data Analytics Systems." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-234219.

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Today, most modern online services make use of big data analytics systems to extract useful information from the raw digital data. The data normally arrives as a continuous data stream at a high speed and in huge volumes. The cost of handling this massive data can be significant. Providing interactive latency in processing the data is often impractical due to the fact that the data is growing exponentially and even faster than Moore’s law predictions. To overcome this problem, approximate computing has recently emerged as a promising solution. Approximate computing is based on the observation
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Canon, Moreno Javier Mauricio 1977. "Leading data analytics transformations." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111472.

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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 77-79).<br>The phenomenal success of big technology companies founded with a strong emphasis on data, has epitomized the rise of the new "digital economy." Large traditional organizations, that were not long ago "on top of the world" are now at a crossroads. Their business models seem threatened by newcomers as they face pressure to "transform" and "modernize." Publicity has reinforced the perception that data can now be
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Ahsan, Ramoza. "Time Series Data Analytics." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/529.

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Given the ubiquity of time series data, and the exponential growth of databases, there has recently been an explosion of interest in time series data mining. Finding similar trends and patterns among time series data is critical for many applications ranging from financial planning, weather forecasting, stock analysis to policy making. With time series being high-dimensional objects, detection of similar trends especially at the granularity of subsequences or among time series of different lengths and temporal misalignments incurs prohibitively high computation costs. Finding trend
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Carle, William R. II. "Active Analytics: Adapting Web Pages Automatically Based on Analytics Data." UNF Digital Commons, 2016. http://digitalcommons.unf.edu/etd/629.

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Web designers are expected to perform the difficult task of adapting a site’s design to fit changing usage trends. Web analytics tools give designers a window into website usage patterns, but they must be analyzed and applied to a website's user interface design manually. A framework for marrying live analytics data with user interface design could allow for interfaces that adapt dynamically to usage patterns, with little or no action from the designers. The goal of this research is to create a framework that utilizes web analytics data to automatically update and enhance web user interfaces.
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Erlandsson, Niklas. "Game Analytics och Big Data." Thesis, Mittuniversitetet, Avdelningen för arkiv- och datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-29185.

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Game Analytics är ett område som vuxit fram under senare år. Spelutvecklare har möjligheten att analysera hur deras kunder använder deras produkter ned till minsta knapptryckning. Detta kan resultera i stora mängder data och utmaning ligger i att lyckas göra något vettigt av sitt data. Utmaningarna med speldata beskrivs ofta med liknande egenskaper som används för att beskriva Big Data: volume, velocity och variability. Detta borde betyda att det finns potential för ett givande samarbete. Studiens syfte är att analysera och utvärdera vilka möjligheter Big Data ger att utveckla området Game Ana
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Doucet, Rachel A., Deyan M. Dontchev, Javon S. Burden, and Thomas L. Skoff. "Big data analytics test bed." Thesis, Monterey, California: Naval Postgraduate School, 2013. http://hdl.handle.net/10945/37615.

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Approved for public release; distribution is unlimited<br>The proliferation of big data has significantly expanded the quantity and breadth of information throughout the DoD. The task of processing and analyzing this data has become difficult, if not infeasible, using traditional relational databases. The Navy has a growing priority for information processing, exploitation, and dissemination, which makes use of the vast network of sensors that produce a large amount of big data. This capstone report explores the feasibility of a scalable Tactical Cloud architecture that will harness and utiliz
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Naumanen, Hampus, Torsten Malmgård, and Eystein Waade. "Analytics tool for radar data." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-353857.

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Analytics tool for radar data was a project that started when radar specialists at Saab needed to modernize their tools that analyzes binary encoded radar data. Today, the analysis is accomplished using inadequate and ineffective applications not designed for that purpose, and consequently this makes the analysis tedious and more difficult compared to using an appropriate interface. The applications had limitations regarding different radar systems too, which restricted their usage significantly. The solution was to design a new software that imports, translates and visualizes the data indepen
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Komolafe, Tomilayo A. "Data Analytics for Statistical Learning." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/87468.

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The prevalence of big data has rapidly changed the usage and mechanisms of data analytics within organizations. Big data is a widely-used term without a clear definition. The difference between big data and traditional data can be characterized by four Vs: velocity (speed at which data is generated), volume (amount of data generated), variety (the data can take on different forms), and veracity (the data may be of poor/unknown quality). As many industries begin to recognize the value of big data, organizations try to capture it through means such as: side-channel data in a manufacturing oper
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10

Miloš, Marek. "Nástroje pro Big Data Analytics." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-199274.

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The thesis covers the term for specific data analysis called Big Data. The thesis firstly defines the term Big Data and the need for its creation because of the rising need for deeper data processing and analysis tools and methods. The thesis also covers some of the technical aspects of Big Data tools, focusing on Apache Hadoop in detail. The later chapters contain Big Data market analysis and describe the biggest Big Data competitors and tools. The practical part of the thesis presents a way of using Apache Hadoop to perform data analysis with data from Twitter and the results are then visual
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Santhanagopalan, Meena. "Biopsychosocial Data Analytics and Modeling." Thesis, Federation University Australia, 2021. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/177435.

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Sustained customisation of digital health intervention (DHI) programs, in the context of community health engagement, requires strong integration of multi-sourced interdisciplinary biopsychosocial health data. The biopsychosocial model is built upon the idea that biological, psychological and social processes are integrally and interactively involved in physical health and illness. One of the longstanding challenges of dealing with healthcare data is the wide variety of data generated from different sources and the increasing need to learn actionable insights that drive performance improvement
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Phillips, Thurman B., and Raymond J. Lanclos. "Data analytics in procurement fraud prevention." Thesis, Monterey, California: Naval Postgraduate School, 2014. http://hdl.handle.net/10945/42708.

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Approved for public release; distribution is unlimited<br>The purpose of this research project is to explore the viability of detecting anomalies through using data analytics software as a tool in procurement fraud prevention and to analyze its potential policy implications on federal procurement stakeholders. According to a survey conducted in 2012 by the Association of Certified Fraud Examiners, organizations lose an estimated 5% of their revenues to fraud each year. In order to relate this estimate to the Department of Defense (DOD), this estimated percentage was applied to the requested DO
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Kapicka, Bryan A. (Bryan Anderson). "Implicit costs of data and analytics." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90221.

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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 69-72).<br>Firms have been able to utilize data and analytics to achieve a variety of economic benefits. To realize this value, firms have to invest in the necessary information technology, process updates, and employee training. These costs are straightforward, but firms also incur implicit costs, the costs of mitigating potential risks and maximizing firm value from data and analytics. These costs are less well underst
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Katzenbach, Alfred, and Holger Frielingsdorf. "Big Data Analytics für die Produktentwicklung." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-214517.

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Aus der Einleitung: "Auf der Hannovermesse 2011 wurde zum ersten Mal der Begriff "Industrie 4.0" der Öffentlichkeit bekannt gemacht. Die Akademie der Technikwissenschaften hat in einer Arbeitsgruppe diese Grundidee der vierten Revolution der Industrieproduktion weiterbearbeitet und 2013 in einem Abschlussbericht mit dem Titel „Umsetzungsempfehlungen für das Zukunftsprojekt Industrie 4.0“ veröffentlicht (BmBF, 2013). Die Grundidee besteht darin, wandlungsfähige und effiziente Fabriken unter Nutzung moderner Informationstechnologie zu entwickeln. Basistechnologien für die Umsetzung der intellige
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Sun, Mingyang. "Big data analytics in power systems." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/45061.

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With the increasing penetration of advanced sensor systems in power systems, an influx of extremely large datasets presents a valuable opportunity to gain insight for improving system operation and planning in the context of the large-scale integration of intermittent energy sources. To this end, it becomes imperative to implement big data methodologies to handle such complex datasets with the challenges of volume, velocity, variety, and veracity (4Vs) for further dealing with the problems in power systems. The large-scale integration of intermittent energy sources, the introduction of shiftab
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DeYoung, Mark E. "Privacy Preserving Network Security Data Analytics." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/82909.

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The problem of revealing accurate statistics about a population while maintaining privacy of individuals is extensively studied in several related disciplines. Statisticians, information security experts, and computational theory researchers, to name a few, have produced extensive bodies of work regarding privacy preservation. Still the need to improve our ability to control the dissemination of potentially private information is driven home by an incessant rhythm of data breaches, data leaks, and privacy exposure. History has shown that both public and private sector organizations are no
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Chen, Xin. "Be the Data: Embodied Visual Analytics." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/72287.

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With the rise of big data, it is becoming increasingly important to educate students about data analytics. In particular, students without a strong mathematical background usually have an unenthusiastic attitude towards high-dimensional data and find it challenging to understand relevant complex analytical methods, such as dimension reduction. In this thesis, we present an embodied approach for visual analytics designed to teach students exploring alternative 2D projections of high dimensional data points using weighted multidimensional scaling. We proposed a novel application, Be the Data, to
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Safari, Leila. "A clinical data analytics language (CliniDAL)." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/12858.

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This work aims to propose CliniDAL as a generic solution for a special purpose query language for Clinical Data Analytics, which can compute answers to any question that is answerable from a Clinical Information System (CIS), with a specific focus on addressing temporal issues in CISs. The research begins with the investigation of common questions in the clinical domain, which led to the definition of 6 classes of question and answer categories. A set of syntactic rules has been generated to support these query categories which enables direct question answering and supports answering research
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Ng, Wartini. "Contemporary data analytics for soil spectroscopy." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/21071.

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No soil, no life. Know soil, know life. Soil provides the basis for life. To promote soil security, soil monitoring is essential. However, conventional methods of soil analysis are costly and time-consuming. This thesis explores contemporary data analytics for analyzing soil infrared spectroscopy data. New data analytics take soil infrared spectral data and convert them to soil properties that are useful for assessing its conditions. This thesis deals with issues of sampling, spectral reduction techniques, deep learning models, and application in soil contamination assessment. Soil spectral da
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Priya, Rashmi. "RETAIL DATA ANALYTICS USING GRAPH DATABASE." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/67.

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Big data is an area focused on storing, processing and visualizing huge amount of data. Today data is growing faster than ever before. We need to find the right tools and applications and build an environment that can help us to obtain valuable insights from the data. Retail is one of the domains that collects huge amount of transaction data everyday. Retailers need to understand their customer’s purchasing pattern and behavior in order to take better business decisions. Market basket analysis is a field in data mining, that is focused on discovering patterns in retail’s transaction data. Our
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Di, Silvestro Lorenzo Paolo. "Data Mining and Visual Analytics Techniques." Doctoral thesis, Università di Catania, 2014. http://hdl.handle.net/10761/1559.

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With the beginning of the Information Age and the following spread of the information overload phenomenon, it has been mandatory to develop a means to simply explore, analyze and summarize large quantity of data. To achieve this purposes a data mining techniques and information visualization methods are used since decades. In the last years a new research field is gaining importance: Visual Analytics, an outgrowth of the fields of scientific and information visualization but includes technologies from many other fields, including knowledge management, statistical analysis, cognitive science an
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Cvetkov-Iliev, Alexis. "Embedding models for relational data analytics." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG004.

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L'analyse de données, par exemple via des modèles d'apprentissage automatique, requiert généralement qu'elles soient regroupées en une table unique décrivant les entités analysées par un nombre fixe d'attributs ou features. En pratique cependant, la plupart des jeux de données sont relationnels (cf. bases de données relationnelles et graphes de connaissance), où l'information sur les entités d'intérêt est irrégulière et dispersée à travers plusieurs sources. Pour analyser de telles données, il est alors nécessaire de les assembler dans une structure unique (généralement une table), ce qui dema
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ZHANG, YANG. "Data Challenges and Data Analytics Solutions for Power Systems." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2842489.

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Fors, Anton, and Emelie Ohlson. "Business analytics in traditional industries – tackling the new age of data and analytics." Thesis, Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-10450.

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Decision-making is no longer based on human preferences and expertise alone. The era of big data brings up new challenges with business analytics for organizations that want a competitive advantage. Previous research shows that a lot of studies have been made on why this era is now crucial to organizations but not how they can adapt it. In this case study there is a glimpse of how a traditional organization with an old mindset can catch up on the new technological advantages. The purpose of this study is to understand how a traditional company in Sweden is affected by analytics and if it is va
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Koza, Jacob. "Active Analytics: Suggesting Navigational Links to Users Based on Temporal Analytics Data." UNF Digital Commons, 2019. https://digitalcommons.unf.edu/etd/892.

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Front-end developers are tasked with keeping websites up-to-date while optimizing user experiences and interactions. Tools and systems have been developed to give these individuals granular analytic insight into who, with what, and how users are interacting with their sites. These systems maintain a historical record of user interactions that can be leveraged for design decisions. Developing a framework to aggregate those historical usage records and using it to anticipate user interactions on a webpage could automate the task of optimizing web pages. In this research a system called Active An
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Akula, Venkata Ganesh Ashish. "Implementation of Advanced Analytics on Customer Satisfaction Process in Comparison to Traditional Data Analytics." University of Akron / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=akron1555612496986004.

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Nagin, Gleb. "Competing on analytics." Master's thesis, Vysoká škola ekonomická v Praze, 2011. http://www.nusl.cz/ntk/nusl-164067.

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Business analytics refers to the skills, technologies, applications and practisies for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. Business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning, which is also based on data and statistical methods. Analytics makes extensive use of data, statistical or qua
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Kruse, Gustav, Lotta Åhag, Samuel Dahlback, and Albin Åbrink. "Seco Analytics." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414862.

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Forecasting is a powerful tool that can enable companies to save millions in revenue every year if the forecast is good enough. The problem lies in the good enough part. Many companies today use Excel topredict their future sales and trends. While this is a start it is far from optimal. Seco Analytics aim to solve this issue by forecasting in an informative and easy manner. The web application uses the ARIMA analysis method to accurately calculate the trend given any country and product area selection. It also features external data that allow the user to compare internal data with relevant ex
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Poppe, Olga. "Event stream analytics." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-dissertations/530.

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Advances in hardware, software and communication networks have enabled applications to generate data at unprecedented volume and velocity. An important type of this data are event streams generated from financial transactions, health sensors, web logs, social media, mobile devices, and vehicles. The world is thus poised for a sea-change in time-critical applications from financial fraud detection to health care analytics empowered by inferring insights from event streams in real time. Event processing systems continuously evaluate massive workloads of Kleene queries to detect and aggregate eve
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Liu, Xiao, and Xiao Liu. "Health Data Analytics: Data and Text Mining Approaches for Pharmacovigilance." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/620913.

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Pharmacovigilance is defined as the science and activities relating to the detection, assessment, understanding, and prevention of adverse drug events (WHO 2004). Post-approval adverse drug events are a major health concern. They attribute to about 700,000 emergency department visits, 120,000 hospitalizations, and $75 billion in medical costs annually (Yang et al. 2014). However, certain adverse drug events are preventable if detected early. Timely and accurate pharmacovigilance in the post-approval period is an urgent goal of the public health system. The availability of various sources of h
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Zheng, Yao. "Privacy Preservation for Cloud-Based Data Sharing and Data Analytics." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/73796.

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Data privacy is a globally recognized human right for individuals to control the access to their personal information, and bar the negative consequences from the use of this information. As communication technologies progress, the means to protect data privacy must also evolve to address new challenges come into view. Our research goal in this dissertation is to develop privacy protection frameworks and techniques suitable for the emerging cloud-based data services, in particular privacy-preserving algorithms and protocols for the cloud-based data sharing and data analytics services. Cloud co
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Soukup, Petr. "High-Performance Analytics (HPA)." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-165252.

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The aim of the thesis on the topic of High-Performance Analytics is to gain a structured overview of solutions of high performance methods for data analysis. The thesis introduction concerns with definitions of primary and secondary data analysis, and with the primary systems which are not appropriate for analytical data analysis. The usage of mobile devices, modern information technologies and other factors caused a rapid change of the character of data. The major part of this thesis is devoted particularly to the historical turn in the new approaches towards analytical data analysis, which w
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Nguyen, Phong Hai. "Statistical flow data applied to visual analytics." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70978.

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Statistical flow data such as commuting, migration, trade and money flows has gained manyinterests from policy makers, city planners, researchers and ordinary citizens as well. Therehave appeared numerous statistical data visualisations; however, there is a shortage of applicationsfor visualising flow data. Moreover, among these rare applications, some are standaloneand only for expert usages, some do not support interactive functionalities, and somecan only provide an overview of data. Therefore, in this thesis, I develop a web-enabled,highly interactive and analysis support statistical flow
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Khemiri, Wael. "Data-intensive interactive workflows for visual analytics." Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00659227.

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The increasing amounts of electronic data of all forms, produced by humans (e.g. Web pages, structured content such as Wikipedia or the blogosphere etc.) and/or automatic tools (loggers, sensors, Web services, scientific programs or analysis tools etc.) leads to a situation of unprecedented potential for extracting new knowledge, finding new correlations, or simply making sense of the data.Visual analytics aims at combining interactive data visualization with data analysis tasks. Given the explosion in volume and complexity of scientific data, e.g., associated to biological or physical process
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Bitto, Nicholas. "Adding big data analytics to GCSS-MC." Thesis, Monterey, California: Naval Postgraduate School, 2014. http://hdl.handle.net/10945/43879.

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Approved for public release; distribution is unlimited<br>Global Combat Support System - Marine Corp is a large logistics system designed to replace numerous legacy systems used by theMarine Corps. While it has been in existence for a while, its intended potential has not been fully realized. Therefore, various teams are working hard to develop the analytics that will benefit the community. With the growth of data, the only way these analytics (in Structured Query Language [SQL]) will run efficiently will be on proprietary hardware from Oracle. This research looks at running the same analytics
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TANNEEDI, NAREN NAGA PAVAN PRITHVI. "Customer Churn Prediction Using Big Data Analytics." Thesis, Blekinge Tekniska Högskola, Institutionen för kommunikationssystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13518.

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Customer churn is always a grievous issue for the Telecom industry as customers do not hesitate to leave if they don’t find what they are looking for. They certainly want competitive pricing, value for money and above all, high quality service. Customer churning is directly related to customer satisfaction. It’s a known fact that the cost of customer acquisition is far greater than cost of customer retention, that makes retention a crucial business prototype. There is no standard model which addresses the churning issues of global telecom service providers accurately. BigData analytics with Ma
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Papadopoulos, Stavros. "Novel graph analytics for enhancing data insight." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/42992.

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Graph analytics is a fast growing and significant field in the visualization and data mining community, which is applied on numerous high-impact applications such as, network security, finance, and health care, providing users with adequate knowledge across various patterns within a given system. Although a series of methods have been developed in the past years for the analysis of unstructured collections of multi-dimensional points, graph analytics has only recently been explored. Despite the significant progress that has been achieved recently, there are still many open issues in the area,
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Neupane, Bijay. "Predictive Data Analytics for Energy Demand Flexibility." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-236309.

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The depleting fossil fuel and environmental concerns have created a revolutionary movement towards the installation and utilization of Renewable Energy Sources (RES) such as wind and solar energy. The RES entails challenges, both in regards to the physical integration into a grid system and regarding management of the expected demand. The flexibility in energy demand can facilitate the alignment of the supply and demand to achieve a dynamic Demand Response (DR). The flexibility is often not explicitly available or provided by a user and has to be analyzed and extracted automatically from hist
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Glasgo, Brock. "Device-Level Data Analytics to Guide Policy." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/896.

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This dissertation is comprised of four studies that examine issues where submetered device-level energy use data can be used to inform energy efficiency policy and investment decision making in residential buildings. In addition to identifying applications and developing the methods for incorporating these data in engineering and economic analyses, the nontechnical aspects of these issues are also considered as implementation of these solutions depends on more than their technical feasibility. The first study frames the existing methods being used today to build a device-level accounting of wh
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Al-Shiakhli, Sarah. "Big Data Analytics: A Literature Review Perspective." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-74173.

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Big data is currently a buzzword in both academia and industry, with the term being used todescribe a broad domain of concepts, ranging from extracting data from outside sources, storingand managing it, to processing such data with analytical techniques and tools.This thesis work thus aims to provide a review of current big data analytics concepts in an attemptto highlight big data analytics’ importance to decision making.Due to the rapid increase in interest in big data and its importance to academia, industry, andsociety, solutions to handling data and extracting knowledge from datasets need
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Li, Yuanxu Li. "HealthyLifeData Analytics: A DATA ANALYTICS TOOL FOR THE HealthyLifeHRA HEALTH RISK ASSESSMENT SYSTEM." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1465510000.

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Zhang, Liangwei. "Big Data Analytics for eMaintenance : Modeling of high-dimensional data streams." Licentiate thesis, Luleå tekniska universitet, Drift, underhåll och akustik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-17012.

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Big Data analytics has attracted intense interest from both academia and industry recently for its attempt to extract information, knowledge and wisdom from Big Data. In industry, with the development of sensor technology and Information & Communication Technologies (ICT), reams of high-dimensional data streams are being collected and curated by enterprises to support their decision-making. Fault detection from these data is one of the important applications in eMaintenance solutions with the aim of supporting maintenance decision-making. Early discovery of system faults may ensure the reliabi
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43

Killada, Parimala. "Data Analytics using Regression Models for Health Insurance Market place Data." University of Toledo / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1501721348961437.

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Törnqvist, Christian. "Evaluating the Importance of Disk-locality for Data Analytics Workloads : Evaluating the Importance of Disk-locality for Data Analytics Workloads." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-410212.

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Designing on-premise hardware platforms to deal with big data analytics should be done in a way in which the available resources can be scaled both up and down depending on future needs. Two of the main components of an analytics cluster is the data storage and computational part. Separating those two components yields great value but can come with the price of performance loss if not set up properly. The objective of this thesis is to examine how much the performance gets impacted when the computational and storage part gets divided into different hardware nodes. To get data on how well this
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Leis, Machín Angela 1974. "Studying depression through big data analytics on Twitter." Doctoral thesis, TDX (Tesis Doctorals en Xarxa), 2021. http://hdl.handle.net/10803/671365.

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Mental disorders have become a major concern in public health, since they are one of the main causes of the overall disease burden worldwide. Depressive disorders are the most common mental illnesses, and they constitute the leading cause of disability worldwide. Language is one of the main tools on which mental health professionals base their understanding of human beings and their feelings, as it provides essential information for diagnosing and monitoring patients suffering from mental disorders. In parallel, social media platforms such as Twitter, allow us to observe the activity, though
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Wang, Ting. "Data analytics for networked and possibly private sources." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/39598.

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This thesis focuses on two grand challenges facing data analytical system designers and operators nowadays. First, how to fuse information from multiple autonomous, yet correlated sources and to provide consistent views of underlying phenomena? Second, how to respect externally imposed constraints (privacy concerns in particular) without compromising the efficacy of analysis? To address the first challenge, we apply a general correlation network model to capture the relationships among data sources, and propose Network-Aware Analysis (NAA), a library of novel inference models, to capture (i)
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Azarm, Mana. "Tool Support and Data Management for Business Analytics." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/20059.

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The data delivery architectures in most enterprises are complex and under documented. Conceptual business models and business analytics applications are created to provide a simplified, and easy to navigate view of enterprise data for analysts. But the construction of such interfaces is tedious, manually intensive to build, requiring specialized technical expertise, and it is especially difficult to map exactly where data came from in the organization. In this paper we investigate how two aspects (lineage and requests for data i.e. semantics and new reports) can be addressed by tying metadat
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Rothschedl, Christopher, Roland Ritt, Paul O'Leary, Matthew Harker, Michael Habacher, and Michael Brandner. "Real-time-data analytics in raw materials handling." Technische Universitaet Bergakademie Freiberg Universitaetsbibliothek "Georgius Agricola", 2018. http://nbn-resolving.de/urn:nbn:de:bsz:105-qucosa-231350.

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This paper proposes a system for the ingestion and analysis of real-time sensor and actor data of bulk materials handling plants and machinery. It references issues that concern mining sensor data in cyber physical systems (CPS) as addressed in O’Leary et al. [2015].
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Nannoni, Nicolas. "Message-oriented Middleware for Scalable Data Analytics Architectures." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-167139.

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The democratization of Internet allowed many more people to use online services and enjoy their benefits. The traffic towards websites became tremendous those recent years, especially with the apparition of social networks. Mobile application, televisions and other non--‐computer devices also get connected to the Internet and use it to provide services to the end--‐users: Video on--‐demand, music streaming and so on. These applications rely on powerful backend servers that handle the requests made by devices and provide statistics and metrics about application usage. These metrics can be gener
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Brydon, Humphrey Charles. "Missing imputation methods explored in big data analytics." University of the Western Cape, 2018. http://hdl.handle.net/11394/6605.

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Philosophiae Doctor - PhD (Statistics and Population Studies)<br>The aim of this study is to look at the methods and processes involved in imputing missing data and more specifically, complete missing blocks of data. A further aim of this study is to look at the effect that the imputed data has on the accuracy of various predictive models constructed on the imputed data and hence determine if the imputation method involved is suitable. The identification of the missingness mechanism present in the data should be the first process to follow in order to identify a possible imputation method. The
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