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Relevant bibliographies by topics / Data-Driven Markets / Dissertations / Theses

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Dissertations / Theses on the topic 'Data-Driven Markets'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Macias, Lloret Mario. "Business-driven resource allocation and management for data centres in cloud computing markets." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/144562.

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Cloud Computing markets arise as an efficient way to allocate resources for the execution of tasks and services within a set of geographically dispersed providers from different organisations. Client applications and service providers meet in a market and negotiate for the sales of services by means of the signature of a Service Level Agreement that contains the Quality of Service terms that the Cloud provider has to guarantee by managing properly its resources. Current implementations of Cloud markets suffer from a lack of information flow between the negotiating agents, which sell the resou

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2

Carriere, Thomas. "Towards seamless value-oriented forecasting and data-driven market valorisation of photovoltaic production." Thesis, Université Paris sciences et lettres, 2020. http://www.theses.fr/2020UPSLM019.

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La décarbonation de la production d’électricité à échelle mondiale est un élément de réponse clé face aux pressions exercées par les différents enjeux environnementaux. Par ailleurs, la baisse des coûts de la filière photovoltaïque (PV) ouvre la voie à une augmentation significative de la production PV dans le monde. L’objectif principal de cette thèse est alors de maximiser le revenu d’un producteur d’énergie PV sous incertitude des prix de marché et de la production. Pour cela, un modèle de prévision probabiliste de la production PV à court (5 minutes) et moyen (24 heures) terme est proposé.

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3

Törngren, Oskar. "Mergers in big data-driven markets : is the dimension of privacy and protection of personal data something to consider in the merger review?" Thesis, Stockholms universitet, Juridiska institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-153509.

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4

Mandl, Christian [Verfasser], Stefan [Akademischer Betreuer] Minner, Stefan [Gutachter] Minner, and Srinagesh [Gutachter] Gavirneni. "Optimal Procurement and Inventory Control in Volatile Commodity Markets : Advances in Stochastic and Data-Driven Optimization / Christian Mandl ; Gutachter: Stefan Minner, Srinagesh Gavirneni ; Betreuer: Stefan Minner." München : Universitätsbibliothek der TU München, 2019. http://d-nb.info/1190818779/34.

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5

Schaubhut, Geoffrey J. "Markers Of Alcohol Use Disorder Outpatient Treatment Outcome: Prediction Modeling Of Day One Treatment." ScholarWorks @ UVM, 2020. https://scholarworks.uvm.edu/graddis/1120.

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ABSTRACT Background: Alcohol use disorders (AUD) affect health and wellbeing, and have broad societal costs (Bouchery, Harwood, Sacks, Simon, & Brewer, 2011; Rehm et al., 2009; Sudhinaraset, Wigglesworth, Takeuchi, & Tsuker, 2016). While treatments have existed for decades, they are limited in success and expensive to administer. As such, understanding which factors best predict who will benefit most from treatment remains a laudable goal. Prior attempts to predict factors associated with positive treatment outcome are limited by methodology including statistical methods that lead to poor pred

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6

Dikshit, Anubhav. "Omnichannel path to purchase : Viability of Bayesian Network as Market Attribution Models." Thesis, Linköpings universitet, Filosofiska fakulteten, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-165443.

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Market attribution is the problem of interpreting the influence of advertisements onthe user’s decision process. Market attribution is a hard problem, and it happens to be asignificant reason for Google’s revenue. There are broadly two types of attribution models- data-driven and heuristics.This thesis focuses on the data driven attribution modeland explores the viability of using Bayesian Network as market attribution models andbenchmarks the performance against a logistic regression. The data used in this thesiswas prepossessed using undersampling technique. Furthermore, multiple techniques

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7

Garrido, Fernández Aurora. "Towards transport futures using mobile data analytics : Stakeholder identification in the city of Stockholm." Thesis, KTH, Hållbar utveckling, miljövetenskap och teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240604.

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The use of big data in urban transport planning is unstoppably gaining momentum and with the help of strategic business partnerships and technological advancements (e.g. transport apps, mobile device location tracking, data processing) the new mobility models are evolving towards an integrated and multimodal urban mobility: Mobility as a Service (MaaS). From the generation of data by Telecom companies to transport end users, a broad range of stakeholders are involved in the data market. This tighter with the call for sustainable alternatives in passenger traffic highlights that business relati

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8

BUCCI, Frederic. "Market impact for large institutional investors: empirical evidences and theoretical models." Doctoral thesis, Scuola Normale Superiore, 2020. http://hdl.handle.net/11384/90464.

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Advances in technology have deeply changed the way how securities are traded. The introduction of new technologies has enabled exchanges to automate the majority of their trading operations, leading on one side to a considerable cost reductions and on the other side oering a full set of new possibilities for market participants. In parallel to this automation, also brokers, hedge funds, proprietary trading forms, and other market participants have profitted from these new technology approaches for automating a variety of tasks, from optimization of order execution to whole trading strate

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9

Teng, Sin Yong. "Intelligent Energy-Savings and Process Improvement Strategies in Energy-Intensive Industries." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-433427.

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S tím, jak se neustále vyvíjejí nové technologie pro energeticky náročná průmyslová odvětví, stávající zařízení postupně zaostávají v efektivitě a produktivitě. Tvrdá konkurence na trhu a legislativa v oblasti životního prostředí nutí tato tradiční zařízení k ukončení provozu a k odstavení. Zlepšování procesu a projekty modernizace jsou zásadní v udržování provozních výkonů těchto zařízení. Současné přístupy pro zlepšování procesů jsou hlavně: integrace procesů, optimalizace procesů a intenzifikace procesů. Obecně se v těchto oblastech využívá matematické optimalizace, zkušeností řešitele a pr

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10

LI, JUN-XUE, and 李俊學. "New product advantages and market performance driven by data analysis." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/qz6q4n.

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碩士<br>正修科技大學<br>資訊管理研究所<br>107<br>With the rapid development of technology and shortened product life cycle, many companies hope to gain market advantages in the product development stage, but some developed products are failed products, That is to say, some new products produced by some companies cannot meet the needs of most consumers. In this failed experience, companies use customer-oriented methods to develop new products in order to meet customer needs, and in turn enhance the advantages of new products. Looking back at the relevant literature in the past, I found that the advantages of

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11

Dias, Tiago Melo. "Can machine learning algorithms predict football players´ market values? A data-driven approach." Master's thesis, 2020. http://hdl.handle.net/10362/104504.

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Football players transfer fees have been increasing in an unprecedented manner in the last decade. As so, it is crucial for football clubs to correctly assess the value of its players. To tackle this problem, this thesis proposes a data-driven solution. After assembling a dataset with data regarding football players’ characteristics, on-field performance indicators and market values, Machine Learning algorithms were used to construct a prediction model. The final proposed modelis a Random Forest regression, whichregistered a coefficient of determination (R2)of 0.88in the test

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12

Lin, Hsin-Yi, and 林欣宜. "A Dynamic-Data-Driven Application System Based on Reinforcement Learning: A Case Study of Taiwan Stock Market." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/67b732.

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碩士<br>國立交通大學<br>資訊管理研究所<br>104<br>Most analysis on time series data are based on a static approach, such as selecting specific time period in order to analyze relation strength. Although they are simple and intuitive, they lack of considerations on data interaction between themselves and on the fact that the data correlation strength will change as time goes by. Consequently, their predictions usually are not accurate. This thesis proposes a dynamic-data-driven application system based on reinforcement learning. This proposed system uses reinforcement learning to find correlation strength betw

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13

Dobreva, Maria Lubomirova. "Data-driven evaluation of real estate liquidity : predicting days on market to optimize the sales strategy of a startup." Master's thesis, 2019. http://hdl.handle.net/10362/91224.

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Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies Management<br>This is a research project for applying data mining techniques on Real Estate data in cooperation with Homeheed, a startup in the area of real estate, providing a platform solution as a single source of truth in Sofia, Bulgaria. This project suggests the development of a predictive model by using LASSO regression with the premise to determine days on market. As a consequence, the discoveries are expected to contribute

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14

CHEN, HUI-LIN, and 陳惠玲. "Big Data-Driven Innovation to Capture User-Experience for Market Strategic Decision : An Empirical Study for Healthcare Information Industry." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/bp8da3.

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15

Pires, Pedro Miguel Geraldes. "A data-driven approach to predict the value and key features of collectible cars." Master's thesis, 2020. http://hdl.handle.net/10071/21876.

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In our study, a Data Mining approach is proposed to predict the selling price of collectible cars at auction and determine which vehicle's characteristics influence this value. RM Sotheby's, a prestigious auction house, allowed us over 30,000 vehicles in order to build our data set. The use of different models allowed us to analyze a large data set with 18 features. In order to determine which model is most suitable for our study, 11 Data Mining models were compared using 4 metrics (MAE, NMAE, RAE and RMSPE), the 11 models were tested on our data set using a rolling window scheme. "Xgboos

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