Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 98 for “"unstructured data"”.
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Learning structure from unstructured data
… of dynamical systems from noisy input-output data. Specifically, we address the question: "For a fixed length of noisy data generated by an unknown model, what is the best approximation that can be estimated?"; this is in contrast to traditional system identification which answers the question …
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Online Content Design with Unstructured Data
Studying the design and management of digital content is imperative for individuals and organizations that aim to thrive in the digital age. This dissertation investigates the design of digital content in three aspects: (1) consumer reviews for e-commerce, (2) online news headlines, and (3) …
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Statistical Learning for Sequential Unstructured Data
Unstructured data, which cannot be organized into predefined structures, such as texts, human behavior status, and system logs, often presented in a sequential format with inherent dependencies. Probabilistic model are commonly used to capture these dependencies in the data generation process …
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Efficient Indexing for Structured and Unstructured Data
The collection of digital data is growing at an exponential rate. Data originates from wide range of data sources such as text feeds, biological sequencers, internet traffic over routers, through sensors and many other sources. To mine intelligent information from these sources, users have to query …
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Accelerating queries for structured and unstructured data
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01
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Ensemble Learning Techniques for Structured and Unstructured Data
… of classification models. Actual structured and unstructured data sets from industry are utilized during the research process, analysis and subsequent model evaluations. The first research section addresses the consumer demand forecasting and daily capacity management requirements of a nationally …
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Deep Learning for Unstructured Data by Leveraging Domain Knowledge
Unstructured data such as texts, strings, images, audios, videos are everywhere due to the social interaction on the Internet and the high-throughput technology in sciences, e.g., chemistry and biology. However, for traditional machine learning algorithms, classifying a text document is far more …
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Interactive Direct Volume Rendering of Curvilinear and Unstructured Data
… volume rendering of nonrectilinear 3D scientific data sets, such as those generated by the finite element method, are investigated. We focus on the use of projection methods, in particular splatting algorithms, for volume rendering curvilinear and irregular data. The data is rendered without …
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High throughput path selection for unstructured data center networks
… in demand and popularity of cloud and big data applications has driven the need for higher throughput data center network design. Recent work to provide topologies with much denser interconnects pose a difficult challenge for routing of traffic within a data center. Even with proposals like …
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Multi-dimensional mining of unstructured data with limited supervision
As one of the most important data forms, unstructured text data plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to healthcare and scientific research. In many emerging applications, people's information needs from text data are …
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Graph-Based Approach: Bridging Insights from Structured and Unstructured Data
… intricate relationships and patterns in complex data, enabling the integration of structured and unstructured information for insightful decision-making across diverse domains. Our research focuses on constructing graphs from structured and unstructured data, demonstrating their applications in …
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Knowledge extraction from unstructured data and classification through distributed ontologies
… the Web of documents evolved towards a space of data silos, linked each other only through untyped references (such as hypertext references) where only humans were able to understand. A growing desire to programmatically access to pieces of data implicitly enclosed in documents has characterized …
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Influence of the inclusion of unstructured data in recommender systems
La gran cantidad de datos disponibles a través de la red hacen más que patente la necesidad de sistemas que nos ayuden a separar el contenido relevante del que carece de importancia. Esta tarea es realizada por los llamados Sistemas de Recomendación, y en la actualidad podemos encontrarlos en la …
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Developing Event Identification Methods for Structured and Unstructured Data Streams
Data, now more than ever before, are continuously being generated in huge volumes, andat rapid speed. Data may originate from various sources, for instance: sensor readings,financial transactions, social networks, etc.. A data stream is a continuous sequence ofdata arriving in almost real-time and …
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Predictive Model Fusion: A Modular Approach to Big, Unstructured Data
Data sets of increasing size and complexity require new approaches for prediction as the sheer volume of data from disparate sources inhibits joint processing and modeling. Rather modular segmentation is required, in which a set of models process (potentially overlapping) partitions of the data to …
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Machine learning-based analytics of structured and unstructured data for enhanced bridge deterioration prediction
… increasing availability of heterogeneous bridge data from multiple sources opens unprecedented opportunities for data analytics to better predict bridge deterioration for supporting enhanced bridge maintenance decision making. Such data include structured National Bridge Inventory (NBI) and …
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Application of Natural Language Processing to Unstructured Data: A Case Study of Climate Change
… areas of interest. The resurgence of big data and machine learning has brought a high hope that designers can learn from past successes and failures. However, when the available data is in a mixture of textual, numerical or graphical form, then the currently popular deep learning tools …
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ARDA : automatic relational data augmentation for machine learning
This thesis is motivated by two major trends in data science: easy access to tremendous amounts of unstructured data and the effectiveness of Machine Learning (ML) in data driven applications. As a result, there is a growing need to integrate ML models and data curation into a homogeneous system …
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Text mining with neural network and MapReduce
… AT AUTHOR'S REQUEST.] Increasing data from internet can provide helpful information to support business process such as product development process, inventory management process and quality management process by measuring customers' satisfaction. This source of information is …
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A study of conceptual data modelling in the era of big data: a case of Zambia.
The way data is collected and stored has evolved over time due to various developments which have led to advancements in database technologies. Few decades ago, only structured data were stored, however, with the increase in the business demand for data in this competitive global market, …
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