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 186 for “"Data streams."”.
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Efficient analysis of data streams
Data streams provide a challenging environment for statistical analysis. Data points can arrive at a high velocity and may need to be deleted once they have been observed. Due to these restrictions, standard techniques may not be applicable to the data streaming scenario. This leads to the need for …
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Active learning for data streams.
With the exponential growth of data amount and sources, access to large collections of data has become easier and cheaper. However, data is generally unlabelled and labels are often difficult, expensive, and time consuming to obtain. Two learning paradigms have been used by machine learning …
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Efficient Algorithms for Mining Data Streams
Data streams are ordered sets of values that are fast, continuous, mutable, and potentially unbounded. Examples of data streams include the pervasive time series which span domains such as finance, medicine, and transportation. Mining data streams require approaches that are efficient, adaptive, …
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Estimating Frequency Distributions in Data Streams
… allow for space-efficient processing of massive datasets. The distribution of the frequencies of items in a large dataset is often used to characterize that data: e.g., the data is heavy-tailed, the data follows a power law, or there are many elements that only appear only once or twice. In this …
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Unsupervised Concept Drift Detection in Data Streams
In data stream mining, efficiently detecting concept drifts is still challenging due to the high cost of collecting true class labels. Traditional detection methods usually need high computation and memory cost and is unable to distinguish between concept drift and novelty. To improve the drift …
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Advanced adaptive classifier methods for data streams
… has resulted in an overwhelming influx of big data. However, traditional batch learning models face significant obstacles in effectively learning from these vast and constantly evolving data streams and generating up-to-date outcomes. To overcome these limitations, Stream Learning (SL) has …
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Bridging Sensor Data Streams and Human Knowledge
Generating useful knowledge out of personal big data in form of sensor streams is a difficult task that presents multiple challenges due to the intrinsic characteristics of these type of data, namely their volume, velocity, variety and noisiness. This problem is a well-known long standing problem …
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Visualizing and analyzing human-centered data streams
… potential to also function as powerful sensory data collectors. These devices are able to record and store a variety of data about their owner's everyday activities, a new development that may significantly impact the way we recall information. Human memory, with its limitations and subjective …
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Structural Model Discovery in Temporal Event Data Streams
… (IRSM). More intelligent ways of conducting data analysis have been explored in recent years. Ma- chine learning and data mining systems that utilize pattern classification and discovery in non-textual data promise to bring new generations of powerful "crawlers" for knowledge discovery, e.g., …
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Learning Recurring Concepts from Data Streams in Ubiquitous Environments
… it is now possible to continuously record data at high speeds in a wide range of devices. The need to make sense of such massive amounts of data opens an opportunity to create new data stream classification techniques to model and predict the behavior of streaming data. When learning from …
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Event Based Retrieval From Digital Libraries Containing Data Streams
… in building a digital library that contains data streams and allows event-based retrieval. “Digital Libraries are storehouses of information available through the Internet that provide ways to collect, store, and organize data and make it accessible for search, retrieval, and processing” …
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An architecture for distributing processing on realtime data streams
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.
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Handling Concept Drift Using the Correlation between Multiple Data Streams
… has been a major issue in handing streaming data in machine learning area. To date, the research on concept drift considers data streams separately, ignoring the correlations between data streams. Motivated by this, this research proposes four methods to deal with the correlations between …
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Learning in Dynamic Data-Streams with a Scarcity of Labels
Analysing data in real-time is a natural and necessary progression from traditional data mining. However, real-time analysis presents additional challenges to batch-analysis; along with strict time and memory constraints, change is a major consideration. In a dynamic stream there is an assumption …
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Clustering to Improve One-Class Classifier Performance in Data Streams
… abnormal conditions from streaming sensor data. The one-class classification (OCC) paradigm addresses this scenario by casting the task as learning a decision boundary around the majority class with no need for minority class instances [110]. OCC has been thoroughly investigated, e.g. [20, …
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Developing Learning Methods for Non-stationary and Imbalanced Data Streams
… of systems to both generate and collect data from a variety of sources. There is an increasing number of Internet of Things devices generating continuous data streams rapidly. Mining these data streams brings new opportunities but also introduces new challenges. Learning from these data …
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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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Estimating post-disaster traffic conditions using real-time data streams
… models with a traffic model and traffic sensor data. Using the earthquake characteristics as an input to the traffic model, the traffic conditions are sequentially estimated given traffic sensor measurements using an ensemble Kalman filter. The proposed algorithm is tested through numerical …
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Improving ensembles and prediction intervals for machine learning on data streams
The rapid growth of streaming data presents significant challenges for traditional machine learning, including popular tasks like regression and classification. This thesis proposes adaptive and dynamic methods to address key issues, including concept drift, uncertainty quantification, and ensemble …
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