Global ETD Search
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Showing 1 to 20 of 136 for “"Temporal data"”.
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Wavelet analysis of temporal data
… to problems involving multiple series of temporal data. Wavelets have proven to be highly effective at extracting frequency information from data. Their multi-scale nature enables the efficient description of both transient and long-term signals. Furthermore, only a small number of wavelet …
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Graph based management of temporal data
… devices and sensors that led to high-volume temporal data generation. Temporal modeling and querying of this huge data have been essential for effective querying and retrieval. However, custom temporal models have the problem of generalizability, whereas the extended temporal models require …
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Algorithms for Analyzing Spatio-Temporal Data
<p>In today's age, huge data sets are becoming ubiquitous. In addition to their size, most of these data sets are often noisy, have outliers, and are incomplete. Hence, analyzing such data is challenging. We look at applying geometric techniques to tackle some of these challenges, with an emphasis …
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A Spatio-Temporal Data Model for Zoning
Planning departments are besieged with temporal/historical information. While for many institutions historical information can be relegated to archives, planning departments have a constant need to access and query their historical information, particularly their historical spatial information such …
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Spatio-temporal data fusion in cerebral angiography
… presented on both clinical and simulated phantom data sets. The 3D time series results are visualized using the following tools: time series of intensity slices, synthetic X-rays from an arbitrary view, time series of isosurfaces, and 3D surfaces that show arrival times of contrast using color. …
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Spatial-Temporal Data Modeling with Graph Neural Networks
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. It aims to model the dynamic node-level inputs by assuming inter-dependency between connected nodes. A basic assumption behind spatial-temporal graph modeling is that …
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Shape Identication and Ranking in Temporal Data Sets
Shapes are a concise way to describe temporal variable behaviors. Some commonly used shapes are spikes, sinks, rises, and drops. A spike describes a set of variable values that rapidly increase, then immediately rapidly decrease. The variable may be the value of a stock or a person's blood sugar …
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Temporal Data Mining in a Dynamic Feature Space
Many interesting real-world applications for temporal data mining are hindered by concept drift. One particular form of concept drift is characterized by changes to the underlying feature space. Seemingly little has been done to address this issue. This thesis presents FAE, an incremental ensemble …
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Efficient Algorithms for Mining Large Spatio-Temporal Data
Knowledge discovery on spatio-temporal datasets has attracted<br />growing interests. Recent advances on remote sensing technology mean<br />that massive amounts of spatio-temporal data are being collected,<br />and its volume keeps increasing at an ever faster pace. It becomes<br />critical to …
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Visualization of spatio-temporal data in two dimensional space
Spatio-temporal data is becoming very popular in the recent times, as there are large number of datasets that collect both location and temporal information in the real time. The main challenge is that extracting useful insights from such large data set is extremely complex and laborious. In this …
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Machine learning models on geographic spatial-temporal data predictions
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01
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Towards Algorithm Transformation for Temporal Data Mining on GPU
Data Mining allows one to analyze large amounts of data. With increasing amounts of data being collected, more computing power is needed to mine these larger and larger sums of data. The GPU is an excellent piece of hardware with a compelling price to performance ratio and has rapidly risen in …
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Functional Norm Regularization for Margin-Based Ranking on Temporal Data
… of our approach. Applications in bio-medical datasets typically have specific additional challenges. First, and the major one, is the limited amount of data examples, due to an expensive measuring technology, and/or infrequency of conditions of interest. Such limited number of examples makes …
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Spatial-temporal data modelling and processing for personalised decision support
… is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of …
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Mining periodicity and object relationship in spatial and temporal data
… sensor networks, and online social media, spatiotemporal data is now widely collected from smartphones carried by people, sensor tags attached to animals, GPS tracking systems on cars and airplanes, RFID tags on merchandise, and location-based services offered by social media. While such tracking …
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Exploring link, text and spatial-temporal data in social media
… of Web 2.0, a huge amount of user generated data in social media sites is attracting the attentions from different research areas. Social media data has heterogenous data types including link, text and spatial-temporal information, which poses many interesting and challenging tasks for data …
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Predictive modeling of spatial-temporal data: A graph-centric approach
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01
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