Theses and Dissertations at Montana State University (MSU)
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Item Extraction of droplet genealogies from high-fidelity atomization simulations(Montana State University - Bozeman, College of Engineering, 2019) Rubel, Roland Francis Clark, IV; Chairperson, Graduate Committee: Mark OwkesMany research groups are performing high-fidelity simulations of atomizing jets that are taking advantage of the continually increasing computational resources and advances in numerical methods. These high-fidelity simulations produce extremely large data-sets characterizing the flow and giving the ability to gather a better understanding of atomization. One of the main challenges with these data sets is their large size, which requires developing tools to extract relevant physics from them. The main goal of this project is to create a physics extraction technique to compute the genealogy of atomization. This information will characterize the process of the coherent liquid core breaking into droplets and ligaments which may proceed to break up further. This event information will be combined with detailed information such as droplet size, shape, flow field characteristics, etc. The extracted information will be stored in a database, allowing the information to be readily and quickly queried to assist in the development and testing of low-fidelity atomization models that agree with the physics predicted by high-fidelity simulations.Item Event-triggered causality: a causality detection tool for big data(Montana State University - Bozeman, College of Engineering, 2018) Davis, Tyler Bruce; Chairperson, Graduate Committee: Ross K. SniderFinding causal relationships in time series data is a well-known problem and methods such as Granger causality or transfer entropy look for it in continuous data sources. However, when data contains discrete events and comes from multiple sources with varying data types, assumptions underlying these methods are often violated. We present a new method called Event Triggered Causality (ETC) that can determine causal relationships between observed events within time series data from very different sensors. The new causality metric takes a data mining approach where events in the data are first identified with data dependent event detectors. The events are then clustered according to their spectral fingerprints and assessed for causality using both similarity and predictability measures. Event similarity is measured using distance metrics while temporal predictability is measured using a temporal entropy metric. ETC is then extended to find successive causal links between events, called Directed Event Triggered Causality, which takes the form of a directed graph. We use these methods to analyze potential causal links in two different situations. The first is searching for causal links between marmoset vocal interactions and related movements. The second is between commands from a farmer, his sheep dog, and the movement of sheep. The construction of these metrics helps to expand the definition of event-based causality and provides a method to further understand complex systems such as social and behavioral interactions.Item Mining spatiotemporal co-occurrence patterns from massive data sets with evolving regions(Montana State University - Bozeman, College of Engineering, 2014) Ganesan Pillai, Karthik; Chairperson, Graduate Committee: John Paxton; Rafal A. AngryK (co-chair)Due to the current rates of data acquisition, the growth of data volumes in nearly all domains of our lives is reaching historic proportions [5], [6], [7]. Spatiotemporal data mining has emerged in recent decades with the main goal focused on developing data-driven mechanisms for the understanding of the spatiotemporal characteristics and patterns occurring in the massive repositories of data. This work focuses on discovering spatiotemporal co-occurrence patterns (STCOPs) from large data sets with evolving regions. Spatiotemporal co-occurrence patterns represent the subset of event types that occur together in both space and time. Major limitations of existing spatiotemporal data mining models and techniques include the following. First, they do not take into account continuously evolving spatiotemporal events that have polygon-like representations. Second, they do not investigate and provide sufficient interest measures for the STCOPs discovery purposes. Third, computationally and storage efficient algorithms to discover STCOPs are missing. These limitations of existing approaches represent important hurdles while analyzing massive spatiotemporal data sets in several application domains that generate big data, including solar physics, which is an application of our interdisciplinary research. In this work, we address these limitations by i) introducing the problem of mining STCOPs from data sets with extended (region-based) spatial representations that evolve over time, ii) developing a set of novel interest measures, and iii) providing a novel framework to model STCOPs. We also present and investigate three novel approaches to STCOPs mining. We follow this investigation by applying our algorithm to perform a novel data-driven discovery of STCOPs from solar physics data.