Discovery of Spatiotemporal Event Sequences

Finding frequent patterns plays a vital role in many analytics tasks such as finding itemsets, associations, correlations, and sequences. In recent decades, spatiotemporal frequent pattern mining has emerged with the main goal focused on developing data-driven analysis frameworks for understanding underlying spatial and temporal characteristics in massive datasets. In this thesis, we will focus on discovering spatiotemporal event sequences from large-scale region trajectory datasetes with event annotations. Spatiotemporal event sequences are the series of event types whose trajectory-based instances follow each other in spatiotemporal context. We introduce new data models for storing and processing evolving region trajectories, provide a novel framework for modeling spatiotemporal follow relationships, and present novel spatiotemporal event sequence mining algorithms

Concealment Conserving the Data Mining of Groups & Individual

We present an overview of privacy preserving data mining, one of the most popular directions in the data mining research community. In the first part of the chapter, we presented approaches that have been proposed for the protection of either the sensitive data itself in the course of data mining or the sensitive data mining results, in the context of traditional (relational) datasets. Following that, in the second part of the chapter, we focused our attention on one of the most recent as well as prominent directions in privacy preserving data mining: the mining of user mobility data. Although still in its infancy, privacy preserving data mining of mobility data has attracted a lot of research attention and already counts a number of methodologies both with respect to sensitive data protection and to sensitive knowledge hiding. Finally, in the end of the chapter, we provided some roadmap along the field of privacy preserving mobility data mining as well as the area of privacy preserving data mining at large

Descoberta de padrões de perseguição em trajetórias de objetos móveis

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-graduação em Ciência da ComputaçãoTecnologias como celulares, GPS e redes de sensores estão ficando cada vez mais populares. Estes dispositivos geram uma grande quantidade de dados chamados de Trajetórias de Objetos Móveis. Uma trajetória é um conjunto de pontos localizados no espaço e no tempo. Estes dados são normalmente volumosos e confusos, sendo necessário criar métodos e algoritmos para extrair informações interessantes destes dados. Vários estudos tem focado na descoberta de padrões em trajetórias como flocks, desvios, recorrência, liderança, etc. Neste trabalho é proposto um novo tipo de padrão: comportamento de perseguição em trajetórias. Mais especificamente, são apresentadas definições formais do comportamento e são definidos diferentes tipos de perseguição, bem como um algoritmo para identificar o padrão. As principais características consideradas são o tempo, a distância e a velocidade, que são utilizadas de forma diferente em relação a trabalhos existentes. O trabalho é validado com experimentos sobre dados sintéticos e dados reais, demonstrando que o método encontra padrões não identificados por outras abordagens

Endurant vs Perdurant: Ontological Motivation for Language Variations

30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016, Seoul, Republic of Korea, 28-30 October 2016Modern ontology focuses on the shared structure of knowledge representation and sheds light on underling motivations of human conceptual structure. This paper addresses the issue of whether ontological structures are linguistically represented, and whether such conceptual underpinning of linguistic representation may motivate language variations. Integrating our recent work showing that the most fundamental endurant vs. perdurant ontological dichotomy is grammaticalized in Chinese and on comparable corpus based studies of variations of Chinese, I will explore the possibilit ENGLy that this basic conceptual dichotomy may in fact provide the motivation of changes of perspectives that underlies language variations. I will also discuss possible implication this approach has in accounting for other language changes and variations such as light verb's argument taking, incorporation, loss of case/agreement, and English -er/-ee asymmetry. In the process, the will resolve three linguistic puzzles and eventually show that the endurant/perdurant dichotomy may in fact be the conceptual basis of the hitherto undefined +N (i.e. nouny) vs. +V (i.e. verby) features prevalent in linguistics. Based on this proposal, the variations involving various types of denominalization and deverbalization can be accounted for.Department of Chinese and Bilingual Studies2016-2017 > Academic research: refereed > Refereed conference paperbcw

Incubating a Future Metaphysics: Quantum Gravity

In this paper, I will argue that metaphysicians ought to utilize quantum theories of gravity (QG) as incubators for a future metaphysics. In §1, I will argue why this ought to be done. In §2, I will present case studies from the history of science where physical theories have challenged both the dogmatic and speculative metaphysician. In §3, I will present two theories of QG and demonstrate the challenge they pose to certain aspects of our current metaphysics; in particular, how they challenge our understanding of the abstract-concrete distinction. In this section I demonstrate how five different accounts of the distinction each fail to hold under the received interpretations of loop quantum gravity and string theory. The central goal of this paper is to encourage metaphysicians to look to physical theories, especially those involving cosmology such as string theory and loop quantum gravity, when doing metaphysics

Regional Geographies of Extreme Heat

Shaped by countless influences from the atmosphere, biosphere, hydrosphere, and anthroposphere acting across a wide spectrum of spatiotemporal scales, spatial variations in climate are ubiquitous. Meanwhile, the warming signal from anthropogenically elevated greenhouse-gas concentrations is emerging as an overriding determinant for more and more aspects of the climate system, extreme heat among them. In this dissertation, I explore the interaction of these two effects, and the implications of the patterns they create. A key finding is that rapid increases in extreme heat are already occurring, by some metrics having already doubled in the past 40 years, and further nonlinear increases are expected. Another is the strong dependence of extreme heat-humidity combinations on atmospheric moisture, creating subseasonal and interannual patterns dictated by the principal source of regional warm-season moisture — pre-monsoonal advection in some cases, local evapotranspiration in others. These relationships lead to the demonstrated potential for improvements in predictive power, on the basis of sea-surface temperatures and other canonical modes of large-scale climate variability. In contrast to this overall confidence in current temporal patterns and long-term projections, I show that extreme heat at small spatial scales is much more poorly characterized in gridded products, and that these biases are especially acute along coastlines. While summer daytime temperature differences between the shoreline of the Northeast U.S. and locations 60 km inland are often 5°C or more, I find that recent high-resolution downscaled Earth-system models typically represent no more than 25% of this difference. Across the globe, ERA-Interim reanalysis similarly underestimates extreme humid heat by >3°C, a highly significant margin given the large sensitivity of health and economic impacts to marginal changes in the most extreme conditions. I find that these biases propagate into projections, and their importance is also amplified by the large populations living in the affected areas. Rapid mean warming is pushing the climate system to more and more frequently include extreme heat-humidity combinations beyond that which the human species has likely ever experienced. Such conditions, which had not been previously reported in weather-station data, are described in detail and some of the associated characteristics examined. Several channels of analysis highlight that these events are driven primarily by rising sea-surface temperatures in shallow subtropical gulfs, and the subsequent impingement of marine air on the coastline. Given the severity of potential impacts on infrastructure and agriculture, and the size of the populations exposed, this result underscores that major research and adaptation efforts are needed to avoid calamitous outcomes from the emergence of extreme heat-humidity combinations too severe to tolerate in the absence of artificial cooling. This dissertation discusses strategies for advancing knowledge of extreme heat’s natural variations and its behavior under climate change, in order to design metrics, models, methodologies, and presentation types such that essential findings are translated into tangible action in the most effective way possible. Sustained and integrated efforts are necessary to transition to a climate-system management style encompassing more foresight than the effectively unplanned experiment which has been pursued so far, and which has already exacerbated extreme heat events so much

Recent advances in low-cost particulate matter sensor: calibration and application

Particulate matter (PM) has been monitored routinely due to its negative effects on human health and atmospheric visibility. Standard gravimetric measurements and current commercial instruments for field measurements are still expensive and laborious. The high cost of conventional instruments typically limits the number of monitoring sites, which in turn undermines the accuracy of real-time mapping of sources and hotspots of air pollutants with insufficient spatial resolution. The new trends of PM concentration measurement are personalized portable devices for individual customers and networking of large quantity sensors to meet the demand of Big Data. Therefore, low-cost PM sensors have been studied extensively due to their price advantage and compact size. These sensors have been considered as a good supplement of current monitoring sites for high spatial-temporal PM mapping. However, a large concern is the accuracy of these low-cost PM sensors. Multiple types of low-cost PM sensors and monitors were calibrated against reference instruments. All these units demonstrated high linearity against reference instruments with high R2 values for different types of aerosols over a wide range of concentration levels. The question of whether low-cost PM monitors can be considered as a substituent of conventional instruments was discussed, together with how to qualitatively describe the improvement of data quality due to calibrations. A limitation of these sensors and monitors is that their outputs depended highly on particle composition and size, resulting in as high as 10 times difference in the sensor outputs. Optical characterization of low-cost PM sensors (ensemble measurement) was conducted by combining experimental results with Mie scattering theory. The reasons for their dependence on the PM composition and size distribution were studied. To improve accuracy in estimation of mass concentration, an expression for K as a function of the geometric mean diameter, geometric standard deviation, and refractive index is proposed. To get rid of the influence of the refractive index, we propose a new design of a multi-wavelength sensor with a robust data inversion routine to estimate the PM size distribution and refractive index simultaneously. The utility of the networked system with improved sensitivity was demonstrated by deploying it in a woodworking shop. Data collected by the networked system was utilized to construct spatiotemporal PM concentration distributions using an ordinary Kriging method and an Artificial Neural Network model to elucidate particle generation and ventilation processes. Furthermore, for the outdoor environment, data reported by low-cost sensors were compared against satellite data. The remote sensing data could provide a daily calibration of these low-cost sensors. On the other hand, low-cost PM sensors could provide better accuracy to demonstrate the microenvironment

Spatial Big Data Analytics: Classification Techniques for Earth Observation Imagery

University of Minnesota Ph.D. dissertation. August 2016. Major: Computer Science. Advisor: Shashi Shekhar. 1 computer file (PDF); xi, 120 pages.Spatial Big Data (SBD), e.g., earth observation imagery, GPS trajectories, temporally detailed road networks, etc., refers to geo-referenced data whose volume, velocity, and variety exceed the capability of current spatial computing platforms. SBD has the potential to transform our society. Vehicle GPS trajectories together with engine measurement data provide a new way to recommend environmentally friendly routes. Satellite and airborne earth observation imagery plays a crucial role in hurricane tracking, crop yield prediction, and global water management. The potential value of earth observation data is so significant that the White House recently declared that full utilization of this data is one of the nation's highest priorities. However, SBD poses significant challenges to current big data analytics. In addition to its huge dataset size (NASA collects petabytes of earth images every year), SBD exhibits four unique properties related to the nature of spatial data that must be accounted for in any data analysis. First, SBD exhibits spatial autocorrelation effects. In other words, we cannot assume that nearby samples are statistically independent. Current analytics techniques that ignore spatial autocorrelation often perform poorly such as low prediction accuracy and salt-and-pepper noise (i.e., pixels predicted as different from neighbors by mistake). Second, spatial interactions are not isotropic and vary across directions. Third, spatial dependency exists in multiple spatial scales. Finally, spatial big data exhibits heterogeneity, i.e., identical feature values may correspond to distinct class labels in different regions. Thus, learned predictive models may perform poorly in many local regions. My thesis investigates novel SBD analytics techniques to address some of these challenges. To date, I have been mostly focusing on the challenges of spatial autocorrelation and anisotropy via developing novel spatial classification models such as spatial decision trees for raster SBD (e.g., earth observation imagery). To scale up the proposed models, I developed efficient learning algorithms via computational pruning. The proposed techniques have been applied to real world remote sensing imagery for wetland mapping. I also had developed spatial ensemble learning framework to address the challenge of spatial heterogeneity, particularly the class ambiguity issues in geographical classification, i.e., samples with the same feature values belong to different classes in different spatial zones. Evaluations on three real world remote sensing datasets confirmed that proposed spatial ensemble learning outperforms current approaches such as bagging, boosting, and mixture of experts when class ambiguity exists