Event Discovery and Classification in Space-Time Series: A Case Study for Storms

Recent advancement in sensor technology has enabled the deployment of wireless sensors for surveillance and monitoring of phenomenon in diverse domains such as environment and health. Data generated by these sensors are typically high-dimensional and therefore difficult to analyze and comprehend. Additionally, high level phenomenon that humans commonly recognize, such as storms, fire, traffic jams are often complex and multivariate which individual univariate sensors are incapable of detecting. This thesis describes the Event Oriented approach, which addresses these challenges by providing a way to reduce dimensionality of space-time series and a way to integrate multivariate data over space and/or time for the purpose of detecting and exploring high level events. The proposed Event Oriented approach is implemented using space-time series data from the Gulf of Maine Ocean Observation System (GOMOOS). GOMOOS is a long standing network of wireless sensors in the Gulf of Maine monitoring the high energy ocean environment. As a case study, high level storm events are detected and classified using the Event Oriented approach. A domain-independent ontology for detecting high level xvi composite events called a General Composite Event Ontology is presented and used as a basis of the Storm Event Ontology. Primitive events are detected from univariate sensors and assembled into Composite Storm Events using the Storm Event Ontology. To evaluate the effectiveness of the Event Oriented approach, the resulting candidate storm events are compared with an independent historic Storm Events Database from the National Climatic Data Center (NCDC) indicating that the Event Oriented approach detected about 92% of the storms recorded by the NCDC. The Event Oriented approach facilitates classification of high level composite event. In the case study, candidate storms were classified based on their spatial progression and profile. Since ontological knowledge is used for constructing high level event ontology, detection of candidate high level events could help refine existing ontological knowledge about them. In summary, this thesis demonstrates the Event Oriented approach to reduce dimensionality in complex space-time series sensor data and the facility to integrate ime series data over space for detecting high level phenomenon

Rational Price Discovery In Experimental And Field Data

The methodology of tests for martingale properties in return series is analyzed. Martingale results obtain frequently in finance. One case is focused on here, namely, rational price discovery. Price discovery is the process by which a market moves towards a new equilibrium after a major event. It is rational if price changes cannot be predicted from commonly available information. The price discovery process, however, cannot be assumed stationary. Hence, to avoid false inference in the presence of nonstationarities, event studies of field data have been advocating the use of cross-sectional information in the computation of test statistics. Under the martingale hypothesis, however, this inference strategy is shown to add little except if higher moments of the return series do not exist. On the contrary, the cross-sectional approach may even be invalid if there is cross-sectional heterogeneity in the price discovery process. The time series statistic of Patell (1976], originally suggested in the context of i.i.d. time series but cross-sectional heterosceclasticity, may be preferable. It will not provide valid inference either, if higher serial correlation coincides with higher volatility. Unfortunately, this appears to be the case in the dataset which is used in the paper to illustrate the methodological issues, namely, transaction price changes from experiments on continuous double auctions with stochastic private valuations

Causal Discovery from Temporal Data: An Overview and New Perspectives

Temporal data, representing chronological observations of complex systems, has always been a typical data structure that can be widely generated by many domains, such as industry, medicine and finance. Analyzing this type of data is extremely valuable for various applications. Thus, different temporal data analysis tasks, eg, classification, clustering and prediction, have been proposed in the past decades. Among them, causal discovery, learning the causal relations from temporal data, is considered an interesting yet critical task and has attracted much research attention. Existing casual discovery works can be divided into two highly correlated categories according to whether the temporal data is calibrated, ie, multivariate time series casual discovery, and event sequence casual discovery. However, most previous surveys are only focused on the time series casual discovery and ignore the second category. In this paper, we specify the correlation between the two categories and provide a systematical overview of existing solutions. Furthermore, we provide public datasets, evaluation metrics and new perspectives for temporal data casual discovery.Comment: 52 pages, 6 figure

Heuristic Approaches for Generating Local Process Models through Log Projections

Local Process Model (LPM) discovery is focused on the mining of a set of process models where each model describes the behavior represented in the event log only partially, i.e. subsets of possible events are taken into account to create so-called local process models. Often such smaller models provide valuable insights into the behavior of the process, especially when no adequate and comprehensible single overall process model exists that is able to describe the traces of the process from start to end. The practical application of LPM discovery is however hindered by computational issues in the case of logs with many activities (problems may already occur when there are more than 17 unique activities). In this paper, we explore three heuristics to discover subsets of activities that lead to useful log projections with the goal of speeding up LPM discovery considerably while still finding high-quality LPMs. We found that a Markov clustering approach to create projection sets results in the largest improvement of execution time, with discovered LPMs still being better than with the use of randomly generated activity sets of the same size. Another heuristic, based on log entropy, yields a more moderate speedup, but enables the discovery of higher quality LPMs. The third heuristic, based on the relative information gain, shows unstable performance: for some data sets the speedup and LPM quality are higher than with the log entropy based method, while for other data sets there is no speedup at all.Comment: paper accepted and to appear in the proceedings of the IEEE Symposium on Computational Intelligence and Data Mining (CIDM), special session on Process Mining, part of the Symposium Series on Computational Intelligence (SSCI

Light Echoes of Transients and Variables in the Local Universe

Astronomical light echoes, the time-dependent light scattered by dust in the vicinity of varying objects, have been recognized for over a century. Initially, their utility was thought to be confined to mapping out the three-dimensional distribution of interstellar dust. Recently, the discovery of spectroscopically-useful light echoes around centuries-old supernovae in the Milky Way and the Large Magellanic Cloud has opened up new scientific opportunities to exploit light echoes. In this review, we describe the history of light echoes in the local Universe and cover the many new developments in both the observation of light echoes and the interpretation of the light scattered from them. Among other benefits, we highlight our new ability to spectroscopically classify outbursting objects, to view them from multiple perspectives, to obtain a spectroscopic time series of the outburst, and to establish accurate distances to the source event. We also describe the broader range of variable objects whose properties may be better understood from light echo observations. Finally, we discuss the prospects of new light echo techniques not yet realized in practice.Comment: 18 pages, 7 figures, 1 table. Accepted for publication in PAS