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

Event and state detection in time series by genetic programming

Event and state detection in time series has significant value in scientific areas and real-world applications. The aim of detecting time series event and state patterns is to identify particular variations of user-interest in one or more channels of time series streams. For example, dangerous driving behaviours such as sudden braking and harsh acceleration can be detected from continuous recordings from inertial sensors. However, the existing methods are highly dependent on domain knowledge such as the size of the time series pattern and a set of effective features. Furthermore, they are not directly suitable for multi-channel time series data. In this study, we establish a genetic programming based method which can perform classification on multi-channel time series data. It does not need the domain knowledge required by the existing methods. The investigation consists of four parts: the methodology, an evaluation on event detection tasks, an evaluation on state detection tasks and an analysis on the suitability for real-world applications. In the methodology, a GP based method is proposed for processing and analysing multi-channel time series streams. The function set includes basic mathematical operations. In addition, specific functions and terminals are introduced to reserve historical information, capture temporal dependency across time points and handle dependency between channels. These functions and terminals help the GP based method to automatically find the pattern size and extract features. This study also investigates two different fitness functions - accuracy and area under the curve. The proposed method is investigated on a range of event detection tasks. The investigation starts from synthetic tasks such as detecting complete sine waves. The performance of the GP based method is compared to traditional classification methods. On the raw data the GP based method achieves 100 percent accuracy, which outperforms all the non-GP methods.The performance of the non-GP methods is comparable to the GP based method only with suitable features. In addition, the GP based method is investigated on two complex real-world event detection tasks - dangerous driving behaviour detection and video shot detection. In the task of detecting three dangerous driving behaviours from 21-channel time series data, the GP based method performs consistently better than the non-GP classifiers even when features are provided. In the video shot detection task, the GP based method achieves comparable performance on 11200-channel time series to the non-GP classifiers on 28 features. The GP based method is more accurate than a commercial product. The GP based method has also been investigated on state detection tasks. This involves synthetic tasks such as detecting concurrent high values in four of five channels and a real-world activity recognition problem. The results also show that the GP based method consistently outperforms the non-GP methods even with the presence of manually constructed features. As part of the investigation, a mobile phone based activity recognition data set was collected as there was no existing publicly available data set. The suitability of the GP based method for solving real-world problems is further analysed. Our analysis shows that the GP based method can be successfully extended for multi-class classification. The analysis of the evolved programs demonstrates that they do capture time series patterns. On synthetic data sets, the injected regularities are revealed in understandable individuals. The best programs for three real-world problems are more difficult to explain but still provide some insight. The selection of relevant channels and data points by the programs are consistent with domain knowledge. In addition, the analysis shows that the proposed method still performs well for time series pattern of different sizes. The effective window sizes of the evolved GP programs are close to the pattern size. Finally, our study on execution performance of the evolved programs shows that these programs are fast in execution and are suitable for real-time applications. In summary, the GP based method is suitable for the kinds of real-world applications studied in this thesis. This thesis concludes that, with a suitable representation, genetic programming can be an effective method for event and state detection in multi-channel time series for a range of synthetic and real-world tasks. This method does not require much domain knowledge such as the pattern size and suitable features. It offers an effective classification method in similar tasks that are studied in this thesis

Multi-Sensor Event Detection using Shape Histograms

Vehicular sensor data consists of multiple time-series arising from a number of sensors. Using such multi-sensor data we would like to detect occurrences of specific events that vehicles encounter, e.g., corresponding to particular maneuvers that a vehicle makes or conditions that it encounters. Events are characterized by similar waveform patterns re-appearing within one or more sensors. Further such patterns can be of variable duration. In this work, we propose a method for detecting such events in time-series data using a novel feature descriptor motivated by similar ideas in image processing. We define the shape histogram: a constant dimension descriptor that nevertheless captures patterns of variable duration. We demonstrate the efficacy of using shape histograms as features to detect events in an SVM-based, multi-sensor, supervised learning scenario, i.e., multiple time-series are used to detect an event. We present results on real-life vehicular sensor data and show that our technique performs better than available pattern detection implementations on our data, and that it can also be used to combine features from multiple sensors resulting in better accuracy than using any single sensor. Since previous work on pattern detection in time-series has been in the single series context, we also present results using our technique on multiple standard time-series datasets and show that it is the most versatile in terms of how it ranks compared to other published results

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care