Exploring time-series motifs through DTW-SOM

Motif discovery is a fundamental step in data mining tasks for time-series data such as clustering, classification and anomaly detection. Even though many papers have addressed the problem of how to find motifs in time-series by proposing new motif discovery algorithms, not much work has been done on the exploration of the motifs extracted by these algorithms. In this paper, we argue that visually exploring time-series motifs computed by motif discovery algorithms can be useful to understand and debug results. To explore the output of motif discovery algorithms, we propose the use of an adapted Self-Organizing Map, the DTW-SOM, on the list of motif's centers. In short, DTW-SOM is a vanilla Self-Organizing Map with three main differences, namely (1) the use the Dynamic Time Warping distance instead of the Euclidean distance, (2) the adoption of two new network initialization routines (a random sample initialization and an anchor initialization) and (3) the adjustment of the Adaptation phase of the training to work with variable-length time-series sequences. We test DTW-SOM in a synthetic motif dataset and two real time-series datasets from the UCR Time Series Classification Archive. After an exploration of results, we conclude that DTW-SOM is capable of extracting relevant information from a set of motifs and display it in a visualization that is space-efficient.Comment: 8 pages, 12 figures, Accepted for presentation at the International Joint Conference on Neural Networks (IJCNN) 202

Solar Intensity Forecasting using Artificial Neural Networks and Support Vector Machines

This paper presents several forecasting methodologies based on the application of Artificial Neural Networks (ANN) and Support Vector Machines (SVM), directed to the prediction of the solar radiance intensity. The methodologies differ from each other by using different information in the training of the methods, i.e, different environmental complementary fields such as the wind speed, temperature, and humidity. Additionally, different ways of considering the data series information have been considered. Sensitivity testing has been performed on all methodologies in order to achieve the best parameterizations for the proposed approaches. Results show that the SVM approach using the exponential Radial Basis Function (eRBF) is capable of achieving the best forecasting results, and in half execution time of the ANN based approaches

Applications of high-frequency telematics for driving behavior analysis

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Statistics and EconometricsProcessing driving data and investigating driving behavior has been receiving an increasing interest in the last decades, with applications ranging from car insurance pricing to policy-making. A popular way of analyzing driving behavior is to move the focus to the maneuvers as they give useful information about the driver who is performing them. Previous research on maneuver detection can be divided into two strategies, namely, 1) using fixed thresholds in inertial measurements to define the start and end of specific maneuvers or 2) using features extracted from rolling windows of sensor data in a supervised learning model to detect maneuvers. While the first strategy is not adaptable and requires fine-tuning, the second needs a dataset with labels (which is time-consuming) and cannot identify maneuvers with different lengths in time. To tackle these shortcomings, we investigate a new way of identifying maneuvers from vehicle telematics data, through motif detection in time-series. Using a publicly available naturalistic driving dataset (the UAH-DriveSet), we conclude that motif detection algorithms are not only capable of extracting simple maneuvers such as accelerations, brakes, and turns, but also more complex maneuvers, such as lane changes and overtaking maneuvers, thus validating motif discovery as a worthwhile line for future research in driving behavior. We also propose TripMD, a system that extracts the most relevant driving patterns from sensor recordings (such as acceleration) and provides a visualization that allows for an easy investigation. We test TripMD in the same UAH-DriveSet dataset and show that (1) our system can extract a rich number of driving patterns from a single driver that are meaningful to understand driving behaviors and (2) our system can be used to identify the driving behavior of an unknown driver from a set of drivers whose behavior we know.Nas últimas décadas, o processamento e análise de dados de condução tem recebido um interesse cada vez maior, com aplicações que abrangem a área de seguros de automóveis até a atea de regulação. Tipicamente, a análise de condução compreende a extração e estudo de manobras uma vez que estas contêm informação relevante sobre a performance do condutor. A investigação prévia sobre este tema pode ser dividida em dois tipos de estratégias, a saber, 1) o uso de valores fixos de aceleração para definir o início e fim de cada manobra ou 2) a utilização de modelos de aprendizagem supervisionada em janelas temporais. Enquanto o primeiro tipo de estratégias é inflexível e requer afinação dos parâmetros, o segundo precisa de dados de condução anotados (o que é moroso) e não é capaz de identificar manobras de diferentes durações. De forma a mitigar estas lacunas, neste trabalho, aplicamos métodos desenvolvidos na área de investigação de séries temporais de forma a resolver o problema de deteção de manobras. Em particular, exploramos área de deteção de motifs em séries temporais e testamos se estes métodos genéricos são bem-sucedidos na deteção de manobras. Também propomos o TripMD, um sistema que extrai os padrões de condução mais relevantes de um conjuntos de viagens e fornece uma simples visualização. TripMD é testado num conjunto de dados públicos (o UAH-DriveSet), do qual concluímos que (1) o nosso sistema é capaz de extrair padrões de condução/manobras de um único condutor que estão relacionados com o perfil de condução do condutor em questão e (2) o nosso sistema pode ser usado para identificar o perfil de condução de um condutor desconhecido de um conjunto de condutores cujo comportamento nos é conhecido

Discovering Patterns from Sequences with Applications to Protein-Protein and Protein-DNA Interaction

Understanding Protein-Protein and Protein-DNA interaction is of fundamental importance in deciphering gene regulation and other biological processes in living cells. Traditionally, new interaction knowledge is discovered through biochemical experiments that are often labor intensive, expensive and time-consuming. Thus, computational approaches are preferred. Due to the abundance of sequence data available today, sequence-based interaction analysis becomes one of the most readily applicable and cost-effective methods. One important problem in sequence-based analysis is to identify the functional regions from a set of sequences within the same family or demonstrating similar biological functions in experiments. The rationale is that throughout evolution the functional regions normally remain conserved (intact), allowing them to be identified as patterns from a set of sequences. However, there are also mutations such as substitution, insertion, deletion in these functional regions. Existing methods, such as those based on position weight matrices, assume that the functional regions have a fixed width and thus cannot not identify functional regions with mutations, particularly those with insertion or deletion mutations. Recently, Aligned Pattern Clustering (APCn) was introduced to identify functional regions as Aligned Pattern Clusters (APCs) by grouping and aligning patterns with variable width. Nevertheless, APCn cannot discover functional regions with substitution, insertion and/or deletion mutations, since their frequencies of occurrences are too low to be considered as patterns. To overcome such an impasse, this thesis proposes a new APC discovery algorithm known as Pattern-Directed Aligned Pattern Clustering (PD-APCn). By first discovering seed patterns from the input sequence data, with their sequence positions located and recorded on an address table, PD-APCn can use the seed patterns to direct the incremental extension of functional regions with minor mutations. By grouping the aligned extended patterns, PD-APCn can recruit patterns adaptively and efficiently with variable width without relying on exhaustive optimal search. Experiments on synthetic datasets with different sizes and noise levels showed that PD-APCn can identify the implanted pattern with mutations, outperforming the popular existing motif-finding software MEME with much higher recall and Fmeasure over a computational speed-up of up to 665 times. When applying PD-APCn on datasets from Cytochrome C and Ubiquitin protein families, all key binding sites conserved in the families were captured in the APC outputs. In sequence-based interaction analysis, there is also a lack of a model for co-occurring functional regions with mutations, where co-occurring functional regions between interaction sequences are indicative of binding sites. This thesis proposes a new representation model Co-Occurrence APCs to capture co-occurring functional regions with mutations from interaction sequences in database transaction format. Applications on Protein-DNA and Protein-Protein interaction validated the capability of Co-Occurrence APCs. In Protein-DNA interaction, a new representation model, Protein-DNA Co-Occurrence APC, was developed for modeling Protein-DNA binding cores. The new model is more compact than the traditional one-to-one pattern associations, as it packs many-to-many associations in one model, yet it is detailed enough to allow site-specific variants. An algorithm, based on Co-Support Score, was also developed to discover Protein-DNA Co-Occurrence APCs from Protein-DNA interaction sequences. This algorithm is 1600x faster in run-time than its contemporaries. New Protein-DNA binding cores indicated by Protein-DNA Co-Occurrence APCs were also discovered via homology modeling as a proof-of-concept. In Protein-Protein interaction, a new representation model, Protein-Protein Co-Occurrence APC, was developed for modeling the co-occurring sequence patterns in Protein-Protein Interaction between two protein sequences. A new algorithm, WeMine-P2P, was developed for sequence-based Protein-Protein Interaction machine learning prediction by constructing feature vectors leveraging Protein-Protein Co-Occurrence APCs, based on novel scores such as Match Score, MaxMatch Score and APC-PPI score. Through 40 independent experiments, it outperformed the well-known algorithm, PIPE2, which also uses co-occurring functional regions while not allowing variable widths and mutations. Both applications on Protein-Protein and Protein-DNA interaction have indicated the potential use of Co-Occurrence APC for exploring other types of biosequence interaction in the future

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

Data Mining

Data mining is a branch of computer science that is used to automatically extract meaningful, useful knowledge and previously unknown, hidden, interesting patterns from a large amount of data to support the decision-making process. This book presents recent theoretical and practical advances in the field of data mining. It discusses a number of data mining methods, including classification, clustering, and association rule mining. This book brings together many different successful data mining studies in various areas such as health, banking, education, software engineering, animal science, and the environment

Expanding Data Imaginaries in Urban Planning:Foregrounding lived experience and community voices in studies of cities with participatory and digital visual methods

“Expanding Data Imaginaries in Urban Planning” synthesizes more than three years of industrial research conducted within Gehl and the Techno–Anthropology Lab at Aalborg University. Through practical experiments with social media images, digital photovoice, and participatory mapmaking, the project explores how visual materials created by citizens can be used within a digital and participatory methodology to reconfigure the empirical ground of data-driven urbanism. Drawing on a data feminist framework, the project uses visual research to elevate community voices and situate urban issues in lived experiences. As a Science and Technology Studies project, the PhD also utilizes its industrial position as an opportunity to study Gehl’s practices up close, unpacking collectively held narratives and visions that form a particular “data imaginary” and contribute to the production and perpetuation of the role of data in urban planning. The dissertation identifies seven epistemological commitments that shape the data imaginary at Gehl and act as discursive closures within their practice. To illustrate how planners might expand on these, the dissertation uses its own data experiments as speculative demonstrations of how to make alternative modes of knowing cities possible through participatory and digital visual methods

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions

Second Generation General System Theory: Perspectives in Philosophy and Approaches in Complex Systems

Following the classical work of Norbert Wiener, Ross Ashby, Ludwig von Bertalanffy and many others, the concept of System has been elaborated in different disciplinary fields, allowing interdisciplinary approaches in areas such as Physics, Biology, Chemistry, Cognitive Science, Economics, Engineering, Social Sciences, Mathematics, Medicine, Artificial Intelligence, and Philosophy. The new challenge of Complexity and Emergence has made the concept of System even more relevant to the study of problems with high contextuality. This Special Issue focuses on the nature of new problems arising from the study and modelling of complexity, their eventual common aspects, properties and approaches—already partially considered by different disciplines—as well as focusing on new, possibly unitary, theoretical frameworks. This Special Issue aims to introduce fresh impetus into systems research when the possible detection and correction of mistakes require the development of new knowledge. This book contains contributions presenting new approaches and results, problems and proposals. The context is an interdisciplinary framework dealing, in order, with electronic engineering problems; the problem of the observer; transdisciplinarity; problems of organised complexity; theoretical incompleteness; design of digital systems in a user-centred way; reaction networks as a framework for systems modelling; emergence of a stable system in reaction networks; emergence at the fundamental systems level; behavioural realization of memoryless functions