Contributions to time series data mining towards the detection of outliers/anomalies

148 p.Los recientes avances tecnológicos han supuesto un gran progreso en la recogida de datos, permitiendo recopilar una gran cantidad de datos a lo largo del tiempo. Estos datos se presentan comúnmente en forma de series temporales, donde las observaciones se han registrado de forma cronológica y están correlacionadas en el tiempo. A menudo, estas dependencias temporales contienen información significativa y útil, por lo que, en los últimos años, ha surgido un gran interés por extraer dicha información. En particular, el área de investigación que se centra en esta tarea se denomina minería de datos de series temporales.La comunidad de investigadores de esta área se ha dedicado a resolver diferentes tareas como por ejemplo la clasificación, la predicción, el clustering o agrupamiento y la detección de valores atípicos/anomalías. Los valores atípicos o anomalías son aquellas observaciones que no siguen el comportamiento esperado en una serie temporal. Estos valores atípicos o anómalos suelen representar mediciones no deseadas o eventos de interés, y, por lo tanto, detectarlos suele ser relevante ya que pueden empeorar la calidad de los datos o reflejar fenómenos interesantes para el analista.Esta tesis presenta varias contribuciones en el campo de la minería de datos de series temporales, más específicamente sobre la detección de valores atípicos o anomalías. Estas contribuciones se pueden dividir en dos partes o bloques. Por una parte, la tesis presenta contribuciones en el campo de la detección de valores atípicos o anomalías en series temporales. Para ello, se ofrece una revisión de las técnicas en la literatura, y se presenta una nueva técnica de detección de anomalías en series temporales univariantes para la detección de fugas de agua, basada en el aprendizaje autosupervisado. Por otra parte, la tesis también introduce contribuciones relacionadas con el tratamiento de las series temporales con valores perdidos y demuestra su aplicabilidad en el campo de la detección de anomalías

Accelerating Event Stream Processing in On- and Offline Systems

Due to a growing number of data producers and their ever-increasing data volume, the ability to ingest, analyze, and store potentially never-ending streams of data is a mission-critical task in today's data processing landscape. A widespread form of data streams are event streams, which consist of continuously arriving notifications about some real-world phenomena. For example, a temperature sensor naturally generates an event stream by periodically measuring the temperature and reporting it with measurement time in case of a substantial change to the previous measurement. In this thesis, we consider two kinds of event stream processing: online and offline. Online refers to processing events solely in main memory as soon as they arrive, while offline means processing event data previously persisted to non-volatile storage. Both modes are supported by widely used scale-out general-purpose stream processing engines (SPEs) like Apache Flink or Spark Streaming. However, such engines suffer from two significant deficiencies that severely limit their processing performance. First, for offline processing, they load the entire stream from non-volatile secondary storage and replay all data items into the associated online engine in order of their original arrival. While this naturally ensures unified query semantics for on- and offline processing, the costs for reading the entire stream from non-volatile storage quickly dominate the overall processing costs. Second, modern SPEs focus on scaling out computations across the nodes of a cluster, but use only a fraction of the available resources of individual nodes. This thesis tackles those problems with three different approaches. First, we present novel techniques for the offline processing of two important query types (windowed aggregation and sequential pattern matching). Our methods utilize well-understood indexing techniques to reduce the total amount of data to read from non-volatile storage. We show that this improves the overall query runtime significantly. In particular, this thesis develops the first index-based algorithms for pattern queries expressed with the Match_Recognize clause, a new and powerful language feature of SQL that has received little attention so far. Second, we show how to maximize resource utilization of single nodes by exploiting the capabilities of modern hardware. Therefore, we develop a prototypical shared-memory CPU-GPU-enabled event processing system. The system provides implementations of all major event processing operators (filtering, windowed aggregation, windowed join, and sequential pattern matching). Our experiments reveal that regarding resource utilization and processing throughput, such a hardware-enabled system is superior to hardware-agnostic general-purpose engines. Finally, we present TPStream, a new operator for pattern matching over temporal intervals. TPStream achieves low processing latency and, in contrast to sequential pattern matching, is easily parallelizable even for unpartitioned input streams. This results in maximized resource utilization, especially for modern CPUs with multiple cores

Advances on Time Series Analysis using Elastic Measures of Similarity

A sequence is a collection of data instances arranged in a structured manner. When this arrangement is held in the time domain, sequences are instead referred to as time series. As such, each observation in a time series represents an observation drawn from an underlying process, produced at a specific time instant. However, other type of data indexing structures, such as space- or threshold-based arrangements are possible. Data points that compose a time series are often correlated with each other. To account for this correlation in data mining tasks, time series are usually studied as a whole data object rather than as a collection of independent observations. In this context, techniques for time series analysis aim at analyzing this type of data structures by applying specific approaches developed to leverage intrinsic properties of the time series for a wide range of problems, such as classification, clustering and other tasks alike. The development of monitoring and storage devices has made time se- ries analysis proliferate in numerous application fields, including medicine, economics, manufacturing and telecommunications, among others. Over the years, the community has gathered efforts towards the development of new data-based techniques for time series analysis suited to address the problems and needs of such application fields. In the related literature, such techniques can be divided in three main groups: feature-, model- and distance-based methods. The first group (feature-based) transforms time series into a collection of features, which are then used by conventional learning algorithms to provide solutions to the task under consideration. In contrast, methods belonging to the second group (model-based) assume that each time series is drawn from a generative model, which is then har- nessed to elicit knowledge from data. Finally, distance-based techniques operate directly on raw time series. To this end, these methods resort to specially defined measures of distance or similarity for comparing time series, without requiring any further processing. Among them, elastic sim- ilarity measures (e.g., dynamic time warping and edit distance) compute the closeness between two sequences by finding the best alignment between them, disregarding differences in time, and thus focusing exclusively on shape differences. This Thesis presents several contributions to the field of distance-based techniques for time series analysis, namely: i) a novel multi-dimensional elastic similarity learning method for time series classification; ii) an adap- tation of elastic measures to streaming time series scenarios; and iii) the use of distance-based time series analysis to make machine learning meth- ods for image classification robust against adversarial attacks. Throughout the Thesis, each contribution is framed within its related state of the art, explained in detail and empirically evaluated. The obtained results lead to new insights on the application of distance-based time series methods for the considered scenarios, and motivates research directions that highlight the vibrant momentum of this research area

Advances on Time Series Analysis using Elastic Measures of Similarity

135 p.A sequence is a collection of data instances arranged in an structured manner. When thisarrangement is held in the time domain, sequences are instead referred to as time series. As such,each observation in a time series represents an observation drawn from an underlying process,produced at a specific time instant. However, other type of data indexing structures, such as spaceorthreshold-based arrangements are possible. Data points that compose a time series are oftencorrelated to each other. To account for this correlation in data mining tasks, time series are usuallystudied as a whole data object rather than as a collection of independent observations. In thiscontext, techniques for time series analysis aim at analyzing this type of data structures by applyingspecific approaches developed to harness intrinsic properties of the time series for a wide range ofproblems such as, classification, clustering and other tasks alike.The development of monitoring and storage devices has made time series analysisproliferate in numerous application fields including medicine, economics, manufacturing andtelecommunications, among others. Over the years, the community has gathered efforts towards thedevelopment of new data-based techniques for time series analysis suited to address the problemsand needs of such application fields. In the related literature, such techniques can be divided in threemain groups: feature-, model- and distance- based methods. The first group (feature-based)transforms time series into a collection of features, which are then used by conventional learningalgorithms to provide solutions to the task under consideration. In contrast, methods belonging to thesecond group (model-based) assume that each time series is drawn from a generative model, whichis then harnessed to elicit information from data. Finally, distance-based techniques operate directlyon raw time series. To this end, these latter methods resort to specially defined measures of distanceor similarity for comparing time series, without requiring any further processing. Among them,elastic similarity measures (e.g., dynamic time warping and edit distance) compute the closenessbetween two sequences by finding the best alignment between them, disregarding differences intime gaps and thus focusing exclusively on shape differences.This Thesis presents several contributions to the field of distance-based techniques for timeseries analysis, namely: i) a novel multi-dimensional elastic similarity learning method for timeseries classification; ii) an adaptation of elastic measures to streaming time series scenarios; and iii)the use of distance-based time series analysis to make machine learning methods for imageclassification robust against adversarial attacks. Throughout the Thesis, each contribution is framedwithin its related state of the art, explained in detail and empirically evaluated. The obtained resultslead to new insights on the application of distance-based time series methods for the consideredscenarios, and motivates research directions that highlight the vibrant momentum of this researcharea

MISNIS: an intelligent platform for Twitter topic mining

Twitter has become a major tool for spreading news, for dissemination of positions and ideas, and for the commenting and analysis of current world events. However, with more than 500 million tweets flowing per day, it is necessary to find efficient ways of collecting, storing, managing, mining and visualizing all this information. This is especially relevant if one considers that Twitter has no ways of indexing tweet contents, and that the only available categorization “mechanism” is the #hashtag, which is totally dependent of a user's will to use it. This paper presents an intelligent platform and framework, named MISNIS - Intelligent Mining of Public Social Networks’ Influence in Society - that facilitates these issues and allows a non-technical user to easily mine a given topic from a very large tweet's corpus and obtain relevant contents and indicators such as user influence or sentiment analysis. When compared to other existent similar platforms, MISNIS is an expert system that includes specifically developed intelligent techniques that: (1) Circumvent the Twitter API restrictions that limit access to 1% of all flowing tweets. The platform has been able to collect more than 80% of all flowing portuguese language tweets in Portugal when online; (2) Intelligently retrieve most tweets related to a given topic even when the tweets do not contain the topic #hashtag or user indicated keywords. A 40% increase in the number of retrieved relevant tweets has been reported in real world case studies. The platform is currently focused on Portuguese language tweets posted in Portugal. However, most developed technologies are language independent (e.g. intelligent retrieval, sentiment analysis, etc.), and technically MISNIS can be easily expanded to cover other languages and locations