Modeling Individual Cyclic Variation in Human Behavior

Cycles are fundamental to human health and behavior. However, modeling cycles in time series data is challenging because in most cases the cycles are not labeled or directly observed and need to be inferred from multidimensional measurements taken over time. Here, we present CyHMMs, a cyclic hidden Markov model method for detecting and modeling cycles in a collection of multidimensional heterogeneous time series data. In contrast to previous cycle modeling methods, CyHMMs deal with a number of challenges encountered in modeling real-world cycles: they can model multivariate data with discrete and continuous dimensions; they explicitly model and are robust to missing data; and they can share information across individuals to model variation both within and between individual time series. Experiments on synthetic and real-world health-tracking data demonstrate that CyHMMs infer cycle lengths more accurately than existing methods, with 58% lower error on simulated data and 63% lower error on real-world data compared to the best-performing baseline. CyHMMs can also perform functions which baselines cannot: they can model the progression of individual features/symptoms over the course of the cycle, identify the most variable features, and cluster individual time series into groups with distinct characteristics. Applying CyHMMs to two real-world health-tracking datasets -- of menstrual cycle symptoms and physical activity tracking data -- yields important insights including which symptoms to expect at each point during the cycle. We also find that people fall into several groups with distinct cycle patterns, and that these groups differ along dimensions not provided to the model. For example, by modeling missing data in the menstrual cycles dataset, we are able to discover a medically relevant group of birth control users even though information on birth control is not given to the model.Comment: Accepted at WWW 201

Prediction of peptides binding to MHC class I alleles by partial periodic pattern mining

MHC (Major Histocompatibility Complex) is a key player in the immune response of an organism. It is important to be able to predict which antigenic peptides will bind to a spe-cific MHC allele and which will not, creating possibilities for controlling immune response and for the applications of immunotherapy. However a problem encountered in the computational binding prediction methods for MHC class I is the presence of bulges and loops in the peptides, changing the total length. Most machine learning methods in use to-day require the sequences to be of same length to success-fully mine the binding motifs. We propose the use of time-based data mining methods in motif mining to be able to mine motifs position-independently. Also, the information for both binding and non-binding peptides are used on the contrary to the other methods which only rely on binding peptides. The prediction results are between 70-80% for the tested alleles

DRSP : Dimension Reduction For Similarity Matching And Pruning Of Time Series Data Streams

Similarity matching and join of time series data streams has gained a lot of relevance in today's world that has large streaming data. This process finds wide scale application in the areas of location tracking, sensor networks, object positioning and monitoring to name a few. However, as the size of the data stream increases, the cost involved to retain all the data in order to aid the process of similarity matching also increases. We develop a novel framework to addresses the following objectives. Firstly, Dimension reduction is performed in the preprocessing stage, where large stream data is segmented and reduced into a compact representation such that it retains all the crucial information by a technique called Multi-level Segment Means (MSM). This reduces the space complexity associated with the storage of large time-series data streams. Secondly, it incorporates effective Similarity Matching technique to analyze if the new data objects are symmetric to the existing data stream. And finally, the Pruning Technique that filters out the pseudo data object pairs and join only the relevant pairs. The computational cost for MSM is O(l*ni) and the cost for pruning is O(DRF*wsize*d), where DRF is the Dimension Reduction Factor. We have performed exhaustive experimental trials to show that the proposed framework is both efficient and competent in comparison with earlier works.Comment: 20 pages,8 figures, 6 Table

Feature-based time-series analysis

This work presents an introduction to feature-based time-series analysis. The time series as a data type is first described, along with an overview of the interdisciplinary time-series analysis literature. I then summarize the range of feature-based representations for time series that have been developed to aid interpretable insights into time-series structure. Particular emphasis is given to emerging research that facilitates wide comparison of feature-based representations that allow us to understand the properties of a time-series dataset that make it suited to a particular feature-based representation or analysis algorithm. The future of time-series analysis is likely to embrace approaches that exploit machine learning methods to partially automate human learning to aid understanding of the complex dynamical patterns in the time series we measure from the world.Comment: 28 pages, 9 figure

Similarity processing in multi-observation data

Many real-world application domains such as sensor-monitoring systems for environmental research or medical diagnostic systems are dealing with data that is represented by multiple observations. In contrast to single-observation data, where each object is assigned to exactly one occurrence, multi-observation data is based on several occurrences that are subject to two key properties: temporal variability and uncertainty. When defining similarity between data objects, these properties play a significant role. In general, methods designed for single-observation data hardly apply for multi-observation data, as they are either not supported by the data models or do not provide sufficiently efficient or effective solutions. Prominent directions incorporating the key properties are the fields of time series, where data is created by temporally successive observations, and uncertain data, where observations are mutually exclusive. This thesis provides research contributions for similarity processing - similarity search and data mining - on time series and uncertain data. The first part of this thesis focuses on similarity processing in time series databases. A variety of similarity measures have recently been proposed that support similarity processing w.r.t. various aspects. In particular, this part deals with time series that consist of periodic occurrences of patterns. Examining an application scenario from the medical domain, a solution for activity recognition is presented. Finally, the extraction of feature vectors allows the application of spatial index structures, which support the acceleration of search and mining tasks resulting in a significant efficiency gain. As feature vectors are potentially of high dimensionality, this part introduces indexing approaches for the high-dimensional space for the full-dimensional case as well as for arbitrary subspaces. The second part of this thesis focuses on similarity processing in probabilistic databases. The presence of uncertainty is inherent in many applications dealing with data collected by sensing devices. Often, the collected information is noisy or incomplete due to measurement or transmission errors. Furthermore, data may be rendered uncertain due to privacy-preserving issues with the presence of confidential information. This creates a number of challenges in terms of effectively and efficiently querying and mining uncertain data. Existing work in this field either neglects the presence of dependencies or provides only approximate results while applying methods designed for certain data. Other approaches dealing with uncertain data are not able to provide efficient solutions. This part presents query processing approaches that outperform existing solutions of probabilistic similarity ranking. This part finally leads to the application of the introduced techniques to data mining tasks, such as the prominent problem of probabilistic frequent itemset mining.Viele Anwendungsgebiete, wie beispielsweise die Umweltforschung oder die medizinische Diagnostik, nutzen Systeme der Sensorüberwachung. Solche Systeme müssen oftmals in der Lage sein, mit Daten umzugehen, welche durch mehrere Beobachtungen repräsentiert werden. Im Gegensatz zu Daten mit nur einer Beobachtung (Single-Observation Data) basieren Daten aus mehreren Beobachtungen (Multi-Observation Data) auf einer Vielzahl von Beobachtungen, welche zwei Schlüsseleigenschaften unterliegen: Zeitliche Veränderlichkeit und Datenunsicherheit. Im Bereich der Ähnlichkeitssuche und im Data Mining spielen diese Eigenschaften eine wichtige Rolle. Gängige Lösungen in diesen Bereichen, die für Single-Observation Data entwickelt wurden, sind in der Regel für den Umgang mit mehreren Beobachtungen pro Objekt nicht anwendbar. Der Grund dafür liegt darin, dass diese Ansätze entweder nicht mit den Datenmodellen vereinbar sind oder keine Lösungen anbieten, die den aktuellen Ansprüchen an Lösungsqualität oder Effizienz genügen. Bekannte Forschungsrichtungen, die sich mit Multi-Observation Data und deren Schlüsseleigenschaften beschäftigen, sind die Analyse von Zeitreihen und die Ähnlichkeitssuche in probabilistischen Datenbanken. Während erstere Richtung eine zeitliche Ordnung der Beobachtungen eines Objekts voraussetzt, basieren unsichere Datenobjekte auf Beobachtungen, die sich gegenseitig bedingen oder ausschließen. Diese Dissertation umfasst aktuelle Forschungsbeiträge aus den beiden genannten Bereichen, wobei Methoden zur Ähnlichkeitssuche und zur Anwendung im Data Mining vorgestellt werden. Der erste Teil dieser Arbeit beschäftigt sich mit Ähnlichkeitssuche und Data Mining in Zeitreihendatenbanken. Insbesondere werden Zeitreihen betrachtet, welche aus periodisch auftretenden Mustern bestehen. Im Kontext eines medizinischen Anwendungsszenarios wird ein Ansatz zur Aktivitätserkennung vorgestellt. Dieser erlaubt mittels Merkmalsextraktion eine effiziente Speicherung und Analyse mit Hilfe von räumlichen Indexstrukturen. Für den Fall hochdimensionaler Merkmalsvektoren stellt dieser Teil zwei Indexierungsmethoden zur Beschleunigung von ähnlichkeitsanfragen vor. Die erste Methode berücksichtigt alle Attribute der Merkmalsvektoren, während die zweite Methode eine Projektion der Anfrage auf eine benutzerdefinierten Unterraum des Vektorraums erlaubt. Im zweiten Teil dieser Arbeit wird die Ähnlichkeitssuche im Kontext probabilistischer Datenbanken behandelt. Daten aus Sensormessungen besitzen häufig Eigenschaften, die einer gewissen Unsicherheit unterliegen. Aufgrund von Mess- oder übertragungsfehlern sind gemessene Werte oftmals unvollständig oder mit Rauschen behaftet. In diversen Szenarien, wie beispielsweise mit persönlichen oder medizinisch vertraulichen Daten, können Daten auch nachträglich von Hand verrauscht werden, so dass eine genaue Rekonstruktion der ursprünglichen Informationen nicht möglich ist. Diese Gegebenheiten stellen Anfragetechniken und Methoden des Data Mining vor einige Herausforderungen. In bestehenden Forschungsarbeiten aus dem Bereich der unsicheren Datenbanken werden diverse Probleme oftmals nicht beachtet. Entweder wird die Präsenz von Abhängigkeiten ignoriert, oder es werden lediglich approximative Lösungen angeboten, welche die Anwendung von Methoden für sichere Daten erlaubt. Andere Ansätze berechnen genaue Lösungen, liefern die Antworten aber nicht in annehmbarer Laufzeit zurück. Dieser Teil der Arbeit präsentiert effiziente Methoden zur Beantwortung von Ähnlichkeitsanfragen, welche die Ergebnisse absteigend nach ihrer Relevanz, also eine Rangliste der Ergebnisse, zurückliefern. Die angewandten Techniken werden schließlich auf Problemstellungen im probabilistischen Data Mining übertragen, um beispielsweise das Problem des Frequent Itemset Mining unter Berücksichtigung des vollen Gehalts an Unsicherheitsinformation zu lösen