Big data technologies have been applied successfully to diverse application domains in order to facilitate analytics of voluminous and heterogeneous databases at scale. Digital sensory typically provides high-content data comprising multiple data recordings with high frequency. One example of such sensory are multi-electrode arrays (MEA), which are able to measure electric cell activity with high spatial and temporal resolution. The resulting multivariate time series and their inherent subsequences can then be analyzed and compared in aspects of time, space and shape. This analytical process is frequently performed manually by domain experts in combination with data analytical methods that help to identify and track signal beginnings, signal ends and signal propagations.In this paper, we propose an unsupervised approach to discover propagating signals in high-content, multivariate time series databases. To this end, we introduce an efficient spatio-temporal subsequence clustering algorithm that detects and tracks spatial and temporal signal progagations by means of density-based clusters. We present a formal propagation model and show how to adapt the DBSCAN algorithm to our specific application setting on pharmacological data. Our empirical investigation shows that our proposal is able to detect signal propagations with high accuracy and efficiency. Our approach hence scales not only to pharmacological settings but also to other biological, medical, and chemical domains making use of high-content multi-electrode array data.

Time series are able to depict various kinds of real-world processes, which are frequently represented by the aggregation of multiple real-world components acting in parallel. Extracting these components and using them for discovering structural regularities is a major challenge in the domain of time series analytics. In this paper, we propose the CATGP+ algorithm for efficient Component Analysis in Time series with Gaussian Processes. For this purpose, we assume time series to be modelled by Gaussian processes, which are probabilistic machine learning models that are able to capture local linearity, trend, periodicity, etc. Based on these models, the proposed CATGP+ algorithm facilitates the determination of structural regularities, i.e. frequently occurring components, within Gaussian processes. By relating the components of the Gaussian processes, modelling the underlying time series data, with those appearing in real-world processes, we are able to gain further inside into behavior and dependencies on a structural and substructural level. Moreover, as our designed solution is compatible with the classical itemset mining problem, we are able to inherent the efficiency of existing algorithmic approaches. Our experimental evaluation indicates that the CATGP+ algorithm is able to efficiently discover frequent components hidden in the underlying time series data.

When multiple sensors record spatially proximate areas of activity, spreading activity patterns appear as temporally shifted signals in multiple time series. This is particularly prominent in the domains of medical and health analysis, where multi-sensory data is the object of time-elastic investigation. Tracing the spread of these patterns still remains a challenge in time series analysis. In this paper, we propose Motif Tracking for Spatially Ordered Time Series (MoTrack), an algorithm to efficiently track the propagation of individual patterns of activity throughout spatially ordered time series. Additionally, we present the concept of propagation trees to represent this propagation for a given point of origin. We investigate our proposal by applying MoTrack to high-frequency recordings of the electrical activity of β-cells located inside the pancreatic islet. The results confirm MoTrack’s capability to trace dynamically evolving signals in such recordings and indicate that future work using this approach can address current challenges in diabetes research.

Gaussian Processes offer non-parametric, probabilistic models that can be used in numerous fields of data analysis. One major drawback is their lack of adjustability in case of drifting and evolving streaming data, where inherent kernels need to be adapted in an efficient manner. To counteract this issue, we propose a novel automated kernel search method that allows us to incrementally adapt Gaussian Process models to evolving IoT data streams. Our approach, denoted as Adjusting Kernel Search (AKS), offers an efficient alternative to searching for suitable kernels from scratch. We evaluate the AKS algorithm on several IoT datasets and show that our approach is able to achieve higher accuracy with lower run-times compared to previous approaches.

Time series data is one of the complex data types commonly encountered in many application areas ranging from automotive, finance, medicine to industry. A prominent task is time series classification, which entails identifying expressive features in oder to predict class labels of time series data. In this paper, we propose a novel approach for time series classification called Local Gaussian Process Model Inference Classification (LOGIC). Our concept consists in (i) learning latent characteristics of given time series data by means of Gaussian processes, (ii) using these characteristics to embed time series into a more expressive feature space and (iii) classifying time series data based on these features via existing classification methods. By making use of various general-purpose classification methods, we show that LOGIC is able to compete with state-of-the-art approaches in terms of accuracy and efficiency.