Enhanced Query Processing on Complex Spatial and Temporal Data

Innovative technologies in the area of multimedia and mechanical engineering as well as novel methods for data acquisition in different scientific subareas, including geo-science, environmental science, medicine, biology and astronomy, enable a more exact representation of the data, and thus, a more precise data analysis. The resulting quantitative and qualitative growth of specifically spatial and temporal data leads to new challenges for the management and processing of complex structured objects and requires the employment of efficient and effective methods for data analysis. Spatial data denote the description of objects in space by a well-defined extension, a specific location and by their relationships to the other objects. Classical representatives of complex structured spatial objects are three-dimensional CAD data from the sector "mechanical engineering" and two-dimensional bounded regions from the area "geography". For industrial applications, efficient collision and intersection queries are of great importance. Temporal data denote data describing time dependent processes, as for instance the duration of specific events or the description of time varying attributes of objects. Time series belong to one of the most popular and complex type of temporal data and are the most important form of description for time varying processes. An elementary type of query in time series databases is the similarity query which serves as basic query for data mining applications. The main target of this thesis is to develop an effective and efficient algorithm supporting collision queries on spatial data as well as similarity queries on temporal data, in particular, time series. The presented concepts are based on the efficient management of interval sequences which are suitable for spatial and temporal data. The effective analysis of the underlying objects will be efficiently supported by adequate access methods. First, this thesis deals with collision queries on complex spatial objects which can be reduced to intersection queries on interval sequences. We introduce statistical methods for the grouping of subsequences. Involving the concept of multi-step query processing, these methods enable the user to accelerate the query process drastically. Furthermore, in this thesis we will develop a cost model for the multi-step query process of interval sequences in distributed systems. The proposed approach successfully supports a cost based query strategy. Second, we introduce a novel similarity measure for time series. It allows the user to focus specific time series amplitudes for the similarity measurement. The new similarity model defines two time series to be similar iff they show similar temporal behavior w.r.t. being below or above a specific threshold. This type of query is primarily required in natural science applications. The main goal of this new query method is the detection of anomalies and the adaptation to new claims in the area of data mining in time series databases. In addition, a semi-supervised cluster analysis method will be presented which is based on the introduced similarity model for time series. The efficiency and effectiveness of the proposed techniques will be extensively discussed and the advantages against existing methods experimentally proofed by means of datasets derived from real-world applications

PULP-HD: Accelerating Brain-Inspired High-Dimensional Computing on a Parallel Ultra-Low Power Platform

Computing with high-dimensional (HD) vectors, also referred to as $\textit{hypervectors}$, is a brain-inspired alternative to computing with scalars. Key properties of HD computing include a well-defined set of arithmetic operations on hypervectors, generality, scalability, robustness, fast learning, and ubiquitous parallel operations. HD computing is about manipulating and comparing large patterns-binary hypervectors with 10,000 dimensions-making its efficient realization on minimalistic ultra-low-power platforms challenging. This paper describes HD computing's acceleration and its optimization of memory accesses and operations on a silicon prototype of the PULPv3 4-core platform (1.5mm$^2$, 2mW), surpassing the state-of-the-art classification accuracy (on average 92.4%) with simultaneous 3.7$\times$ end-to-end speed-up and 2$\times$ energy saving compared to its single-core execution. We further explore the scalability of our accelerator by increasing the number of inputs and classification window on a new generation of the PULP architecture featuring bit-manipulation instruction extensions and larger number of 8 cores. These together enable a near ideal speed-up of 18.4$\times$ compared to the single-core PULPv3

MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework

We propose MeshfreeFlowNet, a novel deep learning-based super-resolution framework to generate continuous (grid-free) spatio-temporal solutions from the low-resolution inputs. While being computationally efficient, MeshfreeFlowNet accurately recovers the fine-scale quantities of interest. MeshfreeFlowNet allows for: (i) the output to be sampled at all spatio-temporal resolutions, (ii) a set of Partial Differential Equation (PDE) constraints to be imposed, and (iii) training on fixed-size inputs on arbitrarily sized spatio-temporal domains owing to its fully convolutional encoder. We empirically study the performance of MeshfreeFlowNet on the task of super-resolution of turbulent flows in the Rayleigh-Benard convection problem. Across a diverse set of evaluation metrics, we show that MeshfreeFlowNet significantly outperforms existing baselines. Furthermore, we provide a large scale implementation of MeshfreeFlowNet and show that it efficiently scales across large clusters, achieving 96.80% scaling efficiency on up to 128 GPUs and a training time of less than 4 minutes.Comment: Supplementary Video: https://youtu.be/mjqwPch9gDo. Accepted to SC2

EAGLE—A Scalable Query Processing Engine for Linked Sensor Data

Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio–temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio–temporal computing in the linked sensor data context.EC/H2020/732679/EU/ACTivating InnoVative IoT smart living environments for AGEing well/ACTIVAGEEC/H2020/661180/EU/A Scalable and Elastic Platform for Near-Realtime Analytics for The Graph of Everything/SMARTE

Action Recognition in Videos: from Motion Capture Labs to the Web

This paper presents a survey of human action recognition approaches based on visual data recorded from a single video camera. We propose an organizing framework which puts in evidence the evolution of the area, with techniques moving from heavily constrained motion capture scenarios towards more challenging, realistic, "in the wild" videos. The proposed organization is based on the representation used as input for the recognition task, emphasizing the hypothesis assumed and thus, the constraints imposed on the type of video that each technique is able to address. Expliciting the hypothesis and constraints makes the framework particularly useful to select a method, given an application. Another advantage of the proposed organization is that it allows categorizing newest approaches seamlessly with traditional ones, while providing an insightful perspective of the evolution of the action recognition task up to now. That perspective is the basis for the discussion in the end of the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4 table