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Summarization: Methods for Approximate Query Processing (AQP) are essential for dealing with massive data. They are often the only means of providing interactive response times when exploring massive datasets, and are also needed to handle high speed data streams. These methods proceed by computing a lossy, compact synopsis of the data, and then executing the query of interest against the synopsis rather than the entire dataset. We describe basic principles and recent developments in AQP. We focus on four key synopses: random samples, histograms, wavelets, and sketches. We consider issues such as accuracy, space and time effi- ciency, optimality, practicality, range of applicability, error bounds on query answers, and incremental maintenance. We also discuss the tradeoffs between the different synopsis types. Presented on: Foundations and Trends in Databases

Time series classification (TSC) is home to a number of algorithm groups that utilise different kinds of discriminatory patterns. One of these groups describes classifiers that predict using phase dependant intervals. The time series forest (TSF) classifier is one of the most well known interval methods, and has demonstrated strong performance as well as relative speed in training and predictions. However, recent advances in other approaches have left TSF behind. TSF originally summarises intervals using three simple summary statistics. The ‘catch22' feature set of 22 time series features was recently proposed to aid time series analysis through a concise set of diverse and informative descriptive characteristics. We propose combining TSF and catch22 to form a new classifier, the Canonical Interval Forest (CIF). We outline additional enhancements to the training procedure, and extend the classifier to include multivariate classification capabilities. We demonstrate a large and significant improvement in accuracy over both TSF and catch22, and show it to be on par with top performers from other algorithmic classes. By upgrading the interval-based component from TSF to CIF, we also demonstrate a significant improvement in the hierarchical vote collective of transformation-based ensembles (HIVE-COTE) that combines different time series representations. HIVE-COTE using CIF is significantly more accurate on the UCR archive than any other classifier we are aware of and represents a new state of the art for TSC.

The graph data structure offers a highly expressive way of representing many real-world constructs such as social networks, chemical compounds, the world wide web, street maps, etc. In essence, any collection of entities and the relationships between them can be modelled using a graph, thus preserving more information about the real-world objects than a simple vector space model. An issue that arises when operating on collections of graphs, however, is that most statistical analysis and machine learning methods expect their input data to be in the form of multidimensional vectors, where all items can be compared with each other using well-understood metrics such as Euclidean or Manhattan distance. This paper presents a variety of approaches for computing distances between graphs with known node correspondence, with the aim of applying those measures alongside clustering algorithms to discover patterns in a given dataset. The performance of each distance measure is then evaluated through its ability to identify communities of graphs with similar features. We show that because the considered distance metrics highlight different structural properties, the method that produces the highest quality result will depend on the characteristics of the processed graph population.

Road damages are of great interest for federal road authorities and their infrastructure management as well as the automated driving task and thus safety and comfort of vehicle occupants. Therefore, we are investigating the automatic detection of different types of road damages by images from a front-facing camera in the vehicle. The data basis of our work is provided by the ’IEEE BigData Cup Challenge’ and its dataset ’RDD-2020’ with a large number of labelled images from Japan, India and the Czech Republic. Our Deep Learning approach utilizes the pre-trained Faster Region Based Convolutional Neural Networks (R-CNN). In the first step, we classify the destination of the image followed by expert networks for each region. Between the explanation of our applied Deep Learning methodology, some remaining sources of errors are discussed and further, partly failed approaches during our development period are displayed, which could be of interest for future work. Our results are convincing and we are able to achieve an F1 score of 0 . 487 across all regions for longitudinal and lateral cracks, alligator cracks and potholes.

Deep learning-based technology is a good key to unlock the object detection tasks in our real world. By using deep neural networks, we could break a problem that is dangerous and very time-consuming but has to be done every day like detecting the road state. This paper describes the solution using YOLO to detect the various types of road damage in the IEEE BigData Cup Challenge 2020. Our YOLOv5x based-solution is light-weight and fast, even it has good accuracy. We achieved an F1 score of 0.58 using our ensemble model with TTA, and it could be an adequate candidate for detecting real road damage in real-time.