In the efforts for safer roads, ensuring adequate vertical clearance above roadways is of great importance. Frequently, trees or other vegetation is growing above the roads, blocking the sight of traffic signs and lights and posing danger to traffic participants. Accurately estimating this space from simple images proves challenging due to a lack of depth information. This is where LiDAR technology comes into play, a laser scanning sensor that reveals a three-dimensional perspective. Thus far, LiDAR point clouds at the street level have mainly been used for applications in the field of autonomous driving. These scans, however, also open up possibilities in urban management. In this paper, we present a new point cloud algorithm that can automatically detect those parts of the trees that grow over the street and need to be trimmed. Our system uses semantic segmentation to filter relevant points and downstream processing steps to create the required volume to be kept clear above the road. Challenges include obscured stretches of road, the noisy unstructured nature of LiDAR point clouds, and the assessment of the road shape. The identified points of non-compliant trees can be projected from the point cloud onto images, providing municipalities with a visual aid for dealing with such occurrences. By automating this process, municipalities can address potential road space constraints, enhancing safety for all. They may also save valuable time by carrying out the inspections more systematically. Our open-source code gives communities inspiration on how to automate the process themselves.

Knowing the number of floors of all buildings in a city is vital in many areas of urban planning such as energy demand prediction, estimation of inhabitant numbers of specific buildings or the calculation of population densities. Also, novel augmented reality use cases strongly rely on exact numbers and positions of floors. However, in many cases floor numbers are unknown, its collection is mostly a manual process or existing data is not up-to-date. A major difficulty in automating floor counting lies in the architectural variety of buildings from different decades. So far approaches are only rough geometric approximations. More recently approaches apply neural networks to achieve more precise results. But, these neural network approaches rely on various sources of input that are not available to every municipality. They also tend to fail on building types they have not been trained on and existing approaches are completely black-box so that it is difficult to determine when and why the prediction is wrong. In this paper we propose a grey-box approach. In a stepwise process we can predict floor counts with high quality and remain explainable and parametrizable. By using data that is easy to obtain, namely the image of a building, we introduce two configurable methods to derive the number of floors. We demonstrate that the correct prediction quality can be significantly improved. In a thorough evaluation we analyze the quality depending on a number of factors such as image quality or building types.

Ensuring the success of big graph processing for the next decade and beyond.

: Duplikaterkennung, ‚suche und ‚konsolidierung für Kunden‚ und Geschčftspartnerdaten, sog. „Identity Resolution“, ist die Voraussetzung für erfolgreiches Customer Relationship Management und Customer Experience Management, aber auch für das Risikomanagement zur Minimierung von Betrugsrisiken und Einhaltung regulatorischer Vorschriften und viele weitere Anwendungsfčlle. Diese Systeme sind jedoch hochkomplex und müssen individuell an die kundenspezifischen Anforderungen angepasst werden. Der Einsatz lernbasierter Verfahren bietet großes Potenzial zur automatisierten Anpassung. In diesem Beitrag prčsentieren wir für ein KMU praxisfčhige, lernbasierte Verfahren zur automatischen Konfiguration von Business‚Regeln in Duplikaterkennungssystemen. Dabei wurden für Fachanwender Möglichkeiten entwickelt, um beispielgetrieben das Match‚System an individuelle Business‚Regeln ˘u.a. Umzugserkennung, Sperrlistenabgleich¯ anzupassen und zu konfigurieren. Die entwickelten Verfahren wurden evaluiert und in einer prototypischen Lösung integriert. Wir konnten zeigen, dass unser Machine‚Learning‚Verfahren, die von einem Domainexperten erstellten Business‚Regeln für das Duplikaterkennungssystem „identity“ verbessern konnte. Zudem konnte der hierzu erforderliche Zeitaufwand verkürzt werden.

We present and evaluate new methods for incremental entity resolution as needed for the completion of knowledge graphs integrating data from multiple sources. Compared to previous approaches we aim at reducing the dependency on the order in which new sources and entities are added. For this purpose, we consider sets of new entities for an optimized assignment of them to entity clusters. We also propose the use of a light-weight approach to repair entity clusters in order to correct wrong clusters. The new approaches are integrated within the FAMER framework for parallel and scalable entity clustering. A detailed evaluation of the new approaches for real-world workloads shows their high effectiveness. In particular, the repair approach outperforms other incremental approaches and achieves the same quality than with batch-like entity resolution showing that its results are independent from the order in which new entities are added.

The analysis of graph data using graph database and distributed graph processing systems has gained significant interest. However, relatively little effort has been devoted to preparing the graph data for analysis, in particular to transform and integrate data from different sources. To support such ETL processes for graph data we investigate transformation operations for property graphs managed by the distributed platform Gradoop. We also provide initial results of a runtime evaluation of the proposed graph data transformations.

Analyzing large amounts of graph data, e.g., from social networks or bioinformatics, has recently gained much attention. Unfortunately, tool support for handling and analyzing such graph data is still weak and scalability to large data volumes is often limited. We introduce the BIGGR approach providing a novel tool for the user-friendly and efficient analysis and visualization of Big Graph Data on top of the open-source software KNIME and gradoop . Users can visually program graph analytics workflows, execute them on top of the distributed processing framework Apache Flink and visualize large graphs within KNIME. For visualization, we apply visualization-driven data reduction techniques by pushing down sampling and layouting to gradoop and Apache Flink. We also discuss an initial application of the tool for the analysis of patent citation graphs.

The analysis of graph data using graph database and distributed graph processing systems has gained significant interest. However, relatively little effort has been devoted to preparing the graph data for analysis, in particular to transform and integrate data from different sources. To support such ETL processes for graph data we investigate transformation operations for property graphs managed by the distributed platform Gradoop. We also provide initial results of a runtime evaluation of the proposed graph data transformations.

Since its launch in October 2014, the Competence Center for Scalable Data Services and Solutions (ScaDS) Dresden/Leipzig carries out collaborative research on Big Data methods and their use in challenging data science applications of different domains, leading to both general, and application-specific solutions and services. In this article, we give an overview about the structure of the competence center, its primary goals and research directions. Furthermore, we outline selected research results on scalable data platforms, distributed graph analytics, data augmentation and integration and visual analytics. We also briefly report on planned activities for the second funding period (2018-2021) of the center.

Knowledge graphs holistically integrate information about entities from multiple sources. A key step in the construction and maintenance of knowledge graphs is the clustering of equivalent entities from different sources. Previous approaches for such an entity clustering suffer from several problems, e.g., the creation of overlapping clusters or the inclusion of several entities from the same source within clusters. We therefore propose a new entity clustering algorithm CLIP that can be applied both to create entity clusters and to repair entity clusters determined with another clustering scheme. In contrast to previous approaches, CLIP not only uses the similarity between entities for clustering but also further features of entity links such as the so-called link strength. To achieve a good scalability we provide a parallel implementation of CLIP based on Apache Flink. Our evaluation for different datasets shows that the new approach can achieve substantially higher cluster quality than previous approaches.

Entity Resolution (ER) identifies semantically equivalent entities, e.g. describing the same product or customer. It is a crucial and challenging step when integrating heterogeneous (big) data sources. ER approaches typically compute a similarity graph where vertices represent entities and edges (links) connect similar entities. Different clustering algorithms can be applied on such similarity graphs to finally determine groups of matching entities. In this demonstration paper, we introduce a new interactive tool to visualize and thus help to analyze large similarity graphs and large sets of ER clusters. Users can intuitively investigate the link and cluster structure to identify potential problems such as overly large clusters, cluster overlaps or singletons that might indicate the need for repair activities on the ER result. To support large graphs, computation-intensive tasks like layouting and sampling are executed on the server side as parallel or serial processes. The demo walks through different matching and clustering tasks and allows users to interactively explore the results.

Abstract The efficient and intelligent handling of large, often distributed and heterogeneous data sets increasingly determines the scientific and economic competitiveness in most application areas. Mobile applications, social networks, multimedia collections, sensor networks, data intense scientific experiments, and complex simulations nowadays generate a huge data deluge. Nonetheless, processing and analyzing these data sets with innovative methods open up new opportunities for its exploitation and new insights. Nevertheless, the resulting resource requirements exceed usually the possibilities of state-of-the-art methods for the acquisition, integration, analysis and visualization of data and are summarized under the term big data. ScaDS Dresden/Leipzig, as one Germany-wide competence center for collaborative big data research, bundles efforts to realize data-intensive applications for a wide range of applications in science and industry. In this article, we present the basic concept of the competence center and give insights in some of its research topics.

Entity resolution identifies semantically equivalent entities, e.g. describing the same product or customer. It is especially challenging for Big Data applications where large volumes of data from many sources have to be matched and integrated. We therefore introduce a scalable entity resolution framework called FAMER (FAst Multi-source Entity Resolution system) that is based on Apache Flink for distributed execution and that can holistically match entities from multiple sources. For the latter purpose, FAMER includes multiple clustering schemes that group matching entities from different sources within clusters. In addition to previously known clustering schemes FAMER includes new approaches tailored to multi-source entity resolution. We perform a detailed comparative evaluation of eight clustering schemes for different real-life and synthetically generated datasets. The evaluation considers both the match quality as well as the scalability for different numbers of machines and data sizes.

Entity resolution identifies semantically equivalent entities, e.g., describing the same product or customer. It is especially challenging for big data applications where large volumes of data from many sources have to be matched and integrated. Entity resolution for multiple data sources is best addressed by clustering schemes that group all matching entities within clusters. While there are many possible clustering schemes for entity resolution, their relative suitability and scalability is still unclear. We therefore implemented and comparatively evaluate distributed versions of six clustering schemes based on Apache Flink within a new entity resolution framework called Famer. Our evaluation for different real-life and synthetically generated datasets considers both the match quality as well as the scalability for different number of machines and data sizes.

The 17th Conference on Database Systems for Business, Technology, and Web (BTW2017) of the German Informatics Society (GI) took place in March 2017 at the University of Stuttgart in Germany. A Data Science Challenge was organized for the first time at a BTW conference by the University of Stuttgart and Sponsor IBM. We challenged the participants to solve a data analysis task within one month and present their results at the BTW. In this article, we give an overview of the organizational process surrounding the Challenge, and introduce the task that the participants had to solve. In the subsequent sections, the final four competitor groups describe their approaches and results.

Text mining services can be used to extract and categorize entities from textual information on the web. Merging results from multiple services could improve extraction quality. This requires to have an integrated extraction taxonomy and corresponding mappings between individual taxonomies that are used for categorizing extracted information. However, current ontology matching approaches cannot be applied since the available meta data within most taxonomies is weak. In this article we propose a novel taxonomy alignment process that allows us to automatically identify equal, hierarchical and associative mappings and integrate those mappings in a global taxonomy. We broadly evaluate our matching approach on real world service taxonomies and compare to state-of-the-art approaches.