http://dl.gi.de/handle/20.500.12116/21090 2026-07-23T12:19:24Z 2026-07-23T12:19:24Z Junghanns, Martin Petermann, André Rahm, Erhard http://dl.gi.de/handle/20.500.12116/678 2019-03-28T10:56:33Z 2017-01-01T00:00:00Z

Distributed Grouping of Property Graphs with GRADOOP Junghanns, Martin; Petermann, André; Rahm, Erhard Mitschang, Bernhard; Nicklas, Daniela; Leymann, Frank; Schöning, Harald; Herschel, Melanie; Teubner, Jens; Härder, Theo; Kopp, Oliver; Wieland, Matthias Property graphs are an intuitive way to model, analyze and visualize complex relationships among heterogeneous data objects, for example, as they occur in social, biological and information networks. These graphs typically contain thousands or millions of vertices and edges and their entire representation can easily overwhelm an analyst. One way to reduce complexity is the grouping of vertices and edges to summary graphs. In this paper, we present an algorithm for graph grouping with support for attribute aggregation and structural summarization by user-defined vertex and edge properties. The algorithm is part of G , an open-source system for graph analytics. G is implemented on top of Apache Flink, a state-of-the-art distributed dataflow framework, and thus allows us to scale graph analytical programs across multiple machines. Our evaluation demonstrates the scalability of the algorithm on real-world and synthetic social network data.

2017-01-01T00:00:00Z Hagedorn, Stefan Götze, Philipp Sattler, Kai-Uwe http://dl.gi.de/handle/20.500.12116/679 2019-03-28T10:56:33Z 2017-01-01T00:00:00Z

The STARK Framework for Spatio-Temporal Data Analytics on Spark Hagedorn, Stefan; Götze, Philipp; Sattler, Kai-Uwe Mitschang, Bernhard; Nicklas, Daniela; Leymann, Frank; Schöning, Harald; Herschel, Melanie; Teubner, Jens; Härder, Theo; Kopp, Oliver; Wieland, Matthias Big Data sets can contain all types of information: from server log files to tracking information of mobile users with their location at a point in time. Apache Spark has been widely accepted for Big Data analytics because of its very fast processing model. However, Spark has no native support for spatial or spatio-temporal data. Spatial filters or joins using, e.g., a contains predicate are not supported and would have to be implemented ine ciently by the users. Also, Spark cannot make use of, e.g., spatial distribution for optimal partitioning. Here we present our STARK framework that adds spatio-temporal support to Spark. It includes spatial partitioners, different modes for indexing, as well as filter, join, and clustering operators. In contrast to existing solutions, STARK integrates seamlessly into any (Scala) Spark program and provides more flexible and comprehensive operators. Furthermore, our experimental evaluation shows that our implementation outperforms existing solutions.

2017-01-01T00:00:00Z Schildgen, Johannes Krück, Yannick Deßloch, Stefan http://dl.gi.de/handle/20.500.12116/677 2019-03-28T10:56:32Z 2017-01-01T00:00:00Z

Transformations on Graph Databases for Polyglot Persistence with NotaQL Schildgen, Johannes; Krück, Yannick; Deßloch, Stefan Mitschang, Bernhard; Nicklas, Daniela; Leymann, Frank; Schöning, Harald; Herschel, Melanie; Teubner, Jens; Härder, Theo; Kopp, Oliver; Wieland, Matthias Polyglot-persistence applications use a combination of many di erent data stores. Often, one of them is a graph database to model relationships between data items. The data-transformation language NotaQL can be used to define transformations from one NoSQL database to a di erent one. In this paper, we present a language extension for NotaQL to allow graph transformations, graph analysis, and data migrations on graph databases. NotaQL is schema-flexible, it o ers filters and aggregation functions, and it allows for graph traversal and edge creation. Our graph-transformation platform can be used for iterative graph algorithms and bulk processing.

2017-01-01T00:00:00Z Panev, Kiril Weisenauer, Nico Michel, Sebastian http://dl.gi.de/handle/20.500.12116/676 2019-03-28T10:56:32Z 2017-01-01T00:00:00Z

Reverse Engineering Top-k Join Queries Panev, Kiril; Weisenauer, Nico; Michel, Sebastian Mitschang, Bernhard; Nicklas, Daniela; Leymann, Frank; Schöning, Harald; Herschel, Melanie; Teubner, Jens; Härder, Theo; Kopp, Oliver; Wieland, Matthias Ranked lists have become a fundamental tool to represent the most important items taken from a large collection of data. Search engines, sports leagues and e-commerce platforms present their results, most successful teams and most popular items in a concise and structured way by making use of ranked lists. This paper introduces the PALEO-J framework which is able to reconstruct top-k database queries, given only the original query output in the form of a ranked list and the database itself. The query to be reverse engineered may contain a wide range of aggregation functions and an arbitrary amount of equality joins, joining several database relations. The challenge of this work is to reconstruct complex queries as fast as possible while operating on large databases and given only the little amount of information provided by the top-k list of entities serving as input. The core contribution is identifying the join predicates in reverse engineering top-k OLAP queries. Furthermore we introduce several optimizations and an advanced classification system to reduce the execution time of the algorithm. Experiments conducted on a large database show the performance of the presented approach and confirm the benefits of our optimizations.

2017-01-01T00:00:00Z