http://dl.gi.de/handle/20.500.12116/10988 2026-07-24T06:25:55Z 2026-07-24T06:25:55Z Paradies, Marcus Voigt, Hannes http://dl.gi.de/handle/20.500.12116/11009 2018-03-29T12:36:14Z 2017-01-01T00:00:00Z

Big Graph Data Analytics on Single Machines – An Overview Paradies, Marcus; Voigt, Hannes Driven by a multitude of use cases, graph data analytics has become a hot topic in research and industry. Particularly on big graphs, performing complex analytical queries efficiently to derive new insights is a challenging task. Systems that aim at solving the technical part of this challenge are often referred to as graph processing systems. They allow expressing and executing analytic algorithms and queries, while hiding most of the technical details related to efficiently storing and processing graph data. Since 2010, work on graph processing systems for distributed systems as well as shared memory systems has virtually exploded. In this article, we give an overview of this work with the particular focus on graph processing systems for large multiprocessor machines. We describe the state of the art established in recent years and outline trends and challenges in research and development that point towards the future of graph processing systems.

2017-01-01T00:00:00Z Dorok, Sebastian Breß, Sebastian Teubner, Jens Läpple, Horstfried Saake, Gunter Markl, Volker http://dl.gi.de/handle/20.500.12116/11012 2018-03-29T12:36:14Z 2017-01-01T00:00:00Z

Efficiently Storing and Analyzing Genome Data in Database Systems Dorok, Sebastian; Breß, Sebastian; Teubner, Jens; Läpple, Horstfried; Saake, Gunter; Markl, Volker Genome-analysis enables researchers to detect mutations within genomes and deduce their consequences. Researchers need reliable analysis platforms to ensure reproducible and comprehensive analysis results. Database systems provide vital support to implement the required sustainable procedures. Nevertheless, they are not used throughout the complete genome-analysis process, because (1) database systems suffer from high storage overhead for genome data and (2) they introduce overhead during domain-specific analysis. To overcome these limitations, we integrate genome-specific compression into database systems using a specialized database schema. Thus, we can reduce the storage consumption of a database approach by up to 35%. Moreover, we exploit genome-data characteristics during query processing allowing us to analyze real-world data sets up to five times faster than specialized analysis tools and eight times faster than a straightforward database approach.

2017-01-01T00:00:00Z Müller-Hannemann, Matthias Rückert, Ralf http://dl.gi.de/handle/20.500.12116/11013 2018-03-29T12:36:14Z 2017-01-01T00:00:00Z

Dynamic Event-Activity Networks in Public Transportation Müller-Hannemann, Matthias; Rückert, Ralf Real-time timetable information and delay management in public transportation systems are two challenging applications which can be modeled as optimization problems on dynamically changing, large and complex graphs, so-called event-activity networks.We describe both applications in detail, review the state-of-the-art and explain the requirements for systems solving these problems in a productive environment. Focussing on recent research on decision support for train dispatchers, we sketch the system architecture for the software prototype PANDA.

2017-01-01T00:00:00Z Guhlemann, Steffen Petersohn, Uwe Meyer-Wegener, Klaus http://dl.gi.de/handle/20.500.12116/11011 2018-03-29T12:36:14Z 2017-01-01T00:00:00Z

Reducing the Distance Calculations when Searching an M‑Tree Guhlemann, Steffen; Petersohn, Uwe; Meyer-Wegener, Klaus Recent years have brought rising interest in efficiently searching for similar entities in a broad range of domains. Such search can be used to facilitate working with unstructured data such as genome sequences, text corpora, complex production information, or multimedia content, where queries always contain an amount of noise. In such domains the only common structure is a distance function obeying the axioms of a metric. As mostly no other structure information is available, a lot of distances have to be computed during the course of a search. Contrary to classical database indexes, where the optimization focus is on reducing the number of disk accesses (or in case of in-memory databases the number of tree traversal operations), a major cost driver in such multimedia domains is this number of distance calculations which can be very computation intense.There exists a range of index structures for supporting similarity search in metric spaces. A very promising one is the M‑Tree, along with a number of compatible extensions (e. g. Slim-Tree, Bulk Loaded M‑Tree, multi way insertion M‑Tree, $$M^{2}$$M2-Tree, etc.). The M‑Tree family uses common algorithms for the $$k$$k-nearest-neighbor and range search. These algorithms leave room for optimization in terms of necessary distance calculations. In this paper we present new algorithms for these tasks to considerably improve retrieval performance of all M‑Tree-compatible data structures.

2017-01-01T00:00:00Z