http://dl.gi.de/handle/20.500.12116/36360 2026-07-23T13:49:25Z 2026-07-23T13:49:25Z Rost, Christopher Thor, Andreas Rahm, Erhard http://dl.gi.de/handle/20.500.12116/36379 2021-05-04T09:43:00Z 2019-01-01T00:00:00Z

Analyzing Temporal Graphs with Gradoop Rost, Christopher; Thor, Andreas; Rahm, Erhard The temporal analysis of evolving graphs is an important requirement in many domains but hardly supported in current graph database and graph processing systems. We therefore have started with extending the distributed graph analysis framework Gradoop for temporal graph analysis by adding time properties to vertices, edges and graphs and using them within graph operators. We outline these extensions and illustrate their use within analysis workflows. We further describe the implementation of the snapshot and diff operators and evaluated them.

2019-01-01T00:00:00Z Pietrzyk, Johannes Habich, Dirk Damme, Patrick Focht, Erich Lehner, Wolfgang http://dl.gi.de/handle/20.500.12116/36380 2021-05-04T09:43:00Z 2019-01-01T00:00:00Z

Evaluating the Vector Supercomputer SX-Aurora TSUBASA as a Co-Processor for In-Memory Database Systems Pietrzyk, Johannes; Habich, Dirk; Damme, Patrick; Focht, Erich; Lehner, Wolfgang In-memory column-store database systems are state of the art for the efficient processing of analytical workloads. In these systems, data compression as well as vectorization play an important role. Currently, the vectorized processing is done using regular SIMD (Single Instruction Multiple Data) extensions of modern processors. For example, Intel’s latest SIMD extension supports 512-bit vector registers which allows the parallel processing of 8× 64-bit values. From a database system perspective, this vectorization technique is not only very interesting for compression and decompression to reduce the computational overhead, but also for all database operators like joins, scan, as well as groupings. In contrast to these SIMD extensions, NEC Corporation has recently introduced a novel pure vector engine (supercomputer) as a co-processor called SX-Aurora TSUBASA. This vector engine features a vector length of 16.384 bits with the world’s highest bandwidth of up to 1.2 TB/s, which perfectly fits to data-intensive applications like in-memory database systems. Therefore, we describe the unique architecture and properties of this novel vector engine in this paper. Moreover, we present selected in-memory column-store-specific evaluation results to show the benefits of this vector engine compared to regular SIMD extensions. Finally, we conclude the paper with an outlook on our ongoing research activities in this direction.

2019-01-01T00:00:00Z Baumstark, Alexander Pohl, Constantin http://dl.gi.de/handle/20.500.12116/36378 2021-05-04T09:43:00Z 2019-01-01T00:00:00Z

Lock-free Data Structures for Data Stream Processing Baumstark, Alexander; Pohl, Constantin Processing data in real-time instead of storing and reading from tables has led to a specialization of DBMS into the so-called data stream processing paradigm. While high throughput and low latency are key requirements to keep up with varying stream behavior and to allow fast reaction to incoming events, there are many possibilities how to achieve them. In combination with modern hardware, like server CPUs with tens of cores, the parallelization of stream queries for multithreading and vectorization is a common schema. High degrees of parallelism, however, need efficient synchronization mechanisms to allow good scaling with threads for shared memory access.In this work, we identify the most time-consuming operations for stream processing exemplarily for our own stream processing engine PipeFabric. In addition, we present different design principles of lock-free data structures which are suited to overcome those bottlenecks. We will finally demonstrate how lock-freedom greatly improves performance for join processing and tuple exchange between operators under different workloads. Nevertheless, the efficient usage of lock-free data structures comes with additional efforts and pitfalls, which we also discuss in this paper.

2019-01-01T00:00:00Z Held, Janis Beer, Anna Seidl, Thomas http://dl.gi.de/handle/20.500.12116/36382 2021-05-04T09:43:00Z 2019-01-01T00:00:00Z

Chain-detection Between Clusters Held, Janis; Beer, Anna; Seidl, Thomas Chains connecting two or more different clusters are a well known problem of clustering algorithms like DBSCAN or Single Linkage Clustering. Since already a small number of points resulting from, e. g., noise can form such a chain and build a bridge between different clusters, it can happen that the results of the clustering algorithm are distorted: several disparate clusters get merged into one. This single-link effect is rather known but to the best of our knowledge there are no satisfying solutions which extract those chains, yet. We present a new algorithm detecting not only straight chains between clusters, but also bent and noisy ones. Users are able to choose between eliminating one dimensional and higher dimensional chains connecting clusters to receive the underlying cluster structure. Also, the desired straightness can be set by the user. As this paper is an extension of [ 8 ], we apply our technique not only in combination with DBSCAN but also with single link hierarchical clustering. On a real world dataset containing traffic accidents in Great Britain we were able to detect chains emerging from streets between cities and villages, which led to clusters composed of diverse villages. Additionally, we analyzed the robustness regarding the variance of chains in synthetic experiments.

2019-01-01T00:00:00Z