Visualizing Large-Scale Streaming Applications

Pauw, Wim De; Andrade, Henrique

doi:10.1057/ivs.2009.5

Cited by 20 publications

(18 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A comparatively small number of works have focused on visualization of graph streams [4,9,12,14,30]. Visualization of graph streams is harder due to the dynamics of the streams.…”

Section: Dynamic Graph Visualizationmentioning

confidence: 99%

“…However, a comparatively tiny number of works have focused on visualization of graph streams [4,9,12,14,30]. Stream graph visualization differs from traditional dynamic graph visualization in that: (a) the size of graph streams can be very huge, which typically involve a long time dimension; (b) the data comes in incrementally as a stream and possibly in considerably high speed; (c) the entire graph is not always available in streaming context; and (d) the response time may be a relevant factor to design visualization methods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

StreamEB: Stream Edge Bundling.

2013

View full text Add to dashboard Cite

Abstract. Graph streams have been studied extensively, such as for data mining, while fairly limitedly for visualizations. Recently, edge bundling promises to reduce visual clutter in large graph visualizations, though mainly focusing on static graphs.This paper presents a new framework, namely StreamEB, for edge bundling of graph streams, which integrates temporal, neighbourhood, data-driven and spatial compatibility for edges. Amongst these metrics, temporal and neighbourhood compatibility are introduced for the first time. We then present force-directed and tree-based methods for stream edge bundling.

show abstract

“…A comparatively small number of works have focused on visualization of graph streams [4,9,12,14,30]. Visualization of graph streams is harder due to the dynamics of the streams.…”

Section: Dynamic Graph Visualizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

StreamEB: Stream Edge Bundling.

2013

View full text Add to dashboard Cite

show abstract

“…Prefuse [32] is a toolkit for interactive information visualization. Streamsight [33] is a visualization tool for large-scale streaming applications.…”

Section: Related Workmentioning

confidence: 99%

A visualization and modeling tool for security metrics and measurements management

Savola

Heinonen

2011

2011 Information Security for South Africa

View full text Add to dashboard Cite

Abstract-Sufficient and credible information security measurement in software-intensive systems requires use of a variety of security metrics offering security-related evidence from different viewpoints. Visualization is needed to facilitate management of security metrics and measurements and to increase the meaningfulness of them in decision-making such as security assurance and risk management. We introduce a novel visualization and modeling tool for hierarchical specification and deployment of security metrics and measurements. The tool connects high-level risk-driven security objectives with detailed measurements and evidence gathering. The tool facilitates the management of a large number of metrics and measurements without losing appropriate granularity that is crucial for informed security decision-making.

show abstract

“…SPL [15,16] is the programming language of System S. The SPL tooling includes a rapid application development environment, as well as visualization and debugging tools [8,13]. The language can be used to compose parallel and distributed stream processing applications, in the form of operator-based dataflow graphs.…”

Section: System S and Splmentioning

confidence: 99%

SpamWatcher

Zou

Gedik

Wang

2011

Proceedings of the 5th ACM International Conference on Distributed Event-Based System

View full text Add to dashboard Cite

The proliferation of mobile devices, coupled with continuous connectivity, has resulted in a world where massive amounts of data is being produced, on a daily basis, as a result of online interactions between people. These interactions are often captured as relationships in a social network graph, by service providers such as mobile carriers or social web applications. Social network analysis is becoming a common technique for extracting business intelligence from social network graphs in order to improve customer experience and provide better service. Some applications in this domain require processing massive data flows with high-throughput and low-latency, in order to deliver timely results. SpamWatcher is a streaming social network analysis application that fits this description. It is used for real-time filtering of short messages in mobile communications, with the goal of preventing spam. The ever increasing volume of mobile users and rates of messages make realtime detection of spam a challenging problem with respect to performance and scalability. In this paper, we present a solution for the SpamWatcher application using the IBM wire-speed processor -a system-on-a-chip with specialized co-processors and integrated network I/O. This solution goes beyond the state-of-the-art by (i) using a novel implementation technique that takes advantage of the pattern matching accelerator to minimize the latency of spam detection, and (ii) employing hardware primitives to reduce the overhead caused by thread synchronization in order to achieve good scalability with respect to number of cores used. Furthermore, the solution is implemented on System S -a commercial-grade stream processing middleware. We evaluate our approach using real-world data sets and experimentally demonstrate the substantial performance improvements it achieves compared to previously published results.

show abstract

Visualizing Large-Scale Streaming Applications

Cited by 20 publications

References 25 publications

StreamEB: Stream Edge Bundling.

StreamEB: Stream Edge Bundling.

A visualization and modeling tool for security metrics and measurements management

SpamWatcher

Contact Info

Product

Resources

About