Towards LarKC: A Platform for Web-Scale Reasoning

Fensel, Dieter; Harmelen, Frank van; Andersson, Bo; Brennan, Paul; Cunningham, Hamish; Valle, Emanuele Della; Fischer, Florian; Huang, Zhisheng; Kiryakov, Atanas; Lee, T.K.-i.; Schooler, Lael J.; Tresp, Volker; Wesner, Stefan; Witbrock, Michael; Zhong, Ning

doi:10.1109/icsc.2008.41

Cited by 100 publications

(48 citation statements)

References 7 publications

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“…As current Semantic Web reasoning systems do not scale to the requirements of emerging applications, it has been acknowledged that there is an urgent need for a distributed platform for massive reasoning that will remove the speed and scalability barriers [17]. Notably, our preliminary work in this direction has resulted in prize-winning contributions to the Billion Triples Challenge at the International Semantic Web Conference in 2008 [2], and the International Scalable Computing Challenge at CCGrid in 2010 [48].…”

Section: Semantic Webmentioning

confidence: 99%

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Jungle Computing: Distributed Supercomputing Beyond Clusters, Grids, and Clouds

Seinstra

Maassen

Nieuwpoort

et al. 2011

Computer Communications and Networks

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In recent years, the application of high-performance and distributed computing in scientific practice has become increasingly wide-spread. Among the most widely available platforms to scientists are clusters, grids, and cloud systems. Such infrastructures currently are undergoing revolutionary change due to the integration of many-core technologies, providing orders-of-magnitude speed improvements for selected compute kernels. With high-performance and distributed computing systems thus becoming more heterogeneous and hierarchical, programming complexity is vastly increased. Further complexities arise because urgent desire for scalability, and issues including data distribution, software heterogeneity, and ad-hoc hardware availability, commonly force scientists into simultaneous use of multiple platforms (e.g. clusters, grids, and clouds used concurrently). A true computing jungle. In this chapter we explore the possibilities of enabling efficient and transparent use of Jungle Computing Systems in every-day scientific practice. To this end, we discuss the fundamental methodologies required for defining programming models that are tailored to the specific needs of scientific researchers. Importantly, we claim that many of these fundamental methodologies already exist today, as integrated in our Ibis high-performance distributed programming system. We also make a case for the urgent need for easy and efficient Jungle Computing in scientific practice, by exploring a set of state-of-the-art application domains. For one of these domains we present results obtained with Ibis on a real-world Jungle Computing System. The chapter concludes by exploring fundamental research questions to be investigated in the years to come.

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Section: Semantic Webmentioning

confidence: 99%

“…Even though the Semantic Web domain is still in its infancy, it already faces problems of staggering proportions [17]. Today, the field is dealing with huge distributed Fig.…”

Section: Semantic Webmentioning

confidence: 99%

Jungle Computing: Distributed Supercomputing Beyond Clusters, Grids, and Clouds

Seinstra

Maassen

Nieuwpoort

et al. 2011

Computer Communications and Networks

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“…By relying on the pheromone trails also used by the storage operations, the reasoning operation cannot guarantee that a part of a basic graph pattern that may be present somewhere in the network is actually found. However, at web-scale, incomplete reasoning methods have been found to be advantageous due to the gained robustness and scalability, and partial reasoning results are often useful as well [18,19]. Furthermore, triples that are frequently requested have stronger pheromone paths leading to them, enabling the reasoning operation to find these triples more reliably.…”

Section: Reasoningmentioning

confidence: 99%

Large-Scale Storage and Reasoning for Semantic Data Using Swarms

Mühleisen

Dentler

2012

IEEE Comput. Intell. Mag.

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“…We are developing the Stream Reasoning vision on top of LarKC [8]. The LarKC platform is aimed to reason on massive heterogeneous information such as social media data.…”

Section: System Architecturementioning

confidence: 99%

Towards BOTTARI: Using Stream Reasoning to Make Sense of Location-Based Micro-posts

Celino

Huang

Lee³

et al. 2012

Lecture Notes in Computer Science

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Abstract. Consider an urban environment and its semi-public realms (e.g., shops, bars, visitors attractions, means of transportation). Who is the maven of a district? How fast and how broad can such maven influence the opinions of others? These are just few of the questions BOTTARI (our Location-based Social Media Analysis mobile app) is getting ready to answer. In this position paper, we recap our investigation on deductive and inductive stream reasoning for social media analysis, and we show how the results of this research form the underpinning of BOTTARI.

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Towards LarKC: A Platform for Web-Scale Reasoning

Cited by 100 publications

References 7 publications

Jungle Computing: Distributed Supercomputing Beyond Clusters, Grids, and Clouds

Jungle Computing: Distributed Supercomputing Beyond Clusters, Grids, and Clouds

Large-Scale Storage and Reasoning for Semantic Data Using Swarms

Towards BOTTARI: Using Stream Reasoning to Make Sense of Location-Based Micro-posts

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