Computational Methods in Science and Engineering : Proceedings of the Workshop SimLabs@KIT, November 29 - 30, 2010, Karlsruhe, Germany

In this proceedings volume we provide a compilation of article contributions equally covering applications from different research fields and ranging from capacity up to capability computing. Besides classical computing aspects such as parallelization, the focus of these proceedings is on multi-scale approaches and methods for tackling algorithm and data complexity. Also practical aspects regarding the usage of the HPC infrastructure and available tools and software at the SCC are presented

Parallel Algorithm for Solving TOV Equations for Sequence of Cold and Dense Nuclear Matter Models

We have introduced parallel algorithm simulation of neutron star configurations for set of equation of state models.The performance of the parallel algorithm has been investigated for testing set of EoS models on two computational systems. It scales when using with MPI on modern CPUs and this investigation allowed us also to compare two different types of computationa lnodes

Mining usage patterns in residential intranet of things

International audienceUbiquitous smart technologies gradually transform modern homes into Intranet of Things, where a multitude of connected devices allow for novel home automation services (e.g., energy or bandwidth savings, comfort enhancement, etc.). Optimizing and enriching the Quality of Experience (QoE) of residential users emerges as a critical differentiator for Internet and Communication Service providers (ISPs and CSPs, respectively) and heavily relies on the analysis of various kinds of data (connectivity, performance , usage) gathered from home networks. In this paper, we are interested in new Machine-to-Machine data analysis techniques that go beyond binary association rule mining for traditional market basket analysis considered by previous works, to analyze individual device logs of home gateways. Based on multidimensional patterns mining framework, we extract complex device co-usage patterns of 201 residential broadband users of an ISP, subscribed to a triple-play service. Such fine-grained device usage patterns provide valuable insights for emerging use cases such as an adaptive usage of home devices, and also " things " recommendation

Extracting Usage Patterns of Home IoT Devices

International audienceUbiquitous connectivity and smart technologies gradually transform homes into Intranet of Things, where a multitude of connected, intelligent devices allow for novel home automation services. Providing new services for home users (e.g., energy saving automations) and Internet Service Providers (e.g., network management and troubleshooting) requires an in-depth analysis of various kinds of data (connectivity, performance, usage) collected from home networks. In this paper, we explore new Machine-to-Machine data analysis techniques that go beyond binary association rule mining for traditional market basket analysis considered by previous studies, to analyze individual device logs of home gateways. We introduce a multidimensional patterns mining framework, to extract complex device co-usage patterns of 201 residential broadband users of an ISP, subscribed to a triple-play service. Our results show that our analytics engine provides valuable insights for emerging use cases such as monitoring for energy efficiency, and “things” recommendation

Real time News Story Detection and Tracking with Hashtags

Computing News Storylines Workshop at EMNLP 2016, Austin, Texas, United States of America, 2-6 November 2016Topic Detection and Tracking (TDT) is an important research topic in data mining and information retrieval and has been explored for many years. Most of the studies have approached the problem from the event tracking point of view. We argue that the definition of stories as events is not reflecting the full picture. In this work we propose a story tracking method built on crowd-tagging in social media, where news articles are labeled with hashtags in real-time. The social tags act as rich metadata for news articles, with the advantage that, if carefully employed, they can capture emerging concepts and address concept drift in a story. We present an approach for employing social tags for the purpose of story detection and tracking and show initial empirical results. We compare our method to classic keyword query retrieval and discuss an example of story tracking over time.Science Foundation Irelan

SocialTree: Socially Augmented Structured Summaries of News Stories

HT ’19: Hypertext and Social Media 2019, Hof University, Germany, 17–20 September 2019News story understanding entails having an effective summary of a related group of articles that may span different time ranges, involve different topics and entities, and have connections to other stories. In this work, we present an approach to efficiently extract structured summaries of news stories by augmenting news media with the structure of social discourse as reflected in social media in the form of social tags. Existing event detection, topic-modeling, clustering and summarization methods yield news story summaries based only on noun phrases and named entities. These representations are sensitive to the article wording and the keyword extraction algorithm. Moreover, keyword-based representations are rarely helpful for highlighting the inter-story connections or for reflecting the inner structure of the news story because of high word ambiguity and clutter from the large variety of keywords describing news stories. Our method combines the news and social media domains to create structured summaries of news stories in the form of hierarchies of keywords and social tags, named SocialTree. We show that the properties of social tags can be exploited to augment the construction of hierarchical summaries of news stories and to alleviate the weaknesses of existing keyword-based representations. In our quantitative and qualitative evaluation the proposed method strongly outperforms the state-of-the-art with regard to both coverage and informativeness of the summaries.Science Foundation IrelandInsight Research CentreUpdate citation details during checkdate report - A

Topy: Real-time Story Tracking via Social Tags

The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD), Riva del Garda, Italy, 19-23 September 2016The Topy system automates real-time story tracking by utilizing crowd- sourced tagging on social media platforms. Topy employs a state-of-the-art Twitter hashtag recommender to continuously annotate news articles with hashtags, a rich meta-data source that allows connecting articles under drastically different timelines than typical keyword based story tracking systems. Employing social tags for story tracking has the following advantages: (1) social annotation of news enables the detection of emerging concepts and topic drift in a story; (2) hashtags go beyond topics by grouping articles based on connected themes (e.g., #rip, #blacklivesmatter, #icantbreath); (3) hashtags link articles that focus on subplots of the same story (e.g., #palmyra, #isis, #refugeecrisis).Science Foundation Irelan