2,952 research outputs found
Visual exploration of semantic-web-based knowledge structures
Humans have a curious nature and seek a better understanding of the world. Data, in-
formation, and knowledge became assets of our modern society through the information
technology revolution in the form of the internet. However, with the growing size of
accumulated data, new challenges emerge, such as searching and navigating in these large
collections of data, information, and knowledge. The current developments in academic
and industrial contexts target the corresponding challenges using Semantic Web techno-
logies. The Semantic Web is an extension of the Web and provides machine-readable
representations of knowledge for various domains. These machine-readable representations
allow intelligent machine agents to understand the meaning of the data and information;
and enable additional inference of new knowledge.
Generally, the Semantic Web is designed for information exchange and its processing
and does not focus on presenting such semantically enriched data to humans. Visualizations
support exploration, navigation, and understanding of data by exploiting humans’ ability
to comprehend complex data through visual representations. In the context of Semantic-
Web-Based knowledge structures, various visualization methods and tools are available,
and new ones are being developed every year. However, suitable visualizations are highly
dependent on individual use cases and targeted user groups.
In this thesis, we investigate visual exploration techniques for Semantic-Web-Based
knowledge structures by addressing the following challenges: i) how to engage various user
groups in modeling such semantic representations; ii) how to facilitate understanding using
customizable visual representations; and iii) how to ease the creation of visualizations
for various data sources and different use cases. The achieved results indicate that visual
modeling techniques facilitate the engagement of various user groups in ontology modeling.
Customizable visualizations enable users to adjust visualizations to the current needs and
provide different views on the data. Additionally, customizable visualization pipelines
enable rapid visualization generation for various use cases, data sources, and user group
Continuous Performance Benchmarking Framework for ROOT
Foundational software libraries such as ROOT are under intense pressure to
avoid software regression, including performance regressions. Continuous
performance benchmarking, as a part of continuous integration and other code
quality testing, is an industry best-practice to understand how the performance
of a software product evolves over time. We present a framework, built from
industry best practices and tools, to help to understand ROOT code performance
and monitor the efficiency of the code for a several processor architectures.
It additionally allows historical performance measurements for ROOT I/O,
vectorization and parallelization sub-systems.Comment: 8 pages, 5 figures, CHEP 2018 - 23rd International Conference on
Computing in High Energy and Nuclear Physic
Visualizing Large Business Process Models: Challenges, Techniques, Applications
Large process models may comprise hundreds or thousands of process elements, like activities, gateways, and data objects. Presenting such process models to users and enabling them to interact with these models constitute crucial tasks of any process-aware information systems (PAISs). Existing PAISs, however, neither provide adequate techniques for visualizing and abstracting process models nor for interacting with them. In particular, PAISs do not provide tailored process visualizations as needed in complex application environments. This paper presents examples of large process models and discusses some of the challenges to be tackled when visualizing and abstracting respective models. Further, it presents a comprehensive framework that allows for personalized process model visualizations, which can be tailored to the specific needs of the different user groups. First, process model complexity can be reduced by abstracting the models, i.e., by eliminating or aggregating process elements not relevant in the given visualization context. Second, the appearance of process elements can be customized independent of the process modeling language used. Third, different visualization formats (e.g., process diagrams, process forms, and process trees) are supported. Finally, it will be discussed how tailored visualizations of process models may serve as basis for changing and evolving process models at a high level of abstraction
A visual exploration workflow as enabler for the exploitation of Linked Open Data
Abstract. Semantically annotating and interlinking Open Data results in Linked Open Data which concisely and unambiguously describes a knowledge domain. However, the uptake of the Linked Data depends on its usefulness to non-Semantic Web experts. Failing to support data consumers to understand the added-value of Linked Data and possible exploitation opportunities could inhibit its diffusion. In this paper, we propose an interactive visual workflow for discovering and ex-ploring Linked Open Data. We implemented the workflow considering academic library metadata and carried out a qualitative evaluation. We assessed the work-flow’s potential impact on data consumers which bridges the offer: published Linked Open Data; and the demand as requests for: (i) higher quality data; and (ii) more applications that re-use data. More than 70 % of the 34 test users agreed that the workflow fulfills its goal: it facilitates non-Semantic Web experts to un-derstand the potential of Linked Open Data.
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