23,182 research outputs found
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-flowās 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.
CancerLinker: Explorations of Cancer Study Network
Interactive visualization tools are highly desirable to biologist and cancer
researchers to explore the complex structures, detect patterns and find out the
relationships among bio-molecules responsible for a cancer type. A pathway
contains various bio-molecules in different layers of the cell which is
responsible for specific cancer type. Researchers are highly interested in
understanding the relationships among the proteins of different pathways and
furthermore want to know how those proteins are interacting in different
pathways for various cancer types. Biologists find it useful to merge the data
of different cancer studies in a single network and see the relationships among
the different proteins which can help them detect the common proteins in cancer
studies and hence reveal the pattern of interactions of those proteins. We
introduce the CancerLinker, a visual analytic tool that helps researchers
explore cancer study interaction network. Twenty-six cancer studies are merged
to explore pathway data and bio-molecules relationships that can provide the
answers to some significant questions which are helpful in cancer research. The
CancerLinker also helps biologists explore the critical mutated proteins in
multiple cancer studies. A bubble graph is constructed to visualize common
protein based on its frequency and biological assemblies. Parallel coordinates
highlight patterns of patient profiles (obtained from cBioportal by WebAPI
services) on different attributes for a specified cancer studyComment: 7 pages, 9 figure
Design Fiction Diegetic Prototyping: A Research Framework for Visualizing Service Innovations
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Purpose: This paper presents a design fiction diegetic prototyping methodology and research framework for investigating service innovations that reflect future uses of new and emerging technologies.
Design/methodology/approach: Drawing on speculative fiction, we propose a methodology that positions service innovations within a six-stage research development framework. We begin by reviewing and critiquing designerly approaches that have traditionally been associated with service innovations and futures literature. In presenting our framework, we provide an example of its application to the Internet of Things (IoT), illustrating the central tenets proposed and key issues identified.
Findings: The research framework advances a methodology for visualizing future experiential service innovations, considering how realism may be integrated into a designerly approach.
Research limitations/implications: Design fiction diegetic prototyping enables researchers to express a range of āwhat ifā or āwhat can it beā research questions within service innovation contexts. However, the process encompasses degrees of subjectivity and relies on knowledge, judgment and projection.
Practical implications: The paper presents an approach to devising future service scenarios incorporating new and emergent technologies in service contexts. The proposed framework may be used as part of a range of research designs, including qualitative, quantitative and mixed method investigations.
Originality: Operationalizing an approach that generates and visualizes service futures from an experiential perspective contributes to the advancement of techniques that enables the exploration of new possibilities for service innovation research
Utilizing scale-free networks to support the search for scientific publications
When searching for scientiļ¬c publications, users today often rely on search engines such as Yahoo.com. Whereas
searching for publications whose titles are known is considered to be an easy task, users who are looking for important publications in research ļ¬elds they are unfamiliar with face greater diffiulties since few or no indications of a publicationās importance to the respective fields are given. In this paper we investigate the application of the theory of scale-free networks to derive importance indicators for a collection of publications. A tool was developed to support the user in his publication search by visualizing the publicationsā importance indicators derived from the number of citations received and the publicationās age as well as visualizing part of the citation network structure. A preliminary user study indicates the utility of our approach and warrants further research in that direction
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Understanding Hidden Memories of Recurrent Neural Networks
Recurrent neural networks (RNNs) have been successfully applied to various
natural language processing (NLP) tasks and achieved better results than
conventional methods. However, the lack of understanding of the mechanisms
behind their effectiveness limits further improvements on their architectures.
In this paper, we present a visual analytics method for understanding and
comparing RNN models for NLP tasks. We propose a technique to explain the
function of individual hidden state units based on their expected response to
input texts. We then co-cluster hidden state units and words based on the
expected response and visualize co-clustering results as memory chips and word
clouds to provide more structured knowledge on RNNs' hidden states. We also
propose a glyph-based sequence visualization based on aggregate information to
analyze the behavior of an RNN's hidden state at the sentence-level. The
usability and effectiveness of our method are demonstrated through case studies
and reviews from domain experts.Comment: Published at IEEE Conference on Visual Analytics Science and
Technology (IEEE VAST 2017
Contextualization of topics - browsing through terms, authors, journals and cluster allocations
This paper builds on an innovative Information Retrieval tool, Ariadne. The
tool has been developed as an interactive network visualization and browsing
tool for large-scale bibliographic databases. It basically allows to gain
insights into a topic by contextualizing a search query (Koopman et al., 2015).
In this paper, we apply the Ariadne tool to a far smaller dataset of 111,616
documents in astronomy and astrophysics. Labeled as the Berlin dataset, this
data have been used by several research teams to apply and later compare
different clustering algorithms. The quest for this team effort is how to
delineate topics. This paper contributes to this challenge in two different
ways. First, we produce one of the different cluster solution and second, we
use Ariadne (the method behind it, and the interface - called LittleAriadne) to
display cluster solutions of the different group members. By providing a tool
that allows the visual inspection of the similarity of article clusters
produced by different algorithms, we present a complementary approach to other
possible means of comparison. More particular, we discuss how we can - with
LittleAriadne - browse through the network of topical terms, authors, journals
and cluster solutions in the Berlin dataset and compare cluster solutions as
well as see their context.Comment: proceedings of the ISSI 2015 conference (accepted
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