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Martins, Rafael Messias, Dr.ORCID iD iconorcid.org/0000-0002-2901-935X
Publikasjoner (10 av 21) Visa alla publikasjoner
Martins, R. M. & Kerren, A. (2019). Efficient Dynamic Time Warping for Big Data Streams. In: Abe, N; Liu, H; Pu, C; Hu, X; Ahmed, N; Qiao, M; Song, Y; Kossmann, D; Liu, B; Lee, K; Tang, J; He, J; Saltz, J (Ed.), Proceedings of the IEEE International Conference on Big Data (Big Data '18): Workshop on Real-time & Stream Analytics in Big Data & Stream Data Management. Paper presented at 3rd Workshop on Real-time & Stream Analytics in Big Data & Stream Data Management at IEEE Big Data '18, 10-13 December, 2018, Seattle, USA (pp. 2924-2929). IEEE
Åpne denne publikasjonen i ny fane eller vindu >>Efficient Dynamic Time Warping for Big Data Streams
2019 (engelsk)Inngår i: Proceedings of the IEEE International Conference on Big Data (Big Data '18): Workshop on Real-time & Stream Analytics in Big Data & Stream Data Management / [ed] Abe, N; Liu, H; Pu, C; Hu, X; Ahmed, N; Qiao, M; Song, Y; Kossmann, D; Liu, B; Lee, K; Tang, J; He, J; Saltz, J, IEEE, 2019, s. 2924-2929Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Many common data analysis and machine learning algorithms for time series, such as classification, clustering, or dimensionality reduction, require a distance measurement between pairs of time series in order to determine their similarity. A variety of measures can be found in the literature, each with their own strengths and weaknesses, but the Dynamic Time Warping (DTW) distance measure has occupied an important place since its early applications for the analysis and recognition of spoken word. The main disadvantage of the DTW algorithm is, however, its quadratic time and space complexity, which limits its practical use to relatively small time series. This issue is even more problematic when dealing with streaming time series that are continuously updated, since the analysis must be re-executed regularly and with strict running time constraints. In this paper, we describe enhancements to the DTW algorithm that allow it to be used efficiently in a streaming scenario by supporting an append operation for new time steps with a linear complexity when an exact, error-free DTW is needed, and even better performance when either a Sakoe-Chiba band is used, or when a sliding window is the desired range for the data. Our experiments with one synthetic and four natural data sets have shown that it outperforms other DTW implementations and the potential errors are, in general, much lower than another state-of-the-art approximated DTW technique.

sted, utgiver, år, opplag, sider
IEEE, 2019
Serie
IEEE International Conference on Big Data, ISSN 2639-1589
Emneord
time series, dynamic time warping, streaming
HSV kategori
Forskningsprogram
Datavetenskap, Informations- och programvisualisering
Identifikatorer
urn:nbn:se:lnu:diva-78735 (URN)10.1109/BigData.2018.8621878 (DOI)000468499303001 ()2-s2.0-85062589330 (Scopus ID)978-1-5386-5035-6 (ISBN)978-1-5386-5036-3 (ISBN)
Konferanse
3rd Workshop on Real-time & Stream Analytics in Big Data & Stream Data Management at IEEE Big Data '18, 10-13 December, 2018, Seattle, USA
Tilgjengelig fra: 2018-11-08 Laget: 2018-11-08 Sist oppdatert: 2019-08-29bibliografisk kontrollert
Espadoto, M., Martins, R. M., Kerren, A., Hirata, N. S. T. & Telea, A. C. (2019). Towards a Quantitative Survey of Dimension Reduction Techniques. IEEE Transactions on Visualization and Computer Graphics
Åpne denne publikasjonen i ny fane eller vindu >>Towards a Quantitative Survey of Dimension Reduction Techniques
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2019 (engelsk)Inngår i: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506Artikkel i tidsskrift (Fagfellevurdert) Epub ahead of print
Abstract [en]

Dimensionality reduction methods, also known as projections, are frequently used in multidimensional data exploration in machine learning, data science, and information visualization. Tens of such techniques have been proposed, aiming to address a wide set of requirements, such as ability to show the high-dimensional data structure, distance or neighborhood preservation, computational scalability, stability to data noise and/or outliers, and practical ease of use. However, it is far from clear for practitioners how to choose the best technique for a given use context. We present a survey of a wide body of projection techniques that helps answering this question. For this, we characterize the input data space, projection techniques, and the quality of projections, by several quantitative metrics. We sample these three spaces according to these metrics, aiming at good coverage with bounded effort. We describe our measurements and outline observed dependencies of the measured variables. Based on these results, we draw several conclusions that help comparing projection techniques, explain their results for different types of data, and ultimately help practitioners when choosing a projection for a given context. Our methodology, datasets, projection implementations, metrics, visualizations, and results are publicly open, so interested stakeholders can examine and/or extend this benchmark.

sted, utgiver, år, opplag, sider
IEEE, 2019
Emneord
Dimensionality reduction, quality metrics, benchmarking, quantitative analysis, design space
HSV kategori
Forskningsprogram
Datavetenskap, Informations- och programvisualisering
Identifikatorer
urn:nbn:se:lnu:diva-89220 (URN)10.1109/TVCG.2019.2944182 (DOI)
Merknad

TO BE PUBLISHED!!!

Tilgjengelig fra: 2019-09-22 Laget: 2019-09-22 Sist oppdatert: 2019-09-29
Kucher, K., Martins, R. M. & Kerren, A. (2018). Analysis of VINCI 2009–2017 Proceedings. In: Karsten Klein, Yi-Na Li, and Andreas Kerren (Ed.), Proceedings of the 11th International Symposium on Visual Information Communication and Interaction (VINCI '18), 13-15 August 2018, Växjö, Sweden: . Paper presented at 11th International Symposium on Visual Information Communication and Interaction (VINCI '18), 13-15 August 2018, Växjö, Sweden (pp. 97-101). Association for Computing Machinery (ACM)
Åpne denne publikasjonen i ny fane eller vindu >>Analysis of VINCI 2009–2017 Proceedings
2018 (engelsk)Inngår i: Proceedings of the 11th International Symposium on Visual Information Communication and Interaction (VINCI '18), 13-15 August 2018, Växjö, Sweden / [ed] Karsten Klein, Yi-Na Li, and Andreas Kerren, Association for Computing Machinery (ACM), 2018, s. 97-101Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Both the metadata and the textual contents of scientific publications can provide us with insights about the development and the current state of the corresponding scientific community. In this short paper, we take a look at the proceedings of VINCI from the previous years and conduct several types of analyses. We summarize the yearly statistics about different types of publications, identify the overall authorship statistics and the most prominent contributors, and analyze the current community structure with a co-authorship network. We also apply topic modeling to identify the most prominent topics discussed in the publications. We hope that the results of our work will provide insights for the visualization community and will also be used as an overview for researchers previously unfamiliar with VINCI.

sted, utgiver, år, opplag, sider
Association for Computing Machinery (ACM), 2018
Emneord
meta-analysis, survey, overview, visualization, scientific literature, topic modeling
HSV kategori
Forskningsprogram
Datavetenskap, Informations- och programvisualisering
Identifikatorer
urn:nbn:se:lnu:diva-75857 (URN)10.1145/3231622.3231641 (DOI)2-s2.0-85055468154 (Scopus ID)978-1-4503-6501-7 (ISBN)
Konferanse
11th International Symposium on Visual Information Communication and Interaction (VINCI '18), 13-15 August 2018, Växjö, Sweden
Tilgjengelig fra: 2018-06-13 Laget: 2018-06-13 Sist oppdatert: 2019-08-29bibliografisk kontrollert
Ulan, M., Hönel, S., Martins, R. M., Ericsson, M., Löwe, W., Wingkvist, A. & Kerren, A. (2018). Quality Models Inside Out: Interactive Visualization of Software Metrics by Means of Joint Probabilities. In: J. Ángel Velázquez Iturbide, Jaime Urquiza Fuentes, Andreas Kerren, and Mircea F. Lungu (Ed.), Proceedings of the 2018 Sixth IEEE Working Conference on Software Visualization, (VISSOFT), Madrid, Spain, 2018: . Paper presented at IEEE Working Conference on Software Visualization (VISSOFT), Madrid, Spain, 24-25 September, 2018 (pp. 65-75). IEEE
Åpne denne publikasjonen i ny fane eller vindu >>Quality Models Inside Out: Interactive Visualization of Software Metrics by Means of Joint Probabilities
Vise andre…
2018 (engelsk)Inngår i: Proceedings of the 2018 Sixth IEEE Working Conference on Software Visualization, (VISSOFT), Madrid, Spain, 2018 / [ed] J. Ángel Velázquez Iturbide, Jaime Urquiza Fuentes, Andreas Kerren, and Mircea F. Lungu, IEEE, 2018, s. 65-75Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Assessing software quality, in general, is hard; each metric has a different interpretation, scale, range of values, or measurement method. Combining these metrics automatically is especially difficult, because they measure different aspects of software quality, and creating a single global final quality score limits the evaluation of the specific quality aspects and trade-offs that exist when looking at different metrics. We present a way to visualize multiple aspects of software quality. In general, software quality can be decomposed hierarchically into characteristics, which can be assessed by various direct and indirect metrics. These characteristics are then combined and aggregated to assess the quality of the software system as a whole. We introduce an approach for quality assessment based on joint distributions of metrics values. Visualizations of these distributions allow users to explore and compare the quality metrics of software systems and their artifacts, and to detect patterns, correlations, and anomalies. Furthermore, it is possible to identify common properties and flaws, as our visualization approach provides rich interactions for visual queries to the quality models’ multivariate data. We evaluate our approach in two use cases based on: 30 real-world technical documentation projects with 20,000 XML documents, and an open source project written in Java with 1000 classes. Our results show that the proposed approach allows an analyst to detect possible causes of bad or good quality.

sted, utgiver, år, opplag, sider
IEEE, 2018
Emneord
hierarchical data exploration, multivariate data visualization, joint probabilities, t-SNE, data abstraction
HSV kategori
Forskningsprogram
Datavetenskap, Informations- och programvisualisering; Datavetenskap, Programvaruteknik
Identifikatorer
urn:nbn:se:lnu:diva-78093 (URN)10.1109/VISSOFT.2018.00015 (DOI)2-s2.0-85058463111 (Scopus ID)978-1-5386-8292-0 (ISBN)978-1-5386-8293-7 (ISBN)
Konferanse
IEEE Working Conference on Software Visualization (VISSOFT), Madrid, Spain, 24-25 September, 2018
Prosjekter
Software technology for self-adaptive systems
Forskningsfinansiär
Knowledge Foundation, 20150088
Tilgjengelig fra: 2018-10-01 Laget: 2018-10-01 Sist oppdatert: 2019-08-29bibliografisk kontrollert
Laitinen, M., Lundberg, J., Levin, M. & Martins, R. M. (2018). The Nordic Tweet Stream: A Dynamic Real-Time Monitor Corpus of Big and Rich Language Data. In: Eetu Mäkelä, Mikko Tolonen, Jouni Tuominen (Ed.), DHN 2018 Digital Humanities in the Nordic Countries 3rd Conference: Proceedings of the Digital Humanities in the Nordic Countries 3rd Conference Helsinki, Finland, March 7-9, 2018. Paper presented at Digital Humanities in the Nordic Countries 3rd Conference, Helsinki, Finland, March 7-9, 2018 (pp. 349-362). CEUR-WS.org
Åpne denne publikasjonen i ny fane eller vindu >>The Nordic Tweet Stream: A Dynamic Real-Time Monitor Corpus of Big and Rich Language Data
2018 (engelsk)Inngår i: DHN 2018 Digital Humanities in the Nordic Countries 3rd Conference: Proceedings of the Digital Humanities in the Nordic Countries 3rd Conference Helsinki, Finland, March 7-9, 2018 / [ed] Eetu Mäkelä, Mikko Tolonen, Jouni Tuominen, CEUR-WS.org , 2018, s. 349-362Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This article presents the Nordic Tweet Stream (NTS), a cross-disciplinarycorpus project of computer scientists and a group of sociolinguists interestedin language variability and in the global spread of English. Our research integratestwo types of empirical data: We not only rely on traditional structured corpusdata but also use unstructured data sources that are often big and rich inmetadata, such as Twitter streams. The NTS downloads tweets and associatedmetadata from Denmark, Finland, Iceland, Norway and Sweden. We first introducesome technical aspects in creating a dynamic real-time monitor corpus, andthe following case study illustrates how the corpus could be used as empiricalevidence in sociolinguistic studies focusing on the global spread of English tomultilingual settings. The results show that English is the most frequently usedlanguage, accounting for almost a third. These results can be used to assess howwidespread English use is in the Nordic region and offer a big data perspectivethat complement previous small-scale studies. The future objectives include annotatingthe material, making it available for the scholarly community, and expandingthe geographic scope of the data stream outside Nordic region.

sted, utgiver, år, opplag, sider
CEUR-WS.org, 2018
Serie
CEUR Workshop Proceedings, ISSN 1613-0073 ; 2084
Emneord
Real-time language data, Nordic Tweet Stream, Twitter
HSV kategori
Forskningsprogram
Humaniora, Engelska med språkvetenskaplig inriktning
Identifikatorer
urn:nbn:se:lnu:diva-78277 (URN)2-s2.0-85045342911 (Scopus ID)
Konferanse
Digital Humanities in the Nordic Countries 3rd Conference, Helsinki, Finland, March 7-9, 2018
Prosjekter
DISA
Tilgjengelig fra: 2018-10-11 Laget: 2018-10-11 Sist oppdatert: 2019-05-24bibliografisk kontrollert
Chatzimparmpas, A., Martins, R. M. & Kerren, A. (2018). t-viSNE: A Visual Inspector for the Exploration of t-SNE. In: Presented at IEEE Information Visualization  (VIS '18), Berlin, Germany, 21-26 October, 2018: . Paper presented at IEEE Information Visualization (VIS '18), Berlin, Germany, 21-26 October, 2018.
Åpne denne publikasjonen i ny fane eller vindu >>t-viSNE: A Visual Inspector for the Exploration of t-SNE
2018 (engelsk)Inngår i: Presented at IEEE Information Visualization  (VIS '18), Berlin, Germany, 21-26 October, 2018, 2018Konferansepaper, Poster (with or without abstract) (Fagfellevurdert)
Abstract [en]

The use of t-Distributed Stochastic Neighborhood Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with applications published in a wide range of domains. Despite their usefulness, t-SNE plots can sometimes be hard to interpret or even misleading, which hurts the trustworthiness of the results. By opening the black box of the algorithm and showing insights into its behavior through visualization, we may learn how to use it in a more effective way. In this work, we present t-viSNE, a visual inspection tool that enables users to explore anomalies and assess the quality of t-SNE results by bringing forward aspects of the algorithm that would normally be lost after the dimensionality reduction process is finished.

Emneord
Visualization, machine learning, visual analytics, information visualization, interaction, dimensionality reduction
HSV kategori
Forskningsprogram
Datavetenskap, Informations- och programvisualisering
Identifikatorer
urn:nbn:se:lnu:diva-76980 (URN)
Konferanse
IEEE Information Visualization (VIS '18), Berlin, Germany, 21-26 October, 2018
Tilgjengelig fra: 2018-07-23 Laget: 2018-07-23 Sist oppdatert: 2019-01-17bibliografisk kontrollert
Kruiger, J. F., Rauber, P. E., Martins, R. M., Kerren, A., Kobourov, S. & Telea, A. C. (2017). Graph Layouts by t-SNE. Paper presented at 19th EG/VGTC Conference on Visualization (EuroVis '17), 12-16 June 2017, Barcelona, Spain. Computer graphics forum (Print), 36(3), 283-294
Åpne denne publikasjonen i ny fane eller vindu >>Graph Layouts by t-SNE
Vise andre…
2017 (engelsk)Inngår i: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 36, nr 3, s. 283-294Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

We propose a new graph layout method based on a modification of the t-distributed Stochastic Neighbor Embedding (t-SNE) dimensionality reduction technique. Although t-SNE is one of the best techniques for visualizing high-dimensional data as 2D scatterplots, t-SNE has not been used in the context of classical graph layout. We propose a new graph layout method, tsNET, based on representing a graph with a distance matrix, which together with a modified t-SNE cost function results in desirable layouts. We evaluate our method by a formal comparison with state-of-the-art methods, both visually and via established quality metrics on a comprehensive benchmark, containing real-world and synthetic graphs. As evidenced by the quality metrics and visual inspection, tsNET produces excellent layouts.

sted, utgiver, år, opplag, sider
John Wiley & Sons, 2017
Emneord
Visualization, Graph Drawing, Information Visualization, t-SNE, Dimensionality Reduction
HSV kategori
Forskningsprogram
Datavetenskap, Informations- och programvisualisering
Identifikatorer
urn:nbn:se:lnu:diva-62137 (URN)10.1111/cgf.13187 (DOI)000404881200027 ()2-s2.0-85022223094 (Scopus ID)
Konferanse
19th EG/VGTC Conference on Visualization (EuroVis '17), 12-16 June 2017, Barcelona, Spain
Tilgjengelig fra: 2017-04-07 Laget: 2017-04-07 Sist oppdatert: 2019-08-29bibliografisk kontrollert
de Oliveira, R. R., Martins, R. M. & Simao, A. d. (2017). Impact of the Vendor Lock-in Problem on Testing as a Service (TaaS). In: 2017 IEEE INTERNATIONAL CONFERENCE ON CLOUD ENGINEERING (IC2E 2017): . Paper presented at 5th IEEE International Conference on Cloud Engineering (IC2E), APR 04-08, 2017, Simon Fraser Univ, Vancouver, CANADA (pp. 190-196). IEEE
Åpne denne publikasjonen i ny fane eller vindu >>Impact of the Vendor Lock-in Problem on Testing as a Service (TaaS)
2017 (engelsk)Inngår i: 2017 IEEE INTERNATIONAL CONFERENCE ON CLOUD ENGINEERING (IC2E 2017), IEEE, 2017, s. 190-196Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Testing as a Service (TaaS) is a new business and service model that provides efficient and effective software quality assurance and enables the use of a cloud for the meeting of quality standards, requirements and consumer's needs. However, problems that limit the effective use of TaaS involve lack of standardization in writing, execution, configuration and management of tests and lack of portability and interoperability among TaaS platforms - the so-called lock-in problem. The lock-in problem is a serious threat to software testing in the cloud and may become critical when a provider decides to suddenly increase prices, or shows serious technical availability problems. This paper proposes a novel approach for solving the lock-in problem in TaaS with the use of design patterns. The aim to assist software engineers and quality control managers in building testing solutions that are both portable and interoperable and promote a more widespread adoption of the TaaS model in cloud computing.

sted, utgiver, år, opplag, sider
IEEE, 2017
Serie
International Conference on Cloud Engineering, ISSN 2373-3845
Emneord
Cloud Computing, Testing as a Service (TaaS), Design Patterns, Vendor Lock-in, Testing Service
HSV kategori
Forskningsprogram
Data- och informationsvetenskap, Datavetenskap
Identifikatorer
urn:nbn:se:lnu:diva-67052 (URN)10.1109/IC2E.2017.30 (DOI)000404168700027 ()2-s2.0-85020190361 (Scopus ID)978-1-5090-5817-4 (ISBN)
Konferanse
5th IEEE International Conference on Cloud Engineering (IC2E), APR 04-08, 2017, Simon Fraser Univ, Vancouver, CANADA
Tilgjengelig fra: 2017-07-20 Laget: 2017-07-20 Sist oppdatert: 2019-08-29bibliografisk kontrollert
Martins, R. M., Kruiger, J. F., Minghim, R., Telea, A. C. & Kerren, A. (2017). MVN-Reduce: Dimensionality Reduction for the Visual Analysis of Multivariate Networks. In: Barbora Kozlikova and Tobias Schreck and Thomas Wischgoll (Ed.), EuroVis 2017 - Short Papers: . Paper presented at 19th EG/VGTC Conference on Visualization (EuroVis '17), 12-16 June 2017, Barcelona, Spain (pp. 13-17). Eurographics - European Association for Computer Graphics
Åpne denne publikasjonen i ny fane eller vindu >>MVN-Reduce: Dimensionality Reduction for the Visual Analysis of Multivariate Networks
Vise andre…
2017 (engelsk)Inngår i: EuroVis 2017 - Short Papers / [ed] Barbora Kozlikova and Tobias Schreck and Thomas Wischgoll, Eurographics - European Association for Computer Graphics, 2017, s. 13-17Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The analysis of Multivariate Networks (MVNs) can be approached from two different perspectives: a multidimensional one, consisting of the nodes and their multiple attributes, or a relational one, consisting of the network’s topology of edges. In order to be comprehensive, a visual representation of an MVN must be able to accomodate both. In this paper, we propose a novel approach for the visualization of MVNs that works by combining these two perspectives into a single unified model, which is used as input to a dimensionality reduction method. The resulting 2D embedding takes into consideration both attribute- and edge-based similarities, with a user-controlled trade-off. We demonstrate our approach by exploring two real-world data sets: a co-authorship network and an open-source software development project. The results point out that our method is able to bring forward features of MVNs that could not be easily perceived from the investigation of the individual perspectives only. 

sted, utgiver, år, opplag, sider
Eurographics - European Association for Computer Graphics, 2017
Emneord
Multivariate Networks, Dimensionality Reduction, Visualization, Information Visualization, Visual Analytics, Network Visualization, Graph Drawing
HSV kategori
Forskningsprogram
Datavetenskap, Informations- och programvisualisering
Identifikatorer
urn:nbn:se:lnu:diva-62133 (URN)10.2312/eurovisshort.20171126 (DOI)978-3-03868-043-7 (ISBN)
Konferanse
19th EG/VGTC Conference on Visualization (EuroVis '17), 12-16 June 2017, Barcelona, Spain
Tilgjengelig fra: 2017-04-06 Laget: 2017-04-06 Sist oppdatert: 2018-01-13bibliografisk kontrollert
Martins, R. M., Simaki, V., Kucher, K., Paradis, C. & Kerren, A. (2017). StanceXplore: Visualization for the Interactive Exploration of Stance in Social Media. In: : . Paper presented at 2nd Workshop on Visualization for the Digital Humanities (VIS4DH '17) at IEEE VIS '17, October 2017, Phoenix, Arizona, USA.
Åpne denne publikasjonen i ny fane eller vindu >>StanceXplore: Visualization for the Interactive Exploration of Stance in Social Media
Vise andre…
2017 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The use of interactive visualization techniques in Digital Humanities research can be a useful addition when traditional automated machine learning techniques face difficulties, as is often the case with the exploration of large volumes of dynamic—and in many cases, noisy and conflicting—textual data from social media. Recently, the field of stance analysis has been moving from a predominantly binary approach—either pro or con—to a multifaceted one, where each unit of text may be classified as one (or more) of multiple possible stance categories. This change adds more layers of complexity to an already hard problem, but also opens up new opportunities for obtaining richer and more relevant results from the analysis of stancetaking in social media. In this paper we propose StanceXplore, a new visualization for the interactive exploration of stance in social media. Our goal is to offer DH researchers the chance to explore stance-classified text corpora from different perspectives at the same time, using coordinated multiple views including user-defined topics, content similarity and dissimilarity, and geographical and temporal distribution. As a case study, we explore the activity of Twitter users in Sweden, analyzing their behavior in terms of topics discussed and the stances taken. Each textual unit (tweet) is labeled with one of eleven stance categories from a cognitive-functional stance framework based on recent work. We illustrate how StanceXplore can be used effectively to investigate multidimensional patterns and trends in stance-taking related to cultural events, their geographical distribution, and the confidence of the stance classifier. 

Emneord
Stance Visualization, Sentiment Analysis, Digital Humanities, Visual Analytics, Social Media Text
HSV kategori
Forskningsprogram
Datavetenskap, Informations- och programvisualisering
Identifikatorer
urn:nbn:se:lnu:diva-67320 (URN)
Konferanse
2nd Workshop on Visualization for the Digital Humanities (VIS4DH '17) at IEEE VIS '17, October 2017, Phoenix, Arizona, USA
Prosjekter
StaViCTADISA-DH
Forskningsfinansiär
Swedish Research Council, 2012-5659
Tilgjengelig fra: 2017-08-21 Laget: 2017-08-21 Sist oppdatert: 2019-04-08bibliografisk kontrollert
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-2901-935X

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