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Bae, Juhee
Publications (10 of 15) Show all publications
Olson, N. & Bae, J. (2019). Biosensors-Publication Trends and Knowledge Domain Visualization. Sensors, 19(11), Article ID 2615.
Open this publication in new window or tab >>Biosensors-Publication Trends and Knowledge Domain Visualization
2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 11, article id 2615Article in journal (Refereed) Published
Abstract [en]

The number of scholarly publications on the topic of biosensors has increased rapidly; as a result, it is no longer easy to build an informed overview of the developments solely by manual means. Furthermore, with many new research results being continually published, it is useful to form an up-to-date understanding of the recent trends or emergent directions in the field. This paper utilizes bibliometric methods to provide an overview of the developments in the topic based on scholarly publications. The results indicate an increasing interest in the topic of biosensor(s) with newly emerging sub-topics. The US is identified as the country with highest total contribution to this area, but as a collective, EU countries top the list of total contributions. An examination of trends over the years indicates that in recent years, China-based authors have been more productive in this area. If research contribution per capita is considered, Singapore takes the top position, followed by Sweden, Switzerland and Denmark. While the number of publications on biosensors seems to have declined in recent years in the PubMed database, this is not the case in the Web of Science database. However, there remains an indication that the rate of growth in the more recent years is slowing. This paper also presents a comparison of the developments in publications on biosensors with the full set of publications in two of the main journals in the field. In more recent publications, synthetic biology, smartphone, fluorescent biosensor, and point-of-care testing are among the terms that have received more attention. The study also identifies the top authors and journals in the field, and concludes with a summary and suggestions for follow up research.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
bibliometrics, biosensors, emerging trends, scholarly publications
National Category
Information Studies Biomedical Laboratory Science/Technology
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-17409 (URN)10.3390/s19112615 (DOI)000472133300193 ()31181820 (PubMedID)2-s2.0-85067796636 (Scopus ID)
Available from: 2019-07-08 Created: 2019-07-08 Last updated: 2019-07-09Bibliographically approved
Bae, J., Karlsson, A., Mellin, J., Ståhl, N. & Torra, V. (2019). Complex Data Analysis. In: Alan Said, Vicenç Torra (Ed.), Data science in Practice: (pp. 157-169). Springer
Open this publication in new window or tab >>Complex Data Analysis
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2019 (English)In: Data science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, p. 157-169Chapter in book (Refereed)
Abstract [en]

Data science applications often need to deal with data that does not fit into the standard entity-attribute-value model. In this chapter we discuss three of these other types of data. We discuss texts, images and graphs. The importance of social media is one of the reason for the interest on graphs as they are a way to represent social networks and, in general, any type of interaction between people. In this chapter we present examples of tools that can be used to extract information and, thus, analyze these three types of data. In particular, we discuss topic modeling using a hierarchical statistical model as a way to extract relevant topics from texts, image analysis using convolutional neural networks, and measures and visual methods to summarize information from graphs.

Place, publisher, year, edition, pages
Springer, 2019
Series
Studies in Big Data, ISSN 2197-6503, E-ISSN 2197-6511 ; 46
National Category
Computer and Information Sciences Computer Sciences Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL); Distributed Real-Time Systems
Identifiers
urn:nbn:se:his:diva-16811 (URN)10.1007/978-3-319-97556-6_9 (DOI)000464719500010 ()978-3-319-97556-6 (ISBN)978-3-319-97555-9 (ISBN)
Available from: 2019-04-24 Created: 2019-04-24 Last updated: 2019-06-10Bibliographically approved
Holst, A., Bouguelia, M.-R., Görnerup, O., Pashami, S., Al-Shishtawy, A., Falkman, G., . . . Soliman, A. (2019). Eliciting structure in data. In: Christoph Trattner, Denis Parra, Nathalie Riche (Ed.), CEUR Workshop Proceedings: . Paper presented at 2019 Joint ACM IUI Workshops, ACMIUI-WS 2019, Los Angeles, United States, 20 March 2019. CEUR-WS, 2327
Open this publication in new window or tab >>Eliciting structure in data
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2019 (English)In: CEUR Workshop Proceedings / [ed] Christoph Trattner, Denis Parra, Nathalie Riche, CEUR-WS , 2019, Vol. 2327Conference paper, Published paper (Refereed)
Abstract [en]

This paper demonstrates how to explore and visualize different types of structure in data, including clusters, anomalies, causal relations, and higher order relations. The methods are developed with the goal of being as automatic as possible and applicable to massive, streaming, and distributed data. Finally, a decentralized learning scheme is discussed, enabling finding structure in the data without collecting the data centrally. 

Place, publisher, year, edition, pages
CEUR-WS, 2019
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 2327
Keywords
Anomaly detection, Causal inference, Clustering, Distributed analytics, Higher-order structure, Information visualization, Information systems, User interfaces, Causal inferences, Data acquisition
National Category
Computer Sciences Human Computer Interaction
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16748 (URN)2-s2.0-85063227224 (Scopus ID)
Conference
2019 Joint ACM IUI Workshops, ACMIUI-WS 2019, Los Angeles, United States, 20 March 2019
Available from: 2019-04-05 Created: 2019-04-05 Last updated: 2019-04-18Bibliographically approved
Said, A., Bae, J., Parra, D. & Pashami, S. (2019). IDM-WSDM 2019: Workshop on interactive data mining. In: WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining: . Paper presented at 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 11 February 2019 through 15 February 2019 (pp. 846-847). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>IDM-WSDM 2019: Workshop on interactive data mining
2019 (English)In: WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining, Association for Computing Machinery (ACM), 2019, p. 846-847Conference paper, Published paper (Refereed)
Abstract [en]

The first Workshop on Interactive Data Mining is held in Melbourne, Australia, on February 15, 2019 and is co-located with 12th ACM International Conference on Web Search and Data Mining (WSDM 2019). The goal of this workshop is to share and discuss research and projects that focus on interaction with and interactivity of data mining systems. The program includes invited speaker, presentation of research papers, and a discussion session. © 2019 held by the owner/author(s).

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2019
Keywords
Data mining, Human-in-the-loop, Interactive classification and clustering, Interactive dashboards, Visual modeling, Information retrieval, Websites, Data mining system, Interactive classification, Interactive data mining, Melbourne, Australia, Research papers, Visual model
National Category
Other Computer and Information Science Information Systems Interaction Technologies
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16671 (URN)10.1145/3289600.3291376 (DOI)000482120400120 ()2-s2.0-85061736320 (Scopus ID)978-1-4503-5940-5 (ISBN)
Conference
12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 11 February 2019 through 15 February 2019
Note

Conference code: 144821; Export Date: 1 March 2019; Conference Paper

Available from: 2019-03-01 Created: 2019-03-01 Last updated: 2019-09-12Bibliographically approved
Holst, A., Pashami, S. & Bae, J. (2019). Incremental causal discovery and visualization. In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019: . Paper presented at 1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019 (pp. 1-6). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Incremental causal discovery and visualization
2019 (English)In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery (ACM), 2019, p. 1-6Conference paper, Published paper (Refereed)
Abstract [en]

Discovering causal relations from limited amounts of data can be useful for many applications. However, all causal discovery algorithms need huge amounts of data to estimate the underlying causal graph. To alleviate this gap, this paper proposes a novel visualization tool which incrementally discovers causal relations as more data becomes available. That is, we assume that stronger causal links will be detected quickly and weaker links revealed when enough data is available. In addition to causal links, the correlation between variables and the uncertainty of the strength of causal links are visualized in the same graph. The tool is illustrated on three example causal graphs, and results show that incremental discovery works and that the causal structure converges as more data becomes available. 

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2019
Keywords
Causal Discovery, Correlation, Incremental Visualization, Correlation methods, Data mining, Visualization, Causal graph, Causal relations, Discovery algorithm, Incremental discoveries, Novel visualizations, Data visualization
National Category
Probability Theory and Statistics
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-17509 (URN)10.1145/3304079.3310287 (DOI)2-s2.0-85069768142 (Scopus ID)978-1-4503-6296-2 (ISBN)
Conference
1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019
Available from: 2019-08-09 Created: 2019-08-09 Last updated: 2019-08-12Bibliographically approved
Holst, A., Karlsson, A., Bae, J. & Bouguelia, M.-R. (2019). Interactive clustering for exploring multiple data streams at different time scales and granularity. In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019: . Paper presented at 1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019. Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Interactive clustering for exploring multiple data streams at different time scales and granularity
2019 (English)In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery (ACM), 2019Conference paper, Published paper (Refereed)
Abstract [en]

We approach the problem of identifying and interpreting clusters over different time scales and granularity in multivariate time series data. We extract statistical features over a sliding window of each time series, and then use a Gaussian mixture model to identify clusters which are then projected back on the data streams. The human analyst can then further analyze this projection and adjust the size of the sliding window and the number of clusters in order to capture the different types of clusters over different time scales. We demonstrate the effectiveness of our approach in two different application scenarios: (1) fleet management and (2) district heating, wherein each scenario, several different types of meaningful clusters can be identified when varying over these dimensions. 

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2019
Keywords
Clustering, Interaction, Time scales, Time series, Fleet operations, Gaussian distribution, Time measurement, Application scenario, Different time scale, Gaussian Mixture Model, Multiple data streams, Multivariate time series, Time-scales, Data mining
National Category
Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-17512 (URN)10.1145/3304079.3310286 (DOI)2-s2.0-85069762696 (Scopus ID)978-1-4503-6296-2 (ISBN)
Conference
1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2019-08-26Bibliographically approved
Bae, J., Havsol, J., Karpefors, M., Karlsson, A. & Mathiason, G. (2019). Short Text Topic Modeling to Identify Trends on Wearable Bio-sensors in Different Media Types. In: Proceedings - 6th International Symposium on Computational and Business Intelligence, ISCBI 2018: . Paper presented at ISCBI 2018 : 2018 6th International Symposium on Computational and Business Intelligence. Basel, Switzerland August 22 - 29 2018 (pp. 89-93). IEEE Computer Society
Open this publication in new window or tab >>Short Text Topic Modeling to Identify Trends on Wearable Bio-sensors in Different Media Types
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2019 (English)In: Proceedings - 6th International Symposium on Computational and Business Intelligence, ISCBI 2018, IEEE Computer Society, 2019, p. 89-93Conference paper, Published paper (Refereed)
Abstract [en]

The technology and techniques for bio-sensors are rapidly evolving. Accordingly, there is significant business interest to identify upcoming technologies and new targets for the near future. Text information from internet reflects much of the recent information and public interests that help to understand the trend of a certain field. Thus, we utilize Dirichlet process topic modeling on different media sources containing short text (e.g., blogs, news) which is able to self-adapt the learned topic space to the data. We share the observations from the domain experts on the results derived from topic modeling on wearable biosensors from multiple media sources over more than eight years. We analyze the topics on wearable devices, forecast and market analysis, and bio-sensing techniques found from our method. 

Place, publisher, year, edition, pages
IEEE Computer Society, 2019
Keywords
Bayesian non-parametrics, Bio-sensor, short text, topic modeling, wearable, Biosensors, Information analysis, Bayesian nonparametrics, Dirichlet process, Market analysis, Short texts, Text information, Wearable devices, Wearable sensors
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16746 (URN)10.1109/ISCBI.2018.00027 (DOI)000462379700017 ()2-s2.0-85063041846 (Scopus ID)978-1-5386-9450-3 (ISBN)978-1-5386-9451-0 (ISBN)
Conference
ISCBI 2018 : 2018 6th International Symposium on Computational and Business Intelligence. Basel, Switzerland August 22 - 29 2018
Available from: 2019-04-05 Created: 2019-04-05 Last updated: 2019-04-11Bibliographically approved
Bae, J., Falkman, G., Helldin, T. & Riveiro, M. (2019). Visual Data Analysis. In: Alan Said, Vicenç Torra (Ed.), Data science in Practice: (pp. 133-155). Springer
Open this publication in new window or tab >>Visual Data Analysis
2019 (English)In: Data science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, p. 133-155Chapter in book (Refereed)
Abstract [en]

Data Science offers a set of powerful approaches for making new discoveries from large and complex data sets. It combines aspects of mathematics, statistics, machine learning, etc. to turn vast amounts of data into new insights and knowledge. However, the sole use of automatic data science techniques for large amounts of complex data limits the human user’s possibilities in the discovery process, since the user is estranged from the process of data exploration. This chapter describes the importance of Information Visualization (InfoVis) and visual analytics (VA) within data science and how interactive visualization can be used to support analysis and decision-making, empowering and complementing data science methods. Moreover, we review perceptual and cognitive aspects, together with design and evaluation methodologies for InfoVis and VA.

Place, publisher, year, edition, pages
Springer, 2019
Series
Studies in Big Data, ISSN 2197-6503, E-ISSN 2197-6511 ; 46
National Category
Computer and Information Sciences Computer Sciences Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16810 (URN)10.1007/978-3-319-97556-6_8 (DOI)000464719500009 ()978-3-319-97556-6 (ISBN)978-3-319-97555-9 (ISBN)
Available from: 2019-04-24 Created: 2019-04-24 Last updated: 2019-06-10Bibliographically approved
Kim, J., Bae, J. & Hastak, M. (2018). Emergency information diffusion on online social media during storm Cindy in U.S.. International Journal of Information Management, 40, 153-165
Open this publication in new window or tab >>Emergency information diffusion on online social media during storm Cindy in U.S.
2018 (English)In: International Journal of Information Management, ISSN 0268-4012, E-ISSN 1873-4707, Vol. 40, p. 153-165Article in journal (Refereed) Published
Abstract [en]

Social media plays a critical role in propagating emergency information during disasters. Governmental agencies have opened social media accounts for emergency communication channels. To understand the underlying mechanism of user behaviors and engagement, this study employs social network analysis to investigate information network and diffusion across news, weather agencies, governmental agencies, organizations and the public during the 2017 Storm Cindy in the U.S. This study identified certain types of Twitter users (news and weather agencies) were dominant as information sources and information diffusers (the public and organizations). However, the information flow in the network was controlled by numerous types of users including news, agency, weather agencies and the public. The results highlighted the importance of understanding the unique characteristics of social media and networks for better emergency communication system. © 2018 Elsevier Ltd

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
disaster communication, disaster management, disaster response, social media, social network analysis, behavioral research, disaster prevention, disasters, information services, storms, disaster communications, emergency communication, emergency information, information networks, online social medias, social networking (online)
National Category
Information Systems, Social aspects
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-14998 (URN)10.1016/j.ijinfomgt.2018.02.003 (DOI)000427764700016 ()2-s2.0-85042680051 (Scopus ID)
Available from: 2018-04-01 Created: 2018-04-03 Last updated: 2019-06-05Bibliographically approved
Karlsson, A., Duarte, D., Mathiason, G. & Bae, J. (2018). Evaluation of the dirichlet process multinomial mixture model for short-text topic modeling. In: Proceedings - 6th International Symposium on Computational and Business Intelligence, ISCBI 2018: . Paper presented at 6th International Symposium on Computational and Business Intelligence (ISCBI), 27-29 August 2018, Basel, Switzerland (pp. 79-83). USA: Institute of Electrical and Electronics Engineers (IEEE), Article ID 8638311.
Open this publication in new window or tab >>Evaluation of the dirichlet process multinomial mixture model for short-text topic modeling
2018 (English)In: Proceedings - 6th International Symposium on Computational and Business Intelligence, ISCBI 2018, USA: Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 79-83, article id 8638311Conference paper, Published paper (Refereed)
Abstract [en]

Fast-moving trends, both in society and in highly competitive business areas, call for effective methods for automatic analysis. The availability of fast-moving sources in the form of short texts, such as social media and blogs, allows aggregation from a vast number of text sources, for an up to date view of trends and business insights. Topic modeling is established as an approach for analysis of large amounts of texts, but the scarcity of statistical information in short texts is considered to be a major problem for obtaining reliable topics from traditional models such as LDA. A range of different specialized topic models have been proposed, but a majority of these approaches rely on rather strong parametric assumptions, such as setting a fixed number of topics. In contrast, recent advances in the field of Bayesian non-parametrics suggest the Dirichlet process as a method that, given certain hyper-parameters, can self-adapt to the number of topics of the data at hand. We perform an empirical evaluation of the Dirichlet process multinomial (unigram) mixture model against several parametric topic models, initialized with different number of topics. The resulting models are evaluated, using both direct and indirect measures that have been found to correlate well with human topic rankings. We show that the Dirichlet Process Multinomial Mixture model is a viable option for short text topic modeling since it on average performs better, or nearly as good, compared to the parametric alternatives, while reducing parameter setting requirements and thereby eliminates the need of expensive preprocessing. 

Place, publisher, year, edition, pages
USA: Institute of Electrical and Electronics Engineers (IEEE), 2018
Keywords
Bayesian-nonparametrics, Dirichlet-process, short-text, text-analysis, topic-modeling, Information analysis, Bayesian nonparametrics, Dirichlet process, Short texts, Text analysis, Topic Modeling, Mixtures
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16747 (URN)10.1109/ISCBI.2018.00025 (DOI)000462379700015 ()2-s2.0-85063024705 (Scopus ID)978-1-5386-9450-3 (ISBN)978-1-5386-9451-0 (ISBN)
Conference
6th International Symposium on Computational and Business Intelligence (ISCBI), 27-29 August 2018, Basel, Switzerland
Available from: 2019-04-05 Created: 2019-04-05 Last updated: 2019-07-05Bibliographically approved
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