PhD student
Publications
2017
![[PDF]](http://vis.test.uib.no/wp-content/plugins/papercite/img/pdf.png)
![[DOI]](http://vis.test.uib.no/wp-content/plugins/papercite/img/external.png){kind=small}
[Bibtex] @ARTICLE {Turkay2017VIS,
author = "C. Turkay and E. Kaya and S. Balcisoy and H. Hauser",
title = "Designing Progressive and Interactive Analytics Processes for High-Dimensional Data Analysis",
journal = "IEEE Transactions on Visualization and Computer Graphics",
year = "2017",
volume = "PP",
number = "99",
pages = "1-1",
month = "jan",
abstract = "In interactive data analysis processes, the dialogue between the human and the computer is the enabling mechanism that can lead to actionable observations about the phenomena being investigated. It is of paramount importance that this dialogue is not interrupted by slow computational mechanisms that do not consider any known temporal human-computer interaction characteristics that prioritize the perceptual and cognitive capabilities of the users. In cases where the analysis involves an integrated computational method, for instance to reduce the dimensionality of the data or to perform clustering, such non-optimal processes are often likely. To remedy this, progressive computations, where results are iteratively improved, are getting increasing interest in visual analytics. In this paper, we present techniques and design considerations to incorporate progressive methods within interactive analysis processes that involve high-dimensional data. We define methodologies to facilitate processes that adhere to the perceptual characteristics of users and describe how online algorithms can be incorporated within these. A set of design recommendations and according methods to support analysts in accomplishing high-dimensional data analysis tasks are then presented. Our arguments and decisions here are informed by observations gathered over a series of analysis sessions with analysts from finance. We document observations and recommendations from this study and present evidence on how our approach contribute to the efficiency and productivity of interactive visual analysis sessions involving high-dimensional data.",
pdf = "pdfs/Turkay2017VIS.pdf",
images = "images/Turkay-2017-VIS.png",
thumbnails = "images/Turkay-2017-VIS.png",
doi = "10.1109/TVCG.2016.2598470",
issn = "1077-2626"
}2014
[Bibtex] @INCOLLECTION {turkay2014computationally,
author = "Turkay, Cagatay and Jeanquartier, Fleur and Holzinger, Andreas and Hauser, Helwig",
title = "On computationally-enhanced visual analysis of heterogeneous data and its application in biomedical informatics",
booktitle = "Interactive Knowledge Discovery and Data Mining in Biomedical Informatics",
publisher = "Springer",
year = "2014",
pages = "117--140",
abstract = "With the advance of new data acquisition and generation technologies, the biomedical domain is becoming increasingly data-driven. Thus, understanding the information in large and complex data sets has been in the focus of several research fields such as statistics, data mining, machine learning, and visualization. While the first three fields predominantly rely on computational power, visualization relies mainly on human perceptual and cognitive capabilities for extracting information. Data visualization, similar to Humanââ¬âComputer Interaction, attempts an appropriate interaction between human and data to interactively exploit data sets. Specifically within the analysis of complex data sets, visualization researchers have integrated computational methods to enhance the interactive processes. In this state-of-the-art report, we investigate how such an integration is carried out. We study the related literature with respect to the underlying analytical tasks and methods of integration. In addition, we focus on how such methods are applied to the biomedical domain and present a concise overview within our taxonomy. Finally, we discuss some open problems and future challenges.",
images = "images/img_Page_12_Image_0001.jpg, images/img_Page_12_Image_0002.jpg, images/img_Page_12_Image_0003.jpg",
thumbnails = "images/img_Page_12_Image_0001.jpg",
doi = "10.1007/978-3-662-43968-5_7)"
}@ARTICLE {turkay2014characterizing,
author = "Turkay, Cagatay and Lex, Alexander and Streit, Marc and Pfister, Hanspeter and Hauser, Helwig",
title = "Characterizing cancer subtypes using dual analysis in caleydo stratomex",
journal = "Computer Graphics and Applications, IEEE",
year = "2014",
volume = "34",
number = "2",
pages = "38--47",
abstract = "Dual analysis uses statistics to describe both the dimensions and rows of a high-dimensional dataset. Researchers have integrated it into StratomeX, a Caleydo view for cancer subtype analysis. In addition, significant-difference plots show the elements of a candidate subtype that differ significantly from other subtypes, thus letting analysts characterize subtypes. Analysts can also investigate how data samples relate to their assigned subtype and other groups. This approach lets them create well-defined subtypes based on statistical properties. Three case studies demonstrate the approach's utility, showing how it reproduced findings from a published subtype characterization.",
images = "images/img_Page_08_Image_0001.jpg, images/img_Page_04_Image_0001.jpg",
thumbnails = "images/img_Page_08_Image_0001.jpg",
publisher = "IEEE",
doi = "10.1109/MCG.2014.1"
}![[VID]](http://vis.test.uib.no/wp-content/papercite-data/images/video.png)
[Bibtex] @ARTICLE {Angelelli14Interactive,
author = "Paolo Angelelli and Steffen Oeltze and Cagatay Turkay and Judit Haasz and Erlend Hodneland and Arvid Lundervold and Astri Johansen Lundervold and Bernhard Preim and Helwig Hauser",
title = "Interactive Visual Analysis of Heterogeneous Cohort Study Data",
journal = "Computer Graphics and Applications, IEEE",
year = "2014",
volume = "PP",
number = "99",
pages = "1-1",
abstract = "Cohort studies are used in medicine to enable the study of medical hypotheses in large samples. Often, a large amount of heterogeneous data is acquired from many subjects. The analysis is usually hypothesis-driven, i.e., a specific subset of such data is studied to confirm or reject specific hypotheses. In this paper, we demonstrate how we enable the interactive visual exploration and analysis of such data, helping with the generation of new hypotheses and contributing to the process of validating them. We propose a data-cube based model which allows to handle partially overlapping data subsets during the interactive visualization. This model enables the seamless integration of the heterogeneous data, as well as the linking of spatial and non-spatial views on these data. We implemented this model in an application prototype, and used it to analyze data acquired in the context of a cohort study on cognitive aging. In this paper we present a case-study analysis of selected aspects of brain connectivity by using a prototype implementation of the presented model, to demonstrate its potential and flexibility.",
vid = "vids/angelelli14CohortExplorer.wmv",
images = "images/angelelli14Cohort.png",
thumbnails = "images/angelelli14Cohort.png",
doi = "10.1109/MCG.2014.40",
url = "http://dx.doi.org/10.1109/MCG.2014.40"
}@ARTICLE {turkay2014attribute,
author = "Turkay, Cagatay and Slingsby, Aidan and Hauser, Helwig and Wood, Jo and Dykes, Jason",
title = "Attribute signatures: Dynamic visual summaries for analyzing multivariate geographical data",
journal = "Visualization and Computer Graphics, IEEE Transactions on",
year = "2014",
volume = "20",
number = "12",
pages = "2033--2042",
abstract = "The visual analysis of geographically referenced datasets with a large number of attributes is challenging due to the fact that the characteristics of the attributes are highly dependent upon the locations at which they are focussed, and the scale and time at which they are measured. Specialized interactive visual methods are required to help analysts in understanding the characteristics of the attributes when these multiple aspects are considered concurrently. Here, we develop attribute signatures-interactively crafted graphics that show the geographic variability of statistics of attributes through which the extent of dependency between the attributes and geography can be visually explored. We compute a number of statistical measures, which can also account for variations in time and scale, and use them as a basis for' our visualizations. We then employ different graphical configurations to show and compare both continuous and discrete variation of location and scale. Our methods allow variation in multiple statistical summaries of multiple attributes to be considered concurrently and geographically, as evidenced by examples in which the census geography of London and the wider UK are explored.",
images = "images/img_Page_06_Image_0003.jpg, images/img_Page_01_Image_0002.jpg, images/img_Page_01_Image_0005.jpg, images/img_Page_07_Image_0003.jpg",
thumbnails = "images/img_Page_06_Image_0003.jpg",
publisher = "IEEE",
doi = "10.1109/TVCG.2014.2346265"
}2013
[Bibtex] @ARTICLE {Parulek13Visual,
author = "Julius Parulek and Cagatay Turkay and Nathalie Reuter and Ivan Viola",
title = "Visual cavity analysis in molecular simulations",
journal = "BMC Bioinformatics",
year = "2013",
volume = "14",
number = "Suppl 19",
pages = "S4",
month = "Nov.",
abstract = "Molecular surfaces provide a useful mean for analyzing interactions between biomolecules; such as identification and characterization of ligand binding sites to a host macromolecule. We present a novel technique, which extracts potential binding sites, represented by cavities, and characterize them by 3D graphs and by amino acids. The binding sites are extracted using an implicit function sampling and graph algorithms. We propose an advanced cavity exploration technique based on the graph parameters and associated amino acids. Additionally, we interactively visualize the graphs in the context of the molecular surface. We apply our method to the analysis of MD simulations of Proteinase 3, where we verify the previously described cavities and suggest a new potential cavity to be studied.",
images = "images/Parulek13Visual01.png, images/Parulek13Visual02.png",
thumbnails = "images/Parulek13Visual01_thumb.png, images/Parulek13Visual02_thumb.png",
url = "http://www.biomedcentral.com/1471-2105/14/S19/S4",
doi = "10.1186/1471-2105-14-S19-S4",
issn = "1471-2105",
project = "physioillustration"
}@PHDTHESIS {turkay13thesis,
author = "Cagatay Turkay",
title = "Integrating Computational Tools in Interactive and Visual Methods for Enhancing High-dimensional Data and Cluster Analysis",
school = "Department of Informatics, University of Bergen, Norway",
year = "2013",
month = "November",
abstract = "With the advance of new data acquisition and generation technologies, our society is becoming increasingly information-driven. The datasets are getting larger and more complex as new technologies emerge and they are posing new challenges to the analysts who are trying to build an understanding of them. Automated computational approaches and interactive visual methods have been widely used to extract and interpret the relevant information in data analysis. However when these methods are used alone on complex datasets, their effectivity is limited due to several factors. Most of the commonly used computational tools often lead to hard to interpret results that may not be reliable most of the time. This thesis aims to enhance data analysis procedures by integrating computational tools with interactive visual methodologies. The contributions of this thesis are mainly focused on the analysis of (very) high-dimensional data, i.e., hundreds and even thousands of dimensions, and cluster analysis. We introduce the dual analysis approach that makes it possible to analyze the items and the dimensions of a dataset in parallel in two linked visualization spaces. This methodology provides a basis to visually characterize and investigate dimensions as first-order analysis objects. We describe structure-aware analysis procedures that are facilitated by representative factors. Moreover, we present several mechanisms to achieve outlier-aware analysis routines. We describe the notion of outlyingness for the dimensions of a dataset and discuss how they can be determined and treated properly. We then focus on enhancing the dialogue between the analyst and the computer when computational methods are used interactively. We describe how different human factors come into play in visual analysis applications and propose optimized analytical processes that try to comply with the human capabilities. All these different approaches are demonstrated with various use-cases performed mostly together with experts from medical, genetic, and molecular biology domain. ",
pdf = "pdfs/turkay13thesis.pdf",
images = "images/turkay13thesis.png",
thumbnails = "images/turkay13thesis.png",
isbn = "?? ",
project = "medviz"
}@INCOLLECTION {Turkay13Hypothesis,
author = "Cagatay Turkay and Arvid Lundervold and Astri Johansen Lundervold and Helwig Hauser",
title = "Hypothesis Generation by Interactive Visual Exploration of Heterogeneous Medical Data",
booktitle = "Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data",
publisher = "Springer Berlin Heidelberg",
year = "2013",
editor = "Holzinger, Andreas and Pasi, Gabriella",
volume = "7947",
series = "Lecture Notes in Computer Science",
pages = "1--12",
images = "images/Turkay13Hypothesis_01.png",
thumbnails = "images/Turkay13Hypothesis_01.png",
isbn = "978-3-642-39145-3",
doi = "10.1007/978-3-642-39146-0_1",
url = "http://dx.doi.org/10.1007/978-3-642-39146-0_1",
keywords = "interactive visual analysis; high dimensional medical data",
pres = "pdfs/Turkay13Hypothesis.pdf"
}