Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery Integrating Automated Analysis with Interactive Exploration - KDD '09

2009 ◽  
Keyword(s):  
Knowledge Discovery ◽  
Visual Analytics ◽  
Automated Analysis ◽  
Interactive Exploration

scholarly journals Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data

2019 ◽  
Author(s):  
Robert Krueger ◽  
Johanna Beyer ◽  
Won-Dong Jang ◽  
Nam Wook Kim ◽  
Artem Sokolov ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Visual Analytics ◽  
Cancer Biology ◽  
Large Scale ◽  
Hierarchical Structures ◽  
Image Data ◽  
Automated Analysis ◽  
Cell Types ◽  
High Dimensional ◽  
Exploration Process

AbstractFacetto is a scalable visual analytics application that is used to discover single-cell phenotypes in high-dimensional multi-channel microscopy images of human tumors and tissues. Such images represent the cutting edge of digital histology and promise to revolutionize how diseases such as cancer are studied, diagnosed, and treated. Highly multiplexed tissue images are complex, comprising 109or more pixels, 60-plus channels, and millions of individual cells. This makes manual analysis challenging and error-prone. Existing automated approaches are also inadequate, in large part, because they are unable to effectively exploit the deep knowledge of human tissue biology available to anatomic pathologists. To overcome these challenges, Facetto enables a semi-automated analysis of cell types and states. It integrates unsupervised and supervised learning into the image and feature exploration process and offers tools for analytical provenance. Experts can cluster the data to discover new types of cancer and immune cells and use clustering results to train a convolutional neural network that classifies new cells accordingly. Likewise, the output of classifiers can be clustered to discover aggregate patterns and phenotype subsets. We also introduce a new hierarchical approach to keep track of analysis steps and data subsets created by users; this assists in the identification of cell types. Users can build phenotype trees and interact with the resulting hierarchical structures of both high-dimensional feature and image spaces. We report on use-cases in which domain scientists explore various large-scale fluorescence imaging datasets. We demonstrate how Facetto assists users in steering the clustering and classification process, inspecting analysis results, and gaining new scientific insights into cancer biology.

scholarly journals Visual analytics of sensor movement data for cheetah behaviour analysis

2021 ◽  
Author(s):  
Karsten Klein ◽  
Sabrina Jaeger ◽  
Jörg Melzheimer ◽  
Bettina Wachter ◽  
Heribert Hofer ◽  
...  
Keyword(s):  
Visual Analytics ◽  
Visual Analysis ◽  
Automated Analysis ◽  
Environmental Data ◽  
Impact Factors ◽  
Data Sets ◽  
Movement Data ◽  
Data Loggers ◽  
Intuitive Interfaces ◽  
Gps Data Loggers

Abstract Current tracking technology such as GPS data loggers allows biologists to remotely collect large amounts of movement data for a large variety of species. Extending, and often replacing interpretation based on observation, the analysis of the collected data supports research on animal behaviour, on impact factors such as climate change and human intervention on the globe, as well as on conservation programs. However, this analysis is difficult, due to the nature of the research questions and the complexity of the data sets. It requires both automated analysis, for example, for the detection of behavioural patterns, and human inspection, for example, for interpretation, inclusion of previous knowledge, and for conclusions on future actions and decision making. For this analysis and inspection, the movement data needs to be put into the context of environmental data, which helps to interpret the behaviour. Thus, a major challenge is to design and develop methods and intuitive interfaces that integrate the data for analysis by biologists. We present a concept and implementation for the visual analysis of cheetah movement data in a web-based fashion that allows usage both in the field and in office environments. Graphic abstract

Visual Analytics and Conceptual Blending Theory

2011 ◽  
pp. 290-310
Author(s):  
Mia Kalish
Keyword(s):  
Information System ◽  
Missing Data ◽  
Visual Analytics ◽  
Automated Analysis ◽  
Time Of Day ◽  
Conceptual Blending ◽  
Complex Information ◽  
Human Scale ◽  
Data Configuration ◽  
New Ideas

One visualization in Diné philosophy is four small dots arranged in a circular sequence at 90°, 0°, 270°, and 180°. Each position is associated with a time of day, a season, a color, a type of stone, a time in the lifecycle, and a process of living and learning. I use Conceptual Blending Theory to explore this complex information space of small spatial stories that combine to form an “information system of information systems.” This approach to visual analytics uses reduction to human scale, which easily adapts itself to automated analysis and data configuration. This process reveals a previously unseen world and contributes new ideas to understanding both the creation of new visualizations and the decomposition of existing visualizations. This verifiable methodology can validate the steps in the decomposition process itself and also be used to predict the content of missing data.

scholarly journals Integrated data access, visualization and analysis for Galactic Plane surveys: the VIALACTEA case

2016 ◽  
Vol 12 (S325) ◽  
pp. 291-298 ◽  
Author(s):  
Sergio Molinari ◽  
Robert Butora ◽  
Stefano Cavuoti ◽  
Marco Molinaro ◽  
Giuseppe Riccio ◽  
...  
Keyword(s):  
Star Formation ◽  
Visual Analytics ◽  
Milky Way ◽  
Galactic Plane ◽  
Data Access ◽  
Automated Analysis ◽  
Spectral Energy ◽  
Data Mining Algorithms ◽  
Molecular Lines ◽  
Stored Information

AbstractThe VIALACTEA project brings to a common forum the major new-generation surveys of the Milky Way Galactic Plane from 1μm to the radio, both in thermal continuum and in atomic and molecular lines, to attack in a systematic way the characterization of the Milky Way as a star formation engine. Images, catalogues, spectroscopic datacubes and radiative transfer models of the Spectral Energy Distributions (SEDs) of sites of star formation have been incorporated and indexed in the VIALACTEA Knowledge Base (VLKB). The VLKB consists of a combination of a relational database where the VIALACTEA data and metadata are homogenised and stored, and a filesystem-based stored information. This infrastructure allowed, among others, the generation of extensive catalogue for compact sources and extended structures in the Galactic Plane, the implementation of data-mining algorithms for the band-merging of multiwavelength data and expert systems for the automated analysis of molecular line surveys to extract critical kinematical information and derive distances using Galaxy rotation curves and new 3D extinction maps. A new VIALACTEA 3D Visual Analytics interface has been developed that provides integrated access and analysis of continuum and spectroscopic images together with catalogue data directly interfacing with the VLKB.

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