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"title": "2022 26th International Conference Information Visualisation (IV)",
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"doi": "10.1109/IV56949.2022.00077",
"title": "Composition of Geospatial Visualizations for Scale-aware Views of Multiple Outcome Variables in Population Surveys",
"normalizedTitle": "Composition of Geospatial Visualizations for Scale-aware Views of Multiple Outcome Variables in Population Surveys",
"abstract": "Population survey data is important for understanding the status of well-being along any dimensions, i.e., social, economic, political, health, etc. This data generates spatial point patterns which can be explored and analyzed using visualization. Given the spatial aspect of the data, there is a requirement of using cartographic maps, which are mostly limited to visualizing a single variable in most cases. Here, it is also important that the choice of visualizations also enable scale-aware analysis when zooming in and out of the maps, since the data is from the smaller political units and can be aggregated to larger political units. Thus, we explore the different visual compositions which use mathematical operators and the composite layouts for visualizing multiple outcome variables in survey data. The mathematical operators allow the use of univariate and bivariate data modeling and representation, and composite layouts of interest are juxtaposition and superimposed views. We demonstrate the inferences from visualizations using a case study on malnutrition in children under five in India. Our work shows that a visual composition of binary relationships represented in a visualization and a juxtaposed layout of such pairwise variables is effective in making inferences from the multivariate spatial point patterns in population data.",
"abstracts": [
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"content": "Population survey data is important for understanding the status of well-being along any dimensions, i.e., social, economic, political, health, etc. This data generates spatial point patterns which can be explored and analyzed using visualization. Given the spatial aspect of the data, there is a requirement of using cartographic maps, which are mostly limited to visualizing a single variable in most cases. Here, it is also important that the choice of visualizations also enable scale-aware analysis when zooming in and out of the maps, since the data is from the smaller political units and can be aggregated to larger political units. Thus, we explore the different visual compositions which use mathematical operators and the composite layouts for visualizing multiple outcome variables in survey data. The mathematical operators allow the use of univariate and bivariate data modeling and representation, and composite layouts of interest are juxtaposition and superimposed views. We demonstrate the inferences from visualizations using a case study on malnutrition in children under five in India. Our work shows that a visual composition of binary relationships represented in a visualization and a juxtaposed layout of such pairwise variables is effective in making inferences from the multivariate spatial point patterns in population data.",
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"Cartography",
"Data Models",
"Data Visualisation",
"Geographic Information Systems",
"Mathematical Operators",
"Bivariate Data Modeling",
"Cartographic Maps",
"Geospatial Visualizations",
"Mathematical Operators",
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"Multivariate Spatial Point Patterns",
"Population Data",
"Population Survey Data",
"Scale Aware Analysis",
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"affiliation": "Graphics-Visualization-Computing Lab, IIIT Bangalore,Bangalore,India",
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"affiliation": "Graphics-Visualization-Computing Lab, IIIT Bangalore,Bangalore,India",
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"title": "2022 IEEE International Conference on Big Data (Big Data)",
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"title": "Thematic Geo-Density Heatmapping for Walking Tourism Analytics using Semi-Ready GPS Trajectories",
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"abstract": "With the aim to generate beneficial reports to improve the content of walking tourism, this paper discusses methods to visualize tourist interest in geographic features in a city by automatically collecting their feedback through mobile applications. Geo-density maps that use raw trajectories generally have difficulty in determining whether the dense areas presented on them are more appealing than the other areas in a city, since environmental, user-behavioral, and technical factors present various degrees of density regardless of tourist activity. Additionally, the complicated point patterns in a trajectory underestimate polyline-shaped features, such as footpaths and streets, which are some of the most important components of walking tourism, and their density maps prevent the inference of hot streets. The proposed framework monitors user behavior, e.g., entering/leaving indoor spaces and walking/stopping, using various mobile sensors to generate semi-ready trajectory data for thematic heatmaps of city-scale tourist analysis by (1) equalizing the inherent density bias in a trajectory, (2) mitigating the errors of GPS recording, (3) adding attributes that describe concrete user events during tourism. Furthermore, our client system demonstrates hot street heatmaps, user-generated content (UGC)-oriented hot spot heatmaps, and indoor-oriented hot spot heatmaps drawn from semi-ready data. The evaluation experiment conducted in this paper illustrates that the proposed data were able to represent the actual walking trajectory more accurately than the raw GPS trajectory and existing line simplification approaches.",
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"content": "With the aim to generate beneficial reports to improve the content of walking tourism, this paper discusses methods to visualize tourist interest in geographic features in a city by automatically collecting their feedback through mobile applications. Geo-density maps that use raw trajectories generally have difficulty in determining whether the dense areas presented on them are more appealing than the other areas in a city, since environmental, user-behavioral, and technical factors present various degrees of density regardless of tourist activity. Additionally, the complicated point patterns in a trajectory underestimate polyline-shaped features, such as footpaths and streets, which are some of the most important components of walking tourism, and their density maps prevent the inference of hot streets. The proposed framework monitors user behavior, e.g., entering/leaving indoor spaces and walking/stopping, using various mobile sensors to generate semi-ready trajectory data for thematic heatmaps of city-scale tourist analysis by (1) equalizing the inherent density bias in a trajectory, (2) mitigating the errors of GPS recording, (3) adding attributes that describe concrete user events during tourism. Furthermore, our client system demonstrates hot street heatmaps, user-generated content (UGC)-oriented hot spot heatmaps, and indoor-oriented hot spot heatmaps drawn from semi-ready data. The evaluation experiment conducted in this paper illustrates that the proposed data were able to represent the actual walking trajectory more accurately than the raw GPS trajectory and existing line simplification approaches.",
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"normalizedAbstract": "With the aim to generate beneficial reports to improve the content of walking tourism, this paper discusses methods to visualize tourist interest in geographic features in a city by automatically collecting their feedback through mobile applications. Geo-density maps that use raw trajectories generally have difficulty in determining whether the dense areas presented on them are more appealing than the other areas in a city, since environmental, user-behavioral, and technical factors present various degrees of density regardless of tourist activity. Additionally, the complicated point patterns in a trajectory underestimate polyline-shaped features, such as footpaths and streets, which are some of the most important components of walking tourism, and their density maps prevent the inference of hot streets. The proposed framework monitors user behavior, e.g., entering/leaving indoor spaces and walking/stopping, using various mobile sensors to generate semi-ready trajectory data for thematic heatmaps of city-scale tourist analysis by (1) equalizing the inherent density bias in a trajectory, (2) mitigating the errors of GPS recording, (3) adding attributes that describe concrete user events during tourism. Furthermore, our client system demonstrates hot street heatmaps, user-generated content (UGC)-oriented hot spot heatmaps, and indoor-oriented hot spot heatmaps drawn from semi-ready data. The evaluation experiment conducted in this paper illustrates that the proposed data were able to represent the actual walking trajectory more accurately than the raw GPS trajectory and existing line simplification approaches.",
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"title": "Explaining Failure: Investigation of Surprise and Expectation in CNNs",
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"abstract": "Ai Convolutional Neural Networks (CNNs) have expanded into everyday use, more rigorous methods of explaining their inner workings are required. Current popular techniques, such as saliency maps, show how a network interprets an input image at a simple level by scoring pixels according to their importance. In this paper, we introduce the concept of surprise and expectation as means for exploring and visualising how a network learns to model the training data through the understanding of filter activations. We show that this is a powerful technique for understanding how the network reacts to an unseen image compared to the training data. We also show that the insights provided by our technique allows us to \"fix\" misclassifica- tions. Our technique can be used with nearly all types of CNN. We evaluate our method both qualitatively and quantitatively using ImageNet.",
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"abstract": "It is becoming more popular to explore data by multi-touch gesture at present. In order to improve the fluency of interaction, it is necessary to construct queries and provide real-time feedback in the process of a gesture. In this paper, a query construction method based on data dependency for gesture interaction, called DDQC(Data Dependency based Query Construction), is proposed. The movement characteristics of multi-touch gesture and the data dependency on table are taken into account to provide query results many times during a continuous multi-touch gesture. In DDQC, the division point of touching trajectory is presented to dynamically determine the time to query, and the query context of current gesture and the functional dependency on table are used to construct the query event. By collecting the real behavior data of gestural query, the experiments compare the query performance of DDQC, SnapToQuery, TouchViz and Binned Aggregation, and the results show that DDQC has better performance in Query Execution Time, I/O Times and Discovery Time.",
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"abstract": "Visual big data analytics aims at supporting big data analytics via visual metaphors, with a plethora of applications in modern settings and scenarios. In all these domains, visual big data analytics paradigms offer several advantages, among which some noticeable ones are: (i) fast knowledge understanding from big data sets; (ii) pattern and trend discovery from big data sets; (iii) entity and model discovery from big data sets; (iv) sharing insights among organizations. Among several proposals, OLAP-based visual big data analytics methodologies and tools represents a successful case of visual big data analytics frameworks, which is entirely based on OLAP analysis. In this context, an OLAP cube is typically explored with multiple aggregations selecting different subsets of cube dimensions to analyze trends or to discover unexpected results. Unfortunately, such analytic process is generally manual and fails to statistically explain results. On the basis of these considerations, in this paper we propose an innovative OLAP-shaped visual big data analytics framework that incorporates a state-of-the-art statistical technique for supporting exploration and visualization of OLAP data cubes. An experimental evaluation with a medical data set presents statistically significant results and interactive visualizations, which link risk factors and degree of disease.",
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"abstract": "Most of molecular interactions often occur in feature regions of molecular surfaces such as convex regions, cavities or pockets. In this paper we propose a method to identify these feature regions by expanding the molecular surface to its surrounding spherical surface, that is, a 1-1 mapping is established between them. According to the expansion distance, feature regions can be determined to be concave or convex and marked with different colors on the spherical surface. The method brings convenience to features comparison and analysis between different molecular surfaces. The experimental results show that this method works well on identifying the concave and convex regions of molecular surfaces.",
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"abstract": "A classical approach for surface classification is to find a compact algebraic representation for each surface that would be similar for objects within the same class and preserve dissimilarities between classes. We introduce Self Functional Maps as a novel surface representation that satisfies these properties, translating the geometric problem of surface classification into an algebraic form of classifying matrices. The proposed map transforms a given surface into a universal isometry invariant form defined by a unique matrix. The suggested representation is realized by applying the functional maps framework to map the surface into itself. The key idea is to use two different metric spaces of the same surface for which the functional map serves as a signature. Specifically, in this paper, we use the regular and the scale invariant surface laplacian operators to construct two families of eigenfunctions. The result is a matrix that encodes the interaction between the eigenfunctions resulted from two different Riemannian manifolds of the same surface. Using this representation, geometric shape similarity is converted into algebraic distances between matrices.",
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"abstract": "Inference of ancestral or extinct genomes is important in evolutionary biology, cancer research and many other research areas. Whole-genome data has become readily available due to advances in large-scale sequencing technology. During the past decades, a number of evolutionary models and related algorithms have been designed to infer ancestral genome sequence or gene order. Since it is so hard or even impossible to know the true scenario of the ancestral genomes, there must be some tools used to test the robustness of the adjacencies obtained from existing methods. However, till now, there is still no systematic work being conducted to tackle this problem. On the other hand, it is a common practice in phylogenetic analysis to assess the confidence rate of the inferred branches, using techniques such as bootstrapping and jackknifing, which have been evaluated as good resampling tools for phylogenetic reconstruction methods. Some of these resampling methods can be potentially used as a robustness test for ancestral genomes. In this paper, we conduct large-scale experiments by using three existing best ancestral genome inference methods and four different resampling techniques. Experimental results show that resampling techniques are useful for assessing the quality of ancestral genomes, while different methods have different preferred resampling techniques. It is suggested the cut-off threshold of 75% should be used to filter bad gene adjacencies.",
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"content": "Inference of ancestral or extinct genomes is important in evolutionary biology, cancer research and many other research areas. Whole-genome data has become readily available due to advances in large-scale sequencing technology. During the past decades, a number of evolutionary models and related algorithms have been designed to infer ancestral genome sequence or gene order. Since it is so hard or even impossible to know the true scenario of the ancestral genomes, there must be some tools used to test the robustness of the adjacencies obtained from existing methods. However, till now, there is still no systematic work being conducted to tackle this problem. On the other hand, it is a common practice in phylogenetic analysis to assess the confidence rate of the inferred branches, using techniques such as bootstrapping and jackknifing, which have been evaluated as good resampling tools for phylogenetic reconstruction methods. Some of these resampling methods can be potentially used as a robustness test for ancestral genomes. In this paper, we conduct large-scale experiments by using three existing best ancestral genome inference methods and four different resampling techniques. Experimental results show that resampling techniques are useful for assessing the quality of ancestral genomes, while different methods have different preferred resampling techniques. It is suggested the cut-off threshold of 75% should be used to filter bad gene adjacencies.",
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"affiliation": "Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China",
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"abstract": "Ever since Darwin the Tree of Life has provided a framework to study the evolution of organisms. However, comparative genome analyses of microorganisms have shown that genomes are mosaics where different parts have different histories. This is due to horizontal gene transfer that transforms the Tree of Life into a Web of Life where different parts of a genome possess different evolutionary histories. Clustering gene families based on the phylogenetic information they retain allows extracting a majority consensus for the genomes' evolution, and the determination of genes that have a conflicting phylogeny. The latter is of interest in the context of comparative genomics of prokaryotes because these conflicts point towards possible horizontal transfers of genes and metabolic pathways between divergent organisms. We have created a web-based tool gene phylogeny eXplorer, GPX (http://www.bioinformatics.cs.uri.edu/gpx/) that facilitates comparative genome analysis of different species. GPX displays results as an interactive map that allows users to explore and interpret genomic data representing gene evolution. It allows the visualization of consensus and conflicting evolutionary histories of genes which in turn supports the visual identification of possible horizontal gene transfer events. The novel aspect of our approach is that we do not try to analyze DNA sequences directly but instead use self-organizing maps to find structure (clusters) in a space spanned by all possible evolutionary relationships between the genomes in questions.",
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"content": "Ever since Darwin the Tree of Life has provided a framework to study the evolution of organisms. However, comparative genome analyses of microorganisms have shown that genomes are mosaics where different parts have different histories. This is due to horizontal gene transfer that transforms the Tree of Life into a Web of Life where different parts of a genome possess different evolutionary histories. Clustering gene families based on the phylogenetic information they retain allows extracting a majority consensus for the genomes' evolution, and the determination of genes that have a conflicting phylogeny. The latter is of interest in the context of comparative genomics of prokaryotes because these conflicts point towards possible horizontal transfers of genes and metabolic pathways between divergent organisms. We have created a web-based tool gene phylogeny eXplorer, GPX (http://www.bioinformatics.cs.uri.edu/gpx/) that facilitates comparative genome analysis of different species. GPX displays results as an interactive map that allows users to explore and interpret genomic data representing gene evolution. It allows the visualization of consensus and conflicting evolutionary histories of genes which in turn supports the visual identification of possible horizontal gene transfer events. The novel aspect of our approach is that we do not try to analyze DNA sequences directly but instead use self-organizing maps to find structure (clusters) in a space spanned by all possible evolutionary relationships between the genomes in questions.",
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"abstract": "Driven by desirable attributes such as topological characterization and invariance to isometric transformations, the use of the Laplace-Beltrami operator (LBO) and its associated spectrum have been widely adopted among the shape analysis community. Here we demonstrate a novel use of the LBO for shape matching and retrieval by estimating probability densities on its Eigen space, and subsequently using the intrinsic geometry of the density manifold to categorize similar shapes. In our framework, each 3D shape's rich geometric structure, as captured by the low order eigenvectors of its LBO, is robustly characterized via a nonparametric density estimated directly on these eigenvectors. By utilizing a probabilistic model where the square root of the density is expanded in a wavelet basis, the space of LBO-shape densities is identifiable with the unit hyper sphere. We leverage this simple geometry for retrieval by computing an intrinsic Karcher mean (on the hyper sphere of LBO-shape densities) for each shape category, and use the closed-form distance between a query shape and the means to classify shapes. Our method alleviates the need for superfluous feature extraction schemes-required for popular bag-of-features approaches-and experiments demonstrate it to be robust and competitive with the state-of-the-art in 3D shape retrieval algorithms.",
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"abstract": "Computer-aided drug design techniques are important assets in pharmaceutical industry because of their support for research and development of new drugs. Molecular docking (MD) predicts specific compound's binding modes within the active site of target proteins. Since MD is a time-consuming process, existing approaches reduce the number of receptors or ligands in docking by evaluating only small sets of compounds. This restriction in the search space reduces the chances to uniformly cover the diverse space of compounds and misses opportunities to recognize whether new drugs can be identified. Another difficulty with large-scale is analyzing the results, e.g. browsing all directories manually to find which pairs were docked successfully. To address these issues we explored the potential of data provenance analysis and parallel processing of SciCumulus, a cloud Scientific Workflow Management System. We present SciDock, a molecular docking-based virtual screening workflow and evaluate its execution using 10,000 receptor-ligand pairs related to proteases enzymes of protozoan genomes. The overall performance of SciDock using 32 cores, in cloud virtual machines, reaches improvements up to 95.4% when running SciDock with AutoDock and 96.1% when running SciDock with Vina. We show how data provenance improved the result analysis and how it may indicate potential proteases drug targets for protozoan treatments.",
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"abstract": "Thyroid surgery is a common surgical procedure.However, due to the rich blood circulation of the thyroid gland, the anatomical structure is complex, and it adjacent to the trachea, there are important nerves such as recurrent laryngeal nerve and superior laryngeal nerve.The special surgical site has a high surgical risk and is potentially dangerous with multiple complications.Therefore, the observation and nursing of postoperative complications have important significance for the rehabilitation of patients.We observed and nursed 150 patients undergoing thyroid surgery with satisfactory results.Through the observation and taking care of 135 patients with thyroid surgery,we realize that the nurses post-operative closely observed the condition,comprehensive and meticulous care,early detection of complications and cooper-ation with doctors for treatment and rescue is very important to save the patient's life, reduce the postoperative complicat-ions, and even can influence the prognosis.",
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"content": "Thyroid surgery is a common surgical procedure.However, due to the rich blood circulation of the thyroid gland, the anatomical structure is complex, and it adjacent to the trachea, there are important nerves such as recurrent laryngeal nerve and superior laryngeal nerve.The special surgical site has a high surgical risk and is potentially dangerous with multiple complications.Therefore, the observation and nursing of postoperative complications have important significance for the rehabilitation of patients.We observed and nursed 150 patients undergoing thyroid surgery with satisfactory results.Through the observation and taking care of 135 patients with thyroid surgery,we realize that the nurses post-operative closely observed the condition,comprehensive and meticulous care,early detection of complications and cooper-ation with doctors for treatment and rescue is very important to save the patient's life, reduce the postoperative complicat-ions, and even can influence the prognosis.",
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"abstract": "In this paper, we present a novel framework for interactive segmentation of glioblastoma in contrast-enhanced T1-weighted magnetic resonance images. U-net based-fully convolutional network is combined with an interactive refinement technique. Initial segmentation of brain tumor is performed using U-net, and the result is further improved by including complex foreground regions or removing background regions in an iterative manner. The method is evaluated on a research database containing post-operative glioblastoma of 15 patients. Radiologists can refine initial segmentation results in about 90 seconds, which is well below the time of interactive segmentation from scratch using state-of-the-art interactive segmentation tools. The experiments revealed that the segmentation results (Dice score) before and after the interaction step (performed by expert users) are similar. This is most likely due to the limited information in the contrast-enhanced T1-weighted magnetic resonance images used for evaluation. The proposed method is computationally fast and efficient, and could be useful for post-surgical treatment follow-up.",
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"abstract": "There has been significant work on learning realistic, articulated, 3D models of the human body. In contrast, there are few such models of animals, despite many applications. The main challenge is that animals are much less cooperative than humans. The best human body models are learned from thousands of 3D scans of people in specific poses, which is infeasible with live animals. Consequently, we learn our model from a small set of 3D scans of toy figurines in arbitrary poses. We employ a novel part-based shape model to compute an initial registration to the scans. We then normalize their pose, learn a statistical shape model, and refine the registrations and the model together. In this way, we accurately align animal scans from different quadruped families with very different shapes and poses. With the registration to a common template we learn a shape space representing animals including lions, cats, dogs, horses, cows and hippos. Animal shapes can be sampled from the model, posed, animated, and fit to data. We demonstrate generalization by fitting it to images of real animals including species not seen in training.",
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"affiliation": "City University of New York",
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"abstract": "3D-2D medical image matching is a crucial task in image-guided surgery, image-guided radiation therapy and minimally invasive surgery. The task relies on identifying the correspondence between a 2D reference image and the 2D projection of 3D target image. In this paper, we propose a novel image matching framework between 3D CT projection and 2D X-ray image, tailored for vertebra images. The main idea is to learn a vertebra detector by means of deep neural network. The detected vertebra is represented by a bounding box in the 3D CT projection. Next, the bounding box annotated by the doctor on the X-ray image is matched to the corresponding box in the 3D projection. We evaluate our proposed method on our own-collected 3D-2D registration dataset. The experimental results show that our framework outperforms the state-of-the-art neural network-based keypoint matching methods.",
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"content": "3D-2D medical image matching is a crucial task in image-guided surgery, image-guided radiation therapy and minimally invasive surgery. The task relies on identifying the correspondence between a 2D reference image and the 2D projection of 3D target image. In this paper, we propose a novel image matching framework between 3D CT projection and 2D X-ray image, tailored for vertebra images. The main idea is to learn a vertebra detector by means of deep neural network. The detected vertebra is represented by a bounding box in the 3D CT projection. Next, the bounding box annotated by the doctor on the X-ray image is matched to the corresponding box in the 3D projection. We evaluate our proposed method on our own-collected 3D-2D registration dataset. The experimental results show that our framework outperforms the state-of-the-art neural network-based keypoint matching methods.",
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"content": "Quantifying and visualizing the shape of developing biological tissues provide information about the morphogenetic processes in multicellular organisms. The size and shape of biological tissues depend on the number, size, shape, and arrangement of the constituting cells. To better understand the mechanisms that guide tissues into their final shape, it is important to investigate and measure the cellular arrangements within tissues. Here we present a set of techniques that produces detailed 3D models of the individual cells in an epithelial sheet. The inputs to the techniques are a volumetric model of an epithelium and a mesh model of the cell boundaries lying on its apical surface. The techniques include: definition of a Region of Interest (ROI), projection of the ROI vertices first to the basal surface then to the apical surface, projection of apical cell faces to the basal surface, creation of 3D epithelial cell models, and calculation and visualization of length and volume for each cell. In their first utilization we have applied these techniques to construct the individual epithelial cells of the wing imaginal disc of Drosophila melanogaster. To date, 3D epithelial cell models have been created, allowing for the calculation and visualization of cell parameters. The results show position-dependent patterns of cell shape in the wing imaginal disc. Our procedures should offer a general data processing pipeline for the construction of detailed 3D models of a wide variety of epithelial tissues.",
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"abstract": "The operational envelope of gas turbine engines such as those employed in the Army Blackhawk helicopter is constrained by the stability limit of the compression system. Technologies developed to improve the stable operating range of gas turbine compressors lack a fundamental understanding beneficial to design guidance. Improved understanding of the stall inception process and how stall control technologies mitigate such will provide compressors with increased tolerance to stall, thereby expanding the operational envelope of military gas turbine engines. Compressors which consist of multiple stages of stationary and rotating blade rows can include shocks, vortices, separations, secondary flows, shock/boundary layer interactions, and turbulent wakes, all of which grow in severity as a compressor approaches stall. As a compressor nears stall the flow field is no longer periodic from passage to passage so all blade passages must be computed. For a typical multistage compressor this becomes a formidable computational challenge requiring access to massively parallel machines in order to meet the computational and memory demands of the problem. We are using a time-accurate CFD code to simulate the unsteady stall inception process, both with and without stall control technology, in the compression system of the T700 engine which is employed in the Army Blackhawk Helicopter. This work provide the first-ever 3-dimensional viscous time-accurate simulation of the stall inception process in a multistage compressor, providing insight into the causal link between compressor blade design parameters and the stable operating limit, which will be used to guide new design practices leading to compressor designs with increased tolerance to stall. This paper presents progress to date on this DoD Challenge Project.",
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"title": "In Situ Prediction Driven Feature Analysis in Jet Engine Simulations",
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"abstract": "Efficient feature exploration in large-scale data sets using traditional post-hoc analysis approaches is becoming prohibitive due to the bottleneck stemming from I/O and output data sizes. This problem becomes more challenging when an ensemble of simulations are required to run for studying the influence of input parameters on the model output. As a result, scientists are inclining more towards analyzing the data in situ while it resides in the memory. In situ analysis aims at minimizing expensive data movement while maximizing the resource utilization for extraction of important information from the data. In this work, we study the evolution of rotating stall in jet engines using data generated from a large-scale flow simulation under various input conditions. Since the features of interest lack a precise descriptor, we adopt a fuzzy rule-based machine learning algorithm for efficient and robust extraction of such features. For scalable exploration, we advocate for an off-line learning and in situ prediction driven strategy that facilitates in-depth study of the stall. Task-specific information estimated in situ is visualized interactively during the post-hoc analysis revealing important details about the inception and evolution of stall. We verify and validate our method through comprehensive expert evaluation demonstrating the efficacy of our approach.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Efficient feature exploration in large-scale data sets using traditional post-hoc analysis approaches is becoming prohibitive due to the bottleneck stemming from I/O and output data sizes. This problem becomes more challenging when an ensemble of simulations are required to run for studying the influence of input parameters on the model output. As a result, scientists are inclining more towards analyzing the data in situ while it resides in the memory. In situ analysis aims at minimizing expensive data movement while maximizing the resource utilization for extraction of important information from the data. In this work, we study the evolution of rotating stall in jet engines using data generated from a large-scale flow simulation under various input conditions. Since the features of interest lack a precise descriptor, we adopt a fuzzy rule-based machine learning algorithm for efficient and robust extraction of such features. For scalable exploration, we advocate for an off-line learning and in situ prediction driven strategy that facilitates in-depth study of the stall. Task-specific information estimated in situ is visualized interactively during the post-hoc analysis revealing important details about the inception and evolution of stall. We verify and validate our method through comprehensive expert evaluation demonstrating the efficacy of our approach.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Efficient feature exploration in large-scale data sets using traditional post-hoc analysis approaches is becoming prohibitive due to the bottleneck stemming from I/O and output data sizes. This problem becomes more challenging when an ensemble of simulations are required to run for studying the influence of input parameters on the model output. As a result, scientists are inclining more towards analyzing the data in situ while it resides in the memory. In situ analysis aims at minimizing expensive data movement while maximizing the resource utilization for extraction of important information from the data. In this work, we study the evolution of rotating stall in jet engines using data generated from a large-scale flow simulation under various input conditions. Since the features of interest lack a precise descriptor, we adopt a fuzzy rule-based machine learning algorithm for efficient and robust extraction of such features. For scalable exploration, we advocate for an off-line learning and in situ prediction driven strategy that facilitates in-depth study of the stall. Task-specific information estimated in situ is visualized interactively during the post-hoc analysis revealing important details about the inception and evolution of stall. We verify and validate our method through comprehensive expert evaluation demonstrating the efficacy of our approach.",
"fno": "142401a066",
"keywords": [
"Data Analysis",
"Feature Extraction",
"Flow Simulation",
"Fuzzy Systems",
"Jet Engines",
"Learning Artificial Intelligence",
"Robust Extraction",
"Post Hoc Analysis",
"Flow Simulation",
"Jet Engine Simulation",
"Feature Exploration",
"I O Stemming",
"Data Analysis",
"Fuzzy Rule Based Machine Learning Algorithm",
"Off Line Learning",
"In Situ Prediction Driven Feature Analysis",
"Data Visualization",
"Analytical Models",
"Feature Extraction",
"Data Models",
"Jet Engines",
"Machine Learning",
"In Situ Analysis",
"Machine Learning In Visualization",
"Fuzzy Rule Based System",
"Multivariate Analysis",
"Rotating Stall",
"Big Data Analytics"
],
"authors": [
{
"affiliation": null,
"fullName": "Soumya Dutta",
"givenName": "Soumya",
"surname": "Dutta",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Han-Wei Shen",
"givenName": "Han-Wei",
"surname": "Shen",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Jen-Ping Chen",
"givenName": "Jen-Ping",
"surname": "Chen",
"__typename": "ArticleAuthorType"
}
],
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"pubDate": "2018-04-01T00:00:00",
"pubType": "proceedings",
"pages": "66-75",
"year": "2018",
"issn": "2165-8773",
"isbn": "978-1-5386-1424-2",
"notes": null,
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"__typename": "ArticleType"
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