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Patterns and Sequences: Interactive exploration of Clickstreams to Understand Common Visitor Paths

IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2017年第1期23卷 321-330页

作者： Liu, Zhicheng Wang, Yang Dontcheva, Mira Hoffman, Matthew Walker, Seth Wilson, Alan Adobe Res San Jose CA USA Univ Calif Davis Davis CA USA Adobe Syst Inc San Jose CA USA

Modern web clickstream data consists of long, high-dimensional sequences of multivariate events, making it difficult to analyze. Following the overarching principle that the visual interface should provide information about the dataset at multiple levels of granularity and allow users to easily navigate across these levels, we identify four levels of granularity in clickstream analysis: patterns, segments, sequences and events. We present an analytic pipeline consisting of three stages: pattern mining, pattern pruning and coordinated exploration between patterns and sequences. Based on this approach, we discuss properties of maximal sequential patterns, propose methods to reduce the number of patterns and describe design considerations for visualizing the extracted sequential patterns and the corresponding raw sequences. We demonstrate the viability of our approach through an analysis scenario and discuss the strengths and limitations of the methods based on user feedback.

关键词： Clickstream data sequence mining visual analytics event sequences

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visualization and analysis of Rotating Stall for Transonic Jet Engine Simulation

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2016年第1期22卷 847-856页

作者： Chen, Chun-Ming Dutta, Soumya Liu, Xiaotong Heinlein, Gregory Shen, Han-Wei Chen, Jen-Ping Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA Ohio State Univ Dept Mech & Aerosp Engn Columbus OH 43210 USA

Identification of early signs of rotating stall is essential for the study of turbine engine stability. With recent advancements of high performance computing. high-resolution unsteady flow fields allow in depth exploration of rotating stall and its possible causes. Performing stall analysis, however, involves significant effort to process large amounts of simulation data, especially when investigating abnormalities across many time steps. In order to assist scientists during the exploration process, we present a visual analytics framework to identify suspected spatiotemporal regions through a comparative visualization so that scientists are able to focus on relevant data in more detail. To achieve this, we propose efficient stall analysis algorithms derived from domain knowledge and convey the analysis results through juxtaposed interactive plots. Using our integrated visualization system, scientists can visually investigate the detected regions for potential stall initiation and further explore these regions to enhance the understanding of this phenomenon. Positive feedback from scientists demonstrate the efficacy of our system in analyzing rotating stall.

关键词： Turbine flow visualization vortex extraction anomaly detection juxtaposition brushing and linking time series

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Packed-Memory Quadtree: A cache-oblivious data structure for visual exploration of streaming spatiotemporal big data

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COMPUTERS & GRAPHICS-UK 2018年 76卷 117-128页

作者： Toss, Julio Pahins, Cicero A. L. Raffin, Bruno Comba, Joao L. D. Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil Univ Grenoble Alpes Inria CNRS Grenoble INPLIG F-38000 Grenoble France

The visual analysis of large multidimensional spatiotemporal datasets poses challenging questions regarding storage requirements and query performance. Several data structures have recently been proposed to address these problems that rely on indexes that pre-compute different aggregations from a known-a-priori dataset. Consider now the problem of handling streaming datasets, in which data arrive as one or more continuous data streams. Such datasets introduce challenges to the data structure, which now has to support dynamic updates (insertions/deletions) and rebalancing operations to perform self reorganizations. In this work, we present the Packed-Memory Quadtree (PMQ), a novel data structure designed to support visual exploration of streaming spatiotemporal datasets. PMQ is cache-oblivious to perform well under different cache configurations. We store streaming data in an internal index that keeps a spatiotemporal ordering over the data following a quadtree representation, with support for real-time insertions and deletions. We validate our data structure under different dynamic scenarios and compare to competing strategies. We demonstrate how PMQ could be used to answer different types of visual spatiotemporal range queries of streaming datasets. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Spatiotemporal datasets Streaming Real-time exploration Range queries

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TransGraph: Hierarchical exploration of Transition Relationships in Time-Varying Volumetric data

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2011年第12期17卷 2015-2024页

作者： Gu, Yi Wang, Chaoli Michigan Technol Univ Dept Comp Sci Houghton MI 49931 USA

A fundamental challenge for time-varying volume data analysis and visualization is the lack of capability to observe and track data change or evolution in an occlusion-free, controllable, and adaptive fashion. In this paper, we propose to organize a time-varying data set into a hierarchy of states. By deriving transition probabilities among states, we construct a global map that captures the essential transition relationships in the time-varying data. We introduce the TransGraph, a graph-based representation to visualize hierarchical state transition relationships. The TransGraph not only provides a visual mapping that abstracts data evolution over time in different levels of detail, but also serves as a navigation tool that guides data exploration and tracking. The user interacts with the TransGraph and makes connection to the volumetric data through brushing and linking. A set of intuitive queries is provided to enable knowledge extraction from time-varying data. We test our approach with time-varying data sets of different characteristics and the results show that the TransGraph can effectively augment our ability in understanding time-varying data.

关键词： Time-varying data visualization hierarchical representation states transition relationship user interface

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visual Analytic System for Subject Matter Expert Document Tagging using Information Retrieval and Semi-Supervised Machine Learning 23

Visual Analytic System for Subject Matter Expert Document Ta...

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23rd International conference on the Information visualisation (iv) - Incorporating Biomedical visualization, Learning Analytics and Geometric Modelling and Imaging

作者： Hagerman, Craig Brath, Richard Langevin, Scott Uncharted Software Toronto ON Canada

ISBN: (纸本)9781728128382

We present a system that combines ambient visualization, information retrieval and machine learning to facilitate the ease and quality of document classification by subject matter experts for the purpose of organizing documents by "tags" inferred by the resultant classifiers. This system includes data collection, a language model, query exploration, feature selection, semi-supervised machine learning and a visual analytic workflow enabling non-data scientists to rapidly define, verify, and refine high-quality document classifiers.

关键词： visual analytics classifiers machine learning ambient visualization mixed-initiative analytics information retrieval

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Information visualisation: AI and visual Analytics and data Science, iv 2021

Information Visualisation: AI and Visual Analytics and Data ...

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25th International conference Information visualisation, iv 2021

ISBN: (纸本)9781665438278

The proceedings contain 50 papers. The topics discussed include: visualization of sub-network sets by iterative graph sampling from large scale networks;multidimensional data visualization for investigation of skin transparency;towards a visual approach for representing analytical provenance in exploration processes;automatic creation of a vowel dataset for performing prosody analysis in ASD screening;visual analytics to support industrial vehicle fleet planning;visualization tool to support fraud detection;real-time visualization reconstruction in a real-world environment using augmented reality;visually exploring a collaborative augmented reality taxonomy;a brief review of dashboard visualizations employed to support management or business decisions;and visual exploration of the inner representation learned by a convolutional neural network.

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PCNET: Exploratory visual analysis of Large-Scale Network

PCNET: Exploratory Visual Analysis of Large-Scale Network

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IEEE International conference on Big data analysis (ICBDA)

作者： Huang, Jian Wei, Yingmei Du, Xiaolei Natl Univ Def Technol Sci & Technol Informat Syst Engn Lab Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781467395915

The enormous growth of data in the last decades led to big data challenge in the network security field. Traditional visual analysis method for large-scale network exploration is inadequate. Efficient methods for visual clutter reduction, network structure exploration and network behavior detection are needed. In this paper, we propose two methods: Enhanced Histogram Brush (EHB) and Flow-based Fast Newman (FFN) algorithm aim to assist the visual analysis task in large-scale network exploration. The EHB is a novel improvement in Parallel Coordinates to guide exploratory interactions especially for big data. The FFN algorithm can efficiently discover the network hierarchy and extremely reduce the visual clutter in the network layout. A visual analysis tool PCNET is designed and implemented on the basis of these two novel methods. PCNET is capable of visually analyzing vast amounts of network data. To better describe and demonstrate the usefulness and performance of PCNET, we utilize the ChinaVis2015 Challenge dataset as a case study.

关键词： visual analysis big data large-scale network multivariate data

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data analysis and visualisation of Cluster-Oriented Genetic Algorithm output 4

Data analysis and visualisation of Cluster-Oriented Genetic ...

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IEEE International conference on Information visualisation

作者： Packham, ISJ Parmee, IC Univ Plymouth Plymouth Engn Design Ctr Plymouth PL4 8AA Devon England

ISBN: (纸本)0769507433

Simple statistical techniques are investigated to support Cluster-Oriented Genetic Algorithm processes. In a real world application this analysis improves efficiency by reducing effort relating to exploration of multiple visual representations. The preliminary results suggest that the designer can easily be guided towards interesting areas of high performance regions by viewing the most appropriate two- and three-dimensional plots.

关键词： Genetic algorithms

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HiPiler: visual exploration of Large Genome Interaction Matrices with Interactive Small Multiples

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2018年第1期24卷 522-531页

作者： Lekschas, Fritz Bach, Benjamin Kerpedjiev, Peter Gehlenborg, Nils Pfister, Hanspeter Harvard Univ Cambridge MA 02138 USA Harvard Med Sch Boston MA USA

This paper presents an interactive visualization interface HiPiler for the exploration and visualization of regions-of-interest in large genome interaction matrices. Genome interaction matrices approximate the physical distance of pairs of regions on the genome to each other and can contain up to 3 million rows and columns with many sparse regions. Regions of interest (ROls) can be defined, e.g., by sets of adjacent rows and columns, or by specific visual patterns in the matrix. However, traditional matrix aggregation or pan-and-zoom interfaces fail in supporting search, inspection, and comparison of ROls in such large matrices. In HiPiler, ROls are first-class objects, represented as thumbnail-like "snippets". Snippets can be interactively explored and grouped or laid out automatically in scatterplots, or through dimension reduction methods. Snippets are linked to the entire navigable genome interaction matrix through brushing and linking. The design of HiPiler is based on a series of semi-structured interviews with 10 domain experts involved in the analysis and interpretation of genome interaction matrices. We describe six exploration tasks that are crucial for analysis of interaction matrices and demonstrate how HiPiler supports these tasks. We report on a user study with a series of data exploration sessions with domain experts to assess the usability of HiPiler as well as to demonstrate respective findings in the data.

关键词： Interactive Small Multiples Matrix Comparison Biomedical visualization Genomics

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visual Process Mining: Event data exploration and analysis

Visual Process Mining: Event Data Exploration and Analysis

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IEEE conference visual Analytics Sci Technology

作者： Bodesinsky, Peter Alsallakh, Bilal Gschwandtner, Theresia Miksch, Silvia Vienna Univ Technol Vienna Austria

ISBN: (纸本)9781479962273

The research field of Process Mining deals with the extraction of information from event logs. Since large amounts of event data are generated every day, analyzing these often unstructured event logs poses a research challenge. To cope with the complexity of the data and the associated mining tasks, appropriate visualizations and interactive means are needed. We present our early prototype for the visual exploration of event sequences and patterns. Our approach combines (1) a visual representation of event sequences emphasizing recurring event patterns, (2) automated pattern mining methods, as well as (3) interactive means for exploration. We provide first steps to support browsing event logs in an interactive environment and to facilitate the inspection of recurring pattern locations within the context of the surrounding events.

关键词： data mining data visualisation interactive systems automated pattern mining method browsing event logs event data exploration interactive environment visual exploration visual process mining Business data mining data visualization Inspection Prototypes Sorting visualization data visualisation Interactive environment data mining visualization interactive systems Business PROTOTYPE event log sorting programs mining Inspection exploration event patterns

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