|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Steering data quality with visual analytics:The complexity challenge显示文摘Data quality management,especially data cleansing,has been extensively studied for many years in the areas of data management and visual analytics.In the paper,we first review and explore the relevant work from the research areas of data management,visual analytics and human-computer interaction.Then for different types of data such as multimedia data,textual data,trajectory data,and graph data,we summarize the common methods for improving data quality by leveraging data cleansing techniques at different analysis stages.Based on a thorough analysis,we propose a general visual analytics framework for interactively cleansing data.Finally,the challenges and opportunities are analyzed and discussed in the context of data and humans. | Shixia Liu Gennady Andrienko Yingcai Wu Nan Cao Liu Jiang Conglei Shi Yu-Shuen Wang Seokhee Hong | 2018 | Visual Informatics2018，2，4： | 5 |
| 2 | A visual analytics framework for spatio-temporal analysis and modelling显示文摘 | Natalia Andrienko Gennady Andrienko | 2013 | Data Mining and Knowledge Discovery2013，，1： | 2 |
| 3 | Blending Aggregation and Selection: Adapting Parallel Coordinates for the Visualization of Large Datasets 显示文摘 | Gennady Andrienko Natalia Andrienko | 2005 | The Cartographic Journa (S0008-7041)2005，42，1： | 1 |
| 4 | Interactive maps for visual data exploration显示文摘 | Gennady L. Andrienko Natalia V. Andrienko | 1999 | International Journal of Geographical Information Science1999，，4： | 1 |
| 5 | An event-based conceptual model for context-aware movement analysis显示文摘 | Gennady Andrienko Natalia Andrienko Marco Heurich | 2011 | International Journal of Geographical Information Science2011，，9： | 1 |
| 6 | Identifying,exploring,and interpreting time series shapes in multivariate time intervals显示文摘We introduce a concept of episode referring to a time interval in the development of a dynamic phenomenon that is characterized by multiple time-variant attributes.A data structure representing a single episode is a multivariate time series.To analyse collections of episodes,we propose an approach that is based on recognition of particular patterns in the temporal variation of the variables within episodes.Each episode is thus represented by a combination of patterns.Using this representation,we apply visual analytics techniques to fulfil a set of analysis tasks,such as investigation of the temporal distribution of the patterns,frequencies of transitions between the patterns in episode sequences,and co-occurrences of patterns of different variables within same episodes.We demonstrate our approach on two examples using real-world data,namely,dynamics of human mobility indicators during the COVID-19 pandemic and characteristics of football team movements during episodes of ball turnover. | Gota Shirato Natalia Andrienko Gennady Andrienko | 2023 | Visual Informatics2023，7，1： | 1 |
| 7 | Semantic trajectories modeling and analysis显示文摘 | Christine Parent Stefano Spaccapietra Chiara Renso Gennady Andrienko Natalia Andrienko Vania Bogorny Maria Luisa Damiani Aris Gkoulalas-Divanis Jose Macedo Nikos Pelekis Yannis Theodoridis Zhixian Yan | 2013 | ACM Computing Surveys (CSUR)2013，，4： | 1 |
| 8 | A theoretical model for pattern discovery in visual analytics显示文摘The word‘pattern’frequently appears in the visualisation and visual analytics literature,but what do we mean when we talk about patterns?We propose a practicable definition of the concept of a pattern in a data distribution as a combination of multiple interrelated elements of two or more data components that can be represented and treated as a unified whole.Our theoretical model describes how patterns are made by relationships existing between data elements.Knowing the types of these relationships,it is possible to predict what kinds of patterns may exist.We demonstrate how our model underpins and refines the established fundamental principles of visualisation.The model also suggests a range of interactive analytical operations that can support visual analytics workflows where patterns,once discovered,are explicitly involved in further data analysis. | Natalia Andrienko Gennady Andrienko Silvia Miksch Heidrun Schumann Stefan Wrobel | 2021 | Visual Informatics2021，5，1： | 1 |
| 9 | Representation and its relationship with cartographic visual- ization: a research agenda显示文摘 | David Fairbairn Gennady Andrienko Natalia Andrienko | 2001 | Cartography and Geographic In- formation Science2001，，1： | 1 |
| 10 | Geovisual Analytics for Spatital Decision Support显示文摘 | GENNADY ANDRIENKO NATALIA ANDRIENKO | 2007 | In- ternational Journal Geographical Information Science2007，21，8： | 1 |
| 11 | Visual exploration of movement and event data with interactive time masks显示文摘We introduce the concept of time mask,which is a type of temporal filter suitable for selection of multiple disjoint time intervals in which some query conditions fulfil.Such a filter can be applied to time-referenced objects,such as events and trajectories,for selecting those objects or segments of trajectories that fit in one of the selected time intervals.The selected subsets of objects or segments are dynamically summarized in various ways,and the summaries are represented visually on maps and/or other displays to enable exploration.The time mask filtering can be especially helpful in analysis of disparate data(e.g.,event records,positions of moving objects,and time series of measurements),which may come from different sources.To detect relationships between such data,the analyst may set query conditions on the basis of one dataset and investigate the subsets of objects and values in the other datasets that co-occurred in time with these conditions.We describe the desired features of an interactive tool for time mask filtering and present a possible implementation of such a tool.By example of analysing two real world data collections related to aviation and maritime traffic,we show the way of using time masks in combination with other types of filters and demonstrate the utility of the time mask filtering. | Natalia Andrienko Gennady Andrienko Elena Camossi Christophe Claramunt Jose Manuel Cordero Garcia Georg Fuchs Melita Hadzagic Anne-Laure Jousselme Cyril Ray David Scarlatti George Vouros | 2017 | Visual Informatics2017，1，1： | 0 |
| 12 | A learning-based approach for efficient visualization construction显示文摘We propose an approach to underpin interactive visual exploration of large data volumes by training Learned Visualization Index(LVI).Knowing in advance the data,the aggregation functions that are used for visualization,the visual encoding,and available interactive operations for data selection,LVI allows to avoid time-consuming data retrieval and processing of raw data in response to user’s interactions.Instead,LVI directly predicts aggregates of interest for the user’s data selection.We demonstrate the efficiency of the proposed approach in application to two use cases of spatio-temporal data at different scales. | Yongjian Sun Jie Li Siming Chen Gennady Andrienko Natalia Andrienko Kang Zhang | 2022 | Visual Informatics2022，6，1： | 0 |
| 13 | Toward flexible visual analytics augmented through smooth display transitions显示文摘Visualizing big and complex multivariate data is challenging.To address this challenge,we propose flexible visual analytics(FVA)with the aim to mitigate visual complexity and interaction complexity challenges in visual analytics,while maintaining the strengths of multiple perspectives on the studied data.At the heart of our proposed approach are transitions that fluidly transform data between userrelevant views to offer various perspectives and insights into the data.While smooth display transitions have been already proposed,there has not yet been an interdisciplinary discussion to systematically conceptualize and formalize these ideas.As a call to further action,we argue that future research is necessary to develop a conceptual framework for flexible visual analytics.We discuss preliminary ideas for prioritizing multi-aspect visual representations and multi-aspect transitions between them,and consider the display user for whom such depictions are produced and made available for visual analytics.With this contribution we aim to further facilitate visual analytics on complex data sets for varying data exploration tasks and purposes based on different user characteristics and data use contexts. | Christian Tominski Gennady Andrienko Natalia Andrienko Susanne Bleisch Sara Irina Fabrikant Eva Mayr Silvia Miksch Margit Pohl AndréSkupin | 2021 | Visual Informatics2021，5，3： | 0 |
| 14 | Exploring and visualizing temporal relations in multivariate time series显示文摘This paper introduces an approach to analyzing multivariate time series(MVTS)data through progressive temporal abstraction of the data into patterns characterizing the behavior of the studied dynamic phenomenon.The paper focuses on two core challenges:identifying basic behavior patterns of individual attributes and examining the temporal relations between these patterns across the range of attributes to derive higher-level abstractions of multi-attribute behavior.The proposed approach combines existing methods for univariate pattern extraction,computation of temporal relations according to the Allen’s time interval algebra,visual displays of the temporal relations,and interactive query operations into a cohesive visual analytics workflow.The paper describes the application of the approach to real-world examples of population mobility data during the COVID-19 pandemic and characteristics of episodes in a football match,illustrating its versatility and effectiveness in understanding composite patterns of interrelated attribute behaviors in MVTS data. | Gota Shirato Natalia Andrienko Gennady Andrienko | 2023 | Visual Informatics2023，7，4： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员