To understand software systems it is common practice to explore runtime information such as method calls. System behavior analysis can further be facilitated by additionally taking runtime data dependencies into accou...
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ISBN:
(纸本)9781424448975
To understand software systems it is common practice to explore runtime information such as method calls. System behavior analysis can further be facilitated by additionally taking runtime data dependencies into account. In object oriented systems, a typical data dependency is the information about which objects are accessed by the traced method calls. To support software engineers in handling the massive amount of information that execution traces typically consist of highly scalable visualizations are needed. In this paper, we propose a trace-visualization technique that (a) explicitly visualizes both, method calls and object accesses, and (b) provides high scalability to handle large execution traces. With regard to the visualization technique proposed, we give a systematic overview of visual patterns that are to be expected and of their meanings with respect to system behavior. Additionally, we present the results of three case-studies to show how our approach facilitates developers in comprehending the behavior of complex C++ software systems.
The rise in the use of social network sites allows us to collect large amounts of user reported data oil social structures and analysis of this data Could provide useful insights for many of the social sciences. This ...
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ISBN:
(纸本)9783642036576
The rise in the use of social network sites allows us to collect large amounts of user reported data oil social structures and analysis of this data Could provide useful insights for many of the social sciences. This analysis is typically the domain of Social Network analysis, and visualization of these structures often proves invaluable ill understanding them. However, currently available visualanalysis tools are not very well suited to handle the massive scale of this network data, and often resolve to displaying small ego networks or heavily abstracted networks. In this paper, we present Honeycomb, a visualization tool that is able to deal with much larger scale data (with millions of connections), which we illustrate by using a large scale corporate social networking site as an example. Additionally, we introduce a new probability based network metric to guide users to potentially interesting or anomalous patterns and discuss lessons learned during design and implementation.
This paper presents a new approach for outlier handling and analysis of geospatial-autocorrelation data. We improved a new outlier detecting model named Comparative Method of Unit Matrix(CMUM) based on Standard-Deviat...
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Neurobiology investigates how anatomical and physiological relationships in the nervous system mediate behavior. Molecular genetic techniques, applied to species such as the common fruit fly Drosophila melanogaster, h...
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Neurobiology investigates how anatomical and physiological relationships in the nervous system mediate behavior. Molecular genetic techniques, applied to species such as the common fruit fly Drosophila melanogaster, have proven to be an important tool in this research. Large databases of transgenic specimens are being built and need to be analyzed to establish models of neural information processing. In this paper we present an approach for the exploration and analysis of neural circuits based on such a database. We have designed and implemented BrainGazer, a system which integrates visualization techniques for volume data acquired through confocal microscopy as well as annotated anatomical structures with an intuitive approach for accessing the available information. We focus on the ability to visually query the data based on semantic as well as spatial relationships. Additionally, we present visualization techniques for the concurrent depiction of neurobiological volume data and geometric objects which aim to reduce visual clutter. The described system is the result of an ongoing interdisciplinary collaboration between neurobiologists and visualization researchers.
With the explosion of social media, scalability becomes a key challenge. There are two main aspects of the problems that arise: 1) data volume: how to manage and analyze huge datasets to efficiently extract patterns, ...
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ISBN:
(纸本)9781615671090
With the explosion of social media, scalability becomes a key challenge. There are two main aspects of the problems that arise: 1) data volume: how to manage and analyze huge datasets to efficiently extract patterns, 2) data understanding: how to facilitate understanding of the patterns by users? To address both aspects of the scalability challenge, we present a hybrid approach that leverages two complementary disciplines, data mining and information visualization. In particular, we propose 1) an analytic data model for content-based networks using tensors;2) an efficient high-order clustering framework for analyzing the data;3) a scalable context-sensitive graph visualization to present the clusters. We evaluate the proposed methods using both synthetic and real datasets. In terms of computational efficiency, the proposed methods are an order of magnitude faster compared to the baseline. In terms of effectiveness, we present several case studies of real corporate social networks.
Hidden Markov Models (HMMs) have been successfully employed in the exploration and modeling of musical structure, with applications in Music Information Retrieval. This paper focuses on an aspect of HMM training that ...
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ISBN:
(纸本)9783642023934
Hidden Markov Models (HMMs) have been successfully employed in the exploration and modeling of musical structure, with applications in Music Information Retrieval. This paper focuses on an aspect of HMM training that remains relatively unexplored in musical applications, namely the determination of HMM topology. We demonstrate that this complex problem can be effectively addressed through search over model topology space, conducted by HMM state merging and/or splitting. Once successfully identified, the HMM topology that is optimal with respect to a given data set can help identify hidden (latent) variables that are important in shaping the data set's visible structure. These variables are identified by suitable interpretation of the HMM states for the selected topology. As an illustration, we present two case studies that successfully tackle two classic problems in music computation, namely (i) algorithmic statistical segmentation and (ii) meter induction from a sequence of durational patterns.
The comprehensive Understanding of today's software systems is a daunting activity, because of the sheer size and complexity that such systems exhibit. Moreover software systems evolve, which dramatically increase...
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ISBN:
(纸本)9781424434947
The comprehensive Understanding of today's software systems is a daunting activity, because of the sheer size and complexity that such systems exhibit. Moreover software systems evolve, which dramatically increases the amount of data one needs to analyze in order to gain insights into such systems. Indeed, software complexity is recognized as one of the major challenges to the development and maintenance of industrial-size software. projects. Our vision is a 3D visualization approach which helps software engineers build knowledge about their systems. We settled on an intuitive metaphor which depicts software systems as cities. To validate the ideas emerging from our research, we implemented a tool called CodeCity. We devised a set of visualization techniques to support tasks related to program comprehension, design quality assessment, and evolution analysis, and applied them on large open-source systems written in Java, C++, or Smalltalk. Our next research goals are enriching our metaphor with meaningful representations for relations and encoding higher-level information.
In this paper we describe the participation of TELECOM ParisTech in the Large Scale visual Concept Detection and Annotation Task at the ImageClef 2009 challenge. This year, the focus was in the extension of (i) the am...
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In this paper we describe the participation of TELECOM ParisTech in the Large Scale visual Concept Detection and Annotation Task at the ImageClef 2009 challenge. This year, the focus was in the extension of (i) the amount of data available for training and testing, and (ii) the number of concepts to be annotated. We use Canonical Correlation analysis in order to infer a latent space where text and visual description are highly correlated. Starting from a visual description of a test image, we first map it into the latent space, then we predict the underlying text features (and also annotations) which best fit the visual ones in the latent space. Our method is very fast while achieving good results.
Congenital nystagmus (CN) is an ocular-motor disorder characterised by involuntary, conjugated ocular oscillations, that can arise since the first months of life. Pathogenesis of congenital nystagmus is still under in...
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ISBN:
(纸本)9783540892076
Congenital nystagmus (CN) is an ocular-motor disorder characterised by involuntary, conjugated ocular oscillations, that can arise since the first months of life. Pathogenesis of congenital nystagmus is still under investigation. In general, CN patients show a considerable decrease of their visual acuity: image fixation on the retina is disturbed by nystagmus continuous oscillations, mainly horizontal. However, image stabilisation is still achieved during the short periods in which eye velocity slows down while the target image is placed onto the fovea (called foveation intervals). To quantify the extent of nystagmus, eye movement recording are routinely employed, allowing physicians to extract and analyse nystagmus main features such as shape, amplitude and frequency. Using eye movement recording, it is also possible to compute estimated visual acuity predictors: analytical functions which estimates expected visual acuity using signal features such as foveation time and foveation position variability. Use of those functions add information to typical visual acuity measurement (e. g. Landolt C test) and could be a support for therapy planning or monitoring. This study focus on robust detection of CN patients' foveations. Specifically, it proposes a method to recognize the exact signal tracts in which a subject foveates, This paper also analyses foveation sequences. About 50 eye-movement recordings, either infrared-oculographic or electrooculographic, from different CN subjects were acquired. Results suggest that an exponential interpolation for the slow phases of nystagmus could improve foveation time computing and reduce influence of breaking saccades and data noise. Moreover a concise description of foveation sequence variability can be achieved using non-fitting splines.
Using data from China, we investigate the relationship between land use structure and socioeconomic structure of the whole country and regions in different types of land utilization. The conclusions are as follows. (1...
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ISBN:
(纸本)9781424439706
Using data from China, we investigate the relationship between land use structure and socioeconomic structure of the whole country and regions in different types of land utilization. The conclusions are as follows. (1) Land use structure and socioeconomic structure interact with each other significantly. The transformation of industrial structure and the optimization of land use structure make significant contributions to Chinese socioeconomic development. It's possible for Chinese government to conduct macroeconomic regulation by using means of land policies. (2) The interaction between land use structure and socioeconomic structure is of significant regional difference in China. Using hierarchical clustering method, land use structures of 31 Chinese provinces can be classified into four types. We prove that further analyzing regions in different types of land utilization is helpful to deepen the exploration of the relationship between land use structure and socioeconomic structure.
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