354 research outputs found
User-centric Visualization of Data Provenance
The need to understand and track files (and inherently, data) in cloud computing systems is in high demand. Over the past years, the use of logs and data representation using graphs have become the main method for tracking and relating information to the cloud users. While it is still in use, tracking and relating information with âData Provenanceâ (i.e. series of chronicles and the derivation history of data on meta-data) is the new trend for cloud users. However, there is still much room for improving representation of data activities in cloud systems for end-users.
In this thesis, we propose âUVisP (User-centric Visualization of Data Provenance with Gestalt)â, a novel user-centric visualization technique for data provenance. This technique aims to facilitate the missing link between data movements in cloud computing environments and the end-usersâ uncertain queries over their filesâ security and life cycle within cloud systems.
The proof of concept for the UVisP technique integrates D3 (an open-source visualization API) with Gestaltsâ theory of perception to provide a range of user-centric visualizations. UVisP allows users to transform and visualize provenance (logs) with implicit prior knowledge of âGestaltsâ theory of perception.â We presented the initial development of the UVisP technique and our results show that the integration of Gestalt and the existence of âperceptual key(s)â in provenance visualization allows end-users to enhance their visualizing capabilities, extract useful knowledge and understand the visualizations better. This technique also enables end-users to develop certain methods and preferences when sighting different visualizations. For example, having the prior knowledge of Gestaltâs theory of perception and integrated with the types of visualizations offers the user-centric experience when using different visualizations. We also present significant future work that will help profile new user-centric visualizations for cloud users
NodeTrix: Hybrid Representation for Analyzing Social Networks
The need to visualize large social networks is growing as hardware
capabilities make analyzing large networks feasible and many new data sets
become available. Unfortunately, the visualizations in existing systems do not
satisfactorily answer the basic dilemma of being readable both for the global
structure of the network and also for detailed analysis of local communities.
To address this problem, we present NodeTrix, a hybrid representation for
networks that combines the advantages of two traditional representations:
node-link diagrams are used to show the global structure of a network, while
arbitrary portions of the network can be shown as adjacency matrices to better
support the analysis of communities. A key contribution is a set of interaction
techniques. These allow analysts to create a NodeTrix visualization by dragging
selections from either a node-link or a matrix, flexibly manipulate the
NodeTrix representation to explore the dataset, and create meaningful summary
visualizations of their findings. Finally, we present a case study applying
NodeTrix to the analysis of the InfoVis 2004 coauthorship dataset to illustrate
the capabilities of NodeTrix as both an exploration tool and an effective means
of communicating results
UVisP: User-centric visualization of data provenance with gestalt principles
The need to understand and track files (and inherently, data) in cloud computing systems is in high demand. Over the past years, the use of logs and data representation using graphs have become the main method for tracking and relating information to the cloud users. While being used, tracking related information with 'data provenance' (i.e. series of chronicles and the derivation history of data on metadata) is the new trend for cloud users. However, there is still much room for improving data activity representation in cloud systems for end-users. We propose 'User-centric Visualization of data provenance with Gestalt (UVisP)', a novel user-centric visualization technique for data provenance. This technique aims to facilitate the missing link between data movements in cloud computing environments and the end-users uncertain queries over their files security and life cycle within cloud systems. The proof of concept for the UVisP technique integrates an open-source visualization API with Gestalt's theory of perception to provide a range of user-centric provenance visualizations. UVisP allows users to transform and visualize provenance (logs) with implicit prior knowledge of 'Gestalt's theory of perception.' We presented the initial development of the UVisP technique and our results show that the integration of Gestalt and 'perceptual key(s)' in provenance visualization allows end-users to enhance their visualizing capabilities, to extract useful knowledge and understand the visualizations better
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