graphVizdb: A scalable platform for interactive large graph visualization

Bikakis, Nikos; Liagouris, John; Krommyda, Maria; Papastefanatos, George; Sellis, Timos

doi:10.1109/icde.2016.7498340

Cited by 31 publications

(14 citation statements)

References 11 publications

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“…Other generic visualization tools that could potentially be adjusted to efficiently visualize bipartite graphs are the 2D standalone applications such as graphVizdb [258], Ondex [259], Proviz [260], VizANT [261], GUESS [262], UCINET [263], MAPMAN [264], PATIKA [265], Medusa [266], and Osprey [267], as well as 3D visualization tools such as BioLayout Express [268].…”

Section: Software and Tools For Bipartite Graphsmentioning

confidence: 99%

Bipartite graphs in systems biology and medicine: a survey of methods and applications

et al. 2018

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The latest advances in high-throughput techniques during the past decade allowed the systems biology field to expand significantly. Today, the focus of biologists has shifted from the study of individual biological components to the study of complex biological systems and their dynamics at a larger scale. Through the discovery of novel bioentity relationships, researchers reveal new information about biological functions and processes. Graphs are widely used to represent bioentities such as proteins, genes, small molecules, ligands, and others such as nodes and their connections as edges within a network. In this review, special focus is given to the usability of bipartite graphs and their impact on the field of network biology and medicine. Furthermore, their topological properties and how these can be applied to certain biological case studies are discussed. Finally, available methodologies and software are presented, and useful insights on how bipartite graphs can shape the path toward the solution of challenging biological problems are provided.

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Section: Software and Tools For Bipartite Graphsmentioning

confidence: 99%

Bipartite graphs in systems biology and medicine: a survey of methods and applications

et al. 2018

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“…GraphVizDB. In [154], large graphs are indexed where nodes are users and edges are social links. GraphVizDB divides the whole graph into distinct regions (i.e., a set of groups) with respect to a Euclidean plane.…”

Section: Boosting Ev Approachesmentioning

confidence: 99%

User Group Analytics Survey and Research Opportunities

Omidvar-Tehrani¹,

Amer-Yahia²

2020

IEEE Trans. Knowl. Data Eng.

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User data can be acquired from various domains and is characterized by a combination of demographics such as age and occupation, and user actions such as rating a movie or recording one's blood pressure. User data is appealing to analysts in their role as data scientists who seek to conduct large-scale population studies, and gain insights on various population segments. It is also appealing to users in their role as information consumers who use the social Web for routine tasks such as finding a book club or choosing a physical activity. User data analytics usually relies on identifying group-level behaviors such as "Asian women who publish regularly in databases". Group analytics addresses peculiarities of user data such as noise and sparsity to enable insights. In this survey, we discuss different approaches for each component of user group analytics, i.e., discovery, exploration, and visualization. We focus on related work which arises from combining those components. We also discuss challenges and future directions of having an all-in-one system, where all those components are combined. This survey has been presented in the form of two tutorials [1], [2].

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“…IsaViz [85] allows users to zoom and navigate over the RDF graph, and also it offers several "edit" operations (e.g., delete/add/rename nodes and edges). In the same context, graphVizdb [18,17] is built on top of spatial and database techniques offering interactive visualization over very large (RDF) graphs. A different approach has been adopted in [98], where sampling techniques have been exploited.…”

Section: Graph-based Visualization Systemsmentioning

confidence: 99%

A hierarchical aggregation framework for efficient multilevel visual exploration and analysis

Bikakis

Papastefanatos

Skourla

et al. 2016

Self Cite

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Abstract. Data exploration and visualization systems are of great importance in the Big Data era, in which the volume and heterogeneity of available information make it difficult for humans to manually explore and analyse data. Most traditional systems operate in an offline way, limited to accessing preprocessed (static) sets of data. They also restrict themselves to dealing with small dataset sizes, which can be easily handled with conventional techniques. However, the Big Data era has realized the availability of a great amount and variety of big datasets that are dynamic in nature; most of them offer API or query endpoints for online access, or the data is received in a stream fashion. Therefore, modern systems must address the challenge of on-the-fly scalable visualizations over large dynamic sets of data, offering efficient exploration techniques, as well as mechanisms for information abstraction and summarization. Further, they must take into account different user-defined exploration scenarios and user preferences. In this work, we present a generic model for personalized multilevel exploration and analysis over large dynamic sets of numeric and temporal data. Our model is built on top of a lightweight tree-based structure which can be efficiently constructed on-the-fly for a given set of data. This tree structure aggregates input objects into a hierarchical multiscale model. We define two versions of this structure, that adopt different data organization approaches, well-suited to exploration and analysis context. In the proposed structure, statistical computations can be efficiently performed on-the-fly. Considering different exploration scenarios over large datasets, the proposed model enables efficient multilevel exploration, offering incremental construction and prefetching via user interaction, and dynamic adaptation of the hierarchies based on user preferences. A thorough theoretical analysis is presented, illustrating the efficiency of the proposed methods. The presented model is realized in a web-based prototype tool, called SynopsViz that offers multilevel visual exploration and analysis over Linked Data datasets. Finally, we provide a performance evaluation and a empirical user study employing real datasets.

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graphVizdb: A scalable platform for interactive large graph visualization

Cited by 31 publications

References 11 publications

Bipartite graphs in systems biology and medicine: a survey of methods and applications

Bipartite graphs in systems biology and medicine: a survey of methods and applications

User Group Analytics Survey and Research Opportunities

A hierarchical aggregation framework for efficient multilevel visual exploration and analysis

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