Visualizing large-scale RDF data using Subsets, Summaries, and Sampling in Oracle

Sundara, Seema; Atre, Medha; Kolovski, Vladimir; Das, Shantanu; Wu, Zhe; Chong, Eugene Inseok; Srinivasan, Jagannathan

doi:10.1109/icde.2010.5447795

Cited by 10 publications

(7 citation statements)

References 12 publications

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“…[28] supports ad-hoc hierarchies which are manually defined by the users. Differents approaches have been adopted in [29,30], where sampling techniques have been exploited. Other works adopt edge bundling techniques which aggregate graph edges to bundles [31,32,33,34,35,36].…”

Section: Hierarchical Explorationmentioning

confidence: 99%

Big Data Visualization Tools

Bikakis¹

2018

Preprint

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Data visualization is the presentation of data in a pictorial or graphical format, and a data visualization tool is the software that generates this presentation. Data visualization provides users with intuitive means to interactively explore and analyze data, enabling them to effectively identify interesting patterns, infer correlations and causalities, and supports sense-making activities. OverviewExploring, visualizing and analysing data is a core task for data scientists and analysts in numerous applications. Data visualization 2 [1] provides intuitive ways for the users to interactively explore and analyze data, enabling them to effectively identify interesting patterns, infer correlations and causalities, and support sense-making activities.This article appears in Encyclopedia of Big Data Technologies, Springer, 2018 2 Throughout the article, terms visualization and visual exploration, as well as terms tool and system are used interchangeably.

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Section: Hierarchical Explorationmentioning

confidence: 99%

Big Data Visualization Tools

Bikakis¹

2018

Preprint

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“…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. Finally, ZoomRDF [114] employs a space-optimized visualization algorithm in order to increase the number of resources which are displayed.…”

Section: Graph-based Visualization Systemsmentioning

confidence: 99%

A hierarchical aggregation framework for efficient multilevel visual exploration and analysis

Bikakis

Papastefanatos

Skourla

et al. 2016

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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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“…Other than naive random sampling [30], extracting relevant parts of Linked Data graphs has not been done before. However, there are a number of related approaches that deserve mentioning: relevance ranking for Linked Data, generating SPARQL benchmark queries, graph rewriting techniques, and non-deterministic network sampling techniques.…”

Section: Related Workmentioning

confidence: 99%

Structural Properties as Proxy for Semantic Relevance in RDF Graph Sampling

Rietveld

Hoekstra

Schlobach

et al. 2014

The Semantic Web – ISWC 2014

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The Linked Data cloud has grown to become the largest knowledge base ever constructed. Its size is now turning into a major bottleneck for many applications. In order to facilitate access to this structured information, this paper proposes an automatic sampling method targeted at maximizing answer coverage for applications using SPARQL querying. The approach presented in this paper is novel: no similar RDF sampling approach exist. Additionally, the concept of creating a sample aimed at maximizing SPARQL answer coverage, is unique. We empirically show that the relevance of triples for sampling (a semantic notion) is influenced by the topology of the graph (purely structural), and can be determined without prior knowledge of the queries. Experiments show a significantly higher recall of topology based sampling methods over random and naive baseline approaches (e.g. up to 90% for Open-BioMed at a sample size of 6%).

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Visualizing large-scale RDF data using Subsets, Summaries, and Sampling in Oracle

Cited by 10 publications

References 12 publications

Big Data Visualization Tools

Big Data Visualization Tools

A hierarchical aggregation framework for efficient multilevel visual exploration and analysis

Structural Properties as Proxy for Semantic Relevance in RDF Graph Sampling

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