Exploration and Visualization in the Web of Big Linked Data: A Survey of the State of the Art

Data exploration and visualization systems are of great importance in the Big Data era. Exploring and visualizing very large datasets has become a major research challenge, of which scalability is a vital requirement. In this survey, we describe the major prerequisites and challenges that should be addressed by the modern exploration and visualization systems. Considering these challenges, we present how state-of-the-art approaches from the Database and Information Visualization communities attempt to handle them. Finally, we survey the systems developed by Semantic Web community in the context of the Web of Linked Data, and discuss to which extent these satisfy the contemporary requirements.Comment: 6th International Workshop on Linked Web Data Management (LWDM 2016

Using SWET-QUM to Compare the Quality in Use of Semantic Web Exploration Tools

In order to make Semantic Web tools more appealing to lay-users, a key factor is their Quality in Use, the quality of the user experience when interacting with them. To assess and motivate the improvement of the quality in use, it is necessary to have a quality model that guides its evaluation and facilitates comparability. The proposal is based on the international standard ISO/IEC 25010:2011 and focuses on Semantic Web exploration tools, those that make it possible for lay-users to browse and visualise it. The model is applied to compare the three main Semantic Web exploration tools that feature facets and the pivoting operation. The analysis assesses that the work being carried out with one of them, as part of a User-Centred Development process with iterative user evaluations, outperforms the other two tools.The work described in this paper has been partially supported by Spanish Ministry of Science and Innovation through the InDAGuS (Sustainable Open Government Data Infrastructures with Geospatial Features) research project (TIN2012-37826-C02)

A Hierarchical Aggregation Framework for Efficient Multilevel Visual Exploration and Analysis

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. 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. 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 model. The proposed model is realized in a web-based prototype tool, called SynopsViz that offers multilevel visual exploration and analysis over Linked Data datasets.Comment: Semantic Web Journal 2016 (to appear

Facets and Pivoting for Flexible and Usable Linked Data Exploration

Abstract. The success of Open Data initiatives has increased the amount of data available on the Web. Unfortunately, most of this data is only available in raw tabular form, what makes analysis and reuse quite difficult for non-experts. Linked Data principles allow for a more sophisticated approach by making explicit both the structure and semantics of the data. However, from the end-user viewpoint, they continue to be monolithic files completely opaque or difficult to explore by making tedious semantic queries. Our objective is to facilitate the user to grasp what kind of entities are in the dataset, how they are interrelated, which are their main properties and values, etc. Rhizomer is a tool for data publishing whose interface provides a set of components borrowed from Information Architecture (IA) that facilitate awareness of the dataset at hand. It automatically generates navigation menus and facets based on the kinds of things in the dataset and how they are described through metadata properties and values. Moreover, motivated by recent tests with end-users, it also provides the possibility to pivot among the faceted views created for each class of resources in the dataset