The wide adaptation of the Semantic Web and the Resource Description Framework (RDF) has made available many important datasets. The SPARQL query language facilitates the exploration of this information, which is available in a semi-structured way that does not comply with relational data models, deviating from exploration techniques that most researchers are familiar with. Usually, only people with training and extensive knowledge of the RDF model can explore and understand it in depth. We present here a platform that supports the users with querying, exploring and visualizing information available in SPARQL endpoints. A dedicated visualization module, built upon a knowledge database, allows us to provide case-specific visualization solutions for SPARQL query results. The selection is based exclusively on features extracted from the result, without any knowledge about the structure, content and characteristics of the underlying dataset.

The wide adoption of the RDF data model, as well as the Linked Open Data initiative, have made available large linked datasets that have the potential to offer invaluable knowledge. Accessing, evaluating and understanding these datasets as published, though, requires extensive training and experience in the field of the Semantic Web, making these valuable sources of information inaccessible to a wider audience. In the recent years, there have been many efforts to create systems that allow the visualization and exploration of this information. Some of there systems rely on techniques that allow them to limit the volume of the displayed information, by providing aggregated, filtered or summarized access to the datasets while others initialize the exploration of the dataset based on actions performed by the users, such as keyword searches and queries. The underlying technique is key for the sustainability of the system, the definition of the requirements that the input must comply with, the datasets that can be visualized as well as the visualization types provided. We present here a survey on these techniques, their strengths and weaknesses as well as the datasets that they can support. The survey will provide the reader with a deep understanding of the challenges regarding the visualization of large linked datasets, a categorization of the developed techniques to resolve them as well as an overview of the available systems and their functionalities.

There has been significant effort in recent years to explore and navigate very large linked datasets, due to the increase of their availability. Many techniques have been developed that extract the information from such datasets and present it to the user as diagrams, while others take advantage of the hierarchies of the datasets to filter and aggregate them, allowing the users to access specific information. In order to overcome the limitations regarding the volume of the presented information, we have developed a novel technique that enables the interactive visualization as one continuous graph of datasets with millions of elements. IVLG is a fully fledged prototype system that implements this technique based on a client-server architecture, enabling many users to have concurrently access to the information through a user-friendly interface. It allows the user to navigate the dataset through different levels of abstraction and locate information using innovative exploration techniques. A carefully designed storage schema along with an API that takes advantage of the appropriate indexing handles datasets with millions of elements without raising any performance issues, even when accessed from devices with limited computational resources. The proposed demonstration showcases the advantages and technical features of IVLG. A series of demonstration scenarios will show how IVLG can adapt accordingly and handle diverse real and synthetic datasets that vary on size and average node degree and how the system functionalities support the user experience and the exploration of the information.

Significant effort has been dedicated in recent years to the exploitation of very large linked datasets due to the importance of the information they contain and the increase of their availability. Some techniques have been developed that handle the volume of these datasets by aggregating their information based on the data structure or model. Other approaches exploit specific characteristics of the datasets, such as semantic annotations, to present the information to the users in semantically defined ways. In an era that the volume and diversity of the available information increases exponentially and more users are interested in exploring it, it is crucial to provide a technique that will allow the exploitation of diverse and very large linked datasets in a scalable way independent of any characteristics of the input dataset. We present here a generic divide and conquer technique that can offer the required scalability to exploit any input dataset regardless its size and characteristics. The proposed technique has been tested in the context of interactive representation of very large linked datasets as graphs.

ICT advances in emerging domains such as Internet of Things (IoT), Augmented Reality/ Virtual Reality (AR/VR), big data analytics, cyber-physical systems and cloud computing have revolutionized and boosted the creation of cyberworlds as information spaces that allow us to augment the way we interact with each other and with the physical world. Naturally, other than businesses cyberworlds can benefit modern hyper connected societies at their entirety (transport, mobility, health, smart living, etc.) and further to that the physical world around us can also be part of such process. In the present paper the focus is given on citizen-powered cyberworlds for Environmental Monitoring which are created from crowdsourced observations engaging, through gamification, citizens and communities. The means of engagement include serious gaming, collection of geo-tagged IoT, such as images, video and sensor measurements as well as management and storage of diverse IoT as OGC compliant observations all conveyed into a dedicated information space.

We present a novel platform for the interactive visualization of very large graphs. The platform enables the user to interact with the visualized graph in a way that is very similar to the exploration of maps at multiple levels. Our approach involves an offline preprocessing phase that builds the layout of the graph by assigning coordinates to its nodes with respect to a Euclidean plane. The respective points are indexed with a spatial data structure, i.e., an R-tree, and stored in a database. Multiple abstraction layers of the graph based on various criteria are also created offline, and they are indexed similarly so that the user can explore the dataset at different levels of granularity, depending on her particular needs. Then, our system translates user operations into simple and very efficient spatial operations (i.e., window queries) in the backend. This technique allows for a fine-grained access to very large graphs with extremely low latency and memory requirements and without compromising the functionality of the tool. Our web-based prototype supports three main operations: (1) interactive navigation, (2) multi-level exploration, and (3) keyword search on the graph metadata.