High-level visualization over big linked data

The Linked Open Data (LOD) Cloud is continuously expanding and the number of complex and large sources is raising. Understanding at a glance an unknown source is a critical task for LOD users but it can be facilitated by visualization or exploration tools. H-BOLD (High-level visualization over Big Open Linked Data) is a tool that allows users with no a-priori knowledge on the domain nor SPARQL skills to start navigating and exploring Big Linked Data. Users can start from a high-level visualization and then focus on an element of interest to incrementally explore the source, as well as perform a visual query on certain classes of interest. At the moment, 32 Big Linked Data (with more than 500.000 triples) exposing a SPARQL endpoint can be explored by using H-BOLD

Providing effective visualizations over big linked data

The number and the size of Linked Data sources are constantly increasing. In some lucky case, the data source is equipped with a tool that guides and helps the user during the exploration of the data, but in most cases, the data are published as an RDF dump through a SPARQL endpoint that can be accessed only through SPARQL queries. Although the RDF format was designed to be processed by machines, there is a strong need for visualization and exploration tools. Data visualizations make big and small linked data easier for the human brain to understand, and visualization also makes it easier to detect patterns, trends, and outliers in groups of data. For this reason, we developed a tool called H-BOLD (Highlevel Visualization over Big Linked Open Data). H-BOLD aims to help the user exploring the content of a Linked Data by providing a high-level view of the structure of the dataset and an interactive exploration that allows users to focus on the connections and attributes of one or more classes. Moreover, it provides a visual interface for querying the endpoint that automatically generates SPARQL queries

Big Data Visualization Tools

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.Comment: This article appears in Encyclopedia of Big Data Technologies, Springer, 201

Online visualization of bibliography Using Visualization Techniques

Visualization is a concept where we can represent some raw data in the form of graphs, images, charts, etc. which will be very helpful for the end-user to correlate and be able to understand the relationships between the data elements in a single screen. Representing the bibliographic information of the computer science journals and proceedings using Visualization technique would help user choose a particular author and navigate through the hierarchy and find out what papers the author has published, the keywords of the papers, what papers cite them, the co-authors along with the main author, and how many papers are published by the author selected by the user and so on in a single page. These information is right now present in a scattered manner and the user has to search on websites like Google Scholar [1], Cite Seer [2] to get these bibliographic records. By the use of visualization techniques, all the information can be accessed on a single page by having a graph like points on the page, where the user can search for a particular author and the author and its co-authors are represented in the form of points. The goal of this project is to enhance current bibliography web services with an intuitive interactive visualization interface and to improve user understanding and conceptualization. In this project, we develop a simple web-interface which will take a search query from the user and find the related information like author\u27s name, the co-authors, number of papers published by him, related keywords, citations referred etc. The project uses the bibliographic records which are available as XML files from the Citeseer database[2], extracts the data into the database and then queries the database for the results using a web service. The data which is extracted is then presented visually to allow the user to conceptualize the results in a better way and help him/her find the articles of interest with utmost ease. In addition the user can interactively navigate the visual results to get more information about any of the article or the author displayed. So here we present both paper centric view and author centric view to the user by representing data in terms of graphs. The nodes in the graphs obtained for paper centric views and author centric views are color coded based on the paper’s weight parameter ( popularity of the paper ). For the paper centric view, the papers which are referring other papers are represented by providing a directed arrow from referred paper to referenced paper. Overall the idea here was to represent this related data in the form of a tree, so that the user can correlate all the data and get the relationships between them

Structuring visual exploratory analysis of skill demand

The analysis of increasingly large and diverse data for meaningful interpretation and question answering is handicapped by human cognitive limitations. Consequently, semi-automatic abstraction of complex data within structured information spaces becomes increasingly important, if its knowledge content is to support intuitive, exploratory discovery. Exploration of skill demand is an area where regularly updated, multi-dimensional data may be exploited to assess capability within the workforce to manage the demands of the modern, technology- and data-driven economy. The knowledge derived may be employed by skilled practitioners in defining career pathways, to identify where, when and how to update their skillsets in line with advancing technology and changing work demands. This same knowledge may also be used to identify the combination of skills essential in recruiting for new roles. To address the challenges inherent in exploring the complex, heterogeneous, dynamic data that feeds into such applications, we investigate the use of an ontology to guide structuring of the information space, to allow individuals and institutions to interactively explore and interpret the dynamic skill demand landscape for their specific needs. As a test case we consider the relatively new and highly dynamic field of Data Science, where insightful, exploratory data analysis and knowledge discovery are critical. We employ context-driven and task-centred scenarios to explore our research questions and guide iterative design, development and formative evaluation of our ontology-driven, visual exploratory discovery and analysis approach, to measure where it adds value to users’ analytical activity. Our findings reinforce the potential in our approach, and point us to future paths to build on