The objectives of the LinDA project are:

  • Objective 1: Enhance the ability of data providers, especially public organisations to provide re-usable, machine-processable linked data.
  • Objective 2: Provide out-of-the-box software components and analytic tools for SMEs that offer the opportunity to combine and link existing public sector information with privatelyowned data in the most resourceful and cost-effective manner.
  • Objective 3: Deliver an ecosystem of Linked Data Publication and Consumption applications that can be bound together in dynamic and unforeseen ways.
  • Objective 4: Demonstrate the feasibility and impact of the LinDA approach in the European SMEs Sector, over a set of pilot applications.
  • Objective 5: Achieve international recognition and spread excellence for the research undertaken during the LinDA implementation towards enterprises, scientific communities,data providers and end-users. Diffuse and communicate readily-exploitable project results, of a pro-normative nature. Contribute to standardisation and education.

Goal of LinDA Project

LinDA aims at assisting SMEs and data providers in renovating public sector information, analysing and interlinking with enterprise data by developing a linked data workbench encompassing:

  • A cross-platform, extensible software framework that provides a rule-based system for renovating and converting a wide range of supported data containers, structures and formats into arbitrary RDF graphs. The framework can be used to develop custom solutions for SMEs and public sector organisations or be integrated into existing open data applications, in order to support the automated conversion of data into linked data. The platform will allow the export of arbitrary RDF graphs as tabular data, allowing SMEs to store the final results of data linking into relational databases or process further with spreadsheet and data analysis software. In this context, an end-user application will be developed in order to provide a visual environment for declaring mapping rules.
  • A repository for accessing and sharing Linked-Data vocabularies and metadata amongst SMEs data marts that can be linked to the LOD (Linked Open Data) cloud. The system will allow SMEs to reference and use metadata shared by multiple SMEs and data providers across different data endpoints, thus allowing automatic interlinking of datasets.
LinDA Linked Data  workbench concept

LinDA workbench concept

  • An ecosystem of Linked Data publication and consumption apps, which can be bound together in a dynamic manner, leading to new, unpredicted insights. While traditional RDF representations and SPARQL query access is provided to support advanced users, a Linked Data API will be deployed as a proxy to provide access in other widely established formats, such as CSV, JSON and XML, based on the internal RDF data (RDF2Any). This will allow both consumers familiar with the linked data paradigm and those unfamiliar with it, to leverage the provided knowledge bases. In particular, this solution enables the re-use of advanced, JavaScript-based data visualisation components for data presentation, as well as Java-based analytics / data mining components. We aim to realise an ecosystem of data extractions and visualisations, which can be bound together in a dynamic and unforeseen way. This will enable users to explore datasets even if the publisher of the data does not provide any exploration or visualisation means. Most existing work related to visualizing RDF is focused on concrete domains and concrete datatypes, so the envisioned visualisation ecosystem is one of the main innovations of LinDA.
  • A library of visualisation tools for different data modalities (e.g. spatial, temporal and statistic) based on HTML, CSS and JavaScript that can consume output from the Linked Data API and generic web APIs. Such visualisations will include map views of spatial information (e.g. for WMS/WFS endpoints, geocoded data) as well as common graphs and charts for statistical information (e.g. statistical data in the DataCube RDF vocabulary as well as CSV time series data).
  • A library of end-user Analytics and data mining apps library, based on existing Javabased components (Weka and Java ML) extended to point to RDF as source format, specifically targeted to leverage the potential of Linked Data sources, especially in terms of pattern and link analysis.
  • End-to-end business scenarios and models for Linked-Data utilisation on analytics by SMEs