Property data are one of the most valuable datasets managed by governments worldwide and extensively used in various domains by private and public organizations. Unfortunately these data are not always easy to access. House and property data is used in a variety of ways to produce value added information within and across several business sectors, including real estate and debt collection. Such sectors suffer from a lack of innovation due to a fragmented data ecosystem which makes it difficult to access relevant datasets. This hampers innovation, protects incumbents and promotes rent-seeking business models. The difficulty in creating a single, open data market partly depends on the fact that some governmental agencies are currently making significant revenues on selling data to a restricted number of business players in the private sector. However, several studies, demonstrated that the transaction costs for government agencies tend to be very high, and often make selling the data non-profitable. proDataMarket aims to disrupt the property data market and demonstrate innovation across sectors where property data is relevant, by integrating a technical framework for effective publishing and consumption of property-related data, showcasing novel data-driven business products and services based on property data. proDataMarket will provide a digital data marketplace for open and non-open property and related contextual data, making it easier for data providers to publish and distribute their data (for free or for a fee) and for data consumers to easily access the data they need for their businesses. The consortium is formed by large companies ensuring high-impact business cases, technology transfer providers ensuring technologies for the creation and maintenance of the data market platform, and large data providers providing data for the business cases. With strong industry involvement proDataMarket will cover a wide range of data value chains related to property data.

Corporate information, including basic company firmographics (e.g., name(s), incorporation data, registered addresses, ownership and related entities), financials (e.g., balance sheets, ratings) as well as contextual data (e.g., cadastral data on corporate properties, geo data, data about directors and shareholders, public tenders data, press mentions) are the foundation that many data value chains are built on. Furthermore, this type of information contributes to the transparency and accountability of enterprises, is instrumental input to the process of marketing and sales, and plays a key role in many business interactions. Existing initiatives to increase the interoperability and access of corporate data are mostly fragmented (across borders), limited in scope and size, and silo-ed within specific business communities with limited accessibility from outside their originating sectors and countries. As a result, collecting and aggregating data about a business entity from several public sources (be it private/public, official or non-official ones), and especially across country borders and languages is a tedious, time consuming, error prone, and very expensive operation which renders many potential business models non-feasible. euBusinessGraph represents a key initiative to simplify and disrupt the cross-border and cross-lingual collection, reconciliation, aggregation, and provisioning and analytics of company-related data from authoritative and non-authoritative public or private sector sources, with the aim of enabling cross-sectorial innovation. By a combination of large companies, SMEs, public organizations, and technology transfer providers euBusinessGraph sets the foundations of a European cross-border and cross-lingual “business graph”, aggregating, linking, and provisioning (open and non-open) high-quality company-related data, demonstrating innovation across sectors where company-related data value chains are relevant.

Data lakes are raw data ecosystems, where large amounts of diverse data are retained and coexist. They facilitate self-service analytics for flexible, fast, ad hoc decision making. SmartDataLake enables extreme-scale analytics over sustainable big data lakes. It provides an adaptive, scalable and elastic data lake management system that offers: (a) data virtualization for abstracting and optimizing access and queries over heterogeneous data, (b) data synopses for approximate query answering and analytics to enable interactive response times, and (c) automated placement of data in different storage tiers based on data characteristics and access patterns to reduce costs. The data lake’s contents are modelled and organised as a heterogeneous information network, containing multiple types of entities and relations. Efficient and scalable algorithms are provided for: (a) similarity search and exploration for discovering relevant information, (b) entity resolution and ranking for identifying and selecting important and representative entities across sources, (c) link prediction and clustering for unveiling hidden associations and patterns among entities, and (d) change detection and incremental update of analysis results to enable faster analysis of new data. Finally, interactive and scalable visual analytics are provided to include and empower the data scientist in the knowledge extraction loop. This includes functionalities for: (a) visually exploring and tuning the space of features, models and parameters, and (b) enabling large-scale visualizations of spatial, temporal and network data. The results of the project are evaluated in real-world use cases from the business intelligence domain, including scenarios for portfolio recommendation, production planning and pricing, and investment decision making. SmartDataLake will foster innovation and enable European SMEs to capitalize on the value of their own data lakes.