GraphDB Cloud: an on-demand enterprise ready RDF database

We, from Ontotext, are excited to announce GraphDB Cloud – the easy way to introduce you to a semantic database like our signature GraphDB product. The automated tasks in GraphDB Cloud save the organizations the time and effort to install and manage hardware and software as well as the cost to buy it. Compared to a do-it-yourself database, DBaaS offers developers the opportunity to cut down the time it took them to work with their databases and spend their valuable time on creating and innovating instead of administrating.

GraphDB Cloud is one part of the Cognitive Cloud solutions for low-cost and on-demand smart data management.

The users are with the following profile:

  • Small cognitive-technology oriented team in a big organization that needs low upfront and ongoing costs for a database.
  • Start-up companies without a database infrastructure, which requires a reliable technology that scales up along with their business.
  • Corporate solution architects working to solve the challenges their enterprise faces when handling huge amounts of data and information

As a next step, we want to invite you to watch our webinar “GraphDB Cloud – Enterprise Ready RDF Database on Demand” where we introduce you to the DraphDB Cloud console and advise you how you could create custom solutions to address your company’s specific data and information needs.

Proof of Concept with Augmented Reality


The potential of the proDataMarket platform is huge, and by letting third party actors use and contribute to the “big data” platform, the potential could be even greater. To show how proDataMarket can be utilized, EVRY is developing two mobile applications that rely on proDataMarket service. The applications combine data from proDataMarket along with “augmented reality technology” to give the user a visual representation of the data. By doing this, EVRY will help contractors, construction or municipalities visualize future building projects. This is done with two iPad applications. The first application show underground infrastructure such as pipes and cables. The other application augments a 3D model in a real world scene.

Augmented reality (AR) is a live direct or indirect view of a physical, real-world environment whose elements are augmented (or supplemented) by computer-generated sensory input such as sound, video, graphics or GPS data [1]. The applications EVRY develops uses augmented reality technology to present cadastral data, distributed by proDataMarket. By doing this the applications can show underground structure on the screen (through the device camera), as well as 3D models of future building projects in a “real world scene” with information about the surroundings. This is done by having a 3D-model with correct measurement data (relative to its real world size), and by knowing the distance between a desired location and the user, the model can be scaled to the correct size according to the distance. Of course, if the user decides to manipulate the model (i.e. scaling it up), the size/distance relationship will be invalid. The 3D model augmentation can ease both private and commercial building projects by giving a visual presentation of how a building may look in a landscape.

The development process has been a process of trail and error and different augmented reality SDK have been examined. In the end the development team chose “Wikitude SDK [2]” to handle the augmentation processing. The task of augmenting a custom 3D model at a desired location is a suitable task for Wikitude SDK. By setting the model as a “Point of Interest” (POI) and using “GeoLocation”, the user can set the model at a desired location in a 2D map (Google map).


The model will be scaled to the correct size relative to the distance from the user. When a model is placed, Wikitude will augment the model and the user can see and manipulate with onscreen controls.


The manipulation controls are necessary because the iPad compass and location service are not accurate enough to get a satisfying result. If a user needs to place a model at a very exact location, there must be some way to tweak and calibrate the model. All in all, there are still some bugs left to fix in the applications, but the main functionality is in place and we are looking forward to show demos of what we have made.



ProDataMarket place as a toll for connecting real-estate data publishers and prospect data consumers

The main objective of the ProDataMarket project is to create a data marketplace for open and proprietary real-estate and related contextual data.

Marketplace is a place where data producers meet prospect data consumers. In addition to basic features for making data accessible and discoverable, marketplace can provide more tools to help data producers “advertise” their data and better engage with potential data consumers. Among such tools are those that help data producers explain the type of their data, its attributes and demonstrate its value. In this post we discuss how these tools are being realised in the ProDataMarket place.

Driving example

Let’s consider a national statistical office, for example, the Italian National Institute of Statistics (ISTAT). ISTAT wants to disseminate one of its datasets, a dataset with census cells that cover the Italian region of Piemonte. This dataset subdivides the region of Piemonte in census sections according to ISTAT’s 2011 National Census. A census section is the smallest geographic unit for which the statistical variables of a population census are taken.

ISTAT is interested in explaining to the prospect data consumers that the data can be useful when it is needed to:

  • determine inter-municipal boundaries
  • describe different areas of a city in terms of some geographically-bound characteristics

Marketplace: initial steps

Figure 1 illustrates initial steps that ISTAT performs at the marketplace to present her data.

Figure 1: The data producer prepares, describes and publishes her data at the marketplace, to make accessible and discoverable.


ISTAT prepares its data for publication, describes and catalogues it. Now, a prospect data consumer can discover and explore the dataset of census cells of the Piemonte region. While ISTAT made the data accessible and discoverable, data consumers still have to figure our themselves what type of data it is, what is inside and what is it useful for.

Marketplace: explaining the data types

To explain the type of the data, ISTAT creates and attaches visualisations to its data, as shown in Fig. 2.

Figure 2: The data producer creates visualisations, to explain the type of the data


In addition to preparing, describing and publishing Piemonte census sections dataset, ISTAT can create a map of all the census cells of the Piemonte region. This gives an illustrative example of the data to the prospect data consumers: when exploring the dataset, the data consumer can immediately see that the data contains polygons, each of which represents a geographic area of a census section.

Now that the type of the data is clearer, ISTAT can go further and explain various attributes of the data.

Marketplace: explaining attributes of the data 

Figure 3 illustrates steps that ISTAT performs at the marketplace, to give the data consumers a glimpse of the data attributes.

Figure 3: The data producer queries the data, to explain data attributes.


As mentioned above, the dataset of the driving example contains census cells’ geometries. Every cell is attach to a certain municipality. This information becomes useful if one wants to represent single municipalities on a map. For example, to represent the city of Turin, ISTAT can prepare a subset of the census cells by filtering on the municipality attribute of each cell. Similarly, other attributes of the data can be highlighted.

Marketplace: putting data into context to explain its value

With the help of the marketplace, ISTAT can prepare, describe and visualise as many subsets of the data, as she wants to. Finally, to showcase the value of the data and explain to the data consumer its value, ISTAT can put census cells into context, as illustrated in Fig. 4.

Figure 4: The data producer augments its data from other data sources, to show the “value in context”.


This last approach is realised through the Augmentation Service that supports querying a co-located data source using several functions to produce a new dataset. Currently, the Augmentation Service uses data from OpenStreetMap, to provide context. For example, ISTAT can use the service to extract the number of bus stops found nearby each census cell, or the distance to the closest train station, or the length of pedestrian paths in each census cell. Once the new augmented dataset is prepared, ISTAT can proceed with visualisations. For example, she can create a coloured map to show density of nearby bus stops in Turin.

Ontotext in the proDataMarket Project

Ontotext is a SME founded in 2000 in Sofia, Bulgaria. For more than a decade Ontotext has successfully delivered Semantic Technology products and solutions that improve data integration, data management and search within enterprises. Key products of Ontotext include GraphDB – one of the leading enterprise RDF graph databases, and the Self-Service Semantic Suite (S4) – a platform for on-demand smart applications and data management. Ontotext is also delivering semantic data management solutions to organizations in various verticals: media & publishing, healthcare & life sciences, museums and digital libraries.

Ontotext’s vision for smart data management is based on using ontologies and vocabularies for modelling data, analyzing free flowing text content and extracting structured information and facts. The RDF graphs data model provides an agile way to manage and query heterogeneous data, and powerful semantic search can be implemented on top of the graph data. This way the business users can ask more complex questions and find more precise answers, than by using traditional enterprise full-text search approaches.

Ontotext is one of the technology partners in the proDataMarket project. Ontotext’s responsibilities include delivering a scalable data management infrastructure, which will allow for data stored in various legacy data sources to be transformed into RDF graphs with proper metadata mappings to popular ontologies and vocabularies. A scalable RDF database-as-a-service running in the Cloud will be one of the key components of the proDataMarket infrastructure, and it will enable quick deployment of new data services on top of 3rd party datasets. This way, the numerous data publishers will not need to deal with the overhead of provisioning and maintaining the access to their data, while developers will get an easy, instant and reliable access to valuable property related via simple RESTful APIs.