proDataMarket at the European Data Forum 2016

On 29 and 30 June proDataMarket participated in the European Data Forum (EDF) 2016, organized by Amsterdam Data Science and Technical University of Eindhoven under the auspices of the Dutch presidency of the European Union.

Evoluon

The conference, held in the Conference Center and former museum of Science and Technology Evoluon (Eindhoven, NE), was attended by Commissioner Günther Oettinger, the Rector of University of Tilburg and Philips, Siemens and TomTom CEOs. The event brought together more than 600 attendees from across Europe and multiple technology sectors.

General View

Likewise, proDataMarket presented a descriptive poster of the project, explaining its development and the conclusions reached so far in the different business cases and data-marketplace central infrastructure, and how proDataMarket aims to disrupt the PD market and demonstrate innovation across sectors where Property Data is relevant, by integrating technical framework for effective publishing, data consumption and showcasing data-driven business products.

poster

Besides the main event, the IQmulus project organized a workshop addressing Geospatial, Mathematical and Linked Big data. This event addressed aspects of big data where geolocation, geospatial or mathematical structures have a central role. In this side-event, the project coordinator, Dr. Dumitru Roman, also explained the whole project and its Business Cases.

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).

1

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.

2

The manipulation controls are necessary because the iPad compass and location service is 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.

Next up, we take a closer look at the application that augments underground infrastructure.

[1] https://en.wikipedia.org/wiki/Augmented_reality

[2] http://www.wikitude.com/

Cerved and SpazioDati at Data Driven Innovation 2016

Cerved and SpazioDati participated in the first edition of Data Driven Innovation 2016 with a presentation and a stand about preliminary results of their collaborative work in the ProDataMarket project.

Cerved & SpazioDati present the first prototype for proDataMarket @DataDrivenInnovation 2016
Cerved & SpazioDati present the first prototype for proDataMarket @DataDrivenInnovation 2016

 

Data Driven Innovation is an open summit about big data hosted by Roma Tre university and organized by Codemotion. During two days of the summit many people have had the possibility to see the first results of Cerved & SpazioDati proDataMarket project: the Cerved Scouting Terrain Service (CST), an interactive map showing Bologna territory scores and social demographic scores, as the social disease index, the economic disease index, the socio-demographic score and much more territory scores.

CST, 2d business case of Cerved: Employees of the working population in Bologna
CST, 2d business case of Cerved: Employees of the working population in Bologna

 

CST is the second business case Cerved is being developed within the proDataMarket project: the goal of this service is to provide target users with a tool to search and see property and territory information on a map. In order to achieve this, Cerved is developing value-added geo-marketing indicators, analyses and visualisations.

Authors: Claudio Castelli & Diego Sanvito

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.

Satellite images applied to property data

The Sentinels are a fleet of satellites designed specifically to deliver the wealth of data and imagery that are central to the European Commission’s Copernicus programme . This unique environmental monitoring programme is making a step change in the way we manage our environment, understand and tackle the effects of climate change and safeguard everyday lives. Sentinel-2 carries an innovative wide swath high-resolution multispectral imager with 13 spectral bands for a new perspective of our land and vegetation. The combination of high resolution, novel spectral capabilities, a swath width of 290 km and frequent revisit times is generating unprecedented views of Earth. Sentinel-2 is providing information for agricultural and forestry practices and for helping manage food security. Satellite images will be used to determine various crop and plant indexes. Some examples of these parameters could be:

  • Normalised Difference Vegetation Index (NDVI)
  • Normalised Difference Snow and Ice Index (NDSI)
  • Enhanced vegetation index (EVI)

This is particularly important for effective crops production prediction and applications related to Earth’s vegetation.

SentinelExampleSentinel use example

Sentinel-2 is the first optical Earth observation mission of its kind to include three bands in the ‘red edge’, which provide key information on the state of vegetation. In the previous image from 6 July 2015 acquired near Toulouse, France, the satellite’s multispectral instrument was able to discriminate between two types of crops: sunflower (in orange) and maize (in yellow).
These new and advanced datasets will be used inside CAPAS Business case to improve and enrich the information already obtained using LIDAR datasets (What is LIDAR?). Indeed, using LIDAR is possible to obtain accurate surface maps. However, data updates frequency is not very high. On the other hand, Sentinel constellation has a very high revisit frequency (five days) and offers information about kind of crops and their evolution. In conclusion, the use and merging of those different datasets answer several question regarding CAP parameters:

  • Is a specific parcel cultivated?
  • What kind of crop is growing in a plot?
  • Has the number of trees of a copse changed? When?
  • What is the ratio between Ecological Surfaces Areas (EFAs) and Productive areas in a given place?

Processing this kind of information could be very complex and laborious. It depends on selected indexes, chosen bands and geographical area. Furthermore, the processing is complicated by the high volumes of data. However, final results will offer a very detailed and accurate overview about land cover changes, environmental monitoring, crop monitoring, food security and detailed vegetation & forest monitoring parameters as leaf area index, chlorophyll concentration or carbon mass estimations. All this information and results have direct relation with Common Agricultural Policy principles and new European “Greening” policies.

Note: Some details about the characteristics and features of these instruments are available here.

Recent proDataMarket presentations

 

 

 

 

proDataMarket business cases at RuleML2015 Industry Track

The proDataMarket SoE and CAPAS business cases have been published/presented at the RuleML2015 Industry Track:

Norwegian State of Estate: A Reporting Service for the State-Owned Properties in Norway by Ling Shi, Bjørg E. Pettersen, Ivar Østhassel, Nikolay Nikolov, Arash Khorramhonarnama, Arne J. Berre, and Dumitru Roman

  • Abstract: Statsbygg is the public sector administration company responsible for reporting the state-owned property data in Norway. Traditionally the reporting process has been resource-demanding and error-prone. The State of Estate (SoE) business case presented in this paper is creating a new reporting service by sharing, integrating and utilizing cross-sectorial property data, aiming to increase the transparency and accessibility of property data from public sectors enabling downstream innovation. This paper explains the ambitions of the SoE business case, highlights the technical challenges related to data integration and data quality, data sharing and analysis, discusses the current solution and potential use of rules technologies.
  • Paper

 

CAPAS: A Service for Improving the Assignments of Common Agriculture Policy Funds to Farmers and Land Owners by Mariano Navarro, Ramón Baiget, Jesús Estrada and Dumitru Roman

  • Abstract: The Tragsa Group is part of the group of companies administered by the Spanish state-owned holding company Sociedad Estatal de Participaciones Industriales (SEPI). Its 37 years of experience have placed this business group at the forefront of different sectors ranging from agricultural, forestry, livestock, and rural development services, to conservation and protection of the environment in Spain. Tragsa is currently developing a business case around the implementation of a Common Agriculture Policy Assignment Service (CAPAS) – an extension of a currently active and widely used service (more than 20 million visits per year). The extension of the service in this business case is based on leveraging new cross-sectorial data sources, and targets a substantial reduction of incorrect agricultural funds assignments to farmers and land owners. This paper provides an overview of the business case, technical challenges related to the implementation of CAPAS (in areas such as data integration), discusses the current solution and potential use of rule technologies.
  • Paper

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.

Cerved in the proDataMarket project

Who we are
Cerved is the Italian leader in credit risk analysis and the top independent market player for credit management. It offers the most complete range of products and services used by around 34,000 businesses and financial institution to assess solvency and credit rating of its business partners, monitor and manage credit risk and define marketing strategies.

What we do
Cerved responds to needs of its customers (financial institutions, corporations, insurance companies, public administration, professional and private customers) through a wide range of services and products divided into three business areas:

  • Credit Information: Cerved provides data and information to assess economic and financial profile and reliability of businesses and individuals, and it supports customers defining assessment models and decision-making systems;
  • Marketing solutions: this business line offers an extensive, in-depth range of services, such as searching for new customers, competition analysis, increasing awareness of its customer base, along with custom design solutions providing the most effective commercial strategies;
  • Credit Management: through its subsidiaries Cerved Credit Management and Finservice, Cerved is the leading independent market player, offering specials skills in different areas, from credit assessment to credit management in and out of the court and to remarketing of securities and properties.

 

Role in the proDataMarket project
Cerved participates as a large industry partner in proDataMarket and acts as a data and business case provider. Cerved develops two business cases in proDataMarket:

  • Business case CCRS (Cerved Cadastral Report Service)
    The correct estimation of the value of properties owned by companies and individuals is one of the crucial elements for understanding their economic behaviour and to predict their financial stability.
    The goal is to increase the precision of the estimation by relying on the data made available through the proDataMarket marketplace. The activities will focus on both increasing the quality of the service thanks to the proDataMarket datasets and data integration (fusion of open dataset, property data and third-party data).
  • Business case CST (Cerved Scouting the Terrain Service)
    In order to optimize their strategies and maximize the rate of success of their marketing actions, businesses need to understand the territory in which their target customers move and live.
    To achieve this goal, the activities will be focused on leveraging the data made available via the proDataMarket platform to integrate it with CERVED’s own business information data with the goal of developing value-added geo-marketing indicators, analyses and visualizations.

SINTEF: Project leader and technology provider in proDataMarket

SINTEF is Scandinavia’s largest independent research organization. SINTEF is multidisciplinary, with international top-level expertise in a wide range of technological and scientific disciplines, including areas such as ICT, medicine, and the social sciences. SINTEF’s company vision is “technology for a better society”, and it is an important aspect of SINTEF’s societal role to contribute to the creation of more jobs. SINTEF acts as an incubator, commercialising technologies through the establishment of new companies. SINTEF is represented in proDataMarket by Information and Communication Technology (SINTEF ICT) through the department for Networked Systems and Services (NSS).

Role in the project: SINTEF is the project leader of proDataMarket, and in addition serves as a technology provider in the project. SINTEF’s technical focus is on the technical infrastructure of the proDataMarket platform related to data management technologies, in particular data publishing and access, helping organizations with cost-effective solutions for (linked open) data management. Our goal is to promote standardisation with mechanisms for defining structure and semantics of data, as well as improve the interoperability and transparency among data publishers and consumers through leveraging the linked data format. Technically, we are constructing a software framework that consists of a frontend and a set of platform services that support reusable data cleaning and reconfiguration based on pluggable static, dynamic or streaming input in various formats (e.g., relational databases, CSV files, WMS/WFS services, etc.). Outputs will be published on the proDataMarket platform and available to end users and other publishers through a secured set of platform services such as SPARQL query endpoints and RESTful APIs. This framework is meant to provide automation for significantly reducing the manual effort involved in the highly laborious process of data retrieval and aggregation.

In proDataMarket, SINTEF reuses and extends its data reconfiguration solutions from the DaPaaS project. In particular, we plan to further develop the Grafterizer tool for data cleaning and linked data mapping of tabular inputs.