Open Data Hub Day 2024
Industry has long been using connected machines and devices (such as drones, robots, wearable devices, etc.): the so-called Internet of Things (IoT). The Data Act lays down new obligations to share data obtained from these devices to stimulate the reuse of such data and technological progress. Thanks to the Data Act, players who previously did not have access to data can finally derive value from them. This could also bring significant benefits to other players in the supply chain (such as SMEs), that will be able to open their horizons to new datasets in order to carry out research and develop new products and services. The Regulation represents a transversal piece of legislation, which impacts, among other things, data processing and intellectual property law.
Value chains have become extended and data is needed to improve processes, follow sustainability imperatives and foster innovation. Data Spaces constitute a key enabler, facilitating data sharing and ensuring data sovereignty. This presentation will bring clarity on what data spaces are, the state of the art and the opportunities that data spaces unlock.
The Mobility Data Space (MDS) brings together companies, organisations and institutions: those who need data for innovative mobility solutions and those who want to monetise their data assets. Benefit from a large network of experts from different mobility sectors such as OEMs, mobility service providers, municipalities, insurance companies and start-ups. As a member, you will have direct access to relevant contacts, be able to develop innovative mobility solutions and help shape tomorrow's mobility. Data spaces are flexible and open IT infrastructures that enable trustful and transparent use of decentrally organised data according to previously defined scopes of use while guaranteeing the full sovereignty of the actors involved. They are based on a federal organisational principle.
Data spaces create equal framework conditions for the sovereign exchange of data. This also means that every actor can benefit from the use of the data in the same way.
The MDS is a pioneering data space in Europe for the mobility sector. Thanks to its technical architecture, the MDS guarantees not only transparent and secure data exchange, but also international interoperability with other data spaces. As a non-profit organisation, MDS provides a trusted data space for the secure and transparent exchange of mobility data. Thanks to the decentralised structure, members have full data and contractual sovereignty at all times, and decide with whom they share their data.
In the past years, significant improvements have been carried out in order to improve the level of digitalization of public transport in South Tyrol. A new standard architecture has been deployed with the EU project Bingo, and allows to collect and distribute high-quality static and real-time data about public transportation service. A new ticketing and intermodal transport control system (ITCS) are at the end of a complex migration phase, and will allow unprecedented possibilities to offer public transportation "as-a-servce". The next years will be characterized by a further evolution of this digital architecture. On one side, thanks to the new EU project MAGO it will be extended so to allow the integration of other mobility services, putting the technological conditions to enable the dream of mobility-as-a-service. On the other side, a new mobility management center is going to be introduced with the EU project MMCS so to allow a joint, more efficient control not only of car traffic, but also of the overall execution of mobility services. From a technological point of view, both projects will be heavily based on the Open Data Hub as an enabling platform that can open up data silos and allow a more efficient usage and distribution of the data by the new system components that will be deployed.
The Cooperation Open Government Data (OGD) D-A-CH-LI (Germany, Austria, Switzerland and Liechtenstein) introduces its activities and presents the current Open Data challenges.
The evaluation of the Vienna Open Data Strategy 2024 involved a thorough assessment process. This included conducting surveys with enterprises and public schools, as well as an internal workshop to gather insights and feedback. The results of this evaluation will be presented in detail, along with recommendations.
In this presentation, we delve into the integration of a specialized Yanovis widget within SkyAlps, harnessing flight data from Open Data Hub to elevate communication solutions. Integrated seamlessly into the frameworks of Brandnamic and Yanovis, this widget facilitates streamlined access to crucial data, optimizing operational efficiency and enriching customer experiences.
Working with Open Data Sources for topographic-projection-mapping. A system composed of physical topography elevation model, Projector, touch interface, computer and software.
The rapid urbanization of modern cities has led to increasing challenges in traffic management, with a significant impact on daily commutes, environmental pollution, and urban planning. Addressing these challenges requires innovative approaches to predict and analyze traffic patterns effectively. Our project presents a cutting-edge solution by designing and implementing a big data system capable of predicting near-time traffic flows in Bolzano, Italy. This system utilizes data obtained from strategically placed Bluetooth sensors, offering a novel approach to traffic management and analysis. The project leverages data from Open Data Hub Südtirol, focusing primarily on Bluetooth stations data, to predict traffic patterns at specific locations.
The data pipeline is elaborately designed, consisting of data collection, ingestion, preparation, computation, and presentation phases. Historical and stream data collection is automated using GitHub Actions. MySQL was chosen for data ingestion due to its simplicity and compatibility with the collected data types, while MongoDB is used for storing model predictions. The core of our system is a predictive model using Keras layers, specifically Sequential, LSTM (Long Short-Term Memory), and Dense layers, trained on historical data using Spark through Databricks. This LSTM-based neural network was chosen for its efficacy in sequential data prediction.
The predictions generated are integrated into a web application hosted on R based Shiny server, offering an interactive and insightful interface into the traffic trends in Bolzano. Specifically, the application's first page showcases real-time traffic predictions for each station. The second one is dedicated to displaying information about the traffic stations, including average traffic by hour, and traffic distribution throughout different times of the day. On the third page, users can explore historical traffic trends across all stations, with the ability to filter data by date, to identify the busiest periods. The fourth page delves into traffic insights, analyzing patterns based on weekdays, hours, months, and seasons, highlighting variations like decreased traffic on weekends and nights, and increased traffic during warmer months.
Our work highlights the potential and challenges of using big data and machine learning for real-time traffic prediction in urban settings. While the current system effectively predicts traffic patterns using available data, future enhancements could include incorporating additional data sources, optimizing script automation, exploring alternative database technologies, and refining the predictive model to incorporate more variables. Despite some limitations, such as the choice of MySQL over other databases and the inherent slowness of Shiny, our project demonstrates a successful application of big data technologies in addressing real-world problems.
Explore datahub.tirol's pioneering role in creating Europe's first regional Data Space. Delve into social and technical essentials, emphasizing relationship-building and digital ethics. Join us to uncover transformative potential in future business models.
The One Health approach is an excellent testing ground for the subject of the Data Space. Bringing together data on human health, animal health, and the environment into a single coherent Data Space exacerbates the requirements of trust, sovereign, and democratic data sharing both from a legal and technical standpoint. In this Meditech talk, we will illustrate the approach followed by the city and the Local Health Authority (ASL) of Taranto in establishing one of the first European One Health Data Spaces: Calliope.
The world of data is constantly in flux. How do we ensure our data keeps pace with this dynamic landscape, where new sources emerge, formats adapt, and consumer needs evolve? In this talk, we'll delve into how to empower open-data solutions to embrace change and foster continuous evolution.
Exploring the examples I’ll present, we’ll discuss ideas on how a system can benefit from data it doesn’t own, practices to ensure persistence isn’t a dragging anchor, and strategies to make a system that contributes back with more data. We’ll understand how to navigate an architecture smoothly in an open-data landscape. This talk is for anyone involved in open data initiatives, particularly those considering a microservices architecture. Whether you're a developer, architect, or data governance leader, let’s share valuable insights on how to build open data solutions that thrive in a constantly evolving environment.
One challenge in software engineering courses is to make students work with realistic projects and solve real-world problems. This presentation shows the experience in the courses Software Systems Architecture and Advanced Software Design Techniques, offered respectively at the bachelor and master levels, using the context of Open Data Hub in students' projects. The talk will present the types of challenges proposed to the students, the results of their work, and their interaction with the community.