PITCH VIDEO JURY VIDEO   TEAM PCITURE Team Name  Council Insights Heidelberg   Track  Public Sector  Use Case  Implementing Lage Language Models LLM for a strong Democracy Technology   SAS Viya Model Manager, Intelligent Decisioning, Visual Text Analytics, Visual Analytics, Python, LLM, NLP, SAS Viya Region  Heidelberg, Germany  Team lead  Benjamin Gärtner @benj_gaertner  Team members  Stephan Frenzel,   Ahsan Ali, Maria Shcheglova, Christoph Blattgerste (Student), Harsh Patel (Student) , Silvio Martin (Scientific Staff, KIT), Lutz Berger,  Ian Doig, Slava Kuliger, Stephan Verclas, Patrik Bannholzer,  Andreas Koch,   Sebastian Warkentin, Maik Röder Sundaresh Sankaran (Mentor), Ulrich Reincke (Mentor) Social media handles   See the team's  LinkedIn Page specific for the #SASHackathon: https://www.linkedin.com/company/hack-team-heidelberg/posts/?feedView=all Is your team interested in participating in an interview?  yes  Optional: Expand on your technology expertise    ==============    Plan   Create automated scripts to regularly retrieve and download relevant documents from the local council website (https://www.gemeinderat.heidelberg.de/info.asp).  Indexation and vectorization of the scraped data  Data Analytics on the vectorized data using NLP, LLM and SAS Viya  Motivation  Our aim is to improve the transparency and accessibility of the work of Heidelberg Municipal Council. We want to enable citizens, the press and other interested parties to find out quickly and easily about the resolutions and activities of the local council. By visualizing and better presenting the work of the committees, we can strengthen citizens' commitment and trust in local government.    Analysis of strategies    We would like to compare the strategic objectives of the city of Heidelberg with the activities and resolutions of the municipal council in order to identify specific resolutions that contribute to the implementation of strategic objectives.    Relevant strategies can be strategies developed by the city of Heidelberg, such as the Heidelberg Biodiversity Strategy, as well as strategies that are valid nationwide, such as the practical guide ‘Climate Protection in Municipalities’ from the German Institute of Urban Studies.    Possible outputs    Analytics on stringency, consistency or legality of council work   Location of resolutions on a map (e.g. by districts, streets, etc.) where possible  Filter function according to districts, topics, committees, time reference (last 6 months or similar)  Search functions (full text, language, locations/regions/environment)  Summary of resolutions in understandable language  Create a chatbot or integration of an intelligent search  Possibly other types of visualizations – Mindmaps, Knowledge Graphs or similar according to keywords or districts (e.g. which topics have been discussed most frequently in the last 6 months)  ... View more

Team Name Mobility Insights Heidelberg Track Public Sector, IoT Use Case Urban Traffic Flow, Mobility Streams Technology Python, JavaScript, SAS Viya Visual Data Mining and Machine Learning,  Integration if Open Source Software in the SAS Viya Platform, SAS Visual Analytics, IOT, Forecasting, Optimization, Text Analytics, NLP, Statistics, Data Management, Timeseries Management, Geocoding with the OSM Api and Google Maps APIs, Model Interpretability, Trustworthy AI, Model Deployment, Model Integration in Online Parking Application. Region Use Case in Heidelberg, Germany (Team Members from UK, Almaty Kazakhstan, Yerevan, Armenia and Germany) Team lead Benjamin Gärtner @benj_gaertner Team members Julian Dinter @JDinter, James Swinnerton @jswinnerton1  @mh-at-fujitsu @pm_at_fujitsu,   Dzmitry Vabishchewich, Denis Ryzhov, Esther Euteneuer, David Häsler, Max Kastner, Dominik Fischer. Team Mentor: @SnowTiger (Ulrich Reincke)             Is your team interested in participating in an interview? Y Optional: Expand on your technology expertise We are looking for the following expertise for our team:  Python (Data Collection, Data Cleaning, Data Exploration, Data Visualization), JavaScript (Data Visualization), Geographic Information Systems ============== ========================================= Plan Discover, analyze, and forecast as well as a simulation of mobility streams in urban areas by intersecting mobility data (traffic-, bike & people-counting, public transport, and parking data) with weather data (own sensor data, as well as public forecasts) and event data (date, location, size) to increase quality of life und foster environmental sustainability In Heidelberg. Motivation Predicting traffic loads and visitor flows is a major challenge in permanent city management and planning.   The idea is to discover dependencies between the different means of transportation and the weather as well as further urban data (like e.g. events, holidays). Based on these insights, forecasting and predicting traffic loads and visitor flows in the urban area are key to create a comprehensive city management.   The main goal is to generate insights to predict possible hotspots and bottlenecks at an early stage to provide city planners and event organizers with a better understanding and basis for planning. Possible questions could be: - Does the weather influence the choice of transport? - Where are the highest traffic flows located? - Which is the most preferred mean of transportation? - Are there any external influences for the choice of the means of transportation (rain, road works, vacations (school or semester))? Jury Video Pitch Video Team Roles         ... View more

Team Name HeavyRain Track Public Sector/IoT Use Case Heavy Rain Forecast  Accurate forecasting of heavy rainfall events and their impact on cities has been a major challenge not only since the heavy rainfall and flooding disaster in the Ahr Valley in July 2021.   In particular, the abrupt and location-specific occurrence of these events should be analyzed within the framework of the project and predicted as accurately as possible with the help of the available data. In order to give residents room for maneuver. Technology Forecast, simulation, and warning for heavy rainfall events in urban areas by intersecting heavy rainfall risk maps, runoff data and weather data (sensors, forecasts, radar information) in near real time. Region Germany Team lead Benjamin Gaertner Team members  @slg @Harishreddy7288  Social media handles *all team members' social media links here* Is your team interested in participating in an interview? Y Optional: Expand on your technology expertise We are looking for the following expertise for our team: SAS, Python, JavaScript, Geographic Information Systems   Video Submission: ... View more