On Integrating Robotic Data with GIS Tools in a Cloud Environment (Application Paper) Robert Wrembel5 , Jean-Paul Kasprzyk1 , Roland Billen1 , Sandro Bimonte2 , Laurent d’Orazio3 , Dimitris Sacharidis4 and Piotr Skrzypczyński5 1 Université de Liège, Liège, Belgium 2 INRAE, Clermont-Ferrand, France 3 Université de Rennes, Rennes, France 4 Université Libre de Bruxelles, Bruxelles, Belgium 5 Poznan University of Technology, Poznań, Poland Abstract Merging robotic technologies, sensor networks, and Geographic Information Systems (GIS) offers significant potential across various domains, including agriculture and urban planning. However, a critical challenge lies in the lack of interoperability between data generated by these technologies and existing GIS tools. The EU-funded GIS4IoRT project addresses this gap by developing a plug-and-play and cloud-based middleware. This middleware facilitates seamless integration and visualization of multi-dimensional and multi-modal data within GIS environments. Key GIS4IoRT components include: a middleware architecture, a scalable cloud-based infrastructure, real-time robot querying capabilities, data quality assurance, spatio- temporal query support within the cloud, integration with GIS tools, and adherence to relevant standards. The middleware supports diverse data types, including LiDaR, imagery, and sensor data. This paper (1) presents an initial data integration architecture specifically designed for the sustainable architecture domain, (2) outlines the challenges encountered in designing such an architecture, and (3) explores novel data processing paradigms enabled by the architecture. Keywords data integration, geographical information system, robots, sensors, images, LiDaR, sustainable agriculture 1. Introduction and motivation temporal, machine learning (ML) / artificial intelligence (AI). Complex, data-driven systems are inevitable in domains The machinery at the edge level produces huge vol- like agriculture and smart cities. Typically, these sys- umes of highly heterogeneous data (a.k.a. big data). The tems deploy computing and robotic machinery, includ- types of data include: text, dates, numbers - generated by ing: sensors, cameras, laser 3D scanners (LiDaR devices) simple sensors, 2D images and video in multiple formats [1], installed on ground and air robots. These systems - generated by cameras, and 3D images - generated by often rely on edge-fog-cloud architectures [2, 3, 4]. For LiDaR devices. Notice that all the aforementioned data example, in agriculture, such an architecture leverages a types are extended with timestamps and geographical distributed computing paradigm to process data gener- coordinates, making new data types - spatial time series ated by sensors and devices deployed across farms. Initial of numerical, images, video, and LiDaR data. To the best data processing takes place at the devices, i.e., at the edge of our knowledge, techniques for analyzing and visu- (e.g., sensors on robots). Fog nodes perform more com- alizing spatial time series of images, video, and LiDaR plex data processing and analysis, based on data from have not been researched or developed yet. Moreover, multiple edge devices. Finally, cloud facilitates integrated data of all these types collected from mobile robots are long-term storage and advanced analytics, e.g., spatio- equipped with geographical coordinates, forming tra- jectories, which represent yet another data type to be Published in the Proceedings of the Workshops of the EDBT/ICDT 2025 analyzed. Joint Conference (March 25-28, 2025), Barcelona, Spain It is evident that at the fog and cloud levels, heteroge- $ robert.wrembel@put.poznan.pl (R. Wrembel); neous data have to be integrated, to provide an overall jp.kasprzyk@uliege.be (J. Kasprzyk); rbillen@ulg.ac.be (R. Billen); view on the whole domain, based on various analytical sandro.bimonte@inrae.fr (S. Bimonte); laurent.dorazio@irisa.fr (L. d’Orazio); dimitris.sacharidis@ulb.be (D. Sacharidis); and ML applications. To this end, data integration ar- piotr.skrzypczynski@put.poznan.pl (P. Skrzypczyński) chitectures and processes are applied [5, 6, 7]. Research  0000-0001-6037-5718 (R. Wrembel); 0000-0002-1663-6332 and development works resulted in a few standard DI (J. Kasprzyk); 0000-0001-8614-1848 (L. d’Orazio); architectures, namely: federated [8] and mediated [9], 0000-0001-5022-1483 (D. Sacharidis); 0000-0002-9843-2404 lambda [10], data warehouse (DW) [11], data lake (DL) (P. Skrzypczyński) © 2025 Copyright © 2025 for this paper by its authors. Use permitted under [12], data lake house (DLH) [13], and data mesh [14]. In Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings http://ceur-ws.org ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org) all of these architectures, data are moved from DSs into CEUR ceur-ws.org Workshop ISSN 1613-0073 Proceedings an integrated system by means of an integration layer. detecting anomalies of spatio-temporal data re- This layer is implemented by a sophisticated software, quire domain-specific knowledge; which runs the so-called DI processes. • complexity: spatio-temporal data are complex, This paper reports initial findings from an EU CHIST- which requires specialized techniques to analyze ERA (https://www.chistera.eu) project on Development the data across space and time dimensions; of a plug-and-play middleware for integrating robot sensor • pattern recognition: discovering patterns and data with GIS tools in a cloud environment (further called trends in trajectory data requires advanced ma- GIS4IoRT ), run by INRAE (France), Université de Liège chine learning techniques; (Belgium), Université de Rennes (France), Université Li- • spatial and temporal granularities: trajectory data bre de Bruxelles (Belgium), and Poznan University of often have varying levels of spatial and temporal Technology (Poland). The focus of this paper is on the granularities, which need advanced data analysis data integration architecture and challenges in process- techniques to produce meaningful results; ing and querying highly heterogeneous data. • spatial autocorrelation: relationships and corre- lations in spatio-temporal data, which may be difficult to detect, can complicate their analysis; 2. Project contribution • temporal dynamics: understanding how spatial patterns evolve over time and capturing dynamic The GIS4IoRT project challenges the existing research and relationships presents challenges in modeling technological paradigms in the field of data integration trends and in building prediction models; and processing in a few ways, discussed in this section. • interpretation: presenting findings spatio- Interoperability and integration: in the project we temporal data analysis in a meaningful and easy address the problem of integrating disjoint and often to understand way is not straightforward, due to mobile and ephemeral data sources (DSs) by proposing the complexity of dependencies between spatial a middleware solution that facilitates interoperability and temporal dimensions. between robotic machinery, sensor networks, and GIS tools. By bridging this gap, the project aims to create a unified ecosystem where data from diverse sources 3. Architecture can be seamlessly integrated, analyzed in the context of spatio-temporal dimensions, and visualized, enabling To address the aforementioned challenges, we proposed more comprehensive analysis and decision-making. a data integration architecture, as shown in Figure 1. Real-time querying and ML/AI-based approaches: Data sources include various types of machinery, fur- by incorporating real-time querying of robots and ML/AI- ther called the Internet of Robotic Things (IoRT). They based approaches, the project challenges traditional include: ground and air robots, sensors, cameras, and Li- methods of data handling and processing. This enables DaRs. The IoRT devices produce streams of data that are the middleware to ensure data reliability and complete- delivered in real-time to the GIS applications through the ness, even in the face of challenges such as signal loss or GIS4IoRT middleware. At the same time, these data are missing data. The utilization of ML/AI algorithms for data uploaded into a central repository. It stores also metadata quality assurance (e.g., profiling, anomaly detection, mon- and ontologies for mapping data from multiple IoRT, i.e., itoring and alerting) and data processing (e.g., wrangling, data in different modalities. analyzing, viusalizing) [15, 16] represents a departure from conventional approaches, highlighting the project’s 3.1. Data integration and querying layer commitment to leveraging cutting-edge technologies for enhanced performance. We based the architecture of the concept of a mediator Spatio-temporal querying: the development of [9]. Components marked as DI process [ROS2], DI process spatio-temporal query support and a user-friendly GIS [sensors], DI process [image, video], DI process [LiDaR], client further challenges existing paradigms by enhanc- and DI process [data repository] represent wrappers to ing accessibility and usability. This empowers users to DSs. Mobile robots are equipped with the ROS2 oper- efficiently browse available data and perform complex ating system, with its proper data formats and access queries, involving space and time dimensions on highly interface. Data provided by these DSs are pre-processed, heterogeneous, multi-modal, and ephemeral data, within integrated (as much as possible), and correlated by the the middleware. Spatio-temporal data introduce addi- data integration and querying layer. The correlation ap- tional specific challenges, which are addressed in this plies to data of different modalities that are related to the project. The challenges include: same real-world phenomenon. For example, text data describing a field (geographical coordinates and dimen- • data pre-processing: transforming, cleaning, and sions, the type of a crop cultivated there, the type of soil) can be correlated with images of this field. Figure 1: The architecture of the GIS4IoRT system This layer is also responsible for translating queries spatio-temporal querying of IoRT data. GIS applications arriving from GIS applications via GIS4IoRT middleware, execute queries in the context of GIS external data (e.g., like in a mediated architecture. As compared to the stan- the map of a given area) from GIS data repository, based dard mediated architecture, the challenge here is to trans- on input GIS data from end-users. This supports users late queries for very diverse data sources that offer differ- in running complex spatio-temporal queries, leveraging ent functionalities. To make it more challenging, these both IoRT-generated data and external GIS data, to gain data sources are ephemeral as they may be temporarily deeper insights and make informed decisions. unavailable and may provide data of qualities changing Notice that in such a system, multiple IoRT devices in time. may provide the same or similar data, e.g., a drone fly- ing over a given field and a ground robot traversing the 3.2. GIS4IoRT middleware same field. Both of them may provide images from two different perspectives, in two different formats, and in Serverless computing at the edge and fog requires par- two different resolutions. ticular functionality, which is provided by the GIS4IoRT Notice also that the system architecture is highly dy- middleware, namely: (1) dynamic resource orchestration, namic. The dynamicity results from: (1) new devices (2) a fine-grained data caching, to optimize data transfers that can be dynamically deployed in fields and (2) unsta- between storage (e.g., MinIO, S3), via the data integration ble, limited, or unavailable WiFi in fields, causing that and querying layer, (3) data caching at the edge, to enable devices moving into areas without network coverage dis- the most efficient processing and energy usage, and (4) appear temporarily from the system. As a consequence, producing data that conform to GIS standards. querying them is limited or impossible. Particular innovation is in considering: (1) caching not only on traditional CPU, but also on FPGA, to reduce re- 3.3. Challenges in querying IoRT sponse time and energy consumption, (2) smart resources allocation, to manage data and functions with respect to In such a setup, it is necessary to equip the user of the different objectives like data quality, response time, and system with options allowing to manage queries and un- energy consumption. derstand their results. To this end, two standard parame- In addition to optimizing data transfers and processing ters, namely the quality of service (QoS) and the quality of at the edge and fog, the GIS4IoRT middleware enables data (QoD) must be extended with the following notions. QoS execution time - a given query has a parameter Additionally, GIS4IoRT enhances spatial-temporal query specified by the user that limits the time to retrieve results. support and optimizes resource management through After exceeding the time, either the query is aborted or intelligent caching and processing at the edge and fog partial results are provided - this depends on another layers. Unlike previous works that rely on centralized parameter provided by the user. To handle this type of architectures, the proposed system leverages a flexible QoS, the system must be able to dynamically estimate middleware approach to dynamically adapt to different the execution time and be able to re-route a query to the IoT infrastructures, making it more suitable for applica- appropriate data source (IoRT device or data repository). tions requiring on-demand, real-time robotic sensing and The query should be executed on an agent that offers the decision-making. fastest response and transmits the lowest volume of data, at the price of lower quality of the results (e.g., lower 4.1. Cloud computing image resolution, data from sensors sample at a lower frequency). During the last decade, cloud computing has enabled QoD freshness - notice that fresh data come from the (big) data processing in various domains, e.g., healthcare, machinery deployed in fields. With a certain delay, these fleet management, banking, sales, social networks. Cloud data are also transmitted to the central repository. Thus, computing offers Infrastructure (IaaS), Platform (PaaS), the freshness parameter guides the system to which data and Software (SaaS) as a Service. IaaS and PaaS rely source send a query. on rented resources, following a pay-as-you-go model, QoD resolution - the machinery may provide data fast enabling elasticity (scale-up and scale-out). In SaaS, soft- but of lower quality. For example, simple sensors may ware hosted on cloud is made available in the form of transmit their measurements in real-time with a given fre- a subscription. Recently, Function-as-a-Service, as an quency, but they may buffer their measurements taken at implementation of serverless computing, has been pro- a much higher frequency. The buffered data are transmit- posed to offer higher elasticity and more fine-grained ted to the central repository when WiFi allows it. While energy consumption and billing [18]. transmitting images in real-time, a device may down- Serverless computing is a recent research field with grade its resolution to assure acceptable QoS execution few projects. For example, in Europe, (1) CloudButton time. The same image is transmitted to the repository (cloudbutton.eu) provides a serverless data analysis plat- at the highest possible resolution, when a suitable band- form, with high performance runtime and a mutable width is available. data middleware; EDGELESS (www.hipeac.net/network/ To provide the aforementioned QoS and QoD, the sys- projects/7247/edgeless/) tackles efficient processing with tem must be able to dynamically select DSs on which a resource-constrained edge-devices, MELODIC (h2020. given query will be executed. To this end, models for melodic.cloud/the-project/) supports data-intensive ap- managing QoS execution time, QoD freshness, QoD resolu- plications to run within security, cost, and performance tion will be built, based on ML/AI techniques. boundaries on distributed cloud computing, and RADON As mentioned before, the results of queries in such a (radon-h2020.eu/overview/) supports a DevOps frame- system must be equipped with metadata describing the work to create and manage micro-service applications. quality of the result. Such metadata include: (1) percent- Commercial solutions have been proposed, for: (1) sim- age of result completeness – it allows to estimate how ple functions, but have shown their limits for stateful pro- much data is missing, due to the unavailability of DSs, (2) cessing [19], (2) extending cloud computing tools, such as downgraded quality of data, due to either low network Spark [20], or (3) using in serverless environments, such throughput or assuring QoS execution time. as Spark-IO. Other contributions, like Pocket or Apache Crail, investigated the management of ephemeral data. 4. Related works and technologies 4.2. Robotics and IoT Compared to existing GIS and IoT integration architec- The consolidation of ML/AI techniques, IoRT, and geo- tures surveyed in [17], the GIS4IoRT middleware intro- spatial technologies is revolutionizing spatial data analy- duces a novel approach by emphasizing real-time data sis and interpretation. Advancements in this area enable acquisition from mobile robotic platforms and integrat- automated geo-spatial feature extraction [21], enhanc- ing it seamlessly with GIS tools. While traditional ar- ing precision and insight in geographical interpretations. chitectures primarily focus on static sensor networks ML algorithms analyze LiDaR data and satellite imagery and cloud-based GIS processing, GIS4IoRT extends these for automatic identification and classification of features capabilities by incorporating dynamic, ephemeral data (e.g., buildings, vegetation) and for providing dynamic sources from ground and aerial robots, addressing key views of Earth’s surface changes over time. Autonomous challenges in data quality, latency, and interoperability. GIS systems, powered by AI, aim for natural language task acceptance and minimal human intervention in spa- ISO standards: the International Organization for Stan- tial problem-solving, enhancing accessibility and user- dardization (ISO) has also contributed to the development friendliness [22]. Additionally, AI plays a crucial role in of standards for geo-spatial data interoperability. ISO managing vast geo-spatial data from sensors, drones, and 19156, also known as Observations and Measurements, satellites, enabling efficient processing beyond human provides a framework for describing and encoding sen- capacity. sor observations, supporting the integration of IoT data However, there is still a huge research gap in inte- into GIS environments. ISO 19115-1 specifies metadata grating GIS solutions and the robotic technology in an standards for describing geographic information and ser- fully automated system. Such a system not only applies vices, including metadata elements relevant to IoT/IoRT ML/AI techniques to data previously collected (also using DSs. Also IEEE has contributed to the standardization of robots), but also can answer GIS user queries dynami- robot map data representation through IEEE 1873-2015, cally, by asking the IoRT machinery for highly specific which defines a common format for exchanging 2D met- data and managing the operation of the IoRT subsystems ric and topological maps among robots, computers, and in (nearly) real-time. The body of existing literature of- GIS platforms. Unlike proprietary formats, IEEE 1873- fers works on GIS supporting UAVs [23], integration with 2015 facilitates long-term comparability and evaluation BIM systems and construction applications [24], and sup- of maps across different systems, making it particularly port for robot navigation [25]. Also a ROS-based plugin relevant for robotic navigation and collaborative map- for the popular QGIS system was developed [26], but it ping applications [31]. is based on outdated ROS1 and is no longer maintained. Semantic interoperability: achieving semantic interop- These examples show that although the existing re- erability between geo-spatial data and IoT/IoRT devices search has explored aspects of integrating IoRT with is essential for meaningful data integration and analy- GIS systems, but comprehensive solutions addressing dy- sis. Standards such as the Semantic Sensor Network On- namic data integration and real-time processing are still tology developed by the World Wide Web Consortium to be developed. This is the gap we bridge in the GIS4IoRT provide a common semantic framework for describing project, providing the low-level software agents to make sensor observations and capabilities, enabling effective the IoRT machinery "understand" the standards and re- communication between IoT devices and GIS systems. quirements of GIS. We develop the middleware in order Geo-spatial data formats: standardized geo-spatial data to effectively manage the data flow and system configu- formats are crucial for interoperability between GIS and ration in the cloud/fog environment, and implement the IoT/IoRT systems. Formats such as GeoJSON, Shapefile, GIS adoption layer that will make the GIS systems (e.g., or KML provide common encodings for representing ge- QGIS) aware of the functionalities provided by GIS4IoRT. ographic data and sensor observations. Preliminary results from the project consortium demonstrate the successful integration of diverse hard- 4.4. Adaptability to Other Domains ware devices [27] and initial algorithms for data process- ing [21, 28, 29, 30] and quality assurance. The GIS4IoRT project leverages precision agriculture as a testbed for the proposed architecture, given the grow- ing need for smart, sustainable farming solutions to ad- 4.3. GIS and IoRT dress economic and environmental challenges in Europe. GIS systems play a pivotal role in integrating spatial data However, the modular design of the GIS4IoRT middle- for analysis, visualization, and decision-making across ware enables adaptation beyond agriculture—extending various domains. With the emergence of the IoT and the to disaster response, autonomous navigation, and urban IoRT, there is a growing need for standards that facilitate planning. the interoperability and integration of geo-spatial data In disaster response, real-time sensor data integration with sensor networks and robotic technologies. Here, we facilitates damage assessment and resource coordination explore the state of the art in GIS standards related to IoT [32, 33, 34], yet ensuring reliable data transmission in and IoRT. disrupted networks remains a challenge. While the con- OGC standards: the Open Geospatial Consortium cept of using distributed sensors for disaster manage- (OGC) is a leading authority in developing standards ment is well established [35], its effective deployment for geo-spatial data interoperability. OGC has developed requires integrating recent advancements in IoRT and a few standards relevant to IoT/IoRT, such as Sensor Web GIS technologies. Similarly, autonomous navigation de- Enablement, which provides protocols and encodings for mands low-latency processing and seamless fusion of the exchange of sensor data over the Web. Additionally, multi-modal sensor data for precise localization and ob- OGC SensorThings API standardizes the way IoT sensor stacle avoidance [27]. data are published and accessed. In urban planning and architecture, scalable data han- dling and interoperability with existing GIS frameworks are essential for integrating diverse spatial data sources References used in traffic analysis, infrastructure monitoring, and environmental assessment [36, 37]. Despite its potential [1] LiDaR measurements, https://www.geoportal.gov. to enhance urban automation and data-driven decision- pl/en/data/lidar-measurements-lidar/, accessed Jan, making, the integration of robots with smart city infras- 2025. tructure remains underexplored. Recent efforts, such as [2] N. V. B. Yogeswaranathan Kalyani, R. Collier, Digi- the Smart City Component in a Robotic Competition [38], tal twin deployment for smart agriculture in cloud- demonstrate how robots can act as both consumers and fog-edge infrastructure, Int. Journal of Parallel, producers of smart city data, underscoring the need for Emergent and Distributed Systems 38 (2023). seamless interoperability between robotic systems and [3] A. Prountzos, E. G. M. Petrakis, Defog: dynamic urban GIS platforms. micro-service placement in hybrid cloud-fog-edge infrastructures, Int. Journal of Web and Grid Ser- vices 20 (2024). 5. Summary and future work [4] T. F. de Barrena Sarasola, A. García, J. L. Ferrando, Iiot protocols for edge/fog and cloud computing in In this paper we outline research challenges encountered industrial AI: A high frequency perspective, Int. while designing an integration architecture for dynamic Journal of Cloud Applications and Computing 14 spatio-temporal, heterogeneous, and multi-modal data (2024). generated by IoRT machinery, with GIS analytical tools, [5] S. Siddiqi, R. Kern, M. Boehm, SAGA: A scalable within the EU GIS4IoRT project. The fundamental chal- framework for optimizing data cleaning pipelines lenges include: (1) correlating multi-modal data within for machine learning applications, SIGMOD 1 a user query, (2) providing query results according to (2023). QoS and QoD parameters, (3) dynamically re-routing [6] T. Timakum, S. Lee, H. Hu, I. Song, M. Song, queries to appropriate DSs, to assure QoS and QoD, (4) DOLAP: A 25 year journey through research trends building cost models for managing QoS and QoD, and (5) and performance (invited talk), in: Int. Workshop analyzing spatial time-series of non-standard data. on Design, Optimization, Languages and Analyti- Open issues that further will be investigated in the cal Processing of Big Data (DOLAP), volume 3653, project include among others: (1) dynamic resource pro- CEUR-WS.org, 2024. visioning for QoS and QoD, (2) reactive heterogeneous [7] J. Zhu, Y. Mao, L. Chen, C. Ge, Z. Wei, Y. Gao, Fu- data caching at the edge, fog, and cloud, (3) proactive data sionquery: On-demand fusion queries over multi- caching, (4) building ontologies for semantic data annota- source heterogeneous data, VLDB Endowment 17 tions and correlations, (5) novel visualization techniques (2024). at the GIS level. [8] A. Bouguettaya, B. Benatallah, A. Elmargamid, In- terconnecting Heterogeneous Information Systems, Acknowledgments Kluwer Academic Publishers, ISBN 0792382161, 1998. This research is funded from the EU project Chist-Era call [9] P. Brezany, A. M. Tjoa, H. Wanek, A. Wöhrer, Me- 2023, entitled Development of a Plug-and-Play Middleware diators in the architecture of grid information sys- for Integrating Robot Sensor Data with GIS Tools in a Cloud tems, in: Int. Conf. on Parallel Processing and Ap- Environment. In particular: the work of P. Skrzypczyński plied Mathematics (PPAM), volume 3019 of LNCS, and R. Wrembel is supported from the National Science Springer, 2003. Centre (NCN), Poland, grant no. 2024/06/Y/ST6/00136; [10] A. Gillet, É. Leclercq, N. Cullot, Lambda+, the re- the work of J.-P. Kasprzyk, R. Billen, and D. Sacharidis newal of the lambda architecture: Category theory is supported from the National Fund for Scientific Re- to the rescue, in: Int. Conf. on Advanced Informa- search (FNRS), Belgium; the work of S. Bimonte and L. tion Systems Engineering (CAiSE), volume 12751 d’Orazio is supported from French National Research of LNCS, Springer, 2021. Agency (ANR), France. [11] S. A. Errami, H. Hajji, K. A. E. Kadi, H. Badir, Spatial big data architecture: From data warehouses and data lakes to the lakehouse, Journal of Parallel and Declaration on Generative AI Distributed Computing 176 (2023). [12] R. Hai, C. Koutras, C. Quix, M. Jarke, Data lakes: A The authors have not employed any Generative AI tools. survey of functions and systems, IEEE Transactions on Knowledge and Data Engineering 35 (2023). [13] A. A. Harby, F. H. Zulkernine, Data lakehouse: A survey and experimental study, Information Sys- tems 127 (2025). mote Sensing 16 (2024). [14] Z. Dehghani, Data Mesh: Delivering Data-Driven [28] K. Cwian, M. R. Nowicki, P. Skrzypczynski, Gnss- Value at Scale, O’Reilly, ISBN 1492092398, 2022. augmented lidar SLAM for accurate vehicle local- [15] O. Romero, R. Wrembel, G. Koutrika, Artificial ization in large scale urban environments, in: Int. intelligence in data analytics (interactive panel ses- Conf. on Control, Automation, Robotics and Vision sion), in: E. Gallinucci, M. Lissandrini (Eds.), Int. (ICARCV), IEEE, 2022. Workshop on Design, Optimization, Languages [29] V. L. N. Huu, L. d’Orazio, E. Casseau, J. Lallet, Cache and Analytical Processing of Big Data (DOLAP) management in MASCARA-FPGA: from coalescing @EDBT/ICDT, 2024. heuristic to replacement policy, in: Int. Conf. on [16] R. Wrembel, Optimizing data integration processes Management of Data (DaMoN), ACM, 2022. with the support of machine learning - is it re- [30] T. Le, V. Kantere, L. d’Orazio, Dynamic estimation ally possible?, in: Int. Workshop on Design, Opti- and grid partitioning approach for multi-objective mization, Languages and Analytical Processing of optimization problems in medical cloud federations, Big Data (DOLAP) @EDBT/ICDT, volume 3653 of Transactions on Large-Scale Data- and Knowledge- CEUR Workshop Proceedings, 2024. Centered Systems 46 (2020). [17] J. Safari Bazargani, A. Sadeghi-Niaraki, S.-M. Choi, [31] F. Amigoni, W. Yu, T. Andre, D. Holz, M. Magnus- A survey of GIS and IoT integration: Applications son, M. Matteucci, H. Moon, M. Yokotsuka, G. Biggs, and architecture, Applied Sciences 11 (2021). doi:10. R. Madhavan, A standard for map data represen- 3390/app112110365. tation: Ieee 1873-2015 facilitates interoperability [18] A. Bauer, H. Pan, R. Chard, Y. N. Babuji, J. Bryan, between robots, IEEE Robotics & Automation Mag- D. Tiwari, I. T. Foster, K. Chard, The globus compute azine 25 (2018) 65–76. doi:10.1109/MRA.2017. dataset: An open function-as-a-service dataset from 2746179. the edge to the cloud, Future Generation Computer [32] P. Du, J. Chen, Z. Sun, Y. Li, Design of an IoT- Systems 153 (2024). GIS emergency management system for public road [19] A. Khandelwal, A. Kejariwal, K. Ramasamy, Le transport networks, in: Proceedings of the 1st ACM taureau: Deconstructing the serverless landscape SIGSPATIAL International Workshop on the Use of & A look forward, in: Int. Conf. on Management of GIS in Emergency Management, EM-GIS ’15, 2015. Data (SIGMOD), ACM, 2020. doi:10.1145/2835596.2835611. [20] M. Armbrust, T. Das, A. Davidson, A. Ghodsi, A. Or, [33] P. P. Ray, M. Mukherjee, L. Shu, Internet of J. Rosen, I. Stoica, P. Wendell, R. Xin, M. Zaharia, things for disaster management: State-of-the-art Scaling spark in the real world: Performance and and prospects, IEEE Access 5 (2017) 18818–18835. usability, VLDB Endowment 8 (2015). doi:10.1109/ACCESS.2017.2752174. [21] P. Aszkowski, B. Ptak, M. Kraft, D. Pieczynski, [34] H. Cao, M. Wachowicz, The design of an IoT-GIS P. Drapikowski, Deepness: Deep neural remote platform for performing automated analytical tasks, sensing plugin for QGIS, SoftwareX 23 (2023). Computers, Environment and Urban Systems 74 [22] Z. Li, H. Ning, Autonomous GIS: the next- (2019) 23–40. doi:https://doi.org/10.1016/ generation AI-powered GIS, Int. Journal of Digital j.compenvurbsys.2018.11.004. Earth 16 (2023). [35] F. Wang, H. Yuan, Challenges of the sensor web [23] M. Mangiameli, G. Muscato, G. Mussumeci, C. Mi- for disaster management, International Journal lazzo, A GIS application for UAV flight planning, of Digital Earth 3 (2010) 260–279. doi:10.1080/ IFAC Workshop on Research, Education and Devel- 17538947.2010.484510. opment of Unmanned Aerial Systems 46 (2013). [36] S. M. Rachel Macrorie, A. While, Robotics and [24] H. Wang, Y. Pan, X. Luo, Integration of BIM and automation in the city: a research agenda, Ur- GIS in sustainable built environment: A review and ban Geography 42 (2021) 197–217. doi:10.1080/ bibliometric analysis, Automation in Construction 02723638.2019.1698868. 103 (2019) 41–52. [37] A. G. Yeh, From urban modelling, GIS, the digital, [25] M. Sun, S. Yang, H. Liu, GLANS: GIS based large- intelligent, and the smart city to the digital twin scale autonomous navigation system, in: Int. Conf. city with AI, Environment and Planning B: Urban Advances in Swarm Intelligence (ICSI), volume Analytics and City Science 51 (2024) 1085–1088. 10942 of LNCS, Springer, 2018. doi:10.1177/23998083241249552. [26] Locus Robotics, ROS QGIS Plugin prototype, https: [38] G. Bardaro, E. Daga, J. Carvalho, A. Chiatti, //github.com/locusrobotics/qgis_ros, 2023. E. Motta, Introducing a smart city component in [27] M. Nijak, P. Skrzypczynski, K. Cwian, M. Zawada, a robotic competition: A field report, Frontiers S. Szymczyk, J. Wojciechowski, On the importance in Robotics and AI 9 (2022). doi:10.3389/frobt. of precise positioning in robotised agriculture, Re- 2022.728628.