Development of an extension of GeoServer for handling 3D spatial data

Recently, several open source software tools such as CesiumJS and iTowns have been developed for dealing with 3-dimensional spatial data. These tools mainly focus on visualization of 3D spatial data based on WebGL. An open-sourced server capable of storing, sharing and querying 3D spatial data has not yet been developed. GeoServer, one of the representative open source spatial data servers, provides many powerful features. In particular, it supports connecting to and publishing spatial data from a variety of data sources. GeoServer also supports Web Feature Service (WFS), which is a standard protocol established by the Open Geospatial Consortium to request geospatial feature data. However, GeoServer provides functions only for two-dimensional geometry, so it provides few functions for handling 3D spatial data. Because JTS Topology Suite, which is an important component of GeoServer, does not support 3D spatial operations, it also does not support solid geometries. In this paper, we discuss extension modules of GeoServer that we have implemented to handle 3D spatial data. First, instead of JTS, our modules adopted a geometry model based on the ISO 19107 standard and support 3D spatial operations from the Simple Feature CGAL library. Based on this geometry model, we have implemented new internal data structures that represent spatial information from the Feature interface in GeoServer. Second, we also extended the DataStore module to handle and store 3D spatial information for several data sources such as GeoJSON, GML and PostGIS. Finally, we extended the WFS module to share 3D spatial data via GeoServer

Survey on indoor map standards and formats

With the adoption of indoor positioning solutions, which enable for a variety of location-based spatial services, a number of indoor map standards and formats have been proposed in the last decade. As each of these indoor map standard has its own purpose, the strengths and weaknesses are necessary to be understood and analyzed before selecting one of them for a given application. The Indoor Map Subcommittee has been established under IPIN/ISC in 2017. Among others, the goal of this working group is to compare available indoor map standards, provide a guideline for their application and advise on changes to their standardization development organizations if necessary. In this paper we present a survey of indoor map standards as an achievement of the subcommittee. The scope of the survey covers official standards such as IFC of BuildingSmart, IndoorGML and CityGML of OGC, and Indoor OpenStreetMap. We present several use-cases to show and discuss how to build indoor maps.The work of K.-J. Li was supported by a grant (19NSIP-B135746-03) from National Spatial Information Research Program (NSIP) funded by MOLIT of Korean government. The work of C. Laoudias has been supported by the European Union's Horizon 2020 research and innovation programme under grant agreement No. 739551 (KIOS CoE) and from the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development. Torres-Sospedra and Perez-Navarro want to thank the Spanish network of excellence, REPNIN+,TEC2017-90808-REDT. The work of A. Moreira has been supported by FCT -Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2019

Causality Join Query Processing for Data Streams via a Spatiotemporal Sliding Window

Data streams collected from sensors contain a large volume of useful information including causal relationships. Causality join query processing involves retrieving a set of pairs (cause, effect) from streams of data. However, some causal pairs may be omitted from the query result, due to the delay between sensors and the data stream management system, and the limited size of the sliding window. In this paper, we first investigate temporal, spatial, and spatiotemporal aspects of causality join query processing for data streams. Second, we propose several strategies for sliding window management based on these results. The accuracy of the proposed strategies is studied via intensive experimentation. The result shows that we can improve the accuracy of causality join query processing in data streams with respect to the simple FIFO strategy

Causality Join Query Processing for Data Streams via a Spatiotemporal Sliding Window

Data streams collected from sensors contain a large volume of useful information including causal relationships. Causality join query processing involves retrieving a set of pairs (cause, effect) from streams of data. However, some causal pairs may be omitted from the query result, due to the delay between sensors and the data stream management system, and the limited size of the sliding window. In this paper, we first investigate temporal, spatial, and spatiotemporal aspects of causality join query processing for data streams. Second, we propose several strategies for sliding window management based on these results. The accuracy of the proposed strategies is studied via intensive experimentation. The result shows that we can improve the accuracy of causality join query processing in data streams with respect to the simple FIFO strategy