Location- and keyword-based querying of geo-textual data: a survey

With the broad adoption of mobile devices, notably smartphones, keyword-based search for content has seen increasing use by mobile users, who are often interested in content related to their geographical location. We have also witnessed a proliferation of geo-textual content that encompasses both textual and geographical information. Examples include geo-tagged microblog posts, yellow pages, and web pages related to entities with physical locations. Over the past decade, substantial research has been conducted on integrating location into keyword-based querying of geo-textual content in settings where the underlying data is assumed to be either relatively static or is assumed to stream into a system that maintains a set of continuous queries. This paper offers a survey of both the research problems studied and the solutions proposed in these two settings. As such, it aims to offer the reader a first understanding of key concepts and techniques, and it serves as an “index” for researchers who are interested in exploring the concepts and techniques underlying proposed solutions to the querying of geo-textual data.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)Nanyang Technological UniversityThis research was supported in part by MOE Tier-2 Grant MOE2019-T2-2-181, MOE Tier-1 Grant RG114/19, an NTU ACE Grant, and the Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU) that is funded by the Singapore Government through the Industry Alignment Fund Industry Collaboration Projects Grant, and by the Innovation Fund Denmark centre, DIREC

Developing an open data portal for the ESA climate change initiative

We introduce the rationale for, and architecture of, the European Space Agency Climate Change Initiative (CCI) Open Data Portal (http://cci.esa.int/data/). The Open Data Portal hosts a set of richly diverse datasets – 13 “Essential Climate Variables” – from the CCI programme in a consistent and harmonised form and to provides a single point of access for the (>100 TB) data for broad dissemination to an international user community. These data have been produced by a range of different institutions and vary across both scientific and spatio-temporal characteristics. This heterogeneity of the data together with the range of services to be supported presented significant technical challenges. An iterative development methodology was key to tackling these challenges: the system developed exploits a workflow which takes data that conforms to the CCI data specification, ingests it into a managed archive and uses both manual and automatically generated metadata to support data discovery, browse, and delivery services. It utilises both Earth System Grid Federation (ESGF) data nodes and the Open Geospatial Consortium Catalogue Service for the Web (OGC-CSW) interface, serving data into both the ESGF and the Global Earth Observation System of Systems (GEOSS). A key part of the system is a new vocabulary server, populated with CCI specific terms and relationships which integrates OGC-CSW and ESGF search services together, developed as part of a dialogue between domain scientists and linked data specialists. These services have enabled the development of a unified user interface for graphical search and visualisation – the CCI Open Data Portal Web Presence

Textually Relevant Spatial Skylines

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A rule-based video database system architecture

We propose a novel architecture for a video database system incorporating both spatio-temporal and semantic (keyword, event/activity and category-based) query facilities. The originality of our approach stems from the fact that we intend to provide full support for spatio-temporal, relative object-motion and similarity-based object-trajectory queries by a rule-based system utilizing a knowledge-base while using an object-relational database to answer semantic-based queries. Our method of extracting and modeling spatio-temporal relations is also a unique one such that we segment video clips into shots using spatial relationships between objects in video frames rather than applying a traditional scene detection algorithm. The technique we use is simple, yet novel and powerful in terms of effectiveness and user query satisfaction: video clips are segmented into shots whenever the current set of relations between objects changes and the video frames, where these changes occur, are chosen as keyframes. The directional, topological and third-dimension relations used for shots are those of the keyframes selected to represent the shots and this information is kept, along with frame numbers of the keyframes, in a knowledge-base as Prolog facts. The system has a comprehensive set of inference rules to reduce the number of facts stored in the knowledge-base because a considerable number of facts, which otherwise would have to be stored explicitly, can be derived by rules with some extra effort. © 2002 Elsevier Science Inc. All rights reserved