Knowledge Graphs are widely used to store facts about real-world entities and events. With the ubiquity of spatial data, vertexes or edges in knowledge graphs can possess spatial location attributes side by side with other non-spatial attributes. For instance, as of June 2018 the Wikidata knowledge graph contains 48; 547; 142 data items (i.e., vertexes) to date and ≈13% of them have spatial location attributes. The co-existence of the graph and spatial data in the same geographic knowledge graph allows users to search the graph with local intent. Many location-based services such as UberEats, GrubHub, and Yelp already employ similar knowledge graphs to enhance the location search experience for their end-users. In this paper, we demonstrate a system, namely Spindra, that provides efficient management of geographic knowledge graphs. We demonstrate the system using an interactive map-based web interface that allows users to issue location-aware search queries over the WikiData knowledge graph. The Front end will then visualize the returned geographic knowledge to the user using OpenStreetMaps.

When a data scientist analyzes mobility data (e.g., using a data visualization tool), she may find out some interesting facts in the dataset. An example of a fact can be: "The number of Taxi trips in NYC on January 23, 2016, dropped drastically as compared to other days of the same month". However, the data scientist may be left clueless if they cannot find a crisp explanation to such a fact. Furthermore, the tedious task of finding an explanation by manually scraping the data becomes even impossible with big data. Existing techniques are designed for non-spatial data which cannot be applied to spatial data because it does not consider the spatial proximity. In this paper, we propose an automatic framework which guides the data scientist to explain the fact discovered from mobility data. Our approach expands on the aggravation and intervention techniques while using spatial partitioning/clustering to improve explanations for spatial data. Experiments show that the proposed approach outperforms the state-of-the-art approaches in finding the explanation for facts extracted from NYC taxi real mobility dataset.

The paper proposes Riso-Tree, a generic indexing framework for geographic knowledge graphs. Riso-Tree enables fast execution of graph queries that involve spatial predicates (aka. GraSp). The proposed framework augments the classic R-Tree structure with pre-materialized sub-graph entries. Riso-Tree first partitions the graph into sub-graphs based on their connectivity to the spatial sub-regions. The proposed index allows for fast execution of GraSp queries by efficiently pruning the traversed vertexes/edges based upon the materialized sub-graph information. The experiments show that the proposed Riso-Tree achieves up to two orders magnitude faster execution time than its counterparts when executing GraSp queries on real knowledge graphs (e.g., WikiData).

Thanks to the wide spread use of mobile and wearable devices, popular social networks, e.g., Facebook, prompts users to add spatial attributes to social entities, e.g., check-ins, traveling posts, and geotagged photos, leading to what is known as, The GeoSocial Graph. In such graph, usersmay issue a Reachability Query with Spatial Range Predicate (abbr. RangeReach). RangeReach finds whether an input vertex can reach any spatial vertex that lies within an input spatial range. The paper proposes GEOREACH, an approach that adds spatial data awareness to a graph database management system. GEOREACH allows efficient execution of RangeReach queries, yet without compromising a lot on the overall system scalability. Experiments based on system implementation inside Neo4j prove that GEOREACH exhibits up to two orders of magnitude better query performance and up to four times less storage than the state-of-the-art spatial and reachability indexing approaches.

Spatial range queries are used on a daily basis in real-life applications, such as Google Maps and Yelp. Many of those applications may need to rank the spatial objects, enclosed by the spatial range, before presenting the result to the end-user. Existing systems rank spatial objects according to different rules, such as average user rating, distance to the user's location, etc... The popularity of social networks allowed many applications to leverage the social graph in delivering a social-proximity aware ranked list of spatial objects. In this paper, we formally define a query that returns the top-k spatial objects in a given spatial region and rank them according to the social proximity of these objects to the querying user (SKNNGEO). Furthermore, a framework that integrates a joint search on both the social and spatial domains is proposed to efficiently solve the SKNNGEO query. The paper evaluates the proposed approach using real dataset extracted from the Yelp application. Extensive experiments show that the proposed approach outperform existing baseline approaches in processing SKNNGEO query.