Technology Integration around the Geographic Information: A State of the Art

One of the elements that have popularized and facilitated the use of geographical information on a variety of computational applications has been the use of Web maps; this has opened new research challenges on different subjects, from locating places and people, the study of social behavior or the analyzing of the hidden structures of the terms used in a natural language query used for locating a place. However, the use of geographic information under technological features is not new, instead it has been part of a development and technological integration process. This paper presents a state of the art review about the application of geographic information under different approaches: its use on location based services, the collaborative user participation on it, its contextual-awareness, its use in the Semantic Web and the challenges of its use in natural languge queries. Finally, a prototype that integrates most of these areas is presented

A study of existing Ontologies in the IoT-domain

Several domains have adopted the increasing use of IoT-based devices to collect sensor data for generating abstractions and perceptions of the real world. This sensor data is multi-modal and heterogeneous in nature. This heterogeneity induces interoperability issues while developing cross-domain applications, thereby restricting the possibility of reusing sensor data to develop new applications. As a solution to this, semantic approaches have been proposed in the literature to tackle problems related to interoperability of sensor data. Several ontologies have been proposed to handle different aspects of IoT-based sensor data collection, ranging from discovering the IoT sensors for data collection to applying reasoning on the collected sensor data for drawing inferences. In this paper, we survey these existing semantic ontologies to provide an overview of the recent developments in this field. We highlight the fundamental ontological concepts (e.g., sensor-capabilities and context-awareness) required for an IoT-based application, and survey the existing ontologies which include these concepts. Based on our study, we also identify the shortcomings of currently available ontologies, which serves as a stepping stone to state the need for a common unified ontology for the IoT domain.Comment: Submitted to Elsevier JWS SI on Web semantics for the Internet/Web of Thing

Investigating the use of semantic technologies in spatial mapping applications

Semantic Web Technologies are ideally suited to build context-aware information retrieval applications. However, the geospatial aspect of context awareness presents unique challenges such as the semantic modelling of geographical references for efficient handling of spatial queries, the reconciliation of the heterogeneity at the semantic and geo-representation levels, maintaining the quality of service and scalability of communicating, and the efficient rendering of the spatial queries' results. In this paper, we describe the modelling decisions taken to solve these challenges by analysing our implementation of an intelligent planning and recommendation tool that provides location-aware advice for a specific application domain. This paper contributes to the methodology of integrating heterogeneous geo-referenced data into semantic knowledgebases, and also proposes mechanisms for efficient spatial interrogation of the semantic knowledgebase and optimising the rendering of the dynamically retrieved context-relevant information on a web frontend

Finding nearest Neighbor in Geo-Social Query Processing

Recording the region of people using location-acquisition technologies, such as GPS, allows generating life patterns, which associate people to places they habitually visit. Considering life patterns as edges that connect users of a social network to geographical entities on a spatial network, improves the social network, providing an integrated geo-social graph. Queries over such graph excerpt information on users, with respect to their location history, and excerpt information on geographical entities in correspondence with users who normally visit these entities. A repeated type of query in spatial networks (e.g., road networks) is to find the k- nearest neighbors (k-NN) of a given query objects. With these networks, the distances between objects depend on their network connectivity and it is expensive to compute the distances (e.g., shortest paths) between objects. We present the concept of a geo-social graph that is based on life patterns, where users are connected to geographical entities using life-pattern edges more specifically to allow finding a group of users in a Geo-Social network whose members are close to each other both socially and geographically. We proposed a new approach to find the group of k users who are geo-socially attached to each other and satisfy the all the query points. We used the Bottom up pruning technique for effective pruning of geo-social group queries. An important contribution of this work is in illustrating the usefulness and the feasibility of maintaining and querying integrated geo-social graphs