Fingerprint-based Wi-Fi indoor localization using map and inertial sensors

It is a common understanding that the localization accuracy can be improved by indoor maps and inertial sensors. However, there is a lack of concrete and generic solutions that combine these two features together and practically demonstrate its validity. This article aims to provide such a solution based on the mainstream fingerprint-based indoor localization approach. First, we introduce the theorem called reference points placement, which gives a theoretical guide to place reference points. Second, we design a Wi-Fi signal propagation-based cluster algorithm to reduce the amount of computation. The paper gives a parameter called reliability to overcome the skewing of inertial sensors. Then we also present Kalman filter and Markov chain to predict the system status. The system is able to provide high-accuracy real-time tracking by integrating indoor map and inertial sensors with Wi-Fi signal strength. Finally, the proposed work is evaluated and compared with the previous Wi-Fi indoor localization systems. In addition, the effect of inertial sensors’ reliability is also discussed. Results are drawn from a campus office building which is about 80 m×140 m with 57 access points

Designing a knowledge representation interface for cognitive agents

The design of cognitive agents involves a knowledge representation (KR) to formally represent and manipulate information relevant for that agent. In practice, agent programming frameworks are dedicated to a specific KR, limiting the use of other possible ones. In this paper we address the issue of creating a flexible choice for agent programmers regarding the technology they want to use. We propose a generic interface, that provides an easy choice of KR for cognitive agents. Our proposal is governed by a number of design principles, an analysis of functional requirements that cognitive agents pose towards a KR, and the identification of various features provided by KR technologies that the interface should capture. We provide two use-cases of the interface by describing its implementation for Prolog and OWL with rules.Interactive Intelligenc

Designing a knowledge representation interface for cognitive agents

The design of cognitive agents involves a knowledge representation (KR) to formally represent and manipulate information relevant for that agent. In practice, agent programming frameworks are dedicated to a specific KR, limiting the use of other possible ones. In this paper we address the issue of creating a flexible choice for agent programmers regarding the technology they want to use. We propose a generic interface, that provides an easy choice of KR for cognitive agents. Our proposal is governed by a number of design principles, an analysis of functional requirements that cognitive agents pose towards a KR, and the identification of various features provided by KR technologies that the interface should capture. We provide two use-cases of the interface by describing its implementation for Prolog and OWL with rules

Transactional and incremental type inference from data updates

A distinctive property of relational database systems is the ability to perform data updates and queries in atomic blocks called transactions, with the well known ACID properties. To date, the ability of systems performing reasoning to maintain the ACID properties, even over data held within a relational database, has been largely ignored. This article studies an approach to reasoning over data from OWL 2 RL ontologies held in a relational database, where the ACID properties of transactions are maintained. Taking an incremental approach to maintaining materialised views of the result of reasoning, the approach is demonstrated to support a query and reasoning performance comparable to or better than other OWL reasoning systems, yet adding the important benefit of supporting transactions

Optique 1.0: Semantic Access to Big Data ⋆ The Case of Norwegian Petroleum Directorate’s FactPages

Abstract. The Optique project aims at developing an end-to-end system for semantic data access to Big Data in industries such as Statoil ASA and Siemens AG. In our demonstration we present the first version of the Optique system customised for the Norwegian Petroleum Directorate’s FactPages, a publicly available dataset relevant for engineers at Statoil ASA. The system provides different options, including visual, to formulate queries over ontologies and to display query answers. Optique 1.0 offers installation wizards that allow to extract ontologies from relational schemata, extract and define mappings connecting ontologies and schemata, and align and approximate ontologies. Moreover, the system offers highly optimised techniques for query answering.

From Georeferenced Data to Socio-Spatial Knowledge. Ontology Design Patterns to Discover Domain-Specific Knowledge from Crowdsourced Data

So far, ontologies developed to support Geographic Information science have been mostly designed from a space-centered rather than a human-centered and social perspective. In the last decades, a wealth of georeferenced data is collected through sensors, mobile and web platforms from the crowd, providing rich information about people’s collective experiences and behaviors in cities. As a consequence, these new data sources require models able to make machine-understandable the social meanings and uses people commonly associate with certain places. This contribution proposes a set of reusable Ontology Design Patterns (ODP) to guide a data mining workflow and to semantically enrich the mined results. The ODPs explicitly aim at representing two facets of the geographic knowledge - the built environment and people social behavior in cities - as well as the way they interact. Modelling the interplay between the physical and the human aspects of the urban environment provides an ontology representation of the socio-spatial knowledge which can be used as baseline domain knowledge for analysing and interpreting georeferenced data collected through crowdsourcing. An experimentation using a TripAdvisor data sample to recognize food consumption practices in the city of Turin is presented