A unified framework for building ontological theories with application and testing in the field of clinical trials

The objective of this research programme is to contribute to the establishment of the emerging science of Formal Ontology in Information Systems via a collaborative project involving researchers from a range of disciplines including philosophy, logic, computer science, linguistics, and the medical sciences. The re­searchers will work together on the construction of a unified formal ontology, which means: a general framework for the construction of ontological theories in specific domains. The framework will be constructed using the axiomatic-deductive method of modern formal ontology. It will be tested via a series of applications relating to on-going work in Leipzig on medical taxonomies and data dictionaries in the context of clinical trials. This will lead to the production of a domain-specific ontology which is designed to serve as a basis for applications in the medical field

A framework for utility data integration in the UK

In this paper we investigate various factors which prevent utility knowledge from being fully exploited and suggest that integration techniques can be applied to improve the quality of utility records. The paper suggests a framework which supports knowledge and data integration. The framework supports utility integration at two levels: the schema and data level. Schema level integration ensures that a single, integrated geospatial data set is available for utility enquiries. Data level integration improves utility data quality by reducing inconsistency, duplication and conflicts. Moreover, the framework is designed to preserve autonomy and distribution of utility data. The ultimate aim of the research is to produce an integrated representation of underground utility infrastructure in order to gain more accurate knowledge of the buried services. It is hoped that this approach will enable us to understand various problems associated with utility data, and to suggest some potential techniques for resolving them

High-resolution SAR images for fire susceptibility estimation in urban forestry

We present an adaptive system for the automatic assessment of both physical and anthropic fire impact factors on periurban forestries. The aim is to provide an integrated methodology exploiting a complex data structure built upon a multi resolution grid gathering historical land exploitation and meteorological data, records of human habits together with suitably segmented and interpreted high resolution X-SAR images, and several other information sources. The contribution of the model and its novelty rely mainly on the definition of a learning schema lifting different factors and aspects of fire causes, including physical, social and behavioural ones, to the design of a fire susceptibility map, of a specific urban forestry. The outcome is an integrated geospatial database providing an infrastructure that merges cartography, heterogeneous data and complex analysis, in so establishing a digital environment where users and tools are interactively connected in an efficient and flexible way

Efficient Image-Space Extraction and Representation of 3D Surface Topography

Surface topography refers to the geometric micro-structure of a surface and defines its tactile characteristics (typically in the sub-millimeter range). High-resolution 3D scanning techniques developed recently enable the 3D reconstruction of surfaces including their surface topography. In his paper, we present an efficient image-space technique for the extraction of surface topography from high-resolution 3D reconstructions. Additionally, we filter noise and enhance topographic attributes to obtain an improved representation for subsequent topography classification. Comprehensive experiments show that the our representation captures well topographic attributes and significantly improves classification performance compared to alternative 2D and 3D representations.Comment: Initial version of the paper accepted at the IEEE ICIP Conference 201

Secure Sharing of Spatio-Temporal Data through Name-based Access Control

Named Data Networking (NDN) is proposed as a future Internet architecture, which provides name-based data publishing and fetching primitive. Compared to TCP/IP, the benefits of NDN are as follows. NDN removes the need to manage IP address; NDN provides semantically meaningful and structured names; NDN has a stateful and name-based forwarding plane; NDN supports data-centric security and in-network caching. Name-based Access Control is an access control solution proposed over NDN, which is a content-based access control by encrypting data at the time of production directly without relying on a third-party service(i.e., Cloud storage), utilizes NDN’s hierarchical naming convention to express access control policy, and enables automation of key distribution. As more and more mobile data (e.g., mobile-health data) are generated dynamically and continuously over time and space, data owners often want to share his data with others for data analysis or healthcare, etc. To protect their privacy, they may want to share a subset of data based on their requirements with time and/or space restrictions. An effective and secure access control solution is required to ensure only authorized users can access certain data with fine granularity. Inspired by Named-based Access Control scheme, we take into account the data attributes (time, location) to make access decisions. In this work, we introduce a spatio-temporal access control scheme that allows data owners to specify access control policy and limit data access to a given time interval and/or location area. Specifically, we design a hierarchically structured naming convention to express fine-grained access control policy on spatio-temporal data, werealize a publish-subscribe functionality based on PSync for real-time data stream sharing, we develop a practical spatial-temporal data access control prototype based on NDN codebase. Moreover, we run experiments using Mini-NDN to evaluate the performance of sharing historical data from storage and sharing.data in real time