Proximal business intelligence on the semantic web

This is the post-print version of this article. The official version can be accessed from the link below - Copyright @ 2010 Springer.Ubiquitous information systems (UBIS) extend current Information System thinking to explicitly differentiate technology between devices and software components with relation to people and process. Adapting business data and management information to support specific user actions in context is an ongoing topic of research. Approaches typically focus on providing mechanisms to improve specific information access and transcoding but not on how the information can be accessed in a mobile, dynamic and ad-hoc manner. Although web ontology has been used to facilitate the loading of data warehouses, less research has been carried out on ontology based mobile reporting. This paper explores how business data can be modeled and accessed using the web ontology language and then re-used to provide the invisibility of pervasive access; uncovering more effective architectural models for adaptive information system strategies of this type. This exploratory work is guided in part by a vision of business intelligence that is highly distributed, mobile and fluid, adapting to sensory understanding of the underlying environment in which it operates. A proof-of concept mobile and ambient data access architecture is developed in order to further test the viability of such an approach. The paper concludes with an ontology engineering framework for systems of this type – named UBIS-ONTO

Winnowing ontologies based on application use

The requirements of specific applications and services are often over estimated when ontologies are reused or built. This sometimes results in many ontologies being too large for their intended purposes. It is not uncommon that when applications and services are deployed over an ontology, only a few parts of the ontology are queried and used. Identifying which parts of an ontology are being used could be helpful to winnow the ontology, i.e., simplify or shrink the ontology to smaller, more fit for purpose size. Some approaches to handle this problem have already been suggested in the literature. However, none of that work showed how ontology-based applications can be used in the ontology-resizing process, or how they might be affected by it. This paper presents a study on the use of the AKT Reference Ontology by a number of applications and services,and investigates the possibility of relying on this usage information to winnow that ontology

Towards a Semantic Gas Source Localization under Uncertainty

Towards a Semantic Gas Source Localization under Uncertainty.Communications in Computer and Information Science book series (CCIS, volume 855), doi:10.1007/978-3-319-91479-4_42This work addresses the problem of efficiently and coherently locating a gas source in a domestic environment with a mobile robot, meaning efficiently the coverage of the shortest distance as possible and coherently the consideration of different gas sources explaining the gas presence. The main contribution is the exploitation, for the first time, of semantic relationships between the gases detected and the objects present in the environment to face this challenging issue. Our proposal also takes into account both the uncertainty inherent in the gas classification and object recognition processes. These uncertainties are combined through a probabilistic Bayesian framework to provide a priority-ordered list of (previously observed) objects to check. Moreover the proximity of the different candidates to the current robot location is also considered by a cost function, which output is used for planning the robot inspection path. We have conducted an initial demonstration of the suitability of our gas source localization approach by simulating this task within domestic environments for a variable number of objects, and comparing it with an greedy approach.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

The ECO Program Construction System: Ways of Increasing its Representational Power and their Effects on the User Interface

There is a growing interest in programs which help users with little experience of computing to construct simulation models. Much recent development work on such systems has utilized comparatively simple mathematical methods (such as system Dynamics) to facilitate the development of a friendly user interface. The problem with these simple modelling languages is that they assume that users have preconceived ideas of the simulation models which they want to build. In the ECO project, which involved the construction and testing of programs to help ecologists build simulation models, it became clear that users could not always adapt their ideas to fit into these mathematical frameworks. They required a more expressive input language in which to describe their modelling problems, rather than being forced directly to specify the programs which solved those problems. However, we found that as the input language became more sophisticated the complexity of the user interface became disproportionally larger. We attempt to clarify the reasons for this phenomenon by comparing the various systems which we built to try to solve this problem. This comparison is facilitated by the use of a sorted logic as a lingua franca for the various formalisms used in each system. Our analysis centres around a small number of key characteristics which we use to highlight the strengths and weaknesses of various dialogue techniques

The Use of Prolog for Improving the Rigour and Accessibility of Ecological Modelling

We introduce three concepts that offer considerable benefit to the process of ecological modelling: the descriptive representation of models; the explicit representation of knowledge about how to model; and the development of knowledge-based systems that can help ecologists construct models. Prolog, a computer language based on formal logic, has much to offer in realising these ideas. We introduce the concept of a ‘model blueprint’, a complete, formal specification of the structure of a model, and show how a blueprint can be represented as a Prolog program, basing our analysis on system dynamics models for simplicity. We consider ways in which the Prolog interpreter can be used selectively to retrieve information about the model, to check for errors in the formulation of the model, and to evaluate the model mathematically. However, there are drawbacks with this approach, so we discuss ways of overcoming these by implementing - also in Prolog - programs which buffer the user from the difficulties of working at the level of the Prolog interpreter. These include the generation of descriptions of model structure, and the development of a program to help in the construction of simulation models