An OWL-Based Mobile GeoBI Context Ontology Enabling Location-Based and Context-Based Reasoning and Supporting Contextual Business Analysis

Mobile professionals need to be assisted with suitable mobile GeoBI (Geospatial Business Intelligence) systems, which are able to capture, organize and structure the user’s reality into a relevant context model and reason on it. GeoBI context modelling and reasoning are still research issues since there is not yet either a model or a relevant taxonomy regarding GeoBI contextual information. To fill this gap, this paper proposes an extended and detailed OWL-based mobile GeoBI context ontology to provide context-aware applications and users with relevant contextual information and context-based reasoning capabilities. Context quality issues are handled an implementation architecture which is provided.

Mobile and Context-Aware GeoBI Applications: A Multilevel Model for Structuring and Sharing of Contextual Information

With the requirements for high performance results in the today’s mobile, global, highly competitive, and technology-based business world, business professionals have to get supported by convenient mobile decision support systems (DSS). To give an improved support to mobile business professionals, it is necessary to go further than just allowing a simple remote access to a Business Intelligence platform. In this paper, the need for actual context-aware mobile Geospatial Business Intelligence (GeoBI) systems that can help capture, filter, organize and structure the user mobile context is exposed and justified. Furthermore, since capturing, structuring, and modeling mobile contextual information is still a research issue, a wide inventory of existing research work on context and mobile context is provided. Then, step by step, we methodologically identify relevant contextual information to capture for mobility purposes as well as for BI needs, organize them into context-dimensions, and build a hierarchical mobile GeoBI context model which (1) is geo-spatial-extended, (2) fits with human perception of mobility, (3) takes into account the local context interactions and information-sharing with remote contexts, and (4) matches with the usual hierarchical aggregated structure of BI data.

Contributors’enrollment in collaborative online communities:the case of OpenStreetMap

The number of people registering in an online community depends on two main factors:interest in,and awareness of,the project.Registering to a project does not,however,imply contributing to it,as lacking the knowledge and skills can be a barrier to participation.In order to identify the nature of events that might have facilitated or hindered enrollments in the OpenStreetMap(OSM)project over time,we analyzed the correlations between the number of new participants and the events that dotted its history.Four different metrics were defined to characterize participants’behaviors:the daily number of registrations,the daily number of participants that made a first contribution,the delays between contributors’registration and their first edits,and a daily contribution ratio built from the number of new contributors and the number of new registered members.Time series analyses were used to identify trends,and outstanding variations of the number of participants.An inventory of events that took place along the OSM project’s history was created and appreciable variations of the metrics have been linked to events that seemed to be meaningful.Although a correlation does not imply causality,many of the explanations these correlations suggest are supported by the results of other studies,either directly or indirectly.For instance,when considering the time participants spend as“lurker”,as well as on the nature of the contribution of early participants.In other cases,they suggest new explanations for the origin of the spam accounts that affect registration statistics,or the decline in the proportion of registered members who actually become contributors.