Ontological representation of time-of-flight camera data to support vision-based AmI

Proceedings of: 4th International Workshop on Sensor Networks and Ambient Intelligence, 19-23 March 2012, Lugano ( Switzerland)Recent advances in technologies for capturing video data have opened a vast amount of new application areas. Among them, the incorporation of Time-of-Flight (ToF) cameras on Ambient Intelligence (AmI) environments. Although theperformance of tracking algorithms have quickly improved, symbolic models used to represent the resulting knowledge have not yet been adapted for smart environments. This paper presents an extension of a previous system in the area of videobased AmI to incorporate ToF information to enhance sceneinterpretation. The framework is founded on an ontologybased model of the scene, which is extended to incorporate ToF data. The advantages and new features of the model are demonstrated in a Social Signal Processing (SSP) application.This work was supported in part by Projects CICYT TIN2011-28620-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029- C02-02.Publicad

Addresing Consistency Checking Of Goal Model For Software Product Line Government Tourism System

Software Product Line Development needed inconsistency checking in order to improve software quality.In order to handle conflict (inconsistency) is one of active areas in the research of Goal Oriented Requirements Engineering (GORE). Ontology is used to capture the knowledge of certain domain wished. One of the features of Ontology described using OWL is the checking of consistency. The case study is Indonesian Government Tourism System. This paper presents how to develop software product line in Indonesian Government with the Ontology OWL that used for the consistency checking in the software product line for e-government applications. It is important, because the software product line would be derived from goal model that has been consistent and no conflict These paper presents how the Ontology OWL act to handle inconsistency checking in goal model. The first step conducted is to convert the goal model into Ontology using Protégé. Parallel with the first step, Ontology with equipped carried out the checking consistency of terminology, designation, and structure. The next step is to conduct the checking of logic consistency (Strong conflict and/or Weak conflict) by defining the rules using SWRL Ta