Claudia Roncancio

Improving source selection in large scale mediation systems through combinatorial optimization techniques

This paper concerns querying in large scale virtual organizations. Such organizations are characterized by a challenging data context involving a large number of distributed data sources with strong heterogeneity and uncontrolled data overlapping. In that context, data source selection during query evaluation is particularly important and complex. To cope with this task, we propose OptiSource, an original strategy for source selection using combinatorial optimization techniques combined to organizational knowledge of the virtual organization. Experiment numerical results show that OptiSource is a robust strategy that improves the precision and the recall of the source selection process. This paper presents the data and knowledge models, the definition of OptiSource, the related mathematical model, the prototype and an extensive experimental study.

The ContextAct@A4H Real-Life Dataset of Daily-Living Activities

Research on context management and activity recognition in smart environments is essential in the development of innovative well adapted services. This paper presents two main contributions. First, we present ContextAct@A4H, a new real-life dataset of daily living activities with rich context data (This research is supported by the Amiqual4Home Innovation Factory, http://amiqual4home.inria.fr funded by the ANR (ANR-11-EQPX-0002)). It is a high quality dataset collected in a smart apartment with a dense but non intrusive sensor infrastructure. Second, we present the experience of using temporal logic and model checking for activity recognition. Temporal logic allows specifying activities as complex events of object usage which can be described at different granularity. It also expresses temporal ordering between events thus palliating a limitation of ontology based activity recognition. The results on using the CADP toolbox for activity recognition in the real life collected data are very good.

Temporal Conditional Preference Queries on Streams

Preference queries on data streams have been proved very useful for many application areas. Despite of the existence of research studies dedicated to this issue, they lack to support the use of an important implicit information of data streams, the temporal preferences. In this paper we define new operators and an algorithm for the efficient evaluation of temporal conditional preference queries on data streams. We also demonstrate how the proposed operators can be translated to the Continuous Query Language (CQL). The experiments performed show that our proposed operators have considerably superior performance when compared to the equivalent operations in CQL.

Diagnosis Service for Software Component and Its Application to a Heterogeneous Sensor Data Management System

Component-based software development paradigm enables the construction of complex applications by assembling existing self-contained components. The cost of the software development process can then be sharply reduced. Current software systems are becoming more distributed and operate in highly dynamic environments. Therefore, dependability of component-based applications is an important research issue. Our contribution consists of a diagnosis service that enhances dependability of component-based applications. This diagnosis service provides the ability of detection and location of faulty components during runtime. In this paper, we describe the application of the proposed diagnosis service to an industrial case study that consists of a heterogeneous sensor data management system.