Virginie Thion

Quality Awareness over Graph Pattern Queries

We examine the problem of quality awareness when querying graph databases. According to quality annotations that denote quality problems appearing in data subgraphs (the annotations typically result from collaborative practices in the context of open data usage like e.g. users feedbacks), we propose a notion of quality aware (graph pattern) query based on (usage-dependent) quality profiles. In this paper, we present the formal foundations of the approach. We also show how to simply extend a generic state-of-the-art algorithm for graph pattern queries evaluation in order to implement quality awareness at evaluation time and we study its complexity. We then expose implementation guidelines, supported by a proof-of-concept prototype based on the Neo4J graph database management system.

Evaluation and Improvement of a Transition Business Process: A Case Study Guided by a Semantic Quality-Based Approach

ABSTRACT A transition in an outsourced IT project is devoted to the transfer of the project from an outgoing project team to an incoming one. It is a complex, risky, and challenging building block of importance, identified as being a critical factor in the success of an outsourced project. Ensuring the good quality of a transition is then fundamental. We present here our experience on quality evaluation and improvement of a real transition in a public institution.