Section: Research Program

Data driven systems

Data-driven systems are systems whose behavior depends both on explicit workflows (scheduling and durations of tasks, calls to possibly distant services,...) and on the data processed by the system (stored data, parameters of a request, results of a request,...). This family of systems covers workflows that convey data (business processes or information systems), transactional systems (web stores), large databases managed with rules (banking systems), collaborative environments (crowds, health systems), etc. These systems are distributed, modular, and open: they integrate components and sub-services distributed over the web and accept requests from clients. Our objective is to provide validation and supervision tools for such systems. To achieve this goal, we have to solve several challenging tasks:

• provide realistic models, and sound automated abstraction techniques, to reason on models that are reasonable abstractions of real systems. These models should be able to encompass modularity, distribution, in a context where workflows and data aspects are tightly connected.

• address design of data driven systems in a declarative way: declarative models are another way to handle data-driven systems. Rather than defining the explicit workflows and their effects on data, rule-based models state how actions are enacted in terms of the shape (pattern matching) or value of the current data. We think that distributed rewriting rules or attributed grammars can provide a practical yet formal framework for maintenance, by providing a solution to update mandatory documentation during the lifetime of an artifact.

• provide tractable solutions for validation of models. Frequent issues are safety questions (can a system reach some bad configuration?), but also liveness (workflows progess), ... These questions should not only remain decidable on our models, but also with efficient computational methods.

• address QoS management in large reconfigurable systems: Data driven distributed systems often have constraints in terms of QoS. This QoS questions adresse performance issues, but also data quality. This calls for an analysis of quantitative features and for reconfiguration techniques to meet desired QoS.