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Section: Research Program

Data driven systems

The term data-driven systems refers to systems the behavior of which 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 (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 implemented systems designed in low-level languages (for instance BPEL (Business Process Execution Language)). These models should be able to encompass modularity, distribution, in a context where workflows and data aspects are tightly connected.

  • provide tractable solutions for validation of models. Important questions that are frequently addressed (for instance safety properties or coverability) should not only remain decidable on our models, but also with a decent complexity.

  • 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. Such declarative models are well accepted in business processes (Companies such as IBM use their own model of business rules  [55] to interact with their clients). Our approach is to design collaborative activities in terms of distributed structured documents, that can be seen as communicating rewriting systems. This modeling paradigm also includes models such as distributed Active XML  [50] , [53] . We think that distributed rewriting rules or attributed grammars can provide a practical but yet formal framework for maintenance, by providing a solution to update mandatory documentation during the lifetime of an artifact.

  • address QoS management in large reconfigurable systems:

Data driven distributed systems such as web services often have constraints in terms of QoS. This calls for an analysis of quantitative features, and for reconfiguration techniques to meet QoS contracts. We will build from our experience on QoS contracts composition  [56] and planning  [49] , [51] to propose optimization and reconfiguration schemes.