The research goals of the Dream project-team concern monitoring complex systems. The challenge is to design smart systems, both adaptable and dependable, to answer the demand for self-healing embedded systems. The considered systems meet a fixed common goal (or contract), possibly expressed by a set of QoS (Quality of Service) constraints. The Dream team investigates and develops model-based approaches. Dealing with dynamic systems, a central role is given to temporal information and the model specification uses event-based formalisms such as discrete-event systems (mainly described by automata), or sets of chronicles (a chronicle is a temporally constrained set of events).
We investigate two main research questions. Firstly, we design and develop distributed architectures and efficient diagnosis/repair algorithms for highly distributed systems. Secondly, we study the automatic acquisition of models from data using symbolic machine learning and data mining methods, with a particular focus on data streams processing. Target applications are of two kinds: large component-based system monitoring applications, like telecommunication networks, and software systems like web services and environmental protection with development of decision support systems to help managing agricultural plots and support high water quality threatened by pollution.
Diagnosis of large scale discrete event systems
The problem we deal with is monitoring complex and large discrete-event systems (DES) such as an orchestration of web services or a fleet of mobile phones. Two approaches have been studied. The first one consists in representing the system model as a discrete-event system by an automaton. In this case, the diagnostic task consists in determining the trajectories (a sequence of states and events) compatible with the sequence of observations. From these trajectories, it is then easy to determine (identify and localize) the possible faults. In the second approach, the model consists in a set of predefined characteristic patterns. We use temporal patterns, called chronicles, represented by a set of temporally constrained events. The diagnostic task consists in recognizing these patterns by analyzing the flow of observed events. More recently, we started research on interacting with large-scale systems in a decision-oriented way. Scenario patterns were defined for exploring complex systems, based on the use of model-checking techniques.
- Distributed monitoring with chronicles – Interleaving diagnosis and repair – Making web services more adaptive
- Scenario patterns for exploring qualitative ecosystems
Machine learning for model acquisitionModel acquisition is an important issue for model-based diagnosis, especially as modeling dynamic systems. We investigate machine learning methods for temporal data recorded by sensors or spatial data resulting from simulation processes. We also investigate efficient methods for storing and accessing large volume of simulations data. Our main interest is extracting knowledge, especially sequential and temporal patterns or prediction rules, from static or dynamic data (data streams). We are particularly interested in mining temporal patterns with numerical information and in incremental mining from sequences recorded by sensors.
- Mining temporal patterns with numerical information
- Incremental sequential mining
- Multiscale segmentation of satellite image time series
Aiding decision with models and simulation dataModels can be very useful for decision aiding as they can be used to play different plausible scenarios for generating the data representing future states of the modeled process. However, the volume of simulation data may be very huge. Thus, efficient tools must be investigated in order to store the simulation data, to focus on relevant parts of the data and to extract interesting knowledge from these data.
- Exploring models thanks to scenarios: a generic framework
- Designing, building and using a datawarehouse for simulation data
- Computing efficiently skyline queries in an interactive context
- Reasoning with influence Diagrams for Multi-Criteria Decision-Making
- Recommending actions from classification rules
Causal reasoning and influence diagrams
- Modeling causal reasoning for diagnosis
- Implementing causal reasoning with ASP (Answer Set Programming)
- Automating the treatment of cognitive maps