Software framework for automatic learning of telescope operation

摘要

The "Gran Telescopio de Canarias" (GTC) is an optical-infrared 10-meter segmented mirror telescope at the ORM observatory in Canary Islands (Spain). The GTC Control System (GCS) is a distributed object and component oriented system based on RT-CORBA and it is responsible for the operation of the telescope, including its instrumentation. The current development state of GCS is mature and fully operational. On the one hand telescope users as PI's implement the sequences of observing modes of future scientific instruments that will be installed in the telescope and operators, in turn, design their own sequences for maintenance. On the other hand engineers develop new components that provide new functionality required by the system. This great work effort is possible to minimize so that costs are reduced, especially if one considers that software maintenance is the most expensive phase of the software life cycle. Could we design a system that allows the progressive assimilation of sequences of operation and maintenance of the telescope, through an automatic self-programming system, so that it can evolve from one Component oriented organization to a Service oriented organization? One possible way to achieve this is to use mechanisms of learning and knowledge consolidation to reduce to the minimum expression the effort to transform the specifications of the different telescope users to the operational deployments. This article proposes a framework for solving this problem based on the combination of the following tools: data mining, self-Adaptive software, code generation, refactoring based on metrics, Hierarchical Agglomerative Clustering and Service Oriented Architectures.