The complexity and intelligence of energy systems has increased in the recent years, whereas using Multi-agents system (MAS) technology has been recommended by IEEE for developing software solutions for modeling, controlling, and simulating their behaviors. Available MAS solutions for energy systems are generally designed for resolving specific problems by proposing ad-hoc solutions, without considering interoperability and reusability. We propose a methodology, based on the Model-Driven Engineering (MDE) technique, for developing MAS solutions for energy systems. Our methodology uses the Common Information Model standard (CIM), recommended by IEEE, and the existing Platform Independent agent metamodel PIM4Agents. Our proposal allows modeling MAS solutions for power engineering applications, by means of a platform independent model that abstracts developers from existing agent-oriented methodologies and platforms. Applying model transformations, the generated models can be transformed and executed within several agent platforms such as JACK and JADE. The proposal has been partially validated by means of a well-known test case.

Web data extractors are used to extract data from web documents in order to feed automated processes. In this article, we propose a technique that works on two or more web documents generated by the same server-side template and learns a regular expression that models it and can later be used to extract data from similar documents. The technique builds on the hypothesis that the template introduces some shared patterns that do not provide any relevant data and can thus be ignored. We have evaluated and compared our technique to others in the literature on a large collection of web documents; our results demonstrate that our proposal performs better than the others and that input errors do not have a negative impact on its effectiveness; furthermore, its efficiency can be easily boosted by means of a couple of parameters, without sacrificing its effectiveness.

Extracting information from web documents has become a research area in which new proposals sprout out year after year. This has motivated several researchers to work on surveys that attempt to provide an overall picture of the many existing proposals. Unfortunately, none of these surveys provide a complete picture, because they do not take region extractors into account. These tools are kind of preprocessors, because they help information extractors focus on the regions of a web document that contain relevant information. With the increasing complexity of web documents, region extractors are becoming a must to extract information from many websites. Beyond information extraction, region extractors have also found their way into information retrieval, focused web crawling, topic distillation, adaptive content delivery, mashups, and metasearch engines. In this paper, we survey the existing proposals regarding region extractors and compare them side by side.

Many authors are researching on information extraction techniques to transform the semi-structured information in typical web pages into structured information. When a researcher devises a new technique, he or she has to validate it, which requires implementing it, experimenting, gathering precision and recall results, comparing it to others, and drawing conclusions. This involves an array of details that are specific to this technique, but many others that are actually shared with other proposals. Unfortunately, the literature does not provide a single up-to-date platform to guide software engineers and researches in the design and implementation of information extractors. In this paper, we present a platform to design and implement learners of information extraction rules. Due to space constraints, we focus on the class of learners that learn hierarchical transducers. We have implemented our platform, and we have validated it by means of three case studies.