Abstract This article addresses major information systems integration problems, approaches, technologies, and tools within the context of Model‐Driven Software Engineering. The Guaraná integration platform is introduced as an innovative platform amongst state‐of‐the‐art technologies available for enterprises to design and implement integration solutions. In this article, we present its domain‐specific modeling language and its industrial cloud‐based web development platform, which supports the design and implementation of integration solutions. A real‐world case study is described and analyzed; then, we delve into its design and implementation, to finally disclose ten measures that empirically help estimating the amount of effort involved in the development of integration solutions.

Wrappers are pieces of software used to extract data from websites and structure them for further application processing. Unfortunately, websites are continuously evolving and structural changes happen with no forewarning, which usually results in wrappers working incorrectly. Thus, wrappers maintenance is necessary for detecting whether wrapper is extracting erroneous data. The solution consists of using verification models to detect whether wrapper output is statistically similar to the output produced by the wrapper itself when it was successfully invoked in the past. Current proposals present some weaknesses, as the data used to build these models are supposed to be homogeneous, independent, or representative enough, or following a single predefined mathematical model. In this paper, we present MAVE, a novel multilevel wrapper verification system that is based on one-class classification techniques to overcome previous weaknesses. The experimental results show that our proposal outperforms accuracy of current solutions.

The Cloud is evolving as a cost-effective solution to run services that support a variety of business processes. It is not surprising then that Orchestration as a Service (OaaS) is gaining importance as a means to integrate the many services a typical company runs or out sources in the Cloud. OaaS requires very efficient orchestration engines: the faster they work, the less customers have to pay and the more customers can be served. In this paper, we report on a new orchestration engine, we have performed a series of stringent experiments that prove that it outperforms a state-of-the-art orchestration engine in widespread use. Our conclusion is that our proposal is an efficient, solid orchestration engine ready for the Cloud.

One the most challenging problems for enterprise information integration is to deal with heterogeneous information sources on the Web. The reason is that they usually provide information that is in human-readable form only, which makes it difficult for a software agent to understand it. Current solutions build on the idea of annotating the information with semantics. If the information is unstructured, proposals such as S-CREAM, MnM, or Armadillo may be effective enough since they rely on using natural language processing techniques; furthermore, their accuracy can be improved by using redundant information on the Web, as C-PANKOW has proved recently. If the information is structured and closely related to a back-end database, deep annotation ranges among the most effective proposals, but it requires the information providers to modify their applications; if deep annotation is not applicable, the easiest solution consists of using a wrapper and transforming its output into annotations. In this paper, we prove that this transformation can be automated by means of an efficient, domain-independent algorithm. To the best of our knowledge, this is the first attempt to devise and formalize such a systematic, general solution

The semantic Web shall enable Web agents an efficient, precise, and comprehensive extraction of knowledge. Nevertheless, this new Web is not likely to be adopted in the immediate future. We present a specification of a new framework in order to extract knowledge from today's dynamics nonsemantic Web. Our proposal is novel in that it associates semantics with the information extracted, which improves agent interoperability; it can also deal with changes to the structure of a Web page, which improves adaptability; furthermore, it achieves to delegate the knowledge extraction procedure to specialist agents, easing software development and promoting software reuse and maintainability.