Integrating the development of data mining and data warehouses via model-driven engineering

Data mining is one of the most important analysis techniques to automatically extract knowledge from large amount of data. Nowadays, data mining is based on low-level specifications of the employed techniques typically bounded to a specific analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Bearing in mind this situation, we propose a model-driven approach which is based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (that is deployed via data-warehousing technology) and the analysis models for data mining (tailored to a specific platform). Thus, analysts can concentrate on understanding the analysis problem via conceptual data-mining models instead of wasting efforts on low-level programming tasks related to the underlying-platform technical details. These time consuming tasks are now entrusted to the model-transformations scaffolding. The feasibility of our approach is shown by means of a hypothetical data-mining scenario where a time series analysis is required.

Towards a unified framework for opinion retrieval, mining and summarization

The exponential increase of subjective, user-generated content since the birth of the Social Web, has led to the necessity of developing automatic text processing systems able to extract, process and present relevant knowledge. In this paper, we tackle the Opinion Retrieval, Mining and Summarization task, by proposing a unified framework, composed of three crucial components (information retrieval, opinion mining and text summarization) that allow the retrieval, classification and summarization of subjective information. An extensive analysis is conducted, where different configurations of the framework are suggested and analyzed, in order to determine which is the best one, and under which conditions. The evaluation carried out and the results obtained show the appropriateness of the individual components, as well as the framework as a whole. By achieving an improvement over 10% compared to the state-of-the-art approaches in the context of blogs, we can conclude that subjective text can be efficiently dealt with by means of our proposed framework.

Construction of Intelligent Virtual Worlds Using a Grammatical Framework

The potential of integrating multiagent systems and virtual environments has not been exploited to its whole extent. This paper proposes a model based on grammars, called Minerva, to construct complex virtual environments that integrate the features of agents. A virtual world is described as a set of dynamic and static elements. The static part is represented by a sequence of primitives and transformations and the dynamic elements by a series of agents. Agent activation and communication is achieved using events, created by the so-called event generators. The grammar defines a descriptive language with a simple syntax and a semantics, defined by functions. The semantics functions allow the scene to be displayed in a graphics device, and the description of the activities of the agents, including artificial intelligence algorithms and reactions to physical phenomena. To illustrate the use of Minerva, a practical example is presented: a simple robot simulator that considers the basic features of a typical robot. The result is a functional simple simulator. Minerva is a reusable, integral, and generic system, which can be easily scaled, adapted, and improved. The description of the virtual scene is independent from its representation and the elements that it interacts with.

Practical experiences on a real pumping system emulated by a hardware model and used as a remote laboratory

Paper submitted to ACE 2013, 10th IFAC Symposium on Advances in Control Education, University of Sheffield, UK, August 28-30, 2013.

Towards automatic tweet generation: A comparative study from the text summarization perspective in the journalism genre

In recent years, Twitter has become one of the most important microblogging services of the Web 2.0. Among the possible uses it allows, it can be employed for communicating and broadcasting information in real time. The goal of this research is to analyze the task of automatic tweet generation from a text summarization perspective in the context of the journalism genre. To achieve this, different state-of-the-art summarizers are selected and employed for producing multi-lingual tweets in two languages (English and Spanish). A wide experimental framework is proposed, comprising the creation of a new corpus, the generation of the automatic tweets, and their assessment through a quantitative and a qualitative evaluation, where informativeness, indicativeness and interest are key criteria that should be ensured in the proposed context. From the results obtained, it was observed that although the original tweets were considered as model tweets with respect to their informativeness, they were not among the most interesting ones from a human viewpoint. Therefore, relying only on these tweets may not be the ideal way to communicate news through Twitter, especially if a more personalized and catchy way of reporting news wants to be performed. In contrast, we showed that recent text summarization techniques may be more appropriate, reflecting a balance between indicativeness and interest, even if their content was different from the tweets delivered by the news providers.

Can model Hamiltonians describe the electron–electron interaction in π-conjugated systems?: PAH and graphene

Model Hamiltonians have been, and still are, a valuable tool for investigating the electronic structure of systems for which mean field theories work poorly. This review will concentrate on the application of Pariser–Parr–Pople (PPP) and Hubbard Hamiltonians to investigate some relevant properties of polycyclic aromatic hydrocarbons (PAH) and graphene. When presenting these two Hamiltonians we will resort to second quantisation which, although not the way chosen in its original proposal of the former, is much clearer. We will not attempt to be comprehensive, but rather our objective will be to try to provide the reader with information on what kinds of problems they will encounter and what tools they will need to solve them. One of the key issues concerning model Hamiltonians that will be treated in detail is the choice of model parameters. Although model Hamiltonians reduce the complexity of the original Hamiltonian, they cannot be solved in most cases exactly. So, we shall first consider the Hartree–Fock approximation, still the only tool for handling large systems, besides density functional theory (DFT) approaches. We proceed by discussing to what extent one may exactly solve model Hamiltonians and the Lanczos approach. We shall describe the configuration interaction (CI) method, a common technology in quantum chemistry but one rarely used to solve model Hamiltonians. In particular, we propose a variant of the Lanczos method, inspired by CI, that has the novelty of using as the seed of the Lanczos process a mean field (Hartree–Fock) determinant (the method will be named LCI). Two questions of interest related to model Hamiltonians will be discussed: (i) when including long-range interactions, how crucial is including in the Hamiltonian the electronic charge that compensates ion charges? (ii) Is it possible to reduce a Hamiltonian incorporating Coulomb interactions (PPP) to an 'effective' Hamiltonian including only on-site interactions (Hubbard)? The performance of CI will be checked on small molecules. The electronic structure of azulene and fused azulene will be used to illustrate several aspects of the method. As regards graphene, several questions will be considered: (i) paramagnetic versus antiferromagnetic solutions, (ii) forbidden gap versus dot size, (iii) graphene nano-ribbons, and (iv) optical properties.