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By Markus Helfert, Andreas Holzinger, Orlando Belo, Chiara Francalanci

This ebook constitutes the completely refereed court cases of the Fourth foreign convention on information applied sciences and functions, facts 2015, held in Colmar, France, in July 2015.

The nine revised complete papers have been conscientiously reviewed and chosen from 70 submissions. The papers take care of the next issues: databases, facts warehousing, facts mining, facts administration, info protection, wisdom and data platforms and applied sciences; complicated software of data.

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Extra resources for Data Management Technologies and Applications: 4th International Conference, DATA 2015, Colmar, France, July 20-22, 2015, Revised Selected Papers

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45 (11) Any supervised feature selection scheme can be used for the term weighting. For example, the gss extension of the χ2 proposed by [15] eliminates N at numerator and the emphasis to rare features and categories at the denominator. gss = A·D−B·C N2 (12) Relevance frequency [17] considers the terms distribution in the positive and negative examples, stating that, in multi-label text categorization, the higher the concentration of high-frequency terms in the positive examples than in the negative ones, the greater the contribution to categorization.

4. Dependency tree for semantic analysis used in [9]. Like the authors of [6], the authors of [11] search for “noun-verb-noun” structures in sentences. They use verbs as designations of links and display nouns in concepts. The authors of [12] use not only verbs but also prepositional groups of the English language which designate possessiveness (of), direction (to), means (by), etc. for designation of links. The authors of [13] propose a novel approach based on combined techniques of automatic generation of exhaustive syntactic rules, restricted-context part-of-speech tagging and vector space intersection.

The higher the similarity between vectors-terms, the less is the angle, the higher is the cosine of the angle (cosine measure). Consequently, maximum similarity is equal to 1, and minimum one is equal to 0. The obtained term-term matrix measures distances between terms based on their co-occurrence in documents (as coordinates of vectors-terms are frequencies of their use in documents). It means that the sparser the initial term-document matrix, the worse is the quality of the term-term distances matrix.

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