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

This e-book constitutes the completely refereed court cases of the Fourth overseas convention on facts applied sciences and functions, info 2015, held in Colmar, France, in July 2015.

The nine revised complete papers have been rigorously reviewed and chosen from 70 submissions. The papers care for the subsequent themes: databases, info warehousing, information mining, facts administration, info protection, wisdom and knowledge platforms and applied sciences; complicated software of data.

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

Example text

Thanks to example of the authors of work [2] this task got the name Concept Map Mining (CMM) similar to Data Mining and Text Mining. In general case the process of CMM consists of three subtasks: extraction of concepts, extraction of links and summarization (see Fig. 1) [3]. Fig. 1. The subtasks of concept map mining process. The aim of this paper is to demonstrate usefulness and efficiency of statistical Text Mining methods for automatic construction of concept maps based on the domain collections of texts.

The classification of the visiting styles is characterized by three different parameters related to the visitor: (a) the number of artworks viewed, (b) the average time spent by interacting with the viewed artworks, and (c) the path determining the order of visit of the exhibit sections. Table 1. Characterization of the visiting styles’ classification. Animal (a) Viewed artworks (b) Average time (c) Path A high - high B high - low F low low - G low high - As we can observe in Table 1, high values for the parameter (a) characterize both As and Bs, while low values are related to Fs and Gs.

Domeniconi et al. To summarize the various methods of supervised term weighting, we show in Table 1 the fundamental elements mostly used in the following formulas to compute the global importance of a term ti for a category ck . – A denotes the number of documents belonging to category ck where the term ti occurs at least once; – B denotes the number of documents belonging to ck where ti does not occur; – dually C denotes the number of documents not belonging to category ck where the term ti occurs at least once; – finally D denotes the number of documents not belonging to ck where ti does not occur.

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