Data Mining Applications in SMEs: An Italian Perspective

Background: From the last decade, data mining techniques, employed in particular in customer relationship management, have assumed a key role in the profitability and operations of companies. To support small and medium companies (SMEs), several innovative and continuously improving tools have been developed that allow SMEs to utilize the internal and external data sources to increase their competitiveness. Objectives: In this paper, an analysis of the impact of digitalization, and in particular data mining techniques, in the context of SMEs development is presented. Methods/Approach: A review of various sources has been conducted, with the focus on open source tools, since in the context of the Italian economy they are used by SMEs the most. Results: First, the analysis presents a brief review of the data mining techniques available and shows how they are practically employed in small companies. Second, an economical review of investments in data mining projects in Italy is presented. Conclusions: The review indicates that data mining techniques can boost a company in the market. However, the awareness of data mining as a company asset is still not strong in Italian SMEs and most investments in Italy are still carried out by large companie

A Multi-Label Machine Learning Approach to Support Pathologist\u27s Histological Analysis

This paper proposes a new tool in the field of telemedicine, defined as a specific branch where IT supports medicine, in case distance impairs the proper care to be delivered to a patient. All the information contained into medical texts, if properly extracted, may be suitable for searching, classification, or statistical analysis. For this reason, in order to reduce errors and improve quality control, a proper information extraction tool may be useful. In this direction, this work presents a Machine Learning Multi-Label approach for the classification of the information extracted from the pathology reports into relevant categories. The aim is to integrate automatic classifiers to improve the current workflow of medical experts, by defining a Multi-Label approach, able to consider all the features of a model, together with their relationships. This work is licensed under a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

A Neuro-Evolutionary Corpus-Based Method for Word Sense Disambiguation

International audienceWe propose a supervised approach to Word Sense Disambiguation based on Neural Networks combined with Evolutionary Algorithms. An established method to automatically design the structure and learn the connection weights of Neural Networks by means of an Evolutionary Algorithm is used to evolve a neural-network disambiguator for each polysemous word, against a dataset extracted from an annotated corpus. Two distributed encoding schemes, based on the orthography of words and characterized by different degrees of information compression, have been used to represent the context in which a word occurs. The performance of such encoding schemes has been compared. The viability of the approach has been demonstrated through experiments carried out on a representative set of polysemous words. Comparison with the best entry of the Semeval-2007 competition has shown that the proposed approach is almost competitive with state-of-the-art WSD approaches

Automated trading on financial instruments with evolved neural networks

This paper presents an approach to single-position, intraday automated trading based on a neuro-genetic algorithm. An artificial neural network is evolved which provides trading signal to a very unsophisticated automated trading agent

Data Mining Applications in SMEs: An Italian Perspective

From the last decade, data mining techniques, employed in particular in customer relationship management, have assumed a key role in the profitability and operations of companies. To support small and medium companies (SMEs), several innovative and continuously improving tools have been developed that allow SMEs to utilize the internal and external data sources to increase their competitiveness

RADAR: Resilient Application for Dependable Aided Reporting

Many organizations must produce many reports for various reasons. Although this activity could appear simple to carry out, this fact is not at all true: indeed, generating reports requires the collection of possibly large and heterogeneous data sets. Furthermore, different professional figures are involved in the process, possibly with different skills (database technicians, domain experts, employees): the lack of common knowledge and of a unifying framework significantly obstructs the effective and efficient definition and continuous generation of reports. This paper presents a novel framework named RADAR, which is the acronym for “Resilient Application for Dependable Aided Reporting”: the framework has been devised to be a ”bridge” between data and employees in charge of generating reports. Specifically, it builds a common knowledge base in which database administrators and domain experts describe their knowledge about the application domain and the gathered data; this knowledge can be browsed by employees to find out the relevant data to aggregate and insert into reports, while designing report layouts; the framework assists the overall process from data definition to report generation. The paper presents the application scenario and the vision by means of a running example, defines the data model and presents the architecture of the framework