Roberto Espinosa

Open business intelligence: on the importance of data quality awareness in user-friendly data mining

Citizens demand more and more data for making decisions in their daily life. Therefore, mechanisms that allow citizens to understand and analyze linked open data (LOD) in a user-friendly manner are highly required. To this aim, the concept of Open Business Intelligence (OpenBI) is introduced in this position paper. OpenBI facilitates non-expert users to (i) analyze and visualize LOD, thus generating actionable information by means of reporting, OLAP analysis, dashboards or data mining; and to (ii) share the new acquired information as LOD to be reused by anyone. One of the most challenging issues of OpenBI is related to data mining, since non-experts (as citizens) need guidance during preprocessing and application of mining algorithms due to the complexity of the mining process and the low quality of the data sources. This is even worst when dealing with LOD, not only because of the different kind of links among data, but also because of its high dimensionality. As a consequence, in this position paper we advocate that data mining for OpenBI requires data quality-aware mechanisms for guiding non-expert users in obtaining and sharing the most reliable knowledge from the available LOD.

Extracting models from web API documentation

In order to develop web mashups, designers need an in-depth understanding of each Web API they are using. However, Web API documentation is rather heterogeneous, represented by big HTML files or collection of files in which it is difficult to identify elements such as API methods and how they can be invoked. Models have been widely recognized as first-citizen artifacts for documenting software applications, abstracting from implementation details, thus becoming good candidates to raise the level of automation of web mashup development. In this paper we present an approach for extracting models from Web API documentation. Our contributions are (i) a metamodel for standardizing the information extracted from Web APIs documentation; and (ii) a method for the extraction of models by parsing HTML files containing the Web API documentation, discovering useful data, and automatically generating the corresponding models (that conform to the defined metamodel).

A set of experiments to consider data quality criteria in classification techniques for data mining

A successful data mining process depends on the data quality of the sources in order to obtain reliable knowledge. Therefore, preprocessing data is required for dealing with data quality criteria. However, preprocessing data has been traditionally seen as a time-consuming and non-trivial task since data quality criteria have to be considered without any guide about how they affect the data mining process. To overcome this situation, in this paper, we propose to analyze the data mining techniques to know the behavior of different data quality criteria on the sources and how they affects the results of the algorithms. To this aim, we have conducted a set of experiments to assess three data quality criteria: completeness, correlation and balance of data. This work is a first step towards considering, in a systematic and structured manner, data quality criteria for supporting and guiding data miners in obtaining reliable knowledge.

Enabling Non-expert Users to Apply Data Mining for Bridging the Big Data Divide

Non-expert users find complex to gain richer insights into the increasingly amount of available heterogeneous data, the so called big data. Advanced data analysis techniques, such as data mining, are difficult to apply due to the fact that (i) a great number of data mining algorithms can be applied to solve the same problem, and (ii) correctly applying data mining techniques always requires dealing with the inherent features of the data source. Therefore, we are attending a novel scenario in which non-experts are unable to take advantage of big data, while data mining experts do: the big data divide. In order to bridge this gap, we propose an approach to offer non-expert miners a tool that just by uploading their data sets, return them the more accurate mining pattern without dealing with algorithms or settings, thanks to the use of a data mining algorithm recommender. We also incorporate a previous task to help non-expert users to specify data mining requirements and a later task in which users are guided in interpreting data mining results. Furthermore, we experimentally test the feasibility of our approach, in particular, the method to build recommenders in an educational context, where instructors of e-learning courses are non-expert data miners who need to discover how their courses are used in order to make informed decisions to improve them.

A Mobile-Sensor Fire Prevention System Based on the Internet of Things

The Internet of Things (IoT) is changing the industrial sectors, business models and processes. In this context, a special area positively affected by IoT is the prevention of risk factors. A permanently connected network of new products, machines, people and organizations, is a technological advance that can be oriented to combat the risks of fires in places of greater vulnerability in the business areas. This paper presents an application project regarding to a fire prevention system in places where temperature can be measured using the new IoT tools. This project and its novel advantages can be helpful in the development of preventive to increase safety in industry.

Linked Open Data mining for democratization of big data

Data is everywhere, and non-expert users must be able to exploit it in order to extract knowledge, get insights and make well-informed decisions. The value of the discovered knowledge from big data could be of greater value if it is available for later consumption and reusing. In this paper, we present an infrastructure that allows non-expert users to (i) apply user-friendly data mining techniques on big data sources, and (ii) share results as Linked Open Data (LOD). The main contribution of this paper is an approach for democratizing big data through reusing the knowledge gained from data mining processes after being semantically annotated as LOD, then obtaining Linked Open Knowledge. Our work is based on a model-driven viewpoint in order to easily deal with the wide diversity of open data formats.

Towards the Development of a Knowledge Base for Realizing User-Friendly Data Mining

Initiatives as open data, make available more and more data to everybody, thus fostering new techniques for enabling non-expert users to analyse data in an easier manner. Data mining techniques allow acquiring knowledge from available data but it requires a high level of expertise in both preparing data sets and selecting the right mining algorithm. This paper is a first step towards a user-friendly data mining approach in which a knowledge base is created with the aim of guiding non-expert users in obtaining reliable knowledge from data sources.