The combination of data and technology is having a high impact on the way we live. The world is getting smarter thanks to the quantity of collected and analyzed data. However, it is necessary to consider that such amount of data is continuously increasing and it is necessary to deal with novel requirements related to variety, volume, velocity, and veracity issues. In this paper we focus on veracity that is related to the presence of uncertain or imprecise data: errors, missing or invalid data can compromise the usefulness of the collected values. In such a scenario, new methods and techniques able to evaluate the quality of the available data are needed. In fact, the literature provides many data quality assessment and improvement techniques, especially for structured data, but in the Big Data era new algorithms have to be designed. We aim to provide an overview of the issues and challenges related to Data Quality assessment in the Big Data scenario. We also propose a possible solution developed by considering a smart city case study and we describe the lessons learned in the design and implementation phases.

As cities are becoming green and smart, public information systems are being revamped to adopt digital technologies. There are several sources (official or not) that can provide information related to a city. The availability of multiple sources enables the design of advanced analyses for offering valuable services to both citizens and municipalities. However, such analyses would fail if the considered data were affected by errors and uncertainties: Data Quality is one of the main requirements for the successful exploitation of the available information. This paper highlights the importance of the Data Quality evaluation in the context of geographical data sources. Moreover, we describe how the Entity Matching task can provide additional information to refine the quality assessment and, consequently, obtain a better evaluation of the reliability data sources. Data gathered from the public transportation and urban areas of Curitiba, Brazil, are used to show the strengths and effectiveness of the presented approach.

Concerns about energy and sustainability are growing everyday involving a wide range of fields. Even Information Systems (ISs) are being influenced by the issue of reducing pollution and energy consumption and new fields are rising dealing with this topic. One of these fields is Green Information Technology (IT), which deals with energy efficiency with a focus on IT. Researchers have faced this problem according to several points of view. The purpose of this paper is to understand the trends and the future development of Green IT by analyzing the state-of-the-art and classifying existing approaches to understand which are the components that have an impact on energy efficiency in ISs and how this impact can be reduced. At first, we explore some guidelines that can help to understand the efficiency level of an organization and of an IS. Then, we discuss measurement and estimation of energy efficiency and identify which are the components that mainly contribute to energy waste and how it is possible to improve energy efficiency, both at the hardware and at the software level.