Cities serve as vital hubs of economic activity and knowledge generation and dissemination. As such, cities bear a significant responsibility to uphold environmental protection measures while promoting the welfare and living comfort of their residents. There are diverse views on the development of smart cities, from integrating Information and Communication Technologies into urban environments for better operational decisions to supporting sustainability, wealth, and comfort of people. However, for all these cases, data are the key ingredient and enabler for the vision and realization of smart cities. This article explores the challenges associated with smart city data. We start with gaining an understanding of the concept of a smart city, how to measure that the city is a smart one, and what architectures and platforms exist to develop one. Afterwards, we research the challenges associated with the data of the cities, including availability, heterogeneity, management, analysis, privacy, and security. Finally, we discuss ethical issues. This article aims to serve as a “one-stop shop” covering data-related issues of smart cities with references for diving deeper into particular topics of interest.

In the modern era, where the global energy sector is transforming to meet the decarbonization goal, cutting-edge information technology integration, artificial intelligence, and machine learning have emerged to boost energy conversion and management innovations. Incorporating artificial intelligence and machine learning into energy conversion, storage, and distribution fields presents exciting prospects for optimizing energy conversion processes and shaping national and global energy markets. This integration rapidly grows and demonstrates promising advancements and successful practical implementations. This paper comprehensively examines the current state of applying artificial intelligence and machine learning algorithms in energy conversion and management evaluation and optimization tasks. It highlights the latest developments and the most promising algorithms and assesses their merits and drawbacks, encompassing specific applications and relevant scenarios. Furthermore, the authors propose recommendations to emphasize the prioritization of acquiring real-world experimental and simulated data and adopting standardized, explicit reporting in research publications. This review paper includes details on data size, accuracy, error rates achieved, and comparisons of algorithm performance against established benchmarks.

Twitter is a social network that provides a powerful source of data. The analysis of those data offers many challenges among those stands out the opportunity to find reputation of a product, a person or any other entity of interest. Several approaches for sentiment analysis have been proposed in the literature to assess the general opinion expressed in tweets on an entity. Nevertheless, these methods aggregate sentiment scores retrieved from tweets, which is a static view to evaluate the overall reputation of an entity. The reputation of an entity is not static; entities collaborate with each other, and they get involved in different events over time. A simple aggregation of sentiment scores is then not sufficient to represent this dynamism. In this paper, we present a new approach to determine the reputation of an entity on the basis of the set of events in which it is involved. To achieve this, we propose a new sampling method driven by a tweet weighting measure to give a better quality and summary of the target entity. We introduce the concept of Frequent Named Entities to determine the events involving the target entity. Our evaluation achieved for different entities shows that 90% of the reputation of an entity originates from the events it is involved in and the breakdown into events allows interpreting the reputation in a transparent and self-explanatory way.

Schema matching is a critical problem in many applications where the main goal is to match attributes coming from heterogeneous sources. In this paper, we propose PROCLAIM (PROfile-based Cluster-Labeling for AttrIbute Matching), an automatic, unsupervised clustering-based approach to match attributes of a large number of heterogeneous sources. We define the concept of attribute profile to characterize the main properties of an attribute using: (i) the statistical distribution and the dimension of the attribute’s values, (ii) the name and textual descriptions related to the attribute. The attribute matchings produced by PROCLAIM give the best representation of heterogeneous sources thanks to the cluster-labeling function we defined. We evaluate PROCLAIM on 45,000 different data sources coming from oil and gas authority open data website (The data is published under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)). The results we obtain are promising and validate our approach.