Machine learning (ML) has already gained the attention of the researchers involved in smart city (SC) initiatives, along with other advanced technologies such as IoT, big data, cloud computing, or analytics. In this context, researchers also realized that data can help in making the SC happen but also, the open data movement has encouraged more research works using machine learning. Based on this line of reasoning, the aim of this paper is to conduct a systematic literature review to investigate open data-based machine learning applications in the six different areas of smart cities. The results of this research reveal that: (a) machine learning applications using open data came out in all the SC areas and specific ML techniques are discovered for each area, with deep learning and supervised learning being the first choices. (b) Open data platforms represent the most frequently used source of data. (c) The challenges associated with open data utilization vary from quality of data, to frequency of data collection, to consistency of data, and data format. Overall, the data synopsis as well as the in-depth analysis may be a valuable support and inspiration for the future smart city projects.

Energy is the sector most strongly connected with climate change moderation, and this correlation and interdependency is largely investigated, in particular as regards renewable energy and sustainability issues. The United Nations, European Union, and all countries around the world declare their support for sustainable development, materialized in agreements, strategies, and action plans. This diversity, combined with significant interdependencies between indicators, brings up challenges for data analysis, which we have tackled in order to decide on relevant indicators. We have built a research framework based on Business Intelligence & Analytics for monitoring the SDG7 indicators that aim at “Ensuring access to affordable, reliable, sustainable, and modern energy for all”, in relation with SDG13 indicators targeting the sustainable aspect of energy. In developing the Business Intelligence & Analytics framework, we have considered Design Science Research in information systems guidelines. We have designed a process for carrying out Design Science Research by describing the demarche to develop information artifacts, which are the essence of a Business Intelligence & Analytics system. The information artifacts, such as data source, preprocessed data, initial and final data model, as well as data visualizations, are designed and implemented in order to support clean and affordable energy data analysis. The proposed research model, applied for Romania in this paper, serves as a point of departure for investigating data in a more integrated way, and can be easily applied to another country case study.

Nowadays, the IT industry is revolving around the build-out and adoption of a new platform, characterized by mobility, cloud-based application and service delivery, and value-generating overlays of social business and pervasive analytics. The paper explores the convergence of mobile, cloud, and social, as well as the effects for the enterprise and the emergence of the new enterprise application platforms. In the beginning we set the stage, while showing the expansion of the mobile, cloud, and social in the business information system, as they were found in the literature. We then look over the IT trends, especially the consumerization of IT, as reasons and basis for the information systems embracing of mobile. Afterwards, we present a mobility roadmap for the enterprise and illustrate the reconfiguration of the enterprise application platform.

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Successful implementation of an ERP system is the result of knowledgeable and dedicated people working together. It entails company-wide commitment, openness to change, good planning and experienced guidance.These primary criteria determine the probability of gaining significant return on investment (ROI) from an ERP system. Using these criteria as guidelines during the system selection process and subsequent implementation can ensure that the chosen system will support and enable the business improvements envisioned.Among the numerous critical success factors (CSFs) in the implementation of ERP systems, identified and demonstrated by practitioners and academic researchers in the last few years, we will synthesize and analyze the vendor selection issues, in connection to the implementation aspects, as we consider implementation the essential "ingredient" of the ERP success.