Dashboards are popular tools for presenting key insights to decision-makers by translating large volumes of data into clear information. However, while individual visualizations may effectively answer specific questions, they often fail to connect in a way that conveys the overall narrative , leaving decision-makers without a cohesive understanding of the area under analysis. This paper presents a novel methodology for the systematic design of holistic dashboards, moving from analytical requirements to storytelling dashboards. Our approach ensures that all visualizations are aligned with the analytical goals of decision-makers. It includes several key steps: capturing analytical requirements through the i* framework; structuring and refining these requirements into a tree model to reflect the decision-maker’s mental analysis; identifying and preparing relevant data; capturing the key concepts and relationships for the composition of the cohesive storytelling dashboard through a novel storytelling conceptual model; finally, implementing and integrating the visualizations into the dashboard, ensuring coherence and alignment with the decision-maker’s needs. Our methodology has been applied in real-world industrial environments. We evaluated its impact through a controlled experiment. The findings show that storytelling dashboards significantly improve data interpretation , reduce misinterpretations, and enhance the overall user experience compared to traditional dashboards.

Nowadays, the concept of Smart City provides a rich analytical context, highlighting the need to store and process vast amounts of heterogeneous data flowing at different velocities. This data is defined as Big Data, which imposes significant difficulties in traditional data techniques and technologies. Data Warehouses (DWs) have long been recognized as a fundamental enterprise asset, providing fact-based decision support for several organizations. The concept of DW is evolving. Traditionally, Relational Database Management Systems (RDBMSs) are used to store historical data, providing different analytical perspectives regarding several business processes. With the current advancements in Big Data techniques and technologies, the concept of Big Data Warehouse (BDW) emerges to surpass several limitations of traditional DWs. This paper presents a novel approach for designing and implementing BDWs, which has been supporting the SusCity data visualization platform. The BDW is a crucial component of the SusCity research project in the context of Smart Cities, supporting analytical tasks based on data collected in the city of Lisbon.

Big Data is currently conceptualized as data whose volume, variety or velocity impose significant difficulties in traditional techniques and technologies. Big Data Warehousing is emerging as a new concept for Big Data analytics. In this context, SQL-on-Hadoop systems increased notoriety, providing Structured Query Language (SQL) interfaces and interactive queries on Hadoop. A benchmark based on a denormalized version of the TPC-H is used to compare the performance of Hive on Tez, Spark, Presto and Drill. Some key contributions of this work include: the direct comparison of a vast set of technologies; unlike previous scientific works, SQL-on-Hadoop systems were connected to Hive tables instead of raw files; allow to understand the behaviour of these systems in scenarios with ever-increasing requirements, but not-so-good hardware. Besides these benchmark results, this paper also makes available interesting findings regarding an architecture and infrastructure in SQL-on-Hadoop for Big Data Warehousing, helping practitioners and fostering future research.

As huge amounts of data become available in organizations and society, specific data analytics skills and techniques are needed to explore this data and extract from it useful patterns, tendencies, models or other useful knowledge, which could be used to support the decision-making process, to define new strategies or to understand what is happening in a specific field. Only with a deep understanding of a phenomenon it is possible to fight it. In this paper, a data-driven analytics approach is used for the analysis of the increasing incidence of fatalities by pneumonia in the Portuguese population, characterizing the disease and its incidence in terms of fatalities, knowledge that can be used to define appropriate strategies that can aim to reduce this phenomenon, which has increased more than 65% in a decade.