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.

Crimes, forest fires, accidents, infectious diseases, human interactions with mobile devices (e.g., tweets) are being logged as spatiotemporal events. For each event, its geographic location, time and related attributes are known with high levels of detail (LoDs). The LoD plays a crucial role when analyzing data, enhancing the user's perception of phenomena. From one LoD to another, some patterns can be easily perceived or different patterns may be detected. Modeling phenomena at different LoDs is needed, as there is no exclusive LoD at which data can be analyzed.Current practices work mainly on a single LoD, driven by the analysts perception, ignoring the fact that the identification of the suitable LoDs is a key issue for pointing relevant patterns.This paper presents a Visual Analytics approach called VAST, that allows users to simultaneously inspect a phenomenon at different LoDs, helping them to see in what LoDs patterns emerge or in what LoDs the perception of the phenomenon is different. In this way, the analysis of vast amounts of spatiotemporal events is assisted, guiding the user in this process.The use of several synthetic and real datasets allowed the evaluation of VAST, which was able to suggest LoDs with different interesting spatiotemporal patterns and the type of expected patterns.

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.

Some phenomena, such as crimes in a city, fires occurred in a country and road accidents can be interpreted as sets of spatio-temporal events. A spatio-temporal event is described by a geographic location, a time instant and other characterising attributes. The cartographic visualisation of spatio-temporal events remains unresolved, due to the challenges related with portraying multiple dimensions simultaneously: the spatial, the temporal and the semantic (zero or more dimensions) phenomenon's components. In this context, this article presents the Attenuation and Accumulation Maps (AA-Maps). The main idea of this visualisation analytic approach consists in showing in a map, the resulting effect of combining attenuation and accumulation, from a temporal reference of observation, given a spatio-temporal Level of Detail (LoD). Imagine the footprints of people crossing a garden in various directions. They leave different traces that summarize the cumulative effect of the footprints on the grass, which is attenuated as time goes by. AA-Maps support different combinations of attenuation and accumulation functions. In addition, this method also enables analysis with different Levels of Detail (LoD), both spatial and temporal. This allows distinct analytic perspectives of the phenomenon while promoting the search for the most suitable parametrization for its characteristics.

Requirements elicitation is a crucial activity and one of the first steps in software development process. A popular way to capture and describe requirements is through UML use case models. Transforming require- ment specifications into software design models is a complex and error prone software development activity. Software design usually involves several models, each one representing a different perspective. One of those perspectives is the data perspective which can be modeled using a data model. Although of the importance of this model, few works has been done to derive a data model from use case model. The 4SRS (4-Step Rule Set) method generates a logical architecture of a software-based system, based on a use case model. This paper proposes an approach to adapt and extend the 4SRS method in order to generate a data model supporting the generated logical architecture and the elicited requirements based in a set of business process models.

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.

Call Level Interfaces (CLI) are difficult to use mainly in intensive database applications with many Create, Read, Update and Delete (CRUD) expressions. As low level API, they are not suited to promote the development process of business tiers as reusable components, leading to the need of writing additional source code whenever a new CRUD expression is needed. To tackle this gap, this paper proposes an architecture for building reusable business tier components herein referred to as Reusable Business Tier Architecture (RBTA). It relies on a single customizable wide typed service to address a business area, such as accounting. The typed service is able to manage all the required CRUD expressions for that business are, which are deployed at runtime in accordance with the user's needs. The only constraint is that the required service to manage each CRUD expression must be a sub-set of the implemented wide typed service. A proof of concept based on Java Database Connectivity (JDBC) is also presented.

Nowadays, organizations are facing several challenges when they try to analyze generated data with the aim of extracting useful information. This analytical capacity needs to be enhanced with tools capable of dealing with big data sets without making the analytical process a difficult task. Clustering is usually used, as this technique does not require any prior knowledge about the data. However, clustering algorithms usually require one or more input parameters that influence the clustering process and the results that can be obtained. This work analyses the relation between the three input parameters of the SNN (Shared Nearest Neighbor) algorithm and proposes specific guidelines for the identification of the appropriate input parameters that optimizes the processing time.

Object-oriented programming is the most successful programming paradigm. Relational database management systems are the most successful data storage components. Despite their individual successes and their desirable tight binding, they rely on different points of view about data entailing difficulties on their integration. Some solutions have been proposed to overcome these difficulties, such as Embedded SQL, object/relational mappings (O/RM), language extensions and even Call Level Interfaces (CLI), as JDBC and ADO.NET. In this paper we present a new model aimed at integrating object-oriented languages and relational databases, named CRUD Data Object Model (CRUD-DOM). CRUD-DOM relies on CLI (JDBC) and aims not only at exploring CLI advantages as preserving its performance and SQL expressiveness but also on providing a type state approach for the implementation of the ResultSet interface. The model design aims to facilitate the development of automatic code generation tools. We also present such a tool, called CRUD Manager (CRUD-M), which provides automatic code generation with a complementary support for software maintenance. This paper shows that CRUD-DOM is an effective model to address the aforementioned objectives.

The transformation of user requirements into system requirements models can be achieved using the 4 Step Rule Set (4SRS) method that transforms UML use case diagrams into system-level object diagrams. These diagrams represent the logical architecture of the system, integrating the system-level entities, their responsibilities and the relationships among them. The logical architecture captures the system functional requirements and its non-functional intentionalities. Although contributing to the formalization of the design of software architectures, the 4SRS method needs to be extended in order to support the design of the database subsystems that may be considered pertinent within the specified logical architecture. This paper presents the extension of the 4SRS method to support the construction of the class diagram that complements the logical architecture, and shows, through the presentation of a demonstration case, the applicability of the proposed approach.