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.

In order to implement an automatic-computer-aided system for the diagnosis of the Attention-Deficit Hyperactivity Disorder (ADHD), a Deep Learning Multihead Convolutional Based model (EEG-MHCNet) was developed in previous work. To obtain the best model performance, five hyperparameters were studied. This procedure led us to train the model with all hyperparameter combination values. As a result, 1920 executions were performed and took 15.5 days to complete the training process. Finally, the model obtained a better f1-score than the state-of-art ones. To improve these results, more values should be tested. The main problem is the high computational cost of executing such a considerable number of combinations. In this paper, a null hypothesis testing procedure has been applied to find which hyperparameter values have statistical confidence in the model’s performance. By doing this procedure, some values could be discarded thus reducing the number of combinations and the execution of training time. Results show that the number of executions can be reduced from 1920 to 320 without loss of performance with a 95% of confidence level, thus enabling us to considerably reduce the computational training cost from 15.5 days to 1.3 days.

Abstract NoSQL technologies have become a common component in many information systems and software applications. These technologies are focused on performance, enabling scalable processing of large volumes of structured and unstructured data. Unfortunately, most developments over NoSQL technologies consider security as an afterthought, putting at risk personal data of individuals and potentially causing severe economic loses as well as reputation crisis. In order to avoid these situations, companies require an approach that introduces security mechanisms into their systems without scrapping already in-place solutions to restart all over again the design process. Therefore, in this paper we propose the first modernization approach for introducing security in NoSQL databases, focusing on access control and thereby improving the security of their associated information systems and applications. Our approach analyzes the existing NoSQL solution of the organization, using a domain ontology to detect sensitive information and creating a conceptual model of the database. Together with this model, a series of security issues related to access control are listed, allowing database designers to identify the security mechanisms that must be incorporated into their existing solution. For each security issue, our approach automatically generates a proposed solution, consisting of a combination of privilege modifications, new roles and views to improve access control. In order to test our approach, we apply our process to a medical database implemented using the popular document-oriented NoSQL database, MongoDB. The great advantages of our approach are that: (1) it takes into account the context of the system thanks to the introduction of domain ontologies, (2) it helps to avoid missing critical access control issues since the analysis is performed automatically, (3) it reduces the effort and costs of the modernization process thanks to the automated steps in the process, (4) it can be used with different NoSQL document-based technologies in a successful way by adjusting the metamodel, and (5) it is lined up with known standards, hence allowing the application of guidelines and best practices.

Big Data constitutes an opportunity for companies to empower their analysis. However, at the moment there is no standard way for approaching Big Data projects. This, coupled with the complex nature of Big Data, is the cause that many Big Data projects fail or rarely obtain the expected return of investment. In this paper, we present a methodology to tackle Big Data projects in a systematic way, avoiding the aforementioned problems. To this end, we review the state of the art, identifying the most prominent problems surrounding Big Data projects, best practices and methods. Then, we define a methodology describing step by step how these techniques could be applied and combined in order to tackle the problems identified and increase the success rate of Big Data projects.

The huge amount of information available and its heterogeneity has surpassed the capacity of current data management technologies. Dealing with huge amounts of structured and unstructured data, often referred as Big Data, is a hot research topic and a technological challenge. In this paper, the authors present an approach aimed to enable OLAP queries over different, heterogeneous, data sources. Their approach is based on a MapReduce paradigm, which integrates different formats into the recent RDF Data Cube format. The benefits of their approach are that it is capable of querying different sources of information, while maintaining at the same time, an integrated, comprehensive view of the data available. The paper discusses the advantages and disadvantages, as well as the implementation challenges that such approach presents. Furthermore, the approach is evaluated in detail by means of a case study.

In data warehouse (DW) development, a series of mappings must be specified between user concepts and data source elements, in order to identify which sources must undergo an integration process. Until now, these mappings are either assumed to be implied by name matching or identified according to the designer's experience. Then, the result is implemented as Extraction/Transformation/Loading (ETL) processes. Since ETL processes relate elements at the logical level, designers cannot adequately analyze how a change in requirements or in the data sources affects the analysis capabilities. Furthermore, this approach makes it difficult to perform incremental changes in DW design, requiring in some cases to perform the whole analysis again. In this paper we present a set of semantic mappings that relate user concepts specified by requirements to those obtained from data sources. In turn, this allows us to accurately identify how any potential change affects the different structures and ETL processes. As a DW evolves over time, our approach easily allows us to incorporate new concepts, as well as any change introduced at requirements or data sources into the DW repository with no need to redesign the whole DW. In order to show the application of our proposal, we show a real case study focusing on the Digital library of the University of Alicante.

OLAP (On-line Analytical Processing) technologies rely on multidimensional models to provide decision makers with appropriate structures allowing them to intuitively analyze data. However, these multidimensional models may be potentially large, thus becoming too complex to be understood at a glance. Current approaches for OLAP design are focused on providing analysts with a single multidimensional schema derived from their previously stated information requirements, but this is not sufficient to lighten the complexity of the decision making process. To overcome this drawback, the authors propose personalizing multidimensional models for OLAP technologies according to the continuously changing user characteristics, context, requirements and behavior. In this paper, they present a new approach for personalizing OLAP systems at the conceptual level based on the underlying multidimensional model, a user model and a set of personalization rules. Transformations are defined by means of a model-driven strategy to assist in the process of obtaining the corresponding personalized OLAP schemas from these models.

ETL processes are the backbone component of a data warehouse, since they supply the data warehouse with the necessary integrated and reconciled data from heterogeneous and distributed data sources. However, the ETL process development, and particularly its design phase, is still perceived as a time-consuming task. This is mainly due to the fact that ETL processes are typically designed by considering a specific technology from the very beginning of the development process. Thus, it is difficult to share and reuse methodologies and best practices among projects implemented with different technologies. To the best of our knowledge, no attempt has been yet dedicated to harmonize the ETL process development by proposing a common and integrated development strategy. To overcome this drawback, in this paper, a framework for model-driven development of ETL processes is introduced. The benefit of our framework is twofold: (i) using vendor-independent models for a unified design of ETL processes, based on the expressive and well-known standard for modeling business processes, the Business Process Modeling Notation (BPMN), and (ii) automatically transforming these models into the required vendor-specific code to execute the ETL process into a concrete platform.

To customize a data warehouse, many organizations develop concrete data marts focused on a particular department or business process. However, the integrated development of these data marts is an open problem for many organizations due to the technical and organizational challenges involved during the design of these repositories as a complete solution. In this article, the authors present a design approach that employs user requirements to build both corporate data warehouses and data marts in an integrated manner. The approach links information requirements to specific data marts elicited by using goal-oriented requirement engineering, which are automatically translated into the implementation of corresponding data repositories by means of model-driven engineering techniques. The authors provide two UML profiles that integrate the design of both data warehouses and data marts and a set of QVT transformations with which to automate this process. The advantage of this approach is that user requirements are captured from the early development stages of a data-warehousing project to automatically translate them into the entire data-warehousing platform, considering the different data marts. Finally, the authors provide screenshots of the CASE tools that support the approach, and a case study to show its benefits.

Data warehouses (DW) integrate different data sources in order to give a multidimensional view of them to the decision-maker. To this aim, the ETL (Extraction, Transformation and Load) processes are responsible for extracting data from heterogeneous operational data sources, their transformation (conversion, cleaning, standardization, etc.), and its load in the DW. In recent years, several conceptual modeling approaches have been proposed for designing ETL processes. Although these approaches are very useful for documenting ETL processes and supporting the designer tasks, these proposals fail to give mechanisms to carry out an automatic code generation stage. Such a stage should be required to both avoid fails and save development time in the implementation of complex ETL process. Therefore, in this paper we define an approach for the automatic code generation of ETL processes. To this aim, we align the modeling of ETL processes in DW with MDA (Model Driven Architecture) by formally defining a set of QVT (Query, View, Transformation) transformations.

Multidimensional analysis allows decision makers to efficiently and effectively use data analysis tools, which mainly depend on multidimensional (MD) structures of a data warehouse such as facts and dimension hierarchies to explore the information and aggregate it at different levels of detail in an accurate way. A conceptual model of such MD structures serves as abstract basis of the subsequent implementation according to one specific technology. However, there is a semantic gap between a conceptual model and its implementation which complicates an adequate treatment of summarizability issues, which in turn may lead to erroneous results of data analysis tools and cause the failure of the whole data warehouse project. To bridge this gap for relationships between facts and dimension, we present an approach at the conceptual level for (i) identifying problematic situations in fact-dimension relationships, (ii) defining these relationships in a conceptual MD model, and (iii) applying a normalization process to transform this conceptual MD model into a summarizability-compliant model that avoids erroneous analysis of data. Furthermore, we also describe our Eclipsebased implementation of this normalization process.

The development of data warehouses is based on a three-stage process that starts specifying both the static and dynamic properties of on-line analytical processing (OLAP) applications by means of an intuitive, semantically rich abstraction, namely the conceptual model. Then, developers design its logical counterpart where platform-specific details such as performance or storage are also considered. Nevertheless, it is well known the existence of a semantic gap between the conceptual and logical levels that decreases the feasibility of their mapping. In order to bridge this gap, we propose the use of conceptual OLAP queries, i.e., platform-independent, that can be automatically traced to their logical implementation in a coherent and integrated way. For this aim, in this paper, we focus on describing the specification of an OLAP algebra at the conceptual level by using the object-constraint language (OCL). Its operations are then translated into a particular OLAP system by using a model-driven architecture (MDA). The great advantage of our approach is that we allow analysts to query data warehouses without being aware of logical details.

Data Warehouses (DW), Multidimensional (MD) databases, and On-Line Analytical Processing (OLAP) applications provide companies with many years of historical information for the decision-making process. Owing to the relevant information managed by these systems, they should provide strong security and confidentiality measures from the early stages of a DW project in the MD modeling and enforce them. In the last years, there have been some proposals to accomplish the MD modeling at the conceptual level. Nevertheless, none of them considers security measures as an important element in their models, and therefore, they do not allow us to specify confidentiality constraints to be enforced by the applications that will use these MD models. In this paper, we present an Access Control and Audit (ACA) model for the conceptual MD modeling. Then, we extend the Unified Modeling Language (UML) with this ACA model, representing the security information (gathered in the ACA model) in the conceptual MD modeling, thereby allowing us to obtain secure MD models. Moreover, we use the OSCL (Object Security Constraint Language) to specify our ACA model constraints, avoiding in this way an arbitrary use of them. Furthermore, we align our approach with the Model-Driven Architecture, the Model-Driven Security and the Model-Driven Data Warehouse, offering a proposal highly compatible with the more recent technologies.

Multidimensional modeling requires specialized design techniques. Though a lot has been written about how a data warehouse should be designed, there is no consensus on a design method yet. This paper follows from a wide discussion that took place in Dagstuhl, during the Perspectives Workshop "Data Warehousing at the Crossroads", and is aimed at outlining some open issues in modeling and design of data warehouses. More precisely, issues regarding conceptual models, logical models, methods for design, interoperability, and design for new architectures and applications are considered.

The design, development and deployment of a data warehouse (DW) is a complex, time consuming and prone to fail task. This is mainly due to the different aspects taking part in a DW architecture such as data sources, processes responsible for Extracting, Transforming and Loading (ETL) data into the DW, the modeling of the DW itself, specifying data marts from the data warehouse or designing end user tools. In the last years, different models, methods and techniques have been proposed to provide partial solutions to cover the different aspects of a data warehouse. Nevertheless, none of these proposals addresses the whole development process of a data warehouse in an integrated and coherent manner providing the same notation for the modeling of the different parts of a DW.In this paper, we propose a data warehouse development method, based on the Unified Modeling Language (UML) and the Unified Process (UP), which addresses the design and development of both the data warehouse back-stage and front-end. We use the extension mechanisms (stereotypes, tagged values and constraints) provided by the UML and we properly extend it in order to accurately model the different parts of a data warehouse (such as the modeling of the data sources, ETL processes or the modeling of the DW itself) by using the same notation. To the best of our knowledge, our proposal provides a seamless method for developing data warehouses. Finally, we apply our approach to a case study to show its benefit.

Several approaches have been proposed to model different aspects of a Data Warehouse (DW) during recent years, such as the modeling of a DW at the conceptual and logical level, the design of the ETL (Extraction, Transformation, Loading) processes, the derivation of the DW models from the enterprise data models, and customization of a DW schema. At the end of the design, a DW has to be deployed in a database environment, requiring many decisions of a physical nature. However, few efforts have been dedicated to the modeling of the physical design of a DW from the early stages of a DW project. In this article, we argue that some physical decisions can be taken from gathering main user requirements. In this article, we present physical modeling techniques for DWs using the component diagrams and deployment diagrams of the Unified Modeling Language (UML). Our approach allows the designer to anticipate important physical design decisions that may reduce the overall development time of a DW, such as replicating dimension tables, vertical and horizontal partitioning of a fact table, and the use of particular servers for certain ETL processes. Moreover, our approach allows the designer to cover all main design phases of DWs from the conceptual modeling phase to the final implementation. To illustrate our techniques, we show a case study that is implemented on top of a commercial DW management server.

Different modeling approaches have been proposed to overcome every design pitfall of the development of the different parts of a data warehouse (DW) system. However, they are all partial solutions which deal with isolated aspects of the DW and do not provide designers with an integrated and standard method for designing the whole DW (ETL processes, data sources, DW repository and so on). On the other hand, the Model Driven Architecture (MDA) is a standard framework for software development that addresses the complete life cycle of designing, deploying, integrating, and managing applications by using models in software development. In this paper, we describe how to align the whole DW development process to MDA. Then, we define MD2A (MultiDimensional Model Driven Architecture), an approach for applying the MDA framework to one of the stages of the DW development: multidimensional (MD) modeling. First, we describe how to build the different MDA artifacts (i.e. models) by using extensions of the Unified Modeling Language (UML). Secondly, transformations between models are clearly and formally established by using the Query/View/Transformation (QVT) approach. Finally, an example is provided to better show how to apply MDA and its transformations to the MD modeling.

During the few last years, several approaches have been proposed to model different aspects of a Data Warehouse (DW), such as the conceptual model of the DW, the design of the ETL (Extraction, Transformation, Loading) processes, the derivation of the DW models from the enterprise data models, etc. At the end, a DW has to be deployed to a database environment and that takes many decisions of a physical nature. However, few efforts have been dedicated to the modeling of the physical design (i.e. the physical structures that will host data together with their corresponding implementations) of a DW from the early stages of a DW project. From our previously presented DW engineering process, in this paper we present our proposal for the modeling of the physical design of DWs by using the component diagrams and deployment diagrams of the Unified Modeling Language (UML). Our approach allows the designer to anticipate important physical design decisions that may reduce the overall development time of a DW such as replicating dimension tables, vertical and horizontal partitioning of a fact table, the use of particular servers for certain ETL processes and so on. Moreover, our approach allows the designer to cover all main design phases of DWs, from the conceptual modeling phase until the final implementation, as we show with an example in this paper.

Multidimensional (MD) modeling is the basis for data warehouses (DW), multidimensional databases (MDB) and on-line analytical processing (OLAP) applications. In this paper, we present how the unified modeling language (UML) can be successfully used to represent both structural and dynamic properties of these systems at the conceptual level. The structure of the system is specified by means of a UML class diagram that considers the main properties of MD modeling with minimal use of constraints and extensions of the UML. If the system to be modeled is too complex, thereby leading us to a considerable number of classes and relationships, we describe how to use the package grouping mechanism provided by the UML to simplify the final model. Furthermore, we provide a UML-compliant class notation (called cube class) to represent OLAP users’ initial requirements. We also describe how we can use the UML state and interaction diagrams to model the behavior of a data warehouse system. To facilitate the interchange of conceptual MD models, we provide a Document Type Definition (DTD) which allows us to represent the same MD modeling properties that can be considered by using our approach. From this DTD, we can directly generate valid eXtensible Markup Language (XML) documents that represent MD models at the conceptual level. We believe that our innovative approach provides a theoretical foundation for simplifying the conceptual design of MD systems and the examples included in this paper clearly illustrate the use of our approach.

The aim of our GOLD model ([7], [9]) is to provide an Object Oriented (OO) Multidimensional data model supported by an OO formal specification language that allows us to automatically generate prototypes from the specification at the conceptual level, and therefore, to animate and check system properties. Within the context of OO modeling and automatic prototyping, the basis of the mapping from modeling to programming is focused on the identification of (cardinality and behavioral) patterns in the design phase and their relationships with the data model, process model and interface design.

. In the recent past, there has been an increasing interest in multidimensional databases (MDB) and On-line Analytical Processing (OLAP) scenarios. Several multidimensional models have been proposed in the last days. However, very few works have been focused on the area of multidimensional database conceptual modeling. Moreover, they are either conceptual extensions to the classical multidimensional model or translations from classical database conceptual models (such as the EntityRelationship model). Nevertheless, we take the concepts and basic ideas of the classical multidimensional model (dimensions and facts) to propose a revolutionary approach based on the Object Oriented (OO) Paradigm to MDB conceptual modeling. Then, the basic elements of our Object Oriented Multidimensional Model (OOMD) such as dimension classes and fact classes are introduced. We then present cube classes as the basic structure to allow a subsequent analysis of the data stored in the system. We fairly believe ...