Alejandro Maté

A trace metamodel proposal based on the model driven architecture framework for the traceability of user requirements in data warehouses

The complexity of the data warehouse (DW) development process requires to follow a methodological approach in order to be successful. A widely accepted approach for this development is the hybrid one, in which requirements and data sources must be accommodated to a new DW model. The main problem is that we lose the relationships between requirements, elements in the multidimensional (MD) conceptual models and data sources in the process, since no traceability is explicitly specified. Therefore, this hurts requirements validation capability and increases the complexity of Extraction, Transformation and Loading processes. In this paper, we propose a novel trace metamodel for DWs and focus on the relationships between requirements and MD conceptual models. We propose a set of Query/View/Transformation rules to include traceability in DWs in an automatic way, allowing us to obtain a MD conceptual model of the DW, as well as a trace model. Therefore, we are able to trace every requirement to the MD elements, further increasing user satisfaction. Finally, we show the implementation in our Lucentia BI tool.

Conceptualizing and specifying key performance indicators in business strategy models

Key Performance Indicators (KPI) measure the performance of an organization relative to its objectives. To monitor organizational performance relative to KPIs, such KPIs need to be manually implemented in the form of data warehouse queries, to be used in dashboards or scorecards. Moreover, dashboards include little if any information about business strategy and offer a scattered view of KPIs and what do they mean relative to business concerns. In this paper, we propose an integrated view of strategic business models and conceptual data warehouse models. The main benefit of our proposal is that it links strategic business models to the data through which objectives can be monitored and assessed. In our proposal, KPIs are defined in Structured English and are implemented in a semi-automatic way, allowing for quick modifications. This enables real-time monitoring and what-if analysis, thereby helping analysts compare expectations with reported results.

Business Intelligence 2.0: A General Overview

Business Intelligence (BI) solutions allow decision makers to query, understand, and analyze business data in order to make better decisions. However, as the technology and society evolve, faster and better informed decisions are required. Nowadays, it is not enough to use only the information from the own organization and making isolated decisions, but rather requiring also to include information present in the web like opinions or information about competitors, while using collective intelligence, collaborating through social networks, and supporting the BI system with cloud computing. In response to this situation, a vision of a new generation of BI, BI 2.0, based on the evolution of the web and the emerging technologies, arises. However, researchers differ in their vision of this BI evolution. In this paper, we provide an overview of the aspects proposed to be included in BI 2.0. We describe which success factors and technologies have motivated each aspect. Finally, we review how tool developers are including these new features in the next generation of BI solutions.

Goal-Oriented Requirements Engineering: A Systematic Literature Map

Over the last two decades, much attention has been paid to the area of Goal-Oriented Requirements Engineering(GORE), where goals are used as a useful conceptualization to elicit, model and analyze requirements, capturing alternatives and conflicts. Goal modeling has been adapted and applied to many sub-topics within RE and beyond, such as agent-orientation, aspect-orientation, business intelligence, model-driven development, security, and so on. Despite extensive efforts in this field, the RE community lacks a recent, general systematic literature review of the area. As a first step towards providing a GORE overview, we present a Systematic Literature Map, focusing on GORE-related publications at a high-level, categorizing and analyzing paper information in order to answer several research questions, while omitting a detailed analysis of individual paper quality. Our Literature Map covers the 246 top-cited GORE-related conference and journal papers, according to Scopus, classifying them into a number of descriptive paper types and topics, providing an analysis of the data, which is made publicly available. We use our analysis results to make recommendations concerning future GORE research.

Current state of Linked Data in digital libraries

The Semantic Web encourages institutions, including libraries, to collect, link and share their data across the Web in order to ease its processing by machines to get better queries and results. Linked Data technologies enable us to connect related data on the Web using the principles outlined by Tim Berners-Lee in 2006. Digital libraries have great potential to exchange and disseminate data linked to external resources using Linked Data. In this paper, a study about the current uses of Linked Data in digital libraries, including the most important implementations around the world, is presented. The study focuses on selected vocabularies and ontologies, benefits and problems encountered in implementing Linked Data in digital libraries. In addition, it also identifies and discusses specific challenges that digital libraries face, offering suggestions for ways in which libraries can contribute to the Semantic Web. The study uses an adapted methodology for literature review, to find data available to answer research questions. It is based on the information found in the library websites recommended by W3C Library Linked Data Incubator Group in 2011, and scientific publications from Google Scholar, Scopus, ACM and Springer from the last 5 years. The selected libraries for the study are the National Library of France, the Europeana Library, the Library of Congress of the USA, the British Library and the National Library of Spain. In this paper, we outline the best practices found in each experience and identify gaps and future trends.

Application of Data Mining techniques to identify relevant Key Performance Indicators

Currently dashboards are the preferred tool across organizations to monitor business performance. Dashboards are often composed of different data visualization techniques, amongst which are Key Performance Indicators (KPIs) which play a crucial role in quickly providing accurate information by comparing current performance against a target required to fulfill business objectives. However, KPIs are not always well known and sometimes it is difficult to find an appropriate KPI to associate with each business objective. In addition, Data Mining techniques are often used when forecasting trends and visualizing data correlations. In this paper we present a new approach to combining these two aspects in order to drive Data Mining techniques to obtain specific KPIs for business objectives in a semi-automated way. The main benefit of our approach is that organizations do not need to rely on existing KPI lists or test KPIs over a cycle as they can analyze their behavior using existing data. In order to show the applicability of our approach, we apply our proposal to the fields of Massive Open Online Courses (MOOCs) and Open Data extracted from the University of Alicante in order to identify the KPIs. HighlightsExtraction of Key Performance Indicators (KPIs).Application of Data Mining techniques to discover relevant KPIs.A new methodology for extracting the relevant KPIs based on Data mining.Case study with MOOCs and Open Data from the University of Alicante.

On the joint use of i ∗ with other modelling frameworks: A vision paper

The use of the i∗ (iStar) framework by the requirements engineering community is many-fold. Among the several possible engineering cases, we are particularly interested here in the joint use of i∗ with other modelling frameworks to obtain what we call i∗-based frameworks. In this context, several challenges need to be overcome: theoretical, technical, methodological and community-related. In this paper, we review current i∗-based frameworks under several possible scenarios and observe that not all of these challenges are always addressed, and even more, there is lack of guidelines or well-accepted methodological design steps on how to overcome these issues. Then, we detail the several engineering artifacts and techniques whose consideration in i∗-based frameworks may help to overcome them. To illustrate the vision, we present the case of combining i∗ with data warehouse models, from the initial definition of the ontology to the final implementation using profiles. Finally, we provide a research agenda to apply the proposed vision including a final reflection on defining a maturity model as a convenient way to support forthcoming research in the topic.

Tracing conceptual models' evolution in data warehouses by using the model driven architecture

Developing a data warehouse is an ongoing task where new requirements are constantly being added. A widely accepted approach for developing data warehouses is the hybrid approach, where requirements and data sources must be accommodated to a reconciliated data warehouse model. During this process, relationships between conceptual elements specified by user requirements and those supplied by the data sources are lost, since no traceability mechanisms are included. As a result, the designer wastes additional time and effort to update the data warehouse whenever user requirements or data sources change. In this paper, we propose an approach to preserve traceability at conceptual level for data warehouses. Our approach includes a set of traces and their formalization, in order to relate the multidimensional elements specified by user requirements with the concepts extracted from data sources. Therefore, we can easily identify how changes should be incorporated into the data warehouse, and derive it according to the new configuration. In order to minimize the effort required, we define a set of general Query/View/Transformation rules to automate the derivation of traces along with data warehouse elements. Finally, we describe a CASE tool that supports our approach and provide a detailed case study to show the applicability of the proposal.

An integrated multidimensional modeling approach to access big data in business intelligence platforms

The huge amount of information available and its heterogeneity has surpassed the capacity of current data management technologies. Dealing with that huge amounts of structured and unstructured data, often referred as Big Data, is a hot research topic and a technological challenge. In this paper, we present an approach aimed to allow OLAP queries over different, heterogeneous, data sources. The modeling approach proposed is based on a MapReduce paradigm, which integrates different formats into the recent RDF Data Cube format. The benefits of our approach are that it allows a user to make queries that need data from different sources 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 illustrated in an example of application.

Goal-oriented requirements engineering: an extended systematic mapping study

Abstract Over the last two decades, much attention has been paid to the area of goal-oriented requirements engineering (GORE), where goals are used as a useful conceptualization to elicit, model, and analyze requirements, capturing alternatives and conflicts. Goal modeling has been adapted and applied to many sub-topics within requirements engineering (RE) and beyond, such as agent orientation, aspect orientation, business intelligence, model-driven development, and security. Despite extensive efforts in this field, the RE community lacks a recent, general systematic literature review of the area. In this work, we present a systematic mapping study, covering the 246 top-cited GORE-related conference and journal papers, according to Scopus. Our literature map addresses several research questions: we classify the types of papers (e.g., proposals, formalizations, meta-studies), look at the presence of evaluation, the topics covered (e.g., security, agents, scenarios), frameworks used, venues, citations, author networks, and overall publication numbers. For most questions, we evaluate trends over time. Our findings show a proliferation of papers with new ideas and few citations, with a small number of authors and papers dominating citations; however, there is a slight rise in papers which build upon past work (implementations, integrations, and extensions). We see a rise in papers concerning adaptation/variability/evolution and a slight rise in case studies. Overall, interest in GORE has increased. We use our analysis results to make recommendations concerning future GORE research and make our data publicly available.