Public Sector Information (PSI) scenarios require tools that support retrieval of tabular open data beyond keyword-based search on metadata. This paper presents a novel interface for searching tabular open data, as well as a search engine that retrieves tabular data by considering table contents apart from metadata. Our search engine uses word embeddings to calculate the semantic similarity between tabular open data, providing a ranking of candidate tabular datasets to be integrated with an input query table according to the different intentions of the open data reuser (e.g. column or row extension as well as data completion). An initial set of experiments have been conducted, showing promising results in this task.

Smart city governments face several barriers in adopting open data initiatives. Some of these barriers are related to the attribution rights of open licenses, as reusers often misattribute open data. As a result, publishers do not know which open data is reused and for what, thus ignoring the return on investment and hindering the sustainability of open data initiatives. In addition, reusers who do not fully comply the right of attribution of open data licences may face legal problems. To overcome these pitfalls, this paper envisions an approach that aims to extend open data publication standards with elements coming from open-source software development standards, to support reusers in achieving proper attribution of open data. Our approach is a first attempt to both (i) enabling publishers to gather information to understand open data reuse in smart cities, and (ii) supporting reusers to avoid lawsuit issues related to open data license violation.

Open Government concept is experiencing an upswing. Open Government is based on three concepts (transparency, participation and collaboration) that require accessing data. To provide this access, Open Data Portals are being implemented around the world by every kind of organizations, mainly in the public sector. The aim of an Open Data Portal is exposing data in such a way that reusing is facilitated. Therefore, it is necessary to define a quality and maturity model to evaluate the characteristics of an Open Data Portal, considering different factors that can contribute to reusing potential, such like visualization, usability, granularity, data integration, reputation, relevancy, availability and reutilization. Also, effectively promoting data reusing implies setting specific norms to promote standardization among institutions, ministries and central governments' offices into the same country. This paper presents a formal proposal to evaluate - based in expert criteria - the quality and the maturity of an open data portal.

Data is everywhere, and non-expert users must be able to exploit it in order to extract knowledge, get insights and make well-informed decisions. The value of the discovered knowledge from big data could be of greater value if it is available for later consumption and reusing. In this paper, we present an infrastructure that allows non-expert users to (i) apply user-friendly data mining techniques on big data sources, and (ii) share results as Linked Open Data (LOD). The main contribution of this paper is an approach for democratizing big data through reusing the knowledge gained from data mining processes after being semantically annotated as LOD, then obtaining Linked Open Knowledge. Our work is based on a model-driven viewpoint in order to easily deal with the wide diversity of open data formats.

The University of Alicante (UA), in Spain, is aligned with an Open Government strategy. Within this strategy, UA is carrying out the OpenData4U (Open Data for Universities) project which aims to provided mechanisms to open data from universities, finding out how open data contributes to open government in universities and to encourage reusing open data (not only for the sake of transparency, but also as a basis of novel data-intensive business models related to universities). This paper describes one of the output of the project: an approach for opening data from universities keeping in mind data quality criteria, and tailored to no specific technological scenario. This approach allowed UA to launch its open data portal http://datos.ua.es that is also reviewed in this paper. Also, some research challenges related to university open data are enumerated.

Data is everywhere, and non-expert users must be able to exploit it in order to extract knowledge, get insights and make well-informed decisions. The value of the discovered knowledge could be of greater value if it is available for later consumption and reusing. In this paper, we present the i¬rst version of the Knowledge Spring Process, an infrastructure that allows non-expert users apply user-friendly data mining techniques on Open Data sources and share results as Linked Open Data. The main contribution of this paper is the concept of reusing the knowledge gained from data mining processes after been semantically annotated in the RDF i¬le as Linked Open Data (Linked Open Knowledge). A model driven approach is proposed in order to maintain a standard structure having into account the diversity of the data formats.

BACKGROUND. The term Rich Internet Applications (RIAs) is generally associated with Web applications that provide the features and functionality of traditional desktop applications. Ten years after the introduction of the term, an ample amount of research has been carried out to study various aspects of RIAs. It has thus become essential to summarize this research and provide an adequate overview. OBJECTIVE. The objective of our study is to assemble, classify, and analyze all RIA research performed in the scientific community, thus providing a consolidated overview thereof, and to identify well-established topics, trends, and open research issues. Additionally, we provide a qualitative discussion of the most interesting findings. This work therefore serves as a reference work for beginning and established RIA researchers alike, as well as for industrial actors that need an introduction in the field, or seek pointers to (a specific subset of) the state-of-the-art. METHOD. A systematic mapping study is performed in order to identify all RIA-related publications, define a classification scheme, and categorize, analyze, and discuss the identified research according to it. RESULTS. Our source identification phase resulted in 133 relevant, peer-reviewed publications, published between 2002 and 2011 in a wide variety of venues. They were subsequently classified according to four facets: development activity, research topic, contribution type, and research type. Pie, stacked bar, and bubble charts were used to depict and analyze the results. A deeper analysis is provided for the most interesting and/or remarkable results. CONCLUSION. Analysis of the results shows that, although the RIA term was coined in 2002, the first RIA-related research appeared in 2004. From 2007 there was a significant increase in research activity, peaking in 2009 and decreasing to pre-2009 levels afterwards. All development phases are covered in the identified research, with emphasis on “design” (33%) and “implementation” (29%). The majority of research proposes a “method” (44%), followed by “model” (22%), “methodology” (18%), and “tools” (16%); no publications in the category “metrics” were found. The preponderant research topic is “models, methods and methodologies” (23%) and, to a lesser extent, “usability and accessibility” and “user interface” (11% each). On the other hand, the topic “localization, internationalization and multilinguality” received no attention at all, and topics such as “deep Web” (under 1%), “business processing”, “usage analysis”, “data management”, “quality and metrics” (all under 2%), “semantics”, and “performance” (slightly above 2%) received very little attention. Finally, there is a large majority of “solution proposals” (66%), few “evaluation research” (14%), and even fewer “validation” (6%), although the latter have been increasing in recent years.

Data that can be reused and redistributed without any restriction is called Open Data. These two features make its quality can be greatly affected. To date, the most used quality criteria of Open Data are those established in the 5-Stars Model. This article aims to extend this model and corroborate the existence of specific quality criteria for Open Data and its corresponding measurement mechanisms. We propose a new Quality Model for Open Data portals which is exposed from two points of view: qualitative and quantitative. To illustrate the use of this model, we implemented a study case based on real open data about Municipality of Perez Zeledon in Costa Rica, which was evaluated with the qualitative model.

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.

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.