Using Ontologies for the Design of Data Warehouses

Obtaining an implementation of a data warehouse is a complex task that forces designers to acquire wide knowledge of the domain, thus requiring a high level of expertise and becoming it a prone-to-fail task. Based on our experience, we have detected a set of situations we have faced up with in real-world projects in which we believe that the use of ontologies will improve several aspects of the design of data warehouses. The aim of this article is to describe several shortcomings of current data warehouse design approaches and discuss the benefit of using ontologies to overcome them. This work is a starting point for discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure

The Research Object Suite of Ontologies: Sharing and Exchanging Research Data and Methods on the Open Web

Research in life sciences is increasingly being conducted in a digital and online environment. In particular, life scientists have been pioneers in embracing new computational tools to conduct their investigations. To support the sharing of digital objects produced during such research investigations, we have witnessed in the last few years the emergence of specialized repositories, e.g., DataVerse and FigShare. Such repositories provide users with the means to share and publish datasets that were used or generated in research investigations. While these repositories have proven their usefulness, interpreting and reusing evidence for most research results is a challenging task. Additional contextual descriptions are needed to understand how those results were generated and/or the circumstances under which they were concluded. Because of this, scientists are calling for models that go beyond the publication of datasets to systematically capture the life cycle of scientific investigations and provide a single entry point to access the information about the hypothesis investigated, the datasets used, the experiments carried out, the results of the experiments, the people involved in the research, etc. In this paper we present the Research Object (RO) suite of ontologies, which provide a structured container to encapsulate research data and methods along with essential metadata descriptions. Research Objects are portable units that enable the sharing, preservation, interpretation and reuse of research investigation results. The ontologies we present have been designed in the light of requirements that we gathered from life scientists. They have been built upon existing popular vocabularies to facilitate interoperability. Furthermore, we have developed tools to support the creation and sharing of Research Objects, thereby promoting and facilitating their adoption.Comment: 20 page

Enabling Micro-level Demand-Side Grid Flexiblity in Resource Constrained Environments

The increased penetration of uncertain and variable renewable energy presents various resource and operational electric grid challenges. Micro-level (household and small commercial) demand-side grid flexibility could be a cost-effective strategy to integrate high penetrations of wind and solar energy, but literature and field deployments exploring the necessary information and communication technologies (ICTs) are scant. This paper presents an exploratory framework for enabling information driven grid flexibility through the Internet of Things (IoT), and a proof-of-concept wireless sensor gateway (FlexBox) to collect the necessary parameters for adequately monitoring and actuating the micro-level demand-side. In the summer of 2015, thirty sensor gateways were deployed in the city of Managua (Nicaragua) to develop a baseline for a near future small-scale demand response pilot implementation. FlexBox field data has begun shedding light on relationships between ambient temperature and load energy consumption, load and building envelope energy efficiency challenges, latency communication network challenges, and opportunities to engage existing demand-side user behavioral patterns. Information driven grid flexibility strategies present great opportunity to develop new technologies, system architectures, and implementation approaches that can easily scale across regions, incomes, and levels of development

A unified view of data-intensive flows in business intelligence systems : a survey

Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft

Automated Functional Testing based on the Navigation of Web Applications

Web applications are becoming more and more complex. Testing such applications is an intricate hard and time-consuming activity. Therefore, testing is often poorly performed or skipped by practitioners. Test automation can help to avoid this situation. Hence, this paper presents a novel approach to perform automated software testing for web applications based on its navigation. On the one hand, web navigation is the process of traversing a web application using a browser. On the other hand, functional requirements are actions that an application must do. Therefore, the evaluation of the correct navigation of web applications results in the assessment of the specified functional requirements. The proposed method to perform the automation is done in four levels: test case generation, test data derivation, test case execution, and test case reporting. This method is driven by three kinds of inputs: i) UML models; ii) Selenium scripts; iii) XML files. We have implemented our approach in an open-source testing framework named Automatic Testing Platform. The validation of this work has been carried out by means of a case study, in which the target is a real invoice management system developed using a model-driven approach.Comment: In Proceedings WWV 2011, arXiv:1108.208

Automated Service Identification Methods: A Review

Service identification represents the first phase in service modelling, a necessary step in SOA. This research study reviewed and analyzed the issues related to automation issues of service identification. However, the importance of service identification methods’ (SIM) automation and their business alignment are emphasized in literature, reviewing existing service identification methods (SIMs) reveals the lack of business alignment, automation as challenging issues. We close the gap by proposing ASIF which relies on automating the SIMs’ steps to identify business aligned services based on business processes and business goals

Topics in Software Engineering

Software engineering is a discipline which specifies, designs, develops, and maintains software applications. It applies practices and technologies from computer science. Software engineering is the backbone of software systems and forms the basis of operational design and development of software systems. Analysts use requirements elicitation techniques to ascertain the needs of customers and users, with the goal being a system that has a high chance of satisfying those needs. Success or failure of system development relies heavily on the quality of requirements gathering. Software modeling is an essential part of the software development process. Models are built and analyzed before the implementation of a system and are used to direct implementation.The Unified Modeling Language (UML) provides a standard way to visualize the design of a system. During the planning and design stages, software engineers must consider the risks involved in developing a system. Software must solve a problem and must respond to both functional and nonfunctional requirements. Software systems generally follow a pattern or an architectural style. We show the initial steps of developing a software system, define its specification and design topics, and demonstrate their creation by presenting a case study