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

Rethinking Ephemeral Architecture. Advanced Geometry for Citizen-Managed Spaces

In recent years there have been a high amount of citizen initiatives that address the complex problems of the contemporary city. There are empty or disused spaces that have been reused for urban gardens, for social use, to encourage integration and civic activities activation, etc. Traditional architectural processes do not provide realistic solutions to these initiatives that, along with limited financial resources, have led to the emergence of architectures and self-constructed facilities, almost as an emergency mode, without necessary planning, media and constructive knowledge. The democratization of technology, thanks to laboratories of digital production, combined with knowledge of the properties of different surfaces through the CAD-CAM tools, offers new opportunities for the development of a lightweight, flexible and low impact architecture, very according to the needs of citizens' initiatives that naturally arise in our cities. The new existing scenario contemplates the figure of the architect, or engineer, not only as an agent of the market, but as a professional able to propose efficient solutions to problems from within, bringing their specific knowledge and serving as bridges between the new technological solutions and the challenges of society

Knowledge management of system interfaces and interactions from product development processes

Thesis (S.M.)--Massachusetts Institute of Technology, System Design & Management Program, 2001.Includes bibliographical references (p. 149).A system architecture was developed and analyzed for a basic elevator system using a limited number of system level components. A Design Structure Matrix was created which represented the complex interactions of the system components. These components were derived from a decomposition of system requirements, code and safety requirements, and evaluation of scenario operational requirements. Clustering routines using cost assignment of interactions aided in optimizing the cluster assignment of components. These cost assignments reflect cost and time associated with managing interactions inside and outside of subsystems. Management and optimization of the interfaces between the clustered components leads to an architecture that minimizes complexity and will hopefully lead to quicker and less costly product development cycles. Using this approach, near-optimal architectures can be analyzed and alternatives can be evaluated for system level impact. As was observed with this test case, highly complex or integrative systems are difficult to analyze, even with the tools utilized. These tools provided a structured approach that utilizes an objective process. This approach provides documentation and analysis of the architecture that is normally managed on the fly as product development progresses. The results of the analysis can provide a framework for an organizational structure of the product development process, provide an avenue for dialogue between design teams responsible for different subsystems, provide a process for evaluation of architecture alternatives, and identify the interactions between subsystems that must be managed carefully.by Ronnie E. Thebeau.S.M

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era