Introducing distributed dynamic data-intensive (D3) science: Understanding applications and infrastructure

A common feature across many science and engineering applications is the amount and diversity of data and computation that must be integrated to yield insights. Data sets are growing larger and becoming distributed; and their location, availability and properties are often time-dependent. Collectively, these characteristics give rise to dynamic distributed data-intensive applications. While "static" data applications have received significant attention, the characteristics, requirements, and software systems for the analysis of large volumes of dynamic, distributed data, and data-intensive applications have received relatively less attention. This paper surveys several representative dynamic distributed data-intensive application scenarios, provides a common conceptual framework to understand them, and examines the infrastructure used in support of applications.Comment: 38 pages, 2 figure

LOGIC AND CONSTRAINT PROGRAMMING FOR COMPUTATIONAL SUSTAINABILITY

Computational Sustainability is an interdisciplinary field that aims to develop computational and mathematical models and methods for decision making concerning the management and allocation of resources in order to help solve environmental problems. This thesis deals with a broad spectrum of such problems (energy efficiency, water management, limiting greenhouse gas emissions and fuel consumption) giving a contribution towards their solution by means of Logic Programming (LP) and Constraint Programming (CP), declarative paradigms from Artificial Intelligence of proven solidity. The problems described in this thesis were proposed by experts of the respective domains and tested on the real data instances they provided. The results are encouraging and show the aptness of the chosen methodologies and approaches. The overall aim of this work is twofold: both to address real world problems in order to achieve practical results and to get, from the application of LP and CP technologies to complex scenarios, feedback and directions useful for their improvement

Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review

[EN] This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper and it is compared with traditional approaches in the development of energy optimization solutions. The different types of agent-based architectures are described, the role played by the environment is analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it. Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field, and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore, we can argue that MAS is a widespread approach in the field of energy optimization and that it is commonly used due to its capacity for the communication, coordination, cooperation of agents and the robustness that this methodology gives in assigning different tasks to agents. Finally, this article considers how MASs can be used for various purposes, from capturing sensor data to decision-making. We propose some research perspectives on the development of electrical optimization solutions through their development using MASs. In conclusion, we argue that researchers in the field of energy optimization should use multi-agent systems at those junctures where it is necessary to model energy efficiency solutions that involve a wide range of factors, as well as context independence that they can achieve through the addition of new agents or agent organizations, enabling the development of energy-efficient solutions for smart cities and intelligent buildings

(I) A Declarative Framework for ERP Systems(II) Reactors: A Data-Driven Programming Model for Distributed Applications

To those who can be swayed by argument and those who know they do not have all the answers This dissertation is a collection of six adapted research papers pertaining to two areas of research. (I) A Declarative Framework for ERP Systems: • POETS: Process-Oriented Event-driven Transaction Systems. The paper describes an ontological analysis of a small segment of the enterprise domain, namely the general ledger and accounts receivable. The result is an event-based approach to designing ERP systems and an abstract-level sketch of the architecture. • Compositional Specification of Commercial Contracts. The paper de-scribes the design, multiple semantics, and use of a domain-specific lan-guage (DSL) for modeling commercial contracts. • SMAWL: A SMAll Workflow Language Based on CCS. The paper show

Ein BIM Ontologie-basiertes Expertensystem für räumliche und zeitliche Programmierungen von Bauten

The effective realization of building construction is closely linked to the construction schedules that, if poorly designed, result in congested site areas, accidents and decline of productivity. In the past decade, many research efforts have been spent in BIM which represents the process of preparation and use of a computer-generated Building Information Model (BIM) even if an effective model to assist construction scheduling is still missing. This PhD thesis proposes an Expert-System able to identify the shortest completion sequence of a given Building Information Model, considering the on-site temporal-space allocation of workspaces. It is supported by an ontology-based system architecture integrated with a rule-based artificial intelligence. Four integrated ontologies, to formally represent construction site entities, constitute the system’s Knowledge-Base (KB): (1) scheduling ontology that maps the necessary components to specify the scheduling task (2) space ontology that contains workspaces requirements in terms of geometries, locations and interactions (3) products ontology that describes geometrical and topological information of the building objects (4) time ontology that describes temporal properties of site entities in their evolution across time. Such a KB was rendered into a Protégé’s script (ontology editing environment) in order to convert it in machine-readable language (i.e., Web Ontology Language –OWL). Furthermore, four automated Reasoning Mechanisms –scripts- were incorporated in the model architecture: (i) an algorithm to define the on-site workspaces configuration pattern, (ii) an algorithm to automatically model workspaces geometries, (iii) a workspaces conflicts checking process and (iv) a rule-engine to deduce the shortest construction sequence and solve the identified conflicts manipulating the KB itself. A validation test was conducted on a BIM-based project of an industrial building composed of 98 building items and 611 workspaces, allocated by means of (i) and modelled with (ii). A construction sequence of 36 construction days was suggested by the system. Moreover, 118 workspaces conflicts were identified (iii) and automatically solved by using the planning rules included in the rule-engine as it was visually verified simulating the sequence itself within a 4D-BIM environment. This prototype can be considered a precursor model in developing BIM-based intelligent systems architectures for spatial construction planning.Eine erfolgreiche Umsetzung eines Gebäudeprojektes ist von der Planung der Montage auf der Baustelle abhängig. Im letzten Jahrzehnt wurden zahlreiche wissenschaftliche Projekte zur Montageplanung unter Verwendung eines Computermodells im Rahmen des Building Information Modelling (BIM) durchgeführt. Momentan fehlt aber noch ein Modell, das auch den Prozess selber auf der Baustelle integriert. In der vorliegenden Arbeit wird ein Expertensystem mit dem Ziel der Findung einer optimalen Montagefolge vorgestellt. Das Expertensystem basiert auf BIM und berücksichtigt die räumliche und zeitliche Interaktion der Arbeitsabläufe auf der Baustelle. Die entwickelte Methode stützt sich auf einer Ontologie-basierten Architektur, die in einer Regel-basierten künstlichen Intelligenz integriert ist. Dabei wird ein neues Objekt in das BIM Modell eingefügt, das den Raumbedarf einer Montagetätigkeit beschreibt. Dies kann beispielsweise ein erforderlicher Freiraum für einen Mobilkran sein oder ein bei der Montage nicht betretbarer Sicherheitsbereich. Die Wissensbasis des Expertensystems besteht aus vier Ontologien, die nötig sind um das Wesen der Baustelle darzustellen: Ontologie der Montageabläufe, die den technischen Ablauf der Aktivitäten bestimmt; Ontologie der baulichen Räume, die den räumlichen Bedarf berücksichtigt; Ontologie der Elemente des Gebäudes, welche die geometrischen und funktionalen Gebäudeelemente beschreibt, um Arbeitsprozesse zu bestimmen; Ontologie der Zeit, welche die Reihenfolge der Bauelemente vorgibt. Die Wissensbasis ist mit einem Protégé-Skript als Ontologie-Editor entwickelt worden, für einen Compiler der Web Ontology Language (OWL). Danach wurde die Wissensbasis mit vier Algorithmen verknüpft: Ein Algorithmus, der den Arbeitsraum definiert; Ein Algorithmus, der die Geometrien der Arbeitsräume modelliert; Ein Kontrollprozess, der die Konfliktstellen des Arbeitsraum identifiziert; Ein Optimierungs-Prozess, der den kürzesten Arbeitsprozess ermittelt. Zur Validierung wurde ein Industriegebäude mit 98 Elementen verwendet. Das Expertensystem hatte 611 Arbeitsräume errechnet und eine geschätzte Bauzeit von 36 Tagen. Das Expertensystem identifizierte 118 Konfliktstellen und entwickelte jeweils Lösungen. Das Ergebnis wurde mit Hilfe einer 4D-BIM Umgebung visualisiert. Das vorgestellte Expertensystem ist ein Prototyp, der einen Beitrag zur Entwicklung automatischer und intelligenter Programmierungen für den Montageablauf unter Verwendung von BIM leistet

Advances in Grid Computing

This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems

Urban load optimization based on agent-based model representation

Tese de mestrado integrado em Engenharia da Energia e do Ambiente, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, em 2018O sistema energético atravessará uma profunda transformação nos próximos anos à medida que a produção renovável distribuída, a flexibilidade no lado do consumo e as funcionalidades de SmartGrid são implementadas. Este processo, conduzido em grande parte pelas imposições causadas pelos efeitos das alterações climáticas, implica profundas transformações na produção e consumo de energia e torna a transição energética extremamente urgente. Simultaneamente, novos players, entidades e modelos de negócio têm emergido em quase todos os níveis da cadeia energética desde a produção, a transmissão, distribuição e comercialização até à gestão da rede elétrica, num movimento conduzido pelo processo de particionamento (unbundling) do sistema elétrico e pela exigência de um sistema mais descentralizado e horizontal. O efeito combinado desta nova paisagem energética torna possíveis novas funcionalidades e arquitecturas de sistema na mesma medida em que coloca enormes problemas de natureza física e matemática mas também enormes questões económicas, sociais e políticas que terão, necessariamente, de ser abordadas e resolvidas. A Gestão do Consumo é um termo abrangente que representa tanto os mecanismos de Resposta na Procura (Demand Response) ou a Gestão no Lado da Procura (Demand-Side Management) e que se impõe como um dos problemas actuais mais importantes em sistemas energéticos inteligentes caracterizados por altas penetrações renováveis e mecanismos de mercado. Para resolver estes problemas, um conjunto de métodos matemáticos e computacionais têm sido propostos nos últimos anos. Otimização distribuída e sistemas inteligentes, sistemas baseados em agentes de software e teoria de jogos encontram-se entre algumas das ferramentas usadas para otimizar o consumo de energia e determinar o agendamento e a alocação ótima de equipamentos e máquinas para consumidores residenciais, comerciais e industriais. Na sequência de trabalhos prévios disponíveis na literatura da especialidade, o presente trabalho propõe um modelo geral para abordar o problema da otimização de cargas através de arquitecturas e métodos baseados no paradigma dos Agentes. O trabalho começa por definir agentes em pontos críticos da rede elétrica e os seus processos internos de raciocínio representados por modelos de otimização matemática. Seguidamente as interações entre agentes são modeladas como um jogo de dois níveis (bi-level game) entre uma entidade gestora da rede e consumidores de energia tipificados de forma a coordenar o carregamento de diversos equipamentos, incluindo veículos elétricos, e determinar uma solução admissível para o sistema global. A funcionalidade geral do modelo proposto é demonstrada através da sua implementação em software proprietário e recorrendo a um conjunto de dados específicos. Está, então, pronto para ser complementado e refinado no futuro de forma a ser aplicado em problemas do mundo real, de grandes dimensões, mas também novas implementações em software open source de forma a ficar acessível a novos utilizadores.The energy system is expected to go through a phase change in coming years as distributed generation, demand flexibility and SmartGrid features gets implemented. The main driver for this process, climate change, imposes constraints on energy production and consumption making energy transition extremely urgent. Simultaneously, new players, entities and business models have emerged at almost all levels of the energy chain from production, transmission, distribution and commercialization down to power grid management driven by the unbundling process and the call for a more decentralized and horizontal energy system. The combined effect of this new energy landscape makes new system’s architectures and functionalities desirable and possible, but poses huge physical, mathematical, engineering, economic and political questions and problems that need to be tackled. Load Management is one broad term depicting Demand-Side Management and Demand Response mechanisms and is one of the pressing problems on smart energy systems. To solve them, a plethora of computational and mathematical methods have been proposed in recent years. Distributed optimization and intelligence, software agents, agent-based systems and game theory are among the tools used to optimize load consumption and determine optimal device scheduling for residential, commercial and industrial power consumers Following previous work found in literature, the present work proposes a general framework to treat the load optimization problem using agent-based architectures and models. We start by defining agents at critical points within the power grid as well as their internal reasoning process depicted by mathematical optimization models. We then proceed to model the cooperative interactions between agents as a Bi-level game between a grid entity and typified power consumers in order to coordinate the charging of several appliances and electrical vehicles and determine a feasible solution for the global system. We show the general functionality of the framework by implementing it in software and applying it to specific datasets. The framework is suitable for further refinement and development when applied to real world problems