Engineering Agile Big-Data Systems

To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems

Forum Session at the First International Conference on Service Oriented Computing (ICSOC03)

The First International Conference on Service Oriented Computing (ICSOC) was held in Trento, December 15-18, 2003. The focus of the conference ---Service Oriented Computing (SOC)--- is the new emerging paradigm for distributed computing and e-business processing that has evolved from object-oriented and component computing to enable building agile networks of collaborating business applications distributed within and across organizational boundaries. Of the 181 papers submitted to the ICSOC conference, 10 were selected for the forum session which took place on December the 16th, 2003. The papers were chosen based on their technical quality, originality, relevance to SOC and for their nature of being best suited for a poster presentation or a demonstration. This technical report contains the 10 papers presented during the forum session at the ICSOC conference. In particular, the last two papers in the report ere submitted as industrial papers

Engineering Agile Big-Data Systems

To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems

Selection of third party software in Off-The-Shelf-based software development: an interview study with industrial practitioners

The success of software development using third party components highly depends on the ability to select a suitable component for the intended application. The evidence shows that there is limited knowledge about current industrial OTS selection practices. As a result, there is often a gap between theory and practice, and the proposed methods for supporting selection are rarely adopted in the industrial practice. This paper's goal is to investigate the actual industrial practice of component selection in order to provide an initial empirical basis that allows the reconciliation of research and industrial endeavors. The study consisted of semi-structured interviews with 23 employees from 20 different software-intensive companies that mostly develop web information system applications. It provides qualitative information that help to further understand these practices, and emphasize some aspects that have been overlooked by researchers. For instance, although the literature claims that component repositories are important for locating reusable components; these are hardly used in industrial practice. Instead, other resources that have not received considerable attention are used with this aim. Practices and potential market niches for software-intensive companies have been also identified. The results are valuable from both the research and the industrial perspectives as they provide a basis for formulating well-substantiated hypotheses and more effective improvement strategies.Peer ReviewedPostprint (author's final draft

Improving Key-Value Database Scalability with Lazy State Determination

Applications keep demanding higher and higher throughput and lower response times from Database systems. Databases leverage concurrency, by using both multiple computer systems (nodes) and the multiple cores available in each node, to execute multiple requests (transactions) concurrently. Executing multiple transactions concurrently requires coordination, which is ensured by the database concurrency control (CC) module. However, excessive control/limitation of concurrency by the CC module negatively impacts the overall performance (latency and throughput) of the database system. The performance limitations imposed by the database CC module can be addressed by exploring new hardware, or by leveraging software-based techniques such as futures and lazy evaluation of transactions. This is where Lazy State Determination (LSD) shines [43, 42]. LSD proposes a new transactional API that decreases the conflicts between concurrent transactions by enabling the use of futures in both SQL and Key-Value database systems. The use of futures allows LSD to better capture the application semantics and to make more informed decisions on what really constitutes a conflict. These two key insights get together to create a system that provides high throughput in high contention scenarios. Our work builds on top of a previous LSD prototype. We identified and diagnosed its shortcomings, and devised and implemented a new prototype that addressed them. We validated our new LSD system and evaluated its behaviour and performance by comparing and contrasting with the original prototype. Our evaluation showed that the throughput of the new LSD prototype is from 3.7× to 4.9× higher, in centralized and distributed settings respectively, while also reducing the latency up to 10 times. With this work, we provide an LSD-based Key-Value Database System that has better vertical and horizontal scalability, and can take advantage of systems with higher core count or high number of nodes, in centralized and distributed settings, respectively.As aplicações continuam a exigir aos sistemas de base de dados (BD) débitos cada vez maiores e tempos de resposta cada vez menores. As BD respondem explorando a concorrência, usando múltiplos sistemas computacionais (nós) e os vários cores disponíveis em cada um desses nós, para executar vários pedidos (transações) simultaneamente. A execução de múltiplas transações simultaneamente requer coordenação, assegurada pelo módulo de controlo de concorrência (CC) da BD. No entanto, o controlo/limitação excessiva de concorrência pelo módulo de CC impacta negativamente o desempenho geral (latência e débito) do sistema de BD. As limitações de desempenho impostas pelo módulo CC da BD podem ser abordadas tanto explorando novo hardware como recorrendo a técnicas baseadas em software, como futuros e avaliação diferida de transações. É aqui que o Lazy State Determination (LSD) brilha [43, 42]. O LSD propõe uma nova API transacional que permite o uso de futuros em sistemas de BD SQL e Chave-Valor, diminuindo os conflitos entre transações concorrentes. O uso de futuros permite também que o LSD capture melhor a semântica da aplicação e tome decisões mais informadas sobre o que realmente constitui um conflito. Estes dois aspetos combinam-se para criar um sistema transacional que fornece elevado débito em cenários de alta contenção. O nosso trabalho foi desenvolvido sobe um protótipo anterior de LSD. Identificamos e diagnosticamos as suas deficiências e limitações, e concebemos e implementamos um novo protótipo que as endereçou. Validamos o novo sistema LSD e avaliamos o seu comportamento e desempenho comparando e contrastando com o protótipo original. A nossa avaliação mostrou que o débito do novo protótipo LSD é de 3,7× a 4,9× maior, em configurações centralizadas e distribuídas, respetivamente, além de reduzir a latência até 10 vezes. Com este trabalho, disponibilizamos um sistema de base de dados de Chave-Valor baseado em LSD que possui melhor escalabilidade vertical e horizontal, fazendo melhor uso de sistemas com múltiplos cores ou com elevado número de nós

Documenting Knowledge Graph Embedding and Link Prediction using Knowledge Graphs

In recent years, sub-symbolic learning, i.e., Knowledge Graph Embedding (KGE) incorporated with Knowledge Graphs (KGs) has gained significant attention in various downstream tasks (e.g., Link Prediction (LP)). These techniques learn a latent vector representation of KG's semantical structure to infer missing links. Nonetheless, the KGE models remain a black box, and the decision-making process behind them is not clear. Thus, the trustability and reliability of the model's outcomes have been challenged. While many state-of-the-art approaches provide data-driven frameworks to address these issues, they do not always provide a complete understanding, and the interpretations are not machine-readable. That is why, in this work, we extend a hybrid interpretable framework, InterpretME, in the field of the KGE models, especially for translation distance models, which include TransE, TransH, TransR, and TransD. The experimental evaluation on various benchmark KGs supports the validity of this approach, which we term Trace KGE. Trace KGE, in particular, contributes to increased interpretability and understanding of the perplexing KGE model's behavior