Ontology-based representation and generation of workflows for micro-task human-machine computation

Doctoral Program in Computer ScienceA crescente popularidade das plataformas de crowdsourcing de micro-tarefas levou ao aparecimento de novas abordagens baseadas em fluxos e workflows de micro-tarefas. Juntamente com estas novas abordagens, surgem novos desafios. A falta de estruturação dos dados das micro-tarefas torna difícil, por parte de quem solicita as tarefas, a inclusão de participantes máquina no processo de execução dos workflows. Outro desafio deve-se á falta de componentes que permitam o controlo do fluxo em workflows de micro-tarefas, embora estes componentes sejam comuns em abordagens de workflow tradicionais e em processos de negócio. Nesta tese, é proposto um método para a representação, construção, instanciação e execução de workflows de tarefas em ambientes de computação pessoa-máquina, baseado em ontologias. A representação é capaz de capturar a estrutura e a semântica das operações e dos seus dados, ao mesmo tempo que se mantém próxima do nível conceptual humano. Os workflows são construidos em duas dimensões: a dimensão de domínio estático e a dimensão (da tarefa) dinâmica. Isto permite que os dados de entrada e de saída dos workflows possam ser descritos exclusivamente de acordo com uma ontologia de domínio, de forma completamente independente da representação do workflow. Para que possa ser efetuada a instanciação e a execução da representação do workflow, foi implementado um motor de workflows baseado no método proposto. Para facilitar o papel do solicitador (ou requester) na criação de novas representações de workflows (ou workflow-definitions), um processo de construção semi-automático baseado em ontologias de domínio é também proposto. O processo foi implementado numa ferramenta de construção que permite a construção assistida, iterativa e visual de representações de workflows. O método de representação e o processo de construção propostos são avaliados através de múltiplos cenários de aplicação em diferentes domínios.The growing popularity of micro-task crowdsourcing platforms has led to new approaches based on workflows of micro-tasks. Along with these new approaches, new challenges have emerged. The unstructured nature of micro-tasks in terms of domain representation makes it difficult for task requesters to include machine workers in the workflow execution process. Also, the representation of these human-machine computation workflows lack the flow control components often found in traditional workflow and business process approaches. In this thesis, a method for the representation, construction, instantiation and execution of human-machine computation task workflows through ontologies is proposed. The representation captures the structure and semantics of the tasks and their domain, while remaining close to the human conceptual level. Workflows are built according to two dimensions: the static domain dimension and the dynamic (task) dimension. This allows the input and the output of workflows to be described according to a domain ontology, completely independent from the workflow representation. The instantiation and execution of the represented workflow can be performed through the implemented workflow engine. To aid the requester in the creation of new workflow representations (or workflowdefinitions), a semi-automatic construction process based on domain ontologies is also proposed. The process has been implemented into a construction framework that allows the aided, iterative and visual construction of workflow-definitions. The proposed method and construction process is evaluated through several application scenarios in different domains.Portuguese Foundation for Science and Technology within the doctoral grant SFRH/BD/ 70302/2010

Test Data Extraction and Comparison with Test Data Generation

Testing an integrated information system that relies on data from multiple sources can be a challenge, particularly when the data is confidential. This thesis describes a novel test data extraction approach, called semantic-based test data extraction for integrated systems (iSTDE) that solves many of the problems associated with creating realistic test data for integrated information systems containing confidential data. iSTDE reads a consistent cross-section of data from the production databases, manipulates that data to obscure individual identities while still preserving overall semantic data characteristics that are critical to thorough system testing, and then moves that test data to an external test environment. This thesis also presents a theoretical study that compares test-data extraction with a competing technique, named test-data generation. Specifically, this thesis a) describes a comparison method that includes a comprehensive list of characteristics essential for testing the database applications organized into seven different areas, b) presents an analysis of the relative strengths and weaknesses of the different test-data creation techniques, and c) reports a number of specific conclusions that will help testers make appropriate choices