Verification of business process workflows

Modeling of Business processes is essential in many areas. Workflows represent the Business processes. It is possible to identify potential problems while performing verification of workflows. One of the objectives of the verification is to assure reachability. This includes analysis of the deadlock and tempo blocking freeness properties. The paper presents verification approach based on using an adjacency matrix. Spreadsheets are used as a verification tool. The approach is illustrated by the examples which justify the importance of verification in workflow processes

Reprodutibilidade e reuso de experimentos em eScience : workflows, ontologias e scripts

Orientadores: Claudia Maria Bauzer Medeiros, Yolanda GilTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Scripts e Sistemas Gerenciadores de Workflows Científicos (SGWfC) são abordagens comumente utilizadas para automatizar o fluxo de processos e análise de dados em experimentos científicos computacionais. Apesar de amplamente usados em diversas disciplinas, scripts são difíceis de entender, adaptar, reusar e reproduzir. Por esta razão, diversas soluções têm sido propostas para auxiliar na reprodutibilidade de experimentos que utilizam ambientes baseados em scripts. Porém, estas soluções não permitem a documentação completa do experimento, nem ajudam quando outros cientistas querem reusar apenas parte do código do script. SGWfCs, por outro lado, ajudam na documentação e reuso através do suporte aos cientistas durante a modelagem e execução dos seus experimentos, que são especificados e executados como componentes interconectados (reutilizáveis) de workflows. Enquanto workflows são melhores que scripts para entendimento e reuso dos experimentos, eles também exigem documentação adicional. Durante a modelagem de um experimento, cientistas frequentemente criam variantes de workflows, e.g., mudando componentes do workflow. Reuso e reprodutibilidade exigem o entendimento e rastreamento da proveniência das variantes, uma tarefa que consome muito tempo. Esta tese tem como objetivo auxiliar na reprodutibilidade e reuso de experimentos computacionais. Para superar estes desafios, nós lidamos com dois problemas de pesquisas: (1) entendimento de um experimento computacional, e (2) extensão de um experimento computacional. Nosso trabalho para resolver estes problemas nos direcionou na escolha de workflows e ontologias como respostas para ambos os problemas. As principais contribuições desta tese são: (i) apresentar os requisitos para a conversão de experimentos baseados em scripts em experimentos reprodutíveis; (ii) propor uma metodologia que guia o cientista durante o processo de conversão de experimentos baseados em scripts em workflow research objects reprodutíveis. (iii) projetar e implementar funcionalidades para avaliação da qualidade de experimentos computacionais; (iv) projetar e implementar o W2Share, um arcabouço para auxiliar a metodologia de conversão, que explora ferramentas e padrões que foram desenvolvidos pela comunidade científica para promover o reuso e reprodutibilidade; (v) projetar e implementar o OntoSoft-VFF, um arcabouço para captura de informação sobre software e componentes de workflow para auxiliar cientistas a gerenciarem a exploração e evolução de workflows. Nosso trabalho é apresentado via casos de uso em Dinâmica Molecular, Bioinformática e Previsão do TempoAbstract: Scripts and Scientific Workflow Management Systems (SWfMSs) are common approaches that have been used to automate the execution flow of processes and data analysis in scientific (computational) experiments. Although widely used in many disciplines, scripts are hard to understand, adapt, reuse, and reproduce. For this reason, several solutions have been proposed to aid experiment reproducibility for script-based environments. However, they neither allow to fully document the experiment nor do they help when third parties want to reuse just part of the code. SWfMSs, on the other hand, help documentation and reuse by supporting scientists in the design and execution of their experiments, which are specified and run as interconnected (reusable) workflow components (a.k.a. building blocks). While workflows are better than scripts for understandability and reuse, they still require additional documentation. During experiment design, scientists frequently create workflow variants, e.g., by changing workflow components. Reuse and reproducibility require understanding and tracking variant provenance, a time-consuming task. This thesis aims to support reproducibility and reuse of computational experiments. To meet these challenges, we address two research problems: (1) understanding a computational experiment, and (2) extending a computational experiment. Our work towards solving these problems led us to choose workflows and ontologies to answer both problems. The main contributions of this thesis are thus: (i) to present the requirements for the conversion of script to reproducible research; (ii) to propose a methodology that guides the scientists through the process of conversion of script-based experiments into reproducible workflow research objects; (iii) to design and implement features for quality assessment of computational experiments; (iv) to design and implement W2Share, a framework to support the conversion methodology, which exploits tools and standards that have been developed by the scientific community to promote reuse and reproducibility; (v) to design and implement OntoSoft-VFF, a framework for capturing information about software and workflow components to support scientists manage workflow exploration and evolution. Our work is showcased via use cases in Molecular Dynamics, Bioinformatics and Weather ForecastingDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação2013/08293-7, 2014/23861-4, 2017/03570-3FAPES

Knowledge-Driven Harmonization of Sensor Observations: Exploiting Linked Open Data for IoT Data Streams

The rise of the Internet of Things leads to an unprecedented number of continuous sensor observations that are available as IoT data streams. Harmonization of such observations is a labor-intensive task due to heterogeneity in format, syntax, and semantics. We aim to reduce the effort for such harmonization tasks by employing a knowledge-driven approach. To this end, we pursue the idea of exploiting the large body of formalized public knowledge represented as statements in Linked Open Data

Semantics-enriched workflow creation and management system with an application to document image analysis and recognition

Scientific workflow systems are an established means to model and execute experiments or processing pipelines. Nevertheless, designing workflows can be a daunting task for users due to the complexities of the systems and the sheer number of available processing nodes, each having different compatibility/applicability characteristics. This Thesis explores how concepts of the Semantic Web can be used to augment workflow systems in order to assist researchers as well as non-expert users in creating valid and effective workflows. A prototype workflow creation/management system has been developed, including components for ontology modelling, workflow composition, and workflow repositories. Semantics are incorporated as a lightweight layer, permeating all aspects of the system and workflows, including retrieval, composition, and validation. Document image analysis and recognition is used as a representative application domain to evaluate the validity of the system. A new semantic model is proposed, covering a wide range of aspects of the target domain and adjacent fields. Real-world use cases demonstrate the assistive features and the automated workflow creation. On that basis, the prototype workflow creation/management system is compared to other state-of-the-art workflow systems and it is shown how those could benefit from the semantic model. The Thesis concludes with a discussion on how a complete infrastructure based on semantics-enriched datasets, workflow systems, and sharing platforms could represent the next step in automation within document image analysis and other domains

Decision-Making with Multi-Step Expert Advice on the Web

This thesis deals with solving multi-step tasks by using advice from experts, which are algorithms to solve individual steps of such tasks. We contribute with methods for maximizing the number of correct task solutions by selecting and combining experts for individual task instances and methods for automating the process of solving tasks on the Web, where experts are available as Web services. Multi-step tasks frequently occur in Natural Language Processing (NLP) or Computer Vision, and as research progresses an increasing amount of exchangeable experts for the same steps are available on the Web. Service provider platforms such as Algorithmia monetize expert access by making expert services available via their platform and having customers pay for single executions. Such experts can be used to solve diverse tasks, which often consist of multiple steps and thus require pipelines of experts to generate hypotheses. We perceive two distinct problems for solving multi-step tasks with expert services: (1) Given that the task is sufficiently complex, no single pipeline generates correct solutions for all possible task instances. One thus must learn how to construct individual expert pipelines for individual task instances in order to maximize the number of correct solutions, while also taking into account the costs adhered to executing an expert. (2) To automatically solve multi-step tasks with expert services, we need to discover, execute and compose expert pipelines. With mostly textual descriptions of complex functionalities and input parameters, Web automation entails to integrate available expert services and data, interpreting user-specified task goals or efficiently finding correct service configurations. In this thesis, we present solutions to both problems: (1) We enable to learn well-performing expert pipelines assuming available reference data sets (comprising a number of task instances and solutions), where we distinguish between centralized and decentralized decision-making. We formalize the problem as specialization of a Markov Decision Process (MDP), which we refer to as Expert Process (EP) and integrate techniques from Statistical Relational Learning (SRL) or Multiagent coordination. (2) We develop a framework for automatically discovering, executing and composing expert pipelines by exploiting methods developed for the Semantic Web. We lift the representations of experts with structured vocabularies modeled with the Resource Description Framework (RDF) and extend EPs to Semantic Expert Processes (SEPs) to enable the data-driven execution of experts in Web-based architectures. We evaluate our methods in different domains, namely Medical Assistance with tasks in Image Processing and Surgical Phase Recognition, and NLP for textual data on the Web, where we deal with the task of Named Entity Recognition and Disambiguation (NERD)