Reo + mCRL2: A Framework for Model-checking Dataflow in Service Compositions

The paradigm of service-oriented computing revolutionized the field of software engineering. According to this paradigm, new systems are composed of existing stand-alone services to support complex cross-organizational business processes. Correct communication of these services is not possible without a proper coordination mechanism. The Reo coordination language is a channel-based modeling language that introduces various types of channels and their composition rules. By composing Reo channels, one can specify Reo connectors that realize arbitrary complex behavioral protocols. Several formalisms have been introduced to give semantics to Reo. In their most basic form, they reflect service synchronization and dataflow constraints imposed by connectors. To ensure that the composed system behaves as intended, we need a wide range of automated verification tools to assist service composition designers. In this paper, we present our framework for the verification of Reo using the toolset. We unify our previous work on mapping various semantic models for Reo, namely, constraint automata, timed constraint automata, coloring semantics and the newly developed action constraint automata, to the process algebraic specification language of , address the correctness of this mapping, discuss tool support, and present a detailed example that illustrates the use of Reo empowered with for the analysis of dataflow in service-based process models

Reo + mCRL2: A Framework for Model-Checking Dataflow in Service Compositions

The paradigm of service-oriented computing revolutionized the field of software engineering. According to this paradigm, new systems are composed of existing stand-alone services to support complex cross-organizational business processes. Correct communication of these services is not possible without a proper coordination mechanism. The Reo coordination language is a channel-based modeling language that introduces various types of channels and their composition rules. By composing Reo channels, one can specify Reo connectors that realize arbitrary complex behavioral protocols. Several formalisms have been introduced to give semantics to Reo. In their most basic form, they reflect service synchronization and dataflow constraints imposed by connectors. To ensure that the composed system behaves as intended, we need a wide range of automated verification tools to assist service composition designers. In this paper, we present our framework for the verification of Reo using the mCRL2 toolset. We unify our previous work on mapping various semantic models for Reo, namely, constraint automata, timed constraint automata, coloring semantics and the newly developed action constraint automata, to the process algebraic specification language of mCRL2, address the correctness of this mapping, discuss tool support, and present a detailed example that illustrates the use of Reo empowered with mCRL2 for the analysis of dataflow in service-based process models

Causality and the semantics of provenance

Provenance, or information about the sources, derivation, custody or history of data, has been studied recently in a number of contexts, including databases, scientific workflows and the Semantic Web. Many provenance mechanisms have been developed, motivated by informal notions such as influence, dependence, explanation and causality. However, there has been little study of whether these mechanisms formally satisfy appropriate policies or even how to formalize relevant motivating concepts such as causality. We contend that mathematical models of these concepts are needed to justify and compare provenance techniques. In this paper we review a theory of causality based on structural models that has been developed in artificial intelligence, and describe work in progress on a causal semantics for provenance graphs.Comment: Workshop submissio

A scientific workflow framework for scientific data querying and processing

We are at the beginning of the new era of ``e-science\u27\u27. Researchers in many areas of science, especially in astrophysics, physics, climatology and biology, are now facing tremendous increases in data volumes, as well as corresponding data analysis tools. These increased data and tools demand a better framework to manage the new generation scientific research cycle from data capture, data curation to data analysis, data query and data visualization. Scientific workflows are proving to be one of the key technologies for scientists to formalize and structure complex scientific processes to enable and accelerate many significant scientific discoveries. Although several scientific workflow management systems (SWFMSs) are developed, a formal scientific workflow composition framework, in which workflows and constructs can be composed arbitrarily to process and query collectional scientific data sets, is still to be proposed. In this thesis, I make several contributions towards formalizing a scientific workflow composition framework. First, We proposed a dataflow-based scientific workflow composition model including a scientific workflow model that separates the declaration of the workflow interface from the definition of its functional body; and a set of workflow constructs, including Map, Reduce, Tree, Loop, Conditional, and Curry, which are fully compositional one with another. Our workflow composition framework is unique in that workflows are the only operands for composition; in this way, our approach elegantly solves the two-world problem in existing composition frameworks, in which composition needs to deal with both the world of tasks and the world of workflows. Second, We formalized a collection-oriented data model, called collectional data model, to model hierarchical collection-oriented scientific data, and a set of well-defined operators to manipulate and query such data. To our best knowledge, this is the first algebraic approach to modeling collection-oriented scientific data. Finally, we developed a prototype scientific workflow management system, called View. The View system implemented the above techniques in its subsystems and integrated them within a service-oriented architecture

Programming Languages and Systems

This open access book constitutes the proceedings of the 30th European Symposium on Programming, ESOP 2021, which was held during March 27 until April 1, 2021, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg and changed to an online format due to the COVID-19 pandemic. The 24 papers included in this volume were carefully reviewed and selected from 79 submissions. They deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems

Web service composition: A survey of techniques and tools

Web services are a consolidated reality of the modern Web with tremendous, increasing impact on everyday computing tasks. They turned the Web into the largest, most accepted, and most vivid distributed computing platform ever. Yet, the use and integration of Web services into composite services or applications, which is a highly sensible and conceptually non-trivial task, is still not unleashing its full magnitude of power. A consolidated analysis framework that advances the fundamental understanding of Web service composition building blocks in terms of concepts, models, languages, productivity support techniques, and tools is required. This framework is necessary to enable effective exploration, understanding, assessing, comparing, and selecting service composition models, languages, techniques, platforms, and tools. This article establishes such a framework and reviews the state of the art in service composition from an unprecedented, holistic perspective

Improving Model-Based Software Synthesis: A Focus on Mathematical Structures

Computer hardware keeps increasing in complexity. Software design needs to keep up with this. The right models and abstractions empower developers to leverage the novelties of modern hardware. This thesis deals primarily with Models of Computation, as a basis for software design, in a family of methods called software synthesis. We focus on Kahn Process Networks and dataflow applications as abstractions, both for programming and for deriving an efficient execution on heterogeneous multicores. The latter we accomplish by exploring the design space of possible mappings of computation and data to hardware resources. Mapping algorithms are not at the center of this thesis, however. Instead, we examine the mathematical structure of the mapping space, leveraging its inherent symmetries or geometric properties to improve mapping methods in general. This thesis thoroughly explores the process of model-based design, aiming to go beyond the more established software synthesis on dataflow applications. We starting with the problem of assessing these methods through benchmarking, and go on to formally examine the general goals of benchmarks. In this context, we also consider the role modern machine learning methods play in benchmarking. We explore different established semantics, stretching the limits of Kahn Process Networks. We also discuss novel models, like Reactors, which are designed to be a deterministic, adaptive model with time as a first-class citizen. By investigating abstractions and transformations in the Ohua language for implicit dataflow programming, we also focus on programmability. The focus of the thesis is in the models and methods, but we evaluate them in diverse use-cases, generally centered around Cyber-Physical Systems. These include the 5G telecommunication standard, automotive and signal processing domains. We even go beyond embedded systems and discuss use-cases in GPU programming and microservice-based architectures