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Workflow

About: Workflow is a research topic. Over the lifetime, 31996 publications have been published within this topic receiving 498339 citations.


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Book ChapterDOI
07 Nov 2004
TL;DR: The experience in applying KAoS services to ensure policy compliance for Semantic Web Services workflow composition and enactment is described and how this work has uncovered requirements for increasing the expressivity of policy beyond what can be done with description logic is described.
Abstract: In this paper we describe our experience in applying KAoS services to ensure policy compliance for Semantic Web Services workflow composition and enactment. We are developing these capabilities within the context of two applications: Coalition Search and Rescue (CoSAR-TS) and Semantic Firewall (SFW). We describe how this work has uncovered requirements for increasing the expressivity of policy beyond what can be done with description logic (e.g., role-value-maps), and how we are extending our representation and reasoning mechanisms in a carefully controlled manner to that end. Since KAoS employs OWL for policy representation, it fits naturally with the use of OWL-S workflow descriptions generated by the AIAI I-X planning system in the CoSAR-TS application. The advanced reasoning mechanisms of KAoS are based on the JTP inference engine and enable the analysis of classes and instances of processes from a policy perspective. As the result of analysis, KAoS concludes whether a particular workflow step is allowed by policy and whether the performance of this step would incur additional policy-generated obligations. Issues in the representation of processes within OWL-S are described. Besides what is done during workflow composition, aspects of policy compliance can be checked at runtime when a workflow is enacted. We illustrate these capabilities through two application examples. Finally, we outline plans for future work.

636 citations

Journal ArticleDOI
TL;DR: In this article, a classification of technology as operations technology, materials technology, and knowledge technology is proposed, based on the broad hypothesis that organizational technology is strongly related to organizational structure, by linear and nonlinear correlational analysis.
Abstract: A classification of concepts of technology as operations technology, materials technology, and knowledge technology, is proposed. The construction of scales measuring operations technology at an organizational level of conceptualization, makes it possible to test the broad hypothesis that organizational technology is strongly related to organizational structure, by linear and nonlinear correlational analysis. On a stratified sample of diverse organizations in the English midlands, and on a subset of manufacturing organizations, this sweeping "technological imperative" hypothesis was generally not supported in successive tests. Operations technology was, however, associated with some variables, which are similar in that all were job-counts denoting the proportions employed in specified categories. This result, together with a detailed comparison with Woodward's findings in south-east Essex, leads to a reinterpretation of the role of technology. Operations technology is shown to affect only those structural variables immediately impinged on by the workflow. Thus the smaller the organization the more completely its structure is pervaded by the immediate effects of this technology; the larger the organization the more these effects are confined to variables such as the proportions employed in activities that are specifically linked with workflow, and technology is not related to the wider administrative and hierarchical structure. This interpretation, it is suggested, offers a synthesis for the long-standing divergence in organization theory between statements by classical management writers of management principles irrespective of technology, and the stress by behavioral scientists on the relevance of technology.

629 citations

Journal ArticleDOI
TL;DR: While using quanteda requires R programming knowledge, its API is designed to enable powerful, efficient analysis with a minimum of steps, which lowers the barriers to learning and using NLP and quantitative text analysis even for proficient R programmers.
Abstract: quanteda is an R package providing a comprehensive workflow and toolkit for natural language processing tasks such as corpus management, tokenization, analysis, and visualization. It has extensive functions for applying dictionary analysis, exploring texts using keywords-in-context, computing document and feature similarities, and discovering multi-word expressions through collocation scoring. Based entirely on sparse operations,it provides highly efficient methods for compiling document-feature matrices and for manipulating these or using them in further quantitative analysis. Using C++ and multi-threading extensively, quanteda is also considerably faster and more efficient than other R and Python packages in processing large textual data. The package is designed for R users needing to apply natural language processing to texts,from documents to final analysis. Its capabilities match or exceed those provided in many end-user software applications, many of which are expensive and not open source. The package is therefore of great benefit to researchers, students, and other analysts with fewer financial resources. While using quanteda requires R programming knowledge, its API is designed to enable powerful, efficient analysis with a minimum of steps. By emphasizing consistent design, furthermore, quanteda lowers the barriers to learning and using NLP and quantitative text analysis even for proficient R programmers.

617 citations

Journal ArticleDOI
02 Apr 2014
TL;DR: An algorithm based on the meta-heuristic optimization technique, particle swarm optimization (PSO), which aims to minimize the overall workflow execution cost while meeting deadline constraints is presented.
Abstract: Cloud computing is the latest distributed computing paradigm and it offers tremendous opportunities to solve large-scale scientific problems. However, it presents various challenges that need to be addressed in order to be efficiently utilized for workflow applications. Although the workflow scheduling problem has been widely studied, there are very few initiatives tailored for cloud environments. Furthermore, the existing works fail to either meet the user's quality of service (QoS) requirements or to incorporate some basic principles of cloud computing such as the elasticity and heterogeneity of the computing resources. This paper proposes a resource provisioning and scheduling strategy for scientific workflows on Infrastructure as a Service (IaaS) clouds. We present an algorithm based on the meta-heuristic optimization technique, particle swarm optimization (PSO), which aims to minimize the overall workflow execution cost while meeting deadline constraints. Our heuristic is evaluated using CloudSim and various well-known scientific workflows of different sizes. The results show that our approach performs better than the current state-of-the-art algorithms.

601 citations

Proceedings ArticleDOI
27 May 2019
TL;DR: A study conducted on observing software teams at Microsoft as they develop AI-based applications finds that various Microsoft teams have united this workflow into preexisting, well-evolved, Agile-like software engineering processes, providing insights about several essential engineering challenges that organizations may face in creating large-scale AI solutions for the marketplace.
Abstract: Recent advances in machine learning have stimulated widespread interest within the Information Technology sector on integrating AI capabilities into software and services. This goal has forced organizations to evolve their development processes. We report on a study that we conducted on observing software teams at Microsoft as they develop AI-based applications. We consider a nine-stage workflow process informed by prior experiences developing AI applications (e.g., search and NLP) and data science tools (e.g. application diagnostics and bug reporting). We found that various Microsoft teams have united this workflow into preexisting, well-evolved, Agile-like software engineering processes, providing insights about several essential engineering challenges that organizations may face in creating large-scale AI solutions for the marketplace. We collected some best practices from Microsoft teams to address these challenges. In addition, we have identified three aspects of the AI domain that make it fundamentally different from prior software application domains: 1) discovering, managing, and versioning the data needed for machine learning applications is much more complex and difficult than other types of software engineering, 2) model customization and model reuse require very different skills than are typically found in software teams, and 3) AI components are more difficult to handle as distinct modules than traditional software components --- models may be "entangled" in complex ways and experience non-monotonic error behavior. We believe that the lessons learned by Microsoft teams will be valuable to other organizations.

597 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20234,414
20229,010
20211,461
20201,579
20191,702