A peer to peer (P2P) architecture for dynamic workflow management

Georgios John Fakas; Bill Karakostas

doi:10.1016/j.infsof.2003.09.015

Dynamic Workflow Restructuring Framework for Long-Running Business Processes

Advances in Database Research - Advanced Topics in Database Research, Volume 4 ◽

10.4018/978-1-59140-471-2.ch001 ◽

2005 ◽

pp. 1-49

Author(s):

Ling Liu ◽

Calton Pu ◽

Duncan Dubugras Ruiz

Keyword(s):

Business Processes ◽

Workflow Management ◽

Specification Language ◽

Organizational Processes ◽

Framework Design ◽

Activity Modeling ◽

Dynamic Workflow ◽

Different Types

This chapter presents a framework for dynamic restructuring of long-running business processes. The framework is composed of the ActivityFlow specification language, a set of workflow restructuring operators, and a dynamic workflow management engine. The ActivityFlow specification language enables the flexible specification, composition, and coordination of workflow activities. There are three unique features of our framework design. First, it supports a collection of specification mechanisms, allowing workflow designers to use a uniform workflow specification interface to describe different types of workflows involved in their organizational processes. A main objective of this characteristic is to help increase the flexibility of workflow processes in accommodating changes. The ActivityFlow language also provides a set of activity modeling facilities, enabling workflow designers to describe the flow of work declaratively and incrementally, and allowing to reason about correctness and security of

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SPMgr: Dynamic workflow manager for sampling and filtering data streams over Apache Storm

International Journal of Distributed Sensor Networks ◽

10.1177/1550147719862206 ◽

2019 ◽

Vol 15 (7) ◽

pp. 155014771986220

Author(s):

Youngkuk Kim ◽

Siwoon Son ◽

Yang-Sae Moon

Keyword(s):

Data Streams ◽

Distributed Processing ◽

Workflow Management ◽

Large Data ◽

Integrated System ◽

Input Processing ◽

Dynamic Workflow ◽

Processing Platform ◽

Apache Storm ◽

Workflow Manager

In this article, we address dynamic workflow management for sampling and filtering data streams in Apache Storm. As many sensors generate data streams continuously, we often use sampling to choose some representative data or filtering to remove unnecessary data. Apache Storm is a real-time distributed processing platform suitable for handling large data streams. Storm, however, must stop the entire work when it changes the input data structure or processing algorithm as it needs to modify, redistribute, and restart the programs. In addition, for effective data processing, we often use Storm with Kafka and databases, but it is difficult to use these platforms in an integrated manner. In this article, we derive the problems when applying sampling and filtering algorithms to Storm and propose a dynamic workflow management model that solves these problems. First, we present the concept of a plan consisting of input, processing, and output modules of a data stream. Second, we propose Storm Plan Manager, which can operate Storm, Kafka, and database as a single integrated system. Storm Plan Manager is an integrated workflow manager that dynamically controls sampling and filtering of data streams through plans. Third, as a key feature, Storm Plan Manager provides a Web client interface to visually create, execute, and monitor plans. In this article, we show the usefulness of the proposed Storm Plan Manager by presenting its design, implementation, and experimental results in order.

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