Kwang-Hoon Kim

Performance Analytic Models and Analyses for Workflow Architectures

The design and implementation of a workflow management system is typically a large and complex task. Decisions need to be made about the hardware and software platforms, the data structures, the algorithms, and network interconnection of various modules utilized by various users and administrators. These decisions are further complicated by requirements such as flexibility, robustness, modifiability, availability, performance, and usability. As the size of workflow systems increases, organizations are finding that the standard server/client architectures, and off-the-shelf solutions are not adequate. We can further see that in the future, very large-scale workflow systems (VLSW) will become more complex, and more prevalent. Thus, one further requirement is an emphasis of this document: scalability. For the purposes of our scalable workflow investigations, we describe a framework, a taxonomy, a model, and a methodology to investigate the performance of various workflow architectures as the size of the system (number of workcases) grows very large.First, this paper presents a novel workflow architectural framework and taxonomy. We survey some example current workflow products and research prototype systems, illustrating some of the taxonomical categories. In fact, most current workflow architectures fall into only one of the many categories of this taxonomy: the centralized server/client category. The paper next explains a performance analysis methodology useful for exploring this taxonomy. The methodology deploys a layered queuing model, and performs mathematical analysis on this model using a modified MOL (method of layers) combined with a linearization algorithm. Finally, the paper utilizes this methodology to compare and contrast the various architectural categories, providing interesting results about performance as the number of workcases increases. Our analytic results suggest that (a) for VLSW performance determination, software architecture is as important as hardware architecture, and (b) alternatives to the client server architecture provide significantly better scalability.

A Low Complexity ICI Cancellation Method for High Mobility OFDM Systems

Orthogonal frequency division multiplexing (OFDM) can significantly reduce a receiver complexity using one-tap frequency domain equalizer in a frequency-selective fading channel. However, the channel variation due to high mobility gives a time-selectivity in one OFDM symbol. As a result, each OFDM subcarrier experiences inter-carrier interference (ICI) that makes the equalization process very complicate. We propose a low complexity ICI cancellation method that takes advantage of the ICI power distribution. Instead of computing the inversion of the whole channel frequency response (CFR) matrix, we split the matrix into several small sub-matrices that contain significant channel information and perform a linear minimum mean squared error (LMMSE) equalization. We also propose an efficient successive interference cancellation (SIC) method that can effectively utilize a time diversity resulting from the mobility. Simulation results show that the performance improvements is remarkable even though the sub-matrix size is much smaller than that of the whole CFR matrix while taking high computational efficiency

A workflow mining method through model rewriting

This work presents a workflow process mining method that is, at least, as powerful as many others presented in the literature, as measured by the examples presented in the literature. The method is based on a grammar of rewriting expressions, by which a model is adapted to include a new execution trace. We also discuss the intrinsic limits of the mining process, which we believe has not been a topic clearly stated and discussed in the published research.

A model-driven workflow fragmentation framework for collaborative workflow architectures and systems

This paper focuses on a workflow distribution methodology for rationally deploying workflow models onto a distributed workflow system running on cloud computing environments, and we particularly lay a stress upon that those workflow systems operable on cloud computing environments are dubbed collaborative workflow systems, which are not only built upon the collaborative workflow architectures proposed in the paper, but pursuing the so-called collaborative computing paradigm characterized by focusing collaboration over cloud computing environments. The essential idea of the workflow distribution methodology is about how to fragment a workflow model and how to allocate its fragments to each of the architectural components configuring the underlying collaborative workflow architecture and system. As a reasonable solution to realize the essential idea, the paper proposes a model-driven workflow fragmentation framework, which provides a series of fragmentation algorithms that semantically fragmentate a workflow model by considering the semantic factors - performer, role, control-flow, data-flow, etc. - of the ICN-based workflow model as fragmentation criteria. The algorithms are classified into the vertical fragmentation approach, the horizontal fragmentation approach, and the hybrid approach of both. Conclusively, this paper conceives a possible set of collaborative workflow architectures embedding the collaborative computing paradigm, and describes the detailed formalism of the framework and about how the framework works on those collaborative workflow architectures and systems.

OFDM Channel Estimation for the Amply-and-Forward Cooperative Channel

OFDM can significantly reduce the receiver complexity by using one-tap equalizer in frequency selective channels, and it can be enhanced the capacity and diversity by using cooperative scheme. However, high data rate transmission with OFDM requires the channel state information (CSI), and therefore OFDM channel estimation for cooperative communication can be a challenging problem. In this paper, we investigate the OFDM channel estimation suitable for the amply-and-forward (AF) cooperative channel. Cramer-Rao lower bound (CRB) of the estimation is derived, and optimal preamble conditions that minimize the CRB with several power constraints are proposed. Moreover, minimum variance unbiased (MVU) estimator that achieves the CRB is introduced for the cooperative channel. Simulations with analytical results confirm the performance of our methods.

A layered workflow knowledge Grid/P2P architecture and its models for future generation workflow systems

This paper addresses the scalability issue on workflow systems using the knowledge Grid/P2P computing environment. As a feasible solution for the scalability issue, we design a Layered Workflow Knowledge Grid/P2P Architecture. The architecture is based upon a conceptual and generic framework that considers various perspectives for creating workflow knowledge Grid/P2P architectures. In order to show how the architecture is applicable in making up a future generation workflow system, we propose three types of workflow knowledge models and a physical architecture that is possibly configurable from the layered workflow knowledge Grid/P2P architecture. That is, the architecture has a 3-layered hierarchical configuration-procedure-layer, role-layer, and actor-layer-and the three workflow knowledge models-ICN-based model, role-based model, and actor-based model-are the workflow knowledge representations on Grid/P2P nodes in the corresponding layers, respectively. In particular, the most critical rationale of this paper is based on the fact that the nature of the knowledge Grid/P2P computing environment ought to be fitted very well into building a platform for future generation workflow systems characterized by maximally parallel and very large scale workflow procedures that are frequently observed in very large scale enterprizes. And we strongly believe that the rationale must be acceptable. Conclusively, the architecture and the models proposed in this paper shall be useful for not only maximizing the usability of computing facilities in the enterprize but also appropriately handling the scalability issue of its underlined future generation workflow management system in order to cope with massively parallel and very large scale workflow applications.

A Framework: Workflow-Based Social Network Discovery and Analysis

The purpose of this paper is to build a fundamental framework of discovering and analyzing a workflow-based social network formed through workflow-based organizational business operations. A little more precisely speaking, the framework formalizes a series of theoretical steps from discovering a workflow-based social network to analyzing the discovered social network. For the sake of the discovery phase, we conceive an algorithm that is able to automatically discover the workflow-based social network from a workflow procedure; while on the other hand, in the analysis phase we apply the degree centrality algorithm to the discovered social network, which is one of the well-known social network analysis algorithms in the literature. Consequently, the crucial implication of the framework is in quantifying the degree of work-intimacy among performers who are involved in enacting the corresponding workflow procedure. Also, as a conceptual extension of the framework, it can be applied to discovering and analyzing d gree centrality or collaborative closeness and betweenness among architectural components and nodes of collaborative cloud workflow computing environments.

Beyond workflow mining

In the domain of Business Process Management and Workflow Management Systems, the log of work transactions executed has been found to be a useful artifact. The ideas, work, and literature on workflow mining have been primarily concerned with examining the workflow event log to rediscover control flow. Workflow mining has generally been defined as “the process of extracting a workflow model from a log of executions of activities”. In fact, most of the literature specifically and narrowly is concerned with rediscovering the precedence relations amongst activities. It is generally a hidden assumption that all activities are known a priori because they are listed by label in the workflow event log. In this position paper, we explore the possibility of removing this assumption, and thus performing workflow discovery rather than precedence rediscovery. Workflow discovery does not assume that process structure or even activities are known a priori and is concerned with discovering a wholistic perspective of workflow. Workflow management systems are people systems that must be designed, deployed, and understood within their social and organizational contexts. Thus, we argue in this document that there is a need to expand the concept of workflow mining beyond the behavioral perspective to encompass the social, organizational, and activity assignment perspectives; as well as other perspectives. To this end, we introduce a general framework and meta-model for workflow discovery, and show one approach to workflow discovery in a multidimensional perspective.

Control-path Oriented Workflow Intelligence Analyses

This paper proposes two kinds of control-path oriented workflow knowledge analysis approaches which will be applied to a workflow intelligence and quality improvement framework aiming at the high degree of the workflow traceability and rediscoverability. The framework needs two kinds of algorithms ⎯ One is for generating the total sequences of the control-paths from a workflow model, and the other is for rediscovering the runtime enactment history of each control-path out of the total sequences from the corresponding workflow’s execution logs. The proposed approaches have something to do with the former, and each of them include a set of detailed algorithms for analyzing the total sequences of the control-path perspective of a workflow model. Eventually, the analyzed results from the approaches will be effectively used for some rediscovery approaches to rediscover control-path oriented workflow intelligence from the runtime enactment history, which is called workflow events log. Based upon those control-path oriented workflow intelligences, it will be realized for a workflow model not only to be gradually refined, but also to be finally revealed to maximize its quality by repeatedly redesigning, reengineering and/or refining during its whole life-long time period.

A XML-Based workflow event logging mechanism for workflow mining

In this paper, we propose a XML-based workflow event logging mechanism, and describe the implementation details of the mechanism so as to be embedded into the e-Chautauqua system that has been recently developed by the CTRL research group as a very large scale workflow management system. Finally, we explain how the XML-based workflow event logs will be applied and used to the workflow mining and rediscovery framework.