J. Leon Zhao

Can e-learning replace classroom learning?

In an e-learning environment that emphasizes learner-centered activity and system interactivity, remote learners can outperform traditional classroom students.

Workflow Automation: Overview and Research Issues

Workflow management systems, a relatively recent technology, are designed to make work more efficient, integrate heterogeneous application systems, and support interorganizational processes in electronic commerce applications. In this paper, we introduce the field of workflow automation, the subject of this special issue of Information Systems Frontiers. In the first part of the paper, we provide basic definitions and frameworks to aid understanding of workflow management technologies. In the remainder of the paper, we discuss technical and management research opportunities in this field and discuss the other contributions to the special issue.

Formulating the Data-Flow Perspective for Business Process Management

Workflow technology has become a standard solution for managing increasingly complex business processes. Successful business process management depends on effective workflow modeling and analysis. One of the important aspects of workflow analysis is the data-flow perspective because, given a syntactically correct process sequence, errors can still occur during workflow execution due to incorrect data-flow specifications. However, there have been only scant treatments of the data-flow perspective in the literature and no formal methodologies are available for systematically discovering data-flow errors in a workflow model. As an indication of this research gap, existing commercial workflow management systems do not provide tools for data-flow analysis at design time. In this paper, we provide a data-flow perspective for detecting data-flow anomalies such as missing data, redundant data, and potential data conflicts. Our data-flow framework includes two basic components: data-flow specification and data-flow analysis; these components add more analytical rigor to business process management.

A case-based reasoning framework for workflow model management

In order to support efficient workflow design, recent commercial workflow systems are providing templates of common business processes. These templates, called cases, can be modified individually or collectively into a new workflow to meet the business specification. However, little research has been done on how to manage workflow models, including issues such as model storage, model retrieval, model reuse and assembly. In this paper, we propose a novel framework to support workflow modeling and design by adapting workflow cases from a repository of process models. Our approach to workflow model management is based on a structured workflow lifecycle and leverages recent advances in model management and case-based reasoning techniques. Our contributions include a conceptual model of workflow cases, a similarity flooding algorithm for workflow case retrieval, and a domain-independent AI planning approach to workflow case composition. We illustrate the workflow model management framework with a prototype system called Case-Oriented Design Assistant for Workflow Modeling (CODAW).

Automatic discovery of similarity relationships through Web mining

This work demonstrates how the World Wide Web can be mined in a fully automated manner for discovering the semantic similarity relationships among the concepts surfaced during an electronic brainstorming session, and thus improving the accuracy of automated clustering meeting messages. Our novel Context Sensitive Similarity Discovery (CSSD) method takes advantage of the meeting context when selecting a subset of Web pages for data mining, and then conducts regular concept co-occurrence analysis within that subset. Our results have implications on reducing information overload in applications of text technologies such as email filtering, document retrieval, text summarization, and knowledge management.

Services computing as the foundation of enterprise agility: Overview of recent advances and introduction to the special issue

The advancement of web services in the last few years has spurred a number of revolutionary concepts in information technology and management including service-oriented architectures, service-oriented computing, and services science, management and engineering, which can be collectively called as “services computing.” Services computing is a new research field that goes beyond traditional computing disciplines as it includes not only architectural, programming, deployment, and other engineering issues, but also management issues such as business component modeling, business process design, and service delivery. In this paper, we provide an overview of emerging research concepts in services computing without attempting to unify them as it will take sometime for the field to become mature. In addition, we take a position that the ultimate goal of services computing is to create the necessary technological and managerial foundation to support enterprise agility. In this short paper, we give an overview of services computing, describe its relationship to enterprise agility, and discuss basic technical and managerial issues. Finally, we introduce the papers that are published in this special issue.

Applying Propositional Logic to Workflow Verification

The increasing complexity of business processes in the era of e-business has heightened the need for workflow verification tools. However, workflow verification remains an open and challenging research area. As an indication, most of commercial workflow management systems do not yet provide workflow designers with formal workflow verification tools. We propose a logic-based verification method that is based on a well-known formalism, i.e., propositional logic. Our logic-based workflow verification approach has distinct advantages such as its rigorous yet simplistic logical formalism and its ability to handle generic activity-based process models. In this paper, we present the theoretical framework for applying propositional logic to workflow verification and demonstrate that logic-based workflow verification is capable of detecting process anomalies in workflow models.

Editorial: web services and process management: a union of convenience or a new area of research?

Two recent trends are reshaping the research landscape in business process management. One such trend is the adoption of process-driven application integration by major e-business middleware vendors, and the other is the advancement of web services as a universal computing platform. In this paper, we investigate the impact of web services on business process technologies and present our viewpoints on research directions in business process management in the presence of web services. Finally, we introduce the papers published in this Special Issue on Web Service and Process Management.

Global Perspectives on Design Science Research

With four inspiring conferences that took place in Claremont, Pasadena, Atlanta and Philadelphia, the International Conference on Design Science Research in Information Systems and Technology (DESRIST) has developed into a premier conference for design-oriented research in Information Systems. Becoming a truly global conference series, DESRIST was held in St.Gallen, Switzerland, in 2010. DESRIST 2010 brought together researchers and practitioners engaged in design science research from all over the world to provide global perspectives on both design science and design research in the broadest sense.The design science research paradigm has been discussed thoroughly in recent years and is now gaining ground for both building knowledge and improving practice in information systems and several related disciplines. As opposed to natural and social research, design research does not crave ultimate truths, grand theories or general laws, but seeks to identify and understand real-world problemsand propose appropriate, useful solutions. It is commonly believed that design research involves building, investigating and evaluating innovative artefacts such as constructs, frameworks, models, methods, and information system instantiations in order to solve practical problems. Moreover, the study of methods, behaviours, and best practices related to the problem analysis and artefact development process are encompassed commonly referred to as design science.An ongoing debate related to the nature, scope and dominant ideologies of design science research, however, shows that the paradigm is still emerging. Its core, its boundaries and its interplay with other research approaches are increasingly being revealed and defined.The topical theme of DESRIST 2010 is Global Perspectives on Design Science Research. Once more, the DESRIST conference successfully serves as a forum for raising and discussing new ideas in the area of design science research.Among others, the papers submitted to DESRIST 2010 contribute to a better understanding of the interplay between design and organisation, design and information, design and behaviour, and design and collaboration. A number of contributions present design research exemplars, while others illuminate design research techniques or design research organisation. All papers were reviewed by at least two reviewers and the selection process was competitive. In total, 80 papers were submitted, out of which 35 were selected as full research papers (acceptance rate of 44%). Furthermore, ten submissions were accepted as short papers and presented as posters. The submissions came from authors located in 29 different countries, geographically distributed as follows: 59% of the authors are located in Europe, 32.5% in the Americas and 4.25% each in Asia and Australia.In conjunction with the main conference, DESRIST 2010 hosted three workshops on design, enterprise architecture management, and enterprise engineering. Papers accepted for CIAO! (one of these workshops) have been published in a separate volume of Springers Lecture Notes in Business Information Processing series.In addition, three invited keynoters and four panels stimulated the discussions on new and emerging issues in line with the conference topics. The panels addressed the following topics: innovation in design science research, design for use, publishing design science research, and organising design science research. We are thankful for the fruitful and inspiring discussions and the interesting impulses for future relevant work in the field of design science research. We wish to thank all the people who submitted papers to the DESRIST 2010 conference for having shared their work with us. We sincerely hope that you find the papers as interesting and inspiring as we did. Moreover, we owe special thanks to all members of the programme committee of DESRIST 2010 as well as all reviewers for their work. We are also very appreciative to the many people who were involved in the organisation of the DESRIST conference and its accompanying events. We believe that DESRIST 2010 provided detailed insights into the current state of the art, set directions for fruitful further research initiatives and truly contributed to the transfer of academic knowledge for practical problem-solving.

Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks

Time series (particularly multivariate) classification has drawn a lot of attention in the literature because of its broad applications for different domains, such as health informatics and bioinformatics. Thus, many algorithms have been developed for this task. Among them, nearest neighbor classification (particularly 1-NN) combined with Dynamic Time Warping (DTW) achieves the state of the art performance. However, when data set grows larger, the time consumption of 1-NN with DTW grows linearly. Compared to 1-NN with DTW, the traditional feature-based classification methods are usually more efficient but less effective since their performance is usually dependent on the quality of hand-crafted features. To that end, in this paper, we explore the feature learning techniques to improve the performance of traditional feature-based approaches. Specifically, we propose a novel deep learning framework for multivariate time series classification. We conduct two groups of experiments on real-world data sets from different application domains. The final results show that our model is not only more efficient than the state of the art but also competitive in accuracy. It also demonstrates that feature learning is worth to investigate for time series classification.