Shaokun Fan

Demystifying Big Data Analytics for Business Intelligence Through the Lens of Marketing Mix

Big data analytics have been embraced as a disruptive technology that will reshape business intelligence, which is a domain that relies on data analytics to gain business insights for better decision-making. Rooted in the recent literature, we investigate the landscape of big data analytics through the lens of a marketing mix framework in this paper. We identify the data sources, methods, and applications related to five important marketing perspectives, namely people, product, place, price, and promotion, that lay the foundation for marketing intelligence. We then discuss several challenging research issues and future directions of research in big data analytics and marketing related business intelligence in general.

Dual Workflow Nets: Mixed Control/Data-Flow Representation for Workflow Modeling and Verification

A spring-balanced, wall-type bed-frame member, which is adapted to support a portion of a mattress assembly, is pivotable between a vertical position adjacent a wall and a horizontal position upon a floor for use. A plurality of the frame members are needed to support the mattress assembly for a bed. The bed contains no permanent cross members linking the respective bed-frame members, so that the location of the frame members can be laterally changed to accommodate mattress assemblies of differing widths.

Business challenges and research directions of management analytics in the big data era

Big data analytics have been embraced as a disruptive technology that will reshape business intelligence, particularly marketing intelligence, which has have traditionally relied on market surveys to understand consumer behavior and product design. In this paper, we investigate how big data analytics will affect the landscape of business intelligence, leading to big data intelligence. Rooted in the recent literature, we delineate business opportunities and managerial challenges brought forward by the emergence of big data analytics and outline a number of research directions in big data intelligence for business.

A collaborative scheduling approach for service-driven scientific workflow execution

Scientific workflow execution often spans multiple self-managing administrative domains to obtain specific processing capabilities. Existing (global) analysis techniques tend to mandate every domain-specific application to unveil all private behaviors for scientific collaboration. In practice, it is infeasible for a domain-specific application to disclose its process details (as a private workflow fragment) for privacy or security reasons. Consequently, it is a challenging endeavor to coordinate scientific workflows and its distributed domain-specific applications. To address this problem, we propose a collaborative scheduling approach that can deal with temporal dependencies between a scientific workflow and a private workflow fragment. Under this collaborative scheduling approach, a private workflow fragment could maintain the temporal consistency with a scientific workflow in resource sharing and task enactments. Further, an evaluation is also presented to demonstrate the proposed approach for coordinating multiple scientific workflow executions in a concurrent environment.

A process ontology based approach to easing semantic ambiguity in business process modeling

Business process modeling continues to increase in complexity, due, in part, to the dynamic business contexts and complicated domain concepts found in todays global economic environment. Although business process modeling is a critical step in workflow automation that powers business around the world, business process modelers often misunderstand domain concepts or relationships due to their lack of precise domain knowledge. Such semantic ambiguity affects the efficiency and quality of business process modeling. To address this problem, a Process Ontology Based Approach is proposed to ease semantic ambiguity by providing a means to capture rich, semantic information on complex business processes through domain specific ontologies. This approach is grounded in the Bunge-Shanks Framework to semantic disambiguation and evaluated using an expert survey as well as a controlled laboratory experiment.

On design, verification, and dynamic modification of the problem-based scientific workflow model

Abstract A science process is a process to solve complex scientific problems which usually have no mature solving methods. Science processes if modeled in workflow forms, i.e. scientific workflows, can be managed more effectively and performed more automatically. However, most current workflow models seldom take account of specific characteristics of science processes and are not very suitable for modeling scientific workflows. Therefore, a new workflow model named problem-based scientific workflow model (PBSWM) is proposed in this paper to accommodate those specific characteristics. Corresponding soundness verification and dynamic modification are discussed accordingly based on the new modelling method. This paper makes three main contributions: (1) three new constructs are proposed for special logic semantics in science processes; (2) verification is deployed with the consideration from both data-specific perspective and control-specific perspective; and (3) a set of rules are provided to automatically infer passive modifications caused by other modifications.

Introduction to the special issue of ECR on E-business innovation with big data

The past half century has witnessed an unrelenting exponential growth in digital information—generated from the activities of individuals, businesses, mobile phones, social networks and the rapidly emerging Internet-of-Things. We live in an era of ‘‘Big Data’’ that has created both immense opportunities and daunting challenges for innovation on electronic business platforms. Big data initiatives are growing in industry and the academy, with the exploration of new designs, fast and cost-effective systems development, and fascinating new business models. The field has given new life to statistical and artificial intelligence analytics, putting these on the center stage in an explosion of value-creation in the twenty-first century. The eight papers in our special issue of Electronic Commerce Research offer fascinating insights into a wide range of topics, including personalized product recommendation, spam detection in social media, sentiment community detection in social networks, and integration of big data analysis into conceptual modeling. We hope that readers of ECR will enjoy these articles and find this special issue valuable. We would like to thank Chris Westland, Editor-in-Chief of ECR for his guidance in the process of creating this issue and all the authors and reviewers for their valuable contributions that made this special issue possible.

A Workflow Engine-Driven SOA-Based Cooperative Computing Paradigm in Grid Environments

The grid has been proposed as a promising service-oriented platform for increasingly complex cooperative computing. The platforms of service-oriented grids are often Web-based where participants collaborate to achieve a common goal by sharing scarce Web-Based Computational/ Computing Resources (WBCR). To share the WBCR effectively is a challenging problem in boundary-spanning grid environments, particularly when these resources are subject to both static and dynamic usage. To set up the certificate-based usage policy described in this paper, we first explore a workflow engine-driven SOA-based resource access control mechanism. Then, aiming at setting up a cooperative computing paradigm from the resource sharing perspective, an infrastructure derived from a specific project of SOA&EDSCCE (SOA-Based&Engine-Driven Structured Cooperative Computing Environment) is proposed for promoting its cooperative computing in grid environment based on the control disciplines and the WBCR usage policy. The main contributions of this paper are twofold: 1) a workflow engine-driven SOA-based WBCR sharing mechanism is presented in accordance with to the certificate-based usage policy; and 2) a specific infrastructure of cooperative computing is put forward for the collaboration based on the WBCR sharing mechanism.

Enabling effective workflow model reuse

With increasingly widespread adoption of workflow technology as a standard solution to business process management, a large number of workflow models have been put in use in companies in the era of electronic commerce. These workflow models form a valuable resource for workflow domain knowledge, which should be reused to support workflow model design. However, current workflow modeling approaches do not facilitate workflow model reuse as a fundamental requirement, leading to a research gap in effective workflow model reuse. In this paper, we propose a novel approach called Data-centric Workflow Model Reuse framework (DWMR) to provide a solution to workflow model reuse. DWMR compliments existing control-flow-focused workflow modeling approaches by explicitly storing workflow data information, such as data dependency, data task relationships, and data similarity scores. DWMR also provides data-driven workflow model search and composition algorithms to satisfy user query requirements by automatically combining multiple workflow models. We demonstrate the feasibility of the DWMR approach by applying it to data from a well-known industry workflow model repository. We propose a formal data-driven approach (DWMR) to facilitate efficient workflow model reuse.DWMR compliments existing control-flow-focused workflow modeling approaches by explicitly storing workflow data information.DWMR provides data-driven workflow model search and composition algorithms to automatically satisfy user query requirements.The result generated by our approach has a high accuracy in terms of precision and recall, comparing with human experts.

Collaboration Process Pattern Approach to Improving Teamwork Performance: A Data Mining-Based Methodology

It is well documented in management literature that characteristics of collaboration processes strongly influence team performance in a business environment. However, little work has been done on how specific collaboration process patterns affect teamwork performance, leading to an open issue in collaboration management. To address this research gap, we develop a Collaboration Process Pattern CPP approach that analyzes teamwork performance by mining collaboration system logs from open source software development. Our research is novel in three ways. First, our research is fact-driven, as the result is based on teamwork tracking logs. Second, we develop a pattern mining approach based on sequence mining and graph mining. Third, using time-dependent Cox regression, our approach derives business insights from real-world collaboration data that are directly applicable to managerial actions. Our empirical study identifies collaboration patterns that can lead to more efficient teamwork. It also shows that the effects of collaboration patterns vary depending on the types of tasks. These findings are of significant business value since they suggest that managers should carefully prioritize their limited attention on certain types of tasks for intervention. Data and the online supplement are available at https://doi.org/10.1287/ijoc.2016.0739 .