In addition to functionalities, business process management (BPM) involves several key indicators such as openness, security, flexibility, and scalability. Optimizing system performance is becoming a great challenge for an ever-increasing large-scale distributed application system in the digital economy on the Internet of Things (IoT) era. In a centralized BPM, many indicators, such as security and openness, or cost and flexibility, are conflicting with each other. For example, inviting new partners across enterprises, domains, and regions to form a service workflow exposes new risks and needs additional security mechanisms for scrutiny; enhancing the flexibility of business workflow compositions increases the cost of security assurance. Blockchain technology (BCT) has thrown the light on the development of vital solutions to various BPM problems. BCT has to be integrated with other BPM system components that often involve IoT devices to implement specified functionalities related to the application. Currently, the potentials of using BCT have been explored although still at an early stage. In this paper, the states of the art are presented to identify emerging research topics, challenges, and promising applications in integrating BCT into the development of BPM.

Workflow is used to support and connect business processes (BP) in organizations. Historically, it is used to define the control of how tasks are coordinated and executed. Its importance has been continuously increasing with the incorporation of rapidly developed Service-oriented Architecture (SoA), Blockchain, and Internet-of-Thing (IoT); new information technologies have expanded the coverage of workflow across various applications. Workflows often interact with services, where SoA is a key driver to smoot service discovery and provide standards for interoperation. In decentralized collaborative environments, a workflow often deals with disparate services dynamically and interacts with services on demand. This not only unlocks the potentials of workflow applications in business process managements (BPM), but also precipitates significant challenges and has brought considerable attentions to the research community. This paper investigates the state-of-the-art of service workflow modelling and enabling technologies. It begins with the identification and examination of workflow and IoT characteristics; it proposes a workflow architecture to classify existing works on service workflows, and it summarizes the methods for workflow modeling, service interoperation to identify the limitations of existing works and clarify future research directions in using service workflows.

Empirical Bayes methods are designed in selecting massive variables, which may be inter-connected following certain hierarchical structures, because of three attributes: taking prior information on model parameters, allowing data-driven hyperparameter values, and free of tuning parameters. We propose an iterated conditional modes/medians (ICM/M) algorithm to implement empirical Bayes selection of massive variables, while incorporating sparsity or more complicated a priori information. The iterative conditional modes are employed to obtain data-driven estimates of hyperparameters, and the iterative conditional medians are used to estimate the model coefficients and therefore enable the selection of massive variables. The ICM/M algorithm is computationally fast, and can easily extend the empirical Bayes thresholding, which is adaptive to parameter sparsity, to complex data. Empirical studies suggest competitive performance of the proposed method, even in the simple case of selecting massive regression predictors.

Currently, genome-wide association studies (GWAS) are conducted by collecting a massive number of SNPs (i.e., large p) for a relatively small number of individuals (i.e., small n) and associations are made between clinical phenotypes and genetic variation one single-nucleotide polymorphism (SNP) at a time. Univariate association approaches like this ignore the linkage disequilibrium between SNPs in regions of low recombination. This results in a low reliability of candidate gene identification. Here we propose to improve the case-control GWAS approach by implementing linear discriminant analysis (LDA) through a penalized orthogonal-components regression (POCRE), a newly developed variable selection method for large p small n data. The proposed POCRE-LDA method was applied to the Genetic Analysis Workshop 16 case-control data for rheumatoid arthritis (RA). In addition to the two regions on chromosomes 6 and 9 previously associated with RA by GWAS, we identified SNPs on chromosomes 10 and 18 as potential candidates for further investigation.