Blockchain technology offers a sizable promise to rethink the way interorganizational business processes are managed because of its potential to realize execution without a central party serving as a single point of trust (and failure). To stimulate research on this promise and the limits thereof, in this article, we outline the challenges and opportunities of blockchain for business process management (BPM). We first reflect how blockchains could be used in the context of the established BPM lifecycle and second how they might become relevant beyond. We conclude our discourse with a summary of seven research directions for investigating the application of blockchain technology in the context of BPM.

Blockchain is an emerging technology for decentralised and transactional data sharing across a large network of untrusted participants. It enables new forms of distributed software architectures, where agreement on shared states can be established without trusting a central integration point. A major difficulty for architects designing applications based on blockchain is that the technology has many configurations and variants. Since blockchains are at an early stage, there is little product data or reliable technology evaluation available to compare different blockchains. In this paper, we propose how to classify and compare blockchains and blockchain-based systems to assist with the design and assessment of their impact on software architectures. Our taxonomy captures major architectural characteristics of blockchains and the impact of their principal design decisions. This taxonomy is intended to help with important architectural considerations about the performance and quality attributes of blockchain-based systems.

Device failure detection is one of most essential problems in industrial internet of things (IIoT). However, in conventional IIoT device failure detection, client devices need to upload raw data to the central server for model training, which might lead to disclosure of sensitive business data. Therefore, in this paper, to ensure client data privacy, we propose a blockchain-based federated learning approach for device failure detection in IIoT. First, we present a platform architecture of blockchain-based federated learning systems for failure detection in IIoT, which enables verifiable integrity of client data. In the architecture, each client periodically creates a Merkle tree in which each leaf node represents a client data record, and stores the tree root on a blockchain. Further, to address the data heterogeneity issue in IIoT failure detection, we propose a novel centroid distance weighted federated averaging (CDW_FedAvg) algorithm taking into account the distance between positive class and negative class of each client dataset. In addition, to motivate clients to participate in federated learning, a smart contact based incentive mechanism is designed depending on the size and the centroid distance of client data used in local model training. A prototype of the proposed architecture is implemented with our industry partner, and evaluated in terms of feasibility, accuracy and performance. The results show that the approach is feasible, and has satisfactory accuracy and performance.

It is a challenge to ensure data integrity for cloud-based Internet of Things (IoT) applications because of the inherently dynamic nature of IoT data. The available frameworks of data integrity verification with public auditability cannot avoid the Third Party Auditors (TPAs). However, in a dynamic environment, such as the IoT, the reliability of the TPA-based frameworks is far from being satisfactory. In this paper, we propose a blockchain-based framework for Data Integrity Service. Under such framework, a more reliable data integrity verification can be provided for both the Data Owners and the Data Consumers, without relying on any Third Party Auditor (TPA). In this paper, the relevant protocols and a subsequent prototype system, which is implemented to evaluate the feasibility of our proposals, are presented. The performance evaluation of the implemented prototype system is conducted, and the test results are discussed. The work lays a foundation for our future work on dynamic data integrity verification in a fully decentralized environment.

In a blockchain-based system, data and the consensus-based process of recording and updating them over distributed nodes are central to enabling the trustless multi-party transactions. Thus, properly understanding what and how the data are stored and manipulated ultimately determines the degree of utility, performance, and cost of a blockchain-based application. While blockchains enhance the quality of the data by providing a transparent, immutable, and consistent data store, the technology also brings new challenges from a data management perspective. In this paper, we analyse blockchains from the viewpoint of a developer to highlight important concepts and considerations when incorporating a blockchain into a larger software system as a data store. The work aims to increase the level of understanding of blockchain technology as a data store and to promote a methodical approach in applying it to large software systems. First, we identify the common architectural layers of a typical software system with data stores and conceptualise each layer in blockchain terms. Second, we examine the placement and flow of data in blockchain-based applications. Third, we explore data administration aspects for blockchains, especially as a distributed data store. Fourth, we discuss the analytics of blockchain data and trustable data analytics enabled by blockchain. Lastly, we examine the data governance issues in blockchains in terms of privacy and quality assurance.