Summary Blockchain platforms, such as Ethereum, allow a set of actors to maintain a ledger of transactions without relying on a central authority and to deploy programs, called smart contracts, that are executed whenever certain transactions occur. These features can be used as basic building blocks for executing collaborative business processes between mutually untrusting parties. However, implementing business processes using the low‐level primitives provided by blockchain platforms is cumbersome and error‐prone. In contrast, established business process management systems (BPMSs), such as those based on the standard Business Process Model and Notation (BPMN), provide convenient abstractions for rapid development of process‐oriented applications. This article demonstrates how to combine the advantages of a BPMS with those of a blockchain platform. The article introduces a blockchain‐based BPMN execution engine, named Caterpillar. Like any BPMN execution engine, Caterpillar supports the creation of instances of a process model and allows users to monitor the state of process instances and to execute tasks thereof. The specificity of Caterpillar is that the state of each process instance is maintained on the (Ethereum) blockchain and the workflow routing is performed by smart contracts generated by a BPMN‐to‐Solidity compiler. The Caterpillar compiler supports a large array of BPMN constructs, including subprocesses, multiple‐instance activities, and event handlers. The paper describes the architecture of Caterpillar and the interfaces it provides to support the monitoring of process instances, the allocation and execution of work items, and the execution of service tasks.

Blockchain technology provides basic building blocks to support the execution of collaborative business processes involving mutually untrusted parties in a decentralized environment. Several research proposals have demonstrated the feasibility of designing blockchain-based collaborative business processes using a high-level notation, such as the Business Process Model and Notation (BPMN), and thereon automatically generating the code artifacts required to execute these processes on a blockchain platform. In this paper, we present the conceptual foundations of model-driven approaches for blockchain-based collaborative process execution and we compare two concrete approaches, namely Caterpillar and Lorikeet.

The integration of business process management with blockchains across organisational borders provides a means to establish transparency of execution and auditing capabilities. To enable process analytics, though, non-trivial extraction and transformation tasks are necessary on the raw data stored in the ledger. In this paper, we describe our approach to retrieve process data from an Ethereum blockchain ledger and subsequently convert those data into an event log formatted according to the IEEE Extensible Event Stream (XES) standard. We show a proof-of-concept software artefact and its application on a data set produced by the smart contracts of a process execution engine stored on the public Ethereum blockchain network.

Blockchain technology enables the execution of collaborative business processes involving mutually untrusted parties. Existing platforms allow such processes to be modeled using high-level notations and compiled into smart contracts that can be deployed on blockchain platforms. However, these platforms brush aside the question of who is allowed to execute which tasks in the process, either by deferring the question altogether or by adopting a static approach where all actors are bound to roles upon process instantiation. Yet, a key advantage of blockchains is their ability to support dynamic sets of actors. This paper presents a model for dynamic binding of actors to roles in collaborative processes and an associated binding policy specification language. The proposed language is endowed with a Petri net semantics, thus enabling policy consistency verification. The paper also outlines an approach to compile policy specifications into smart contracts for enforcement. An experimental evaluation shows that the cost of policy enforcement increases linearly with the number of roles and constraints.

Blockchain technology provides a tamper-proof mechanism to execute inter-organizational business processes involving mutually untrusted parties. Existing approaches to blockchain-based process execution are based on code generation. In these approaches, a process model is compiled into one or more smart contracts, which are then deployed on a blockchain platform. Given the immutability of the deployed smart contracts, these compiled approaches ensure that all process instances conform to the process model. However, this advantage comes at the price of inflexibility. Any changes to the process model require the redeployment of the smart contracts (a costly operation). In addition, changes cannot be applied to running process instances. To address this lack of flexibility, this paper presents an interpreter of BPMN process models based on dynamic data structures. The proposed interpreter is embedded in a business process execution system with a modular multi-layered architecture, supporting the creation, execution, monitoring and dynamic update of process instances. For efficiency purposes, the interpreter relies on compact bitmap-based encodings of process models. An experimental evaluation shows that the proposed interpreted approach achieves comparable or lower costs relative to existing compiled approaches.Index Terms-Blockchain, Model-Driven Engineering, Business Process Management System, Flexible Process Execution Activity [0] Task [3] Sub-Process [5] Call-Activity [4] Multi-Instance Parallel [6] Multi-Instance Sequential [7] Default [10] User [11] Script [12] Service [13] Receive [14] Expanded [11] Collapsed [12] Event-SubProcess [13] Gateway [1] Join / Split [3] Exclusive (XOR) [4] 1 function executeElements(iDataAddr, eInd) public2 if (msg.sender != (iDataAddr or this)) 3 throw 'REJECTED' 4 iFlowAddr = IData(iDataAddr).getIFlowNode(); 5 pState = IData(iDataAddr).getSubProcessState(); 6 queue = new Queue(eInd); 7 while(!queue.isEmpty()) 8 eInd = queue.pop(); 9 (preC, postC, typeInfo) = IFlow(iFlowAddr).find(eInd); 10 if (!isEnabled(preC, typeInfo, pState)) 11 continue; 12 removeTokens(pState, preC); 13 switch (typeInfo) 14 case PARALLEL_MULTI_INST: 15 for(i = 1 to IFlow(iFlowAddr).getCountInst(eInd)) 16 this.createInst(eInd, iDataAddr);