Business process management employs process models and event logs to represent the behavior of the information systems under study. Traditional case-centric notions consider the order of activities and events in isolated process instances. The emerging field of object-centric processes challenges this assumption by putting objects in the center. Object-centric process mining and modeling approaches identify the structure of co-evolving data objects that influence the behavior of an information system to provide a comprehensive view of the system behavior. Object-centricity has been investigated independently in process modeling and in process mining, which resulted in the coexistence of seemingly contradictory assumptions and definitions. As a community effort, this research manifesto relates and aligns existing terminologies, definitions, and perspectives to provide a common ground for current and future research in object-centric business process management. Based on the current state of research, we propose a conceptualization that sets process models and event logs in relation to the information system’s behavior and the execution data it generates. The conceptualization aims at aligning different terminologies and, thus, providing a basis to model and analyze behavioral characteristics. Building on this common ground, we identify open research challenges along the most relevant research areas in object-centric process management. For each research area, its current status is investigated and an outline of the most relevant research challenges is presented.

Blockchain sharding enhances transaction throughput for vote-based blockchains while preserving system decentralization. Existing sharded blockchains can only maintain reasonable performance by tolerating a smaller magnitude of adversarial nodes compared to the upper-bound adversarial population of non-sharded ones. They lack adequate distinction of adversarial behaviors, with safety attacks posing catastrophic consequences and liveness attacks being more manageable. This paper presents WaveSpreader, a novel blockchain sharding protocol in a synchronous communication network that tolerates the same upper-bound adversarial population as non-sharded blockchains. It achieves a notable transaction throughput with the help of a robust mitigation method that effectively mitigates the liveness attacks. Our study provides a comprehensive security analysis and experimental results confirming the efficacy of WaveSpreader.

Test-driven declarative process modelling combines process models with test traces and has been introduced as a means to achieve both the flexibility provided by the declarative approach and the comprehensibility of the imperative approach. Open test-driven modelling adds a notion of context to tests, specifying the activities of concern in the model, and has been introduced as a means to support both iterative test-driven modelling, where the model can be extended without having to change all tests, and unit testing, where tests can define desired properties of parts of the process without needing to reason about the details of the whole process. The openness however makes checking a test more demanding, since actions outside the context are allowed at any point in the test execution and therefore many different traces may validate or invalidate an open test. In this paper we combine previously developed static techniques for effective open test-driven modelling for Dynamic Condition Response Graphs with a novel efficient implementation of dynamic checking of open tests based on alignment checking. We illustrate the static techniques on an example based on a real-life cross-organizational case management system and benchmark the dynamic checking on models and tests of varying size.

Abstract Smart contracts executed on blockchains are interactive programs where external actors generate events that trigger function invocations. Events can be emitted by participants asynchronously. However, some functionalities should be restricted to participants inhabiting specific roles in the system, which might be dynamically adjusted while the system evolves. We argue that current smart contract languages adopting imperative programming paradigms require additional complicated access control code. Furthermore, smart contracts are often developed and evolved independently and cannot share a joint access control policy. This makes it challenging to ensure the correctness of access control properties and to maintain correctness when the contracts are adapted. We propose using dynamic condition response (DCR) graphs for role‐based and declarative access control for smart contracts and techniques for test‐driven modelling and refinement of DCR graphs to support the safe design and evolution of smart contracts. We show that they allow for capturing and visualizing a form of dynamic access control where access rights evolve as the contract state progresses. Their use supports the straightforward declaration of access control rights, improved code auditing, test‐driven modelling, and safe evolution of smart contracts and improves users' understanding.

Object-centric process mining views processes and traces as an interaction between many objects, each with their own life cycle, as opposed to being centred around the concept of a single case. Instead of describing implicit process flows, declarative process modelling focuses on the description of processes as a set of explicit rules or constraints. The declarative Dynamic Condition Response (DCR) Graphs notation has seen wide industry adoption, in particular in the Danish public sector, has seen significant work on the development of methods for the modelling, verification, and enactment of collaborative processes, and has led to the development of the award winning DisCoveR process miner. In this paper we apply object-centric concepts to DCR Graphs modelling and mining, in particular we: (1) show an extension to DCR Graphs that allows capturing of object-centric process relations and (2) introduce a process discovery method for such object-centric DCR Graphs. We showcase these contributions on the BPIC2017 loan application log.

Most contemporary process discovery methods take as inputs only positive examples of process executions, and so they are one-class classification algorithms. However, we have found negative examples to also be available in industry, hence we build on earlier work that treats process discovery as a binary classification problem. This approach opens the door to many well-established methods and metrics from machine learning, in particular to improve the distinction between what should and should not be allowed by the output model. Concretely, we (1) present a verified formalisation of process discovery as a binary classification problem; (2) provide cases with negative examples from industry, including real-life logs; (3) propose the Rejection Miner binary classification procedure, applicable to any process notation that has a suitable syntactic composition operator; (4) implement two concrete binary miners, one outputting Declare patterns, the other Dynamic Condition Response (DCR) graphs; and (5) apply these miners to real world and synthetic logs obtained from our industry partners and the process discovery contest, showing increased output model quality in terms of accuracy and model size.

Asylum is a legal protection granted by a state to individuals who demonstrate a well-founded fear of persecution or who face real risk of being subjected to torture in their country. However, asylum adjudication often depends on the decision maker's subjective assessment of the applicant's credibility. To investigate potential sources of bias in asylum adjudication practices researchers have used statistics and machine learning models, finding significant sources of variation with respect to a number of extra-legal variables. In this paper, we analyse an original dataset of Danish asylum decisions from the Refugee Appeals Board to understand the variables that explain Danish Adjudication. We train a number of classifiers and, while all classifiers agree that candidate credibility is the single most important variable, we find that performance and variable importance change significantly depending on whether data imbalance and temporality are taken into account. We discuss the implications of our findings with respect to the theory and practice of predicting and explaining asylum adjudication.

Smart contracts executed on blockchains are interactive programs where external actors generate events that trigger function invocations. Events can be emitted by participants asynchronously. However, some functionalities should be restricted to participants inhabiting specific roles in the system, which might be dynamically adjusted while the system evolves. We argue that current smart contract languages adopting imperative programming paradigms require additional complicated access control code. Furthermore, smart contracts are often developed independently and cannot share a joint access control policy. We propose to use Dynamic Condition Response Graphs for role-based and declarative access control for smart contracts. We show that they allow to capture and visualize a form of dynamic access control where access rights evolve as the contract state progresses. Their use supports straight-forward declaration of access control rights, improved code auditing, programming error reduction and improves users’ understanding of smart contracts.

Complex process models can hinder the comprehension of the underlying business processes. While several metrics have been suggested in the literature to evaluate the complexity of imperative process models, little is known about their declarative counterparts. In this paper, we address this gap through a suite of metrics that we propose to capture the complexity of declarative process models. Following this, we empirically investigate the impact of complexity, as measured by the suggested metrics, on users’ cognitive load when comprehending declarative process models. Therein, we use a multi-modal approach including eye-tracking and electrodermal activity. The findings of the empirical study provide evidence about the cognitive load emerging as a result of increased model complexity. Overall, the outcome of this paper presents empirically validated metrics to evaluate the complexity of declarative process models. Implementing these metrics and incorporating them in intelligent modeling tools would help assessing the complexity of declarative process models before being deployed. Furthermore, our empirical approach can be adopted by researchers in upcoming empirical studies to provide a multi-perspective assessment of users’ cognitive load when engaging with process models.

Blockchain sharding splits a blockchain into several shards where consensus is reached at the shard level rather than over the entire blockchain. It improves transaction throughput and reduces the computational resources required of individual nodes. But a derivation of trustworthy consensus within a shard becomes an issue as the longest chain based mechanisms used in conventional blockchains can no longer be used. Instead, a vote-based consensus mechanism must be employed. However, existing vote-based Byzantine fault tolerance consensus protocols do not offer sufficient security guarantees for sharded blockchains. First, when used to support consensus where only one block is allowed at a time (binary consensus), these protocols are susceptible to progress-hindering attacks (i.e., unable to reach a consensus). Second, when used to support a stronger type of consensus where multiple concurrent blocks are allowed (strong consensus), their tolerance of adversary nodes is low. This article proposes a new consensus protocol to address all these issues. We call the new protocol MWPoW +, as its basic framework is based on the existing Multiple Winners Proof of Work (MWPoW) protocol but includes new mechanisms to address the issues mentioned previously. MWPoW+ is a vote-based protocol for strong consensus, asynchronous in consensus derivation but synchronous in communication. We prove that it can tolerate up to f < n /2 adversary nodes in a n-node system as if using a binary consensus protocol and does not suffer from progress-hindering attacks.

Blockchain sharding protocols randomly distribute nodes to different shards. They limit the quantity of shards to ensure that the adversary remains a minority inside each shard with a high probability. There can exist only a small number of shards. In this article, we propose a new sharding protocol that links the number of shards with the adversary population in real-time instead of a fixed upper-bounded population. The protocol is a two-phase design. First, several committee shards are constructed where the majority of nodes inside each are honest with high probability; then, each committee shard randomly splits into several worker shards with a high likelihood that at least one honest node is inside each. Each worker shard handles different transactions. Worker shard blocks that did not pass the unanimous voting are collected and voted by the committee shard using the majority voting. We show that (1) in the worst case (extremely unlikely) when all the transactions need to be handled by the committee shards, the transaction throughput and the data requirement only deteriorate to the same level as classical sharded blockchain; (2) when the worker shards handle most transactions, the overall transaction throughput is zoomed by two magnitudes securely while the data requirement for nodes remains at the same level.

Public authorities make decisions that greatly impact both citizens and non-citizens. Decision-making on asylum, which is regulated by international law but administered by states, in particular is characterised by a higher level of secrecy than other public services. The 1951 Refugee Convention defnes refugeehood as the fear of being persecuted for reasons of race, religion, nationality, social group, or political opinion. Although fear of gender-related persecution was not included as one of the grounds meriting asylum, state practice means that it is today generally recognised as such. The United Nations Refugee Agency (UNHCR) recommends that states "ensure a gender-sensitive interpretation of the 1951 Refugee Convention." Using natural language processing (NLP) to analyse an open dataset of Danish asylum case summaries, we frst identify fve empirical categories connected to gender in the case summaries: 1) gender-related persecution, 2) LGBT 3) sexual conditions, 4) marital conditions and 5) other gender-related forms of persecution. Secondly, we illustrate the relationship between these gender-related categories and other categories/topics in asylum motives. Finally, we discuss how data science techniques can be applied to better understand complex, cooperative work practices in an area where access for researchers is limited, but archival data is available.

The research outlined in this paper is focusing on understanding asylum decision-making and outcome variations across the Nordic countries. As a preliminary study, we extract data from an open dataset1 of decision summaries from the Danish Refugee Appeals Board. The approach we propose, offers points for discussion of how prototyping context and participation can help raise questions about such data and engage stakeholders. Combining the application of Machine Learning (ML) and Natural Language Processing (NLP) with participatory methods (e.g. critically designed artefacts) enable us to 1) move beyond “obvious” ML-application areas, 2) through sensemaking of data with stakeholders, and 3) co-develop approaches to data science from a CSCW-perspective.

Legal compliance is an important part of certifying the correct behaviour of a business process. To be compliant, organizations might hard-wire regulations into processes, limiting the discretion that workers have when choosing what activities should be executed in a case. Worse, hard-wired compliant processes are difficult to change when laws change, and this occurs very often. This paper proposes a model-driven approach to process compliance and combines a) reference models from laws, and b) business process models. Both reference and process models are expressed in a declarative process language, The Dynamic Condition Response (DCR) graphs. They are subject to testing and verification, allowing law practitioners to check consistency against the intent of the law. Compliance checking is a combination of alignments between events in laws and events in a process model. In this way, a reference model can be used to check different process variants. Moreover, changes in the reference model due to law changes do not necessarily invalidate existing processes, allowing their reuse and adaptation. We exemplify the framework via the alignment of laws and business rules and a real contract change management process, Finally, we show how compliance checking for declarative processes is decidable, and provide a polynomial time approximation that contrasts NP complexity algorithms used in compliance checking for imperative business processes. All-together, this paper presents technical and methodological steps that are being used by legal practitioners in municipal governments in their efforts towards digitalization of work practices in the public sector.

We report on a new approach to co-creating adaptive case management systems jointly with end-users, developed in the context of the Effective co-created and compliant adaptive case Management Systems for Knowledge Workers (EcoKnow.org) research project. The approach is based on knowledge from prior ethnographic field studies and research in the declarative Dynamic Condition Response (DCR) technology for model-driven design of case management systems. The approach was tested in an operational environment jointly with the danish municipality of Syddjurs by conducting a service-design project and implementing an open source case manager tool and a new highlighter tool for mapping between textual specifications and the DCR notation. The design method and technologies were evaluated by understandability studies with end-users. The study showed that the development could be done in just 6 months, and that the new highlighter tool in combination with the traditional design and simulation tools, supports domain experts formalise and provide traceability between their interpretations of textual specifications and the formal models.

Enterprise computing applications generally consists of several inter-related business processes linked together via shared data objects and events. We address the open challenge of providing formal modelling and implementation techniques for such enterprise computing applications, introducing the declarative, data-centric and event-driven process language RESEDA for REactive SEmi-structured DAta. The language is inspired by the computational model of spreadsheets and recent advances in declarative business process modelling notations. The key idea is to associate either input events or reactive computation events to the individual elements of semi-structured data and declare reactive behaviour as explicit reaction rules and constraints between these events. Moreover, RESEDA comes with a formal operational semantics given as rewrite rules supporting both formal analysis and persistent execution of the application as sequences of rewrites of the data. The data, along with the set of constraints, thereby at the same time constitutes the specification of the data, its behaviour and the run-time execution component. This key contribution of the paper is to introduce the RESEDA language, its formal execution semantics and give a sufficient condition for liveness of programs. We also establish Turing-equivalence of the language independently of the choice of underlying data expressions and exemplify the use of RESEDA by a running example of an online store. A prototype implementation of RESEDA and the examples of the paper are available on-line at http://dcr.tools/reseda.

We describe how the declarative Dynamic Condition Response (DCR) Graphs process model can be used for trustworthy adaptive case management by leveraging the flexible execution, dynamic composition and adaptation supported by DCR Graphs. The dynamically composed and adapted graphs are verified for deadlock freedom and liveness in the SPIN model checker by utilizing a mapping from DCR Graphs to PROMELA code. We exemplify the approach by a small workflow extracted from a field study at a danish hospital.

We present a case study of the use of Dynamic Condition Response (DCR) Graphs, a recently introduced declarative business process model, in the design of a cross-organizational case management system being developed by Exformatics A/S, a Danish provider of knowledge and workflow management systems. We show how DCR Graphs allow to capture directly both the behavioral constraints identified during meetings with the customer and the operational execution as markings of the graph. In comparison, imperative models such as BPMN, Petri Net, UML Sequence or Activity diagrams are only good at describing the operational way to fulfill the constraints, leaving the constraints implicit. In particular, we point out that the BPMN ad-hoc sub process activity, intended to support more loosely structured goal driven ad-hoc processes, is inconsistently described in the final version of the BPMN 2.0 standard. The case study motivated an extension of the DCR Graphs model to nested graphs and the development of graphical design and simulation tools to increase the understanding of the models. The study also revealed a number of challenges for future research in techniques for model-driven design of cross-organizational process-aware information systems combining declarative and imperative models.