arXiv subjects

Claudio Di Ciccio

Publications and source records attributed to Claudio Di Ciccio.

30 records · Page 2Linked to original sources

arXiv2022

Variants in managing supply chains on distributed ledgers

Smart contracts show a high potential for ensuring that Supply Chain Management strategies make a qualitative leap toward higher levels of optimality, not only in terms of efficiency and profitability but also in the aggregation of skills aimed at creating the best products and services to bring to the market. In this article, we illustrate an architecture that employs smart contracts to implement various algorithmic versions of the Income Sharing principle between companies participating in a supply chain. We implement our approach on Hyperledger Fabric, the most widespread platform for private and consortium distributed ledgers, and discuss its suitability to our purposes by comparing this design choice with the alternative given by public blockchains, with particular attention to Ethereum.

cs.DC↗

arXiv2022

Computing unsatisfiable cores for LTLf specifications

Linear-time temporal logic on finite traces (LTLf) is rapidly becoming a de-facto standard to produce specifications in many application domains (e.g., planning, business process management, run-time monitoring, reactive synthesis). Several studies approached the respective satisfiability problem. In this paper, we investigate the problem of extracting the unsatisfiable core in LTLf specifications. We provide four algorithms for extracting an unsatisfiable core leveraging the adaptation of state-of-the-art approaches to LTLf satisfiability checking. We implement the different approaches within the respective tools and carry out an experimental evaluation on a set of reference benchmarks, restricting to the unsatisfiable ones. The results show the feasibility, effectiveness, and complementarities of the different algorithms and tools.

cs.LO↗

arXiv2021

Detection of statistically significant differences between process variants through declarative rules

Services and products are often offered via the execution of processes that vary according to the context, requirements, or customisation needs. The analysis of such process variants can highlight differences in the service outcome or quality, leading to process adjustments and improvement. Research in the area of process mining has provided several methods for process variants analysis. However, very few of those account for a statistical significance analysis of their output. Moreover, those techniques detect differences at the level of process traces, single activities, or performance. In this paper, we aim at describing the distinctive behavioural characteristics between variants expressed in the form of declarative process rules. The contribution to the research area is two-pronged: the use of declarative rules for the explanation of the process variants and the statistical significance analysis of the outcome. We assess the proposed method by comparing its results to the most recent process variants analysis methods. Our results demonstrate not only that declarative rules reveal differences at an unprecedented level of expressiveness, but also that our method outperforms the state of the art in terms of execution time.

cs.SE↗

arXiv2021

Visual Drift Detection for Sequence Data Analysis of Business Processes

Event sequence data is increasingly available in various application domains, such as business process management, software engineering, or medical pathways. Processes in these domains are typically represented as process diagrams or flow charts. So far, various techniques have been developed for automatically generating such diagrams from event sequence data. An open challenge is the visual analysis of drift phenomena when processes change over time. In this paper, we address this research gap. Our contribution is a system for fine-granular process drift detection and corresponding visualizations for event logs of executed business processes. We evaluated our system both on synthetic and real-world data. On synthetic logs, we achieved an average F-score of 0.96 and outperformed all the state-of-the-art methods. On real-world logs, we identified all types of process drifts in a comprehensive manner. Finally, we conducted a user study highlighting that our visualizations are easy to use and useful as perceived by process mining experts. In this way, our work contributes to research on process mining, event sequence analysis, and visualization of temporal data.

cs.HC↗

arXiv2020

Conformance Checking of Mixed-paradigm Process Models

Mixed-paradigm process models integrate strengths of procedural and declarative representations like Petri nets and Declare. They are specifically interesting for process mining because they allow capturing complex behaviour in a compact way. A key research challenge for the proliferation of mixed-paradigm models for process mining is the lack of corresponding conformance checking techniques. In this paper, we address this problem by devising the first approach that works with intertwined state spaces of mixed-paradigm models. More specifically, our approach uses an alignment-based replay to explore the state space and compute trace fitness in a procedural way. In every state, the declarative constraints are separately updated, such that violations disable the corresponding activities. Our technique provides for an efficient replay towards an optimal alignment by respecting all orthogonal Declare constraints. We have implemented our technique in ProM and demonstrate its performance in an evaluation with real-world event logs.

cs.FL↗

arXiv2020

The Internet-of-Things Meets Business Process Management: A Manifesto

The Internet of Things (IoT) refers to a network of connected devices collecting and exchanging data over the Internet. These things can be artificial or natural, and interact as autonomous agents forming a complex system. In turn, Business Process Management (BPM) was established to analyze, discover, design, implement, execute, monitor and evolve collaborative business processes within and across organizations. While the IoT and BPM have been regarded as separate topics in research and practice, we strongly believe that the management of IoT applications will strongly benefit from BPM concepts, methods and technologies on the one hand; on the other one, the IoT poses challenges that will require enhancements and extensions of the current state-of-the-art in the BPM field. In this paper, we question to what extent these two paradigms can be combined and we discuss the emerging challenges.

cs.CY↗

arXiv2020

Entropia: A Family of Entropy-Based Conformance Checking Measures for Process Mining

This paper presents a command-line tool, called Entropia, that implements a family of conformance checking measures for process mining founded on the notion of entropy from information theory. The measures allow quantifying classical non-deterministic and stochastic precision and recall quality criteria for process models automatically discovered from traces executed by IT-systems and recorded in their event logs. A process model has "good" precision with respect to the log it was discovered from if it does not encode many traces that are not part of the log, and has "good" recall if it encodes most of the traces from the log. By definition, the measures possess useful properties and can often be computed quickly.

cs.AI↗

arXiv2020

Foundational Oracle Patterns: Connecting Blockchain to the Off-chain World

Blockchain has evolved into a platform for decentralized applications, with beneficial properties like high integrity, transparency, and resilience against censorship and tampering. However, blockchains are closed-world systems which do not have access to external state. To overcome this limitation, oracles have been introduced in various forms and for different purposes. However so far common oracle best practices have not been dissected, classified, and studied in their fundamental aspects. In this paper, we address this gap by studying foundational blockchain oracle patterns in two foundational dimensions characterising the oracles: (i) the data flow direction, i.e., inbound and outbound data flow, from the viewpoint of the blockchain; and (ii) the initiator of the data flow, i.e., whether it is push or pull-based communication. We provide a structured description of the four patterns in detail, and discuss an implementation of these patterns based on use cases. On this basis we conduct a quantitative analysis, which results in the insight that the four different patterns are characterized by distinct performance and costs profiles.

cs.SE↗

arXiv2020

Monotone Precision and Recall Measures for Comparing Executions and Specifications of Dynamic Systems

The behavioural comparison of systems is an important concern of software engineering research. For example, the areas of specification discovery and specification mining are concerned with measuring the consistency between a collection of execution traces and a program specification. This problem is also tackled in process mining with the help of measures that describe the quality of a process specification automatically discovered from execution logs. Though various measures have been proposed, it was recently demonstrated that they neither fulfil essential properties, such as monotonicity, nor can they handle infinite behaviour. In this paper, we address this research problem by introducing a new framework for the definition of behavioural quotients. We proof that corresponding quotients guarantee desired properties that existing measures have failed to support. We demonstrate the application of the quotients for capturing precision and recall measures between a collection of recorded executions and a system specification. We use a prototypical implementation of these measures to contrast their monotonic assessment with measures that have been defined in prior research.

cs.FL↗

arXiv2019

Comprehensive Process Drift Detection with Visual Analytics

Recent research has introduced ideas from concept drift into process mining to enable the analysis of changes in business processes over time. This stream of research, however, has not yet addressed the challenges of drift categorization, drilling-down, and quantification. In this paper, we propose a novel technique for managing process drifts, called Visual Drift Detection (VDD), which fulfills these requirements. The technique starts by clustering declarative process constraints discovered from recorded logs of executed business processes based on their similarity and then applies change point detection on the identified clusters to detect drifts. VDD complements these features with detailed visualizations and explanations of drifts. Our evaluation, both on synthetic and real-world logs, demonstrates all the aforementioned capabilities of the technique.

cs.AI↗

arXiv2018

QRFA: A Data-Driven Model of Information-Seeking Dialogues

Understanding the structure of interaction processes helps us to improve information-seeking dialogue systems. Analyzing an interaction process boils down to discovering patterns in sequences of alternating utterances exchanged between a user and an agent. Process mining techniques have been successfully applied to analyze structured event logs, discovering the underlying process models or evaluating whether the observed behavior is in conformance with the known process. In this paper, we apply process mining techniques to discover patterns in conversational transcripts and extract a new model of information-seeking dialogues, QRFA, for Query, Request, Feedback, Answer. Our results are grounded in an empirical evaluation across multiple conversational datasets from different domains, which was never attempted before. We show that the QRFA model better reflects conversation flows observed in real information-seeking conversations than models proposed previously. Moreover, QRFA allows us to identify malfunctioning in dialogue system transcripts as deviations from the expected conversation flow described by the model via conformance analysis.

cs.IR↗

arXiv2018

Blockchains for Business Process Management - Challenges and Opportunities

Blockchain technology promises a sizable potential for executing inter-organizational business processes without requiring a central party serving as a single point of trust (and failure). This paper analyzes its impact on business process management (BPM). We structure the discussion using two BPM frameworks, namely the six BPM core capabilities and the BPM lifecycle. This paper provides research directions for investigating the application of blockchain technology to BPM.

cs.SE↗