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Item does not contain fulltext On the Move to Meaningful Internet Systems. OTM 2018 Conferences.Confederated International Conferences: CoopIS, C&TC, and ODBASE 2018, Valletta, Malta, October 22-26, 2018

Hyperledger Fabric is a very large project under the umbrella of the Linux Foundation, with hundreds of developers involved. In this paper we will illustrate how the application of fact-based modeling will help us in understanding some basic features of the blockchain concept as is used in Hyperledger Fabric (HLF) and that it can serve as a conceptual blueprint of HLF for all involved to use.

Process mining is not only about discovery and conformance checking of business processes. It is also focused on enhancing processes to improve the business performance. While from a business perspective this third main stream is definitely as important as the others if not even more, little research work has been conducted. The existing body of work on process enhancement mainly focuses on ensuring that the process model is adapted to incorporate behavior that is observed in reality. It is less focused on improving the performance of the process. This paper reports on a methodology that creates an enhanced model with an improved performance level. The enhancements of the model limit incorporated behavior to only those parts that do not violate any business rules. Finally, the enhanced model is kept as close to the original model as possible. The practical relevance and feasibility of the methodology is assessed through two case studies. The result shows that the process models improved through our methodology, in comparison with state-of the art techniques, have improved KPI levels while still adhering to the desired prescriptive model.

Process mining combines model-based process analysis with data-driven analysis techniques. The role of process mining is to extract knowledge and gain insights from event logs. Most existing techniques focus on process discovery (the automated extraction of process models) and conformance checking (aligning observed and modeled behavior). Relatively little research has been performed on the analysis of business process performance. Cooperative business processes often exhibit a high degree of variability and depend on many factors. Finding root causes for inefficiencies such as delays and long waiting times in such flexible processes remains an interesting challenge. This paper introduces a novel approach to analyze key process performance indicators by considering the process context. A generic context-aware analysis framework is presented that analyzes performance characteristics from multiple perspectives. A statistical approach is then utilized to evaluate and find significant differences in the results. Insights obtained can be used for finding high-impact points for optimization, prediction, and monitoring. The practical relevance of the approach is shown in a case study using real-life data.

In the area of business process modelling, declarative notations have been proposed as alternatives to notations that follow the dominant, imperative paradigm. Yet, the choice between an imperative or declarative style of modelling is not always easy to make. Instead, a mixture of these styles is sometimes preferable. This observation has underpinned recent calls for so-called hybrid process modelling notations. In this paper, we present a formal semantics for these. In our proposal, a hybrid process model is hierarchical, where each of its sub-processes may be specified in either an imperative or declarative fashion. The semantics we provide will allow modelling communities to build on the benefits of existing imperative and declarative modelling notations, instead of spending their energy on inventing new ones.

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Business Process Management is a powerful approach forthe automation of collaborative business processes. Recently conceptshave been introduced to allow batch processing in business processesaddressing the needs of different industries. The existing batch activityconcepts are limited in their flexibility. In this paper we contribute differentstrategies for modeling and executing processes including batch workto improve the flexibility 1) of business processes in general and 2) of thebatch activity concept. The strategies support different flexibility aspects(i.e., variability, looseness, adaptation, and evolution) of batch activities.The strategies provide a systematic approach to categorize existing andfuture batch-enabled BPM systems. Furthermore, the paper provides asystem architecture independent from existing BPM systems, which allowsfor the support of all the strategies. The architecture can be usedwith different process languages and existing execution environments ina non-intrusive manner.

Smart contracts gain rapid exposure since the inception of blockchain technology. Yet there is no unified ontology for smart contracts. Being categorized as coded contracts or substitutes of conventional legal contracts, there is a need to reduce the conceptual ambiguity of smart contracts. We applied enterprise ontology and model driven architectures to abstract smart contracts at the essential, infological and datalogical level to explain the system behind computation and platform independent smart contracts rather than its functional behavior. This conceptual paper introduces commitment-based smart contracts, in which a contract is viewed as a business exchange consisting of a set of reciprocal commitments. A smart contract ensures the automated execution of most of these commitments.

Distributing software processes by software producing organizations (SPOs) is emerging increasingly due to benefits that global software engineering (GSE) brings in terms of cost reduction, leveraging competencies, and market expansion. However, these organizations are facing communication and project control issues that can slow down the overall organization performance. Therefore, SPOs should be able to manage inter-dependencies among the tasks distributed to the globally dispersed teams. We analyze existing works and product software companies’ best practices in coordinating tasks in GSE. This paper specifically focuses on constructing methodological support for task coordination that can be influenced by the situational factors at the companies. The support comprises a framework and a method developed by using a method engineering approach. We introduce the framework that depicts the aspects that should be examined by companies and the method that elaborates the practices to guide companies to coordinate tasks in GSE projects. The validation results show that the framework and the method are accepted by experts regarding completeness and applicability to help SPOs in managing coordination among globally distributed teams.

The analysis of event data is particularly challenging when there is a lot of variability. Existing approaches can detect variants in very specific settings (e.g., changes of control-flow over time), or do not use statistical testing to decide whether a variant is relevant or not. In this paper, we introduce an unsupervised and generic technique to detect significant variants in event logs by applying existing, well-proven data mining techniques for recursive partitioning driven by conditional inference over event attributes. The approach has been fully implemented and is freely available as a ProM plugin. Finally, we validated our approach by applying it to a real-life event log obtained from a multinational Spanish telecommunications and broadband company, obtaining valuable insights directly from the event data.

Contains fulltext : 35890.pdf (Publisher’s version ) (Closed access) On the Move to Meaningful Internet Systems 2006: OTM 2006 Workshops,Montpellier, France,, 29 oktober 2006

Process discovery algorithms typically aim at discovering process models from event logs that best describe the recorded behavior. Often, the quality of a process discovery algorithm is measured by quantifying to what extent the resulting model can reproduce the behavior in the log, i.e. replay fitness. At the same time, there are many other metrics that compare a model with recorded behavior in terms of the precision of the model and the extent to which the model generalizes the behavior in the log. Furthermore, several metrics exist to measure the complexity of a model irrespective of the log. In this paper, we show that existing process discovery algorithms typically consider at most two out of the four main quality dimensions: replay fitness, precision, generalization and simplicity. Moreover, existing approaches can not steer the discovery process based on user-defined weights for the four quality dimensions. This paper also presents the ETM algorithm which allows the user to seamlessly steer the discovery process based on preferences with respect to the four quality dimensions. We show that all dimensions are important for process discovery. However, it only makes sense to consider precision, generalization and simplicity if the replay fitness is acceptable.

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Process mining is a data-driven technique aiming to provide novel insights and help organizations to improve their business processes. In this paper, we focus on the cooperative aspect of process mining, i.e., discovering networks of cooperating resources that together perform processes. We use online streams of events as an input rather than event logs, which are typically used in an off-line setting. We present the Online Cooperative Network (OCN) framework, which defines online cooperative resource network discovery in a generic way. A prototypical implementation of the framework is available in the open source process mining toolkit ProM. By means of an empirical evaluation we show the applicability of the framework in the streaming domain. The techniques presented operate in a real time fashion and are able to handle unlimited amounts of data. Moreover, the implementation allows to visualize network dynamics, which helps in gaining insights in changes in the execution of the underlying business process.

In this paper we will focus on the interplay of mandatory role and set-comparison (equality-, subset- and exclusion-) constraints in fact based modeling. We will present an algorithm that can be used to derive mandatory role constraints in combination with non-implied set-comparison constraints as a result of the acceptance or rejection of real-life user examples by the domain expert.

The entailment of an RDF graph under the RDF Schema standard can easily become too costly to compute and maintain. It is often more desirable to compute on-demand whether a triple exists in the entailment. This is a non-trivial task likely to incur I/O costs, since RDF graphs are often too large to fit in internal memory. As disk I/O is expensive in terms of time, I/O costs should be minimized to achieve better performance. We investigate three physical indexing methods for RDF storage on disk, comparing them using the state of the art RDF Schema entailment algorithm of Muñoz et al. In particular, the I/O behavior during entailment checking over these graph representations is studied. Extensive empirical analysis shows that an enhanced version of the state of the art indexing method, which we propose here, yields in general the best I/O performance.

In this paper we will address the conceptual schema design procedure (CSDP) in fact-based modeling. We will focus on the modeling procedure of 'cook-book' for deriving set-comparison constraints. We will give an algorithm that can be applied by an analyst in an analyst-user dialogue in which all set-comparison constraints can be derived as a result of the acceptance or rejection of real-life user examples by the domain expert.

We present a new approach to anomaly-based network intrusion detection for web applications. This approach is based on dividing the input parameters of the monitored web application in two groups: the "regular" and the "irregular" ones, and applying a new method for anomaly detection on the "regular" ones based on the inference of a regular language. We support our proposal by realizing Sphinx, an anomaly-based intrusion detection system based on it. Thorough benchmarks show that Sphinx performs better than current state-of-the-art systems, both in terms of false positives/false negatives as well as needing a shorter training period.

The last years have seen the definition of many languages, models and standards tailored to specify and enforce access control policies, but such frameworks do not provide methodological support during the policy specification process. In particular, they do not provide facilities for the analysis of the social context where the system operates. In this paper we propose a model-driven approach for the specification and analysis of access control policies. We build this framework on top of SI*, a modeling language tailored to capture and analyze functional and security requirements of socio-technical systems. The framework also provides formal mechanisms to assist policy writers and system administrators in the verification of access control policies and of the actual user-permission assignment.

Two major problems in constructing data federations (for example, data warehouses and database federations) concern achieving and maintaining consistency and a uniform representation of the data on the global level of the federation. The first step in creating uniform representations of data is known as data extraction, whereas data reconciliation is concerned with resolving data inconsistencies. Our approach to constructing a global conceptual schema as the result of integrating a collection of (semantically) heterogeneous component schemas is based on the concept of exact views. We show that a global schema constructed in terms of exact views integrates component schemas in such a way that the global schema is populated by exactly those instances allowed by the local schemas (and in special cases, also the other way around). In this sense, the global schema is equivalent to the set of component schemas from which the global schema is derived. This paper describes a modeling framework for data federations based on the Object-Role Modeling (ORM) approach. In particular, we show that we can represent exact views within ORM, providing the means to resolve in a combined setting data extraction and reconciliation problems on the global level of the federation.

Business process models play an important role for the management, design, and improvement of process organizations and process-aware information systems. Despite the extensive application of process modeling in practice, there are hardly empirical results available on quality aspects of process models. This paper aims to advance the understanding of this matter by analyzing the connection between formal errors (such as deadlocks) and a set of metrics that capture various structural and behavioral aspects of a process model. In particular, we discuss the theoretical connection between errors and metrics, and provide a comprehensive validation based on an extensive sample of EPC process models from practice. Furthermore, we investigate the capability of the metrics to predict errors in a second independent sample of models. The high explanatory power of the metrics has considerable consequences for the design of future modeling guidelines and modeling tools.