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Contains fulltext : 219437.pdf (Publisher’s version ) (Closed access) OTM 2019

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

Contains fulltext : 195308.pdf (Publisher’s version ) (Closed access) On the Move to Meaningful Internet Systems. OTM 2017 Workshops Confederated International Workshops, EI2N, FBM, ICSP, Meta4eS, OTMA 2017 and ODBASE Posters 2017, Rhodes, Greece, October 23–28, 2017

Purpose: Immune components of the tumor microenvironment (TME) have been associated with disease outcome. We prospectively evaluated the association of an immune-related gene signature (GS) with clinical outcome in melanoma and non-small cell lung cancer (NSCLC) tumor samples from two phase III studies. Experimental Design: The GS was prospectively validated using an adaptive signature design to optimize it for the sample type and technology used in phase III studies. One-third of the samples were used as “training set”; the remaining two thirds, constituting the “test set,” were used for the prospective validation of the GS. Results: In the melanoma training set, the expression level of eight Th1/IFNg-related genes in tumor-positive lymph node tissue predicted the duration of disease-free survival (DFS) and overall survival (OS) in the placebo arm. This GS was prospectively and independently validated as prognostic in the test set. Building a multivariate Cox model in the test set placebo patients from clinical covariates and the GS score, an increased number of melanoma-involved lymph nodes and the GS were associated with DFS and OS. This GS was not associated with DFS in NSCLC, although expression of the Th1/IFNg -related genes was associated with the presence of lymphocytes in tumor samples in both indications. Conclusions: These findings provide evidence that expression of Th1/IFNg genes in the TME, as measured by this GS, is associated with clinical outcome in melanoma. This suggests that, using this GS, patients with stage IIIB/C melanoma can be classified into different risk groups. © 2019 American Association for Cancer Research.

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.

This paper presents a novel extension to the process tree model to support cancelation behavior, and proposes a novel process discovery technique to discover sound, fitting models with cancelation features. The proposed discovery technique relies on a generic error oracle function, and allows us to discover complex combinations of multiple, possibly nested cancelation regions based on observed behavior. An implementation of the proposed approach is available as a ProM plugin. Experimental results based on real-life event logs demonstrate the feasibility and usefulness of the approach.

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.

Achieving seamless interoperability among heterogeneous communication systems and technologies is critical in todays’ networked business world, ranging from joint collaborative military operations that need to exchange critical information in real-time, to complex e-business scenarios of organizations from different industry sectors using heterogeneous technologies and communication infrastructures to execute complex inter-organizational business processes. The aim of this article is to present a review of current domain-specific standardization initiatives towards seamless communication, discussing challenges and highlighting directions for further research and development work. This article has two main contributions. First, it provides an up-to-date review of the most relevant domain-specific standardization initiatives aiming at achieving seamless interoperability among communication systems. Second, by comparing these initiatives, a set of recommendations is advanced towards improving further interoperability developments, offering a unique niche for researchers, practitioners and developers to make significant contributions.

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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.

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.

Humanities scholars agree that the visualization of their data should bring order and insight, reveal patterns and provide leads for new research questions. However, simple two-dimensional visualizations are often too static and too generic to meet these needs. Visualization tools for the humanities should be able to deal with the observer dependency, heterogeneity, uncertainty and provenance of data and the complexity of humanities research questions. They should furthermore offer scholars the opportunity to interactively manipulate their data sets and queries. In this paper, we introduce Storyteller, an open source visualization tool designed to interactively explore complex data sets for the humanities. We present the tool, and demonstrate its applicability in three very different humanities projects.

Process participants need to learn how to perform in the context of their business processes. Process training is challenging due to cognitive difficulties in relating process model elements to real world concepts. In this paper we present a 3D VirtualWorld (VW) process training approach for office environments. In this VW, process participants can experience a process in an immersive environment. They interact with VW representations of process elements in changing locations, based on process activities. By means of embodied 3D representation, deep immersion and engagement in this environment, enhancements in long term memory learning and episodic memory usage for knowledge retrieval are expected. Our illustration of an example process model shows the applicability of the approach. We list a number of future directions to extend the use and the benefits.

This paper defines a language for expressing a durable semantic application model as major part of the specifications for business applications This language consists of the modeling constructs from Natural Language Modeling (NLM). It will be shown in this article how these modeling constructs constitute a hierarchy within any durable semantic application model.

Since its inception in the 1970’s, fact-based modeling has evolved. In this article we will sketch this evolution mainly from the referencing mode’s point of view. In this article we compare referencing modes from the N-ary 1989 NIAM model to the contemporary incarnation of fact-based modeling known as CogNIAM.

PURPOSE: To assess the impact on survival of increasing dose-intensity (DI) of cyclophosphamide, doxorubicin, and etoposide (CDE) in small-cell lung cancer (SCLC). PATIENTS AND METHODS: Previously untreated SCLC patients were randomized to standard CDE (cyclophosphamide 1,000 mg/m2 and doxorubicin 45 mg/m2 on day 1, and etoposide 100 mg/m2 on days 1 to 3 every 3 weeks, for five cycles) or intensified CDE (cyclophosphamide 1,250 mg/m2 and doxorubicin 55 mg/m2 on day 1, and etoposide 125 mg/m2 on days 1 to 3 with granulocyte colony-stimulating factor [G-CSF] 5 μg/kg/d on days 4 to 13 every 2 weeks, for four cycles). Projected cumulative dose was almost identical on the two arms, whereas projected DI was nearly 90% higher on the intensified arm. Two hundred forty-four patients were enrolled. The first 163 patients were also randomized (2 × 2 factorial design) to prophylactic antibiotics or placebo to assess their impact on preventing febrile leukopenia (FL). This report focuses on chemotherapy DI results. RESULTS: With a median follow-up of 54 months, 216 deaths have occurred. Actually delivered DI on the intensified arm was 70% higher than on the standard arm. Intensified CDE was associated with more grade 4 leukopenia (79% v 50%), grade 4 thrombocytopenia (44% v 11%), anorexia, nausea, and mucositis. FL and number of toxic deaths were similar on the two arms. The objective response rate was 79% for the standard arm and 84% for the intensified arm (P = .315). Median survival was 54 weeks and 52 weeks, and the 2-year survival rates were 15% and 18%, respectively (P = .885). CONCLUSION: A 70% increase of CDE actual DI does not translate into an improved outcome in SCLC patients.

By counting the step command pulses, stepping motors can be straightforwardly used for open loop positioning. However, open-loop control is often insufficient to guarantee accurate and energy efficient movements. More intelligent stepping motor algorithms can meet these concerns, however, this requires position information. The back EMF signal contains useful information on the rotor position. This information can be used to monitor the motor condition and to implement a more advanced position control algorithm. A theoretical analysis gives insight into the back EMF generated in a two-phase hybrid stepping motor. In this paper a, by the authors, patented sampling method is considered to measure the back EMF signal. The opportunities of this method are considered theoretically. Moreover this paper presents extensive measurement results proving the opportunities of the method, to develop more intelligent stepping motor algorithms.

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