RBS/channelling study of ion-implanted and proton-exchanged LiNbO3:Nd:MgO planar waveguides

We present a structural study of two Nd³⁺/MgO codoped LiNbO₃ waveguides fabricated by two different methods: ion-implantation and proton-exchange. Rutherford backscattering spectrometry in combination with channelling was used to study the influence of the waveguide fabrication method on the possible modification of the LiNbO₃ crystal structure within the waveguiding layer. The results show that the ion-implanted waveguide mainly maintains the same properties as the virgin crystal, apart from a deterioration of a thin surface layer, whilst the proton-exchanged waveguide exhibits a quite different behaviour, although its surface is less damaged.     

Cloning of a novel retinoic acid-inducible mRNA from human osteoblast-like cells

Retinoids have long been known to influence skeletal development and bone remodeling. Cells of the osteoblastic lineage play a key role in these processes. In this study we have used the differential display PCR technique to identify retinoic acid (RA)-induced mRNAs in human osteoblast-like cells. We report the cloning and sequencing of one such mRNA, AT-RA 6, which was specifically induced by all-trans RA both in normal human osteoblast-like cells and in MG-63 osteosarcoma cells. Maximal expression was found after 60 min, suggesting that this may be an early response gene. Expression was found in all tissues examined. No homology to known mRNA sequences was detected.     

Scientific workflows for process mining: building blocks,scenarios, and implementation

Over the past decade process mining has emerged as a new analytical discipline able to answer a variety of questions based on event data. Event logs have a very particular structure; events have timestamps, refer to activities and resources, and need to be correlated to form process instances. Process mining results tend to be very different from classical data mining results, e.g., process discovery may yield end-to-end process models capturing different perspectives rather than decision trees or frequent patterns. A process-mining tool like ProM provides hundreds of different process mining techniques ranging from discovery and conformance checking to filtering and prediction. Typically, a combination of techniques is needed and, for every step, there are different techniques that may be very sensitive to parameter settings. Moreover, event logs may be huge and may need to be decomposed and distributed for analysis. These aspects make it very cumbersome to analyze event logs manually. Process mining should be repeatable and automated. Therefore, we propose a framework to support the analysis of process mining workflows. Existing scientific workflow systems and data mining tools are not tailored towards process mining and the artifacts used for analysis (process models and event logs). This paper structures the basic building blocks needed for process mining and describes various analysis scenarios. Based on these requirements we implemented RapidProM, a tool supporting scientific workflows for process mining. Examples illustrating the different scenarios are provided to show the feasibility of the approach.     

Questionnaire-based variability modeling for system configuration

Variability management is a recurrent issue in systems engineering. It arises for example in enterprise systems, where modules are configured and composed to meet the requirements of individual customers based on modifications to a reference model. It also manifests itself in the context of software product families, where variants of a system are built from a common code base. This paper proposes an approach to capture system variability based on questionnaire models that include order dependencies and domain constraints. The paper presents analysis techniques to detect circular dependencies and contradictory constraints in questionnaire models, as well as techniques to incrementally prevent invalid configurations by restricting the space of allowed answers to a question based on previous answers. The approach has been implemented as a toolset and has been used in practice to capture configurable process models for film post-production.

Preserving correctness during business process model configuration

A configurable process model captures a family of related process models in a single artifact. Such models are intended to be configured to fit the requirements of specific organizations or projects, leading to individualized process models that are subsequently used for domain analysis or solution design. This article proposes a formal foundation for individualizing configurable process models incrementally, while preserving correctness, both with respect to syntax and behavioral semantics. Specifically, assuming the configurable process model is behaviorally sound, the individualized process models are guaranteed to be sound. The theory is first developed in the context of Petri nets and then extended to a process modeling notation widely used in practice, namely Event-driven Process Chains.     

APROMORE: An advanced process model repository

Business process models are becoming available in large numbers due to their widespread use in many industrial applications such as enterprise and quality engineering projects. On the one hand, this raises a challenge as to their proper management: how can it be ensured that the proper process model is always available to the interested stakeholder? On the other hand, the richness of a large set of process models also offers opportunities, for example with respect to the re-use of existing model parts for new models. This paper describes the functionality and architecture of an advanced process model repository, named APROMORE. This tool brings together a rich set of features for the analysis, management and usage of large sets of process models, drawing from state-of-the art research in the field of process modeling. A prototype of the platform is presented in this paper, demonstrating its feasibility, as well as an outlook on the further development of APROMORE.     

Process diagnostics using trace alignment: Opportunities, issues, and challenges

Business processes leave trails in a variety of data sources (e.g., audit trails, databases, and transaction logs). Hence, every process instance can be described by a trace, i.e., a sequence of events. Process mining techniques are able to extract knowledge from such traces and provide a welcome extension to the repertoire of business process analysis techniques. Recently, process mining techniques have been adopted in various commercial BPM systems (e.g., BPM|one, Futura Reflect, ARIS PPM, Fujitsu Interstage, Businesscape, Iontas PDF, and QPR PA). Unfortunately, traditional process discovery algorithms have problems dealing with less structured processes. The resulting models are difficult to comprehend or even misleading. Therefore, we propose a new approach based on trace alignment. The goal is to align traces in such a way that event logs can be explored easily. Trace alignment can be used to explore the process in the early stages of analysis and to answer specific questions in later stages of analysis. Hence, it complements existing process mining techniques focusing on discovery and conformance checking. The proposed techniques have been implemented as plugins in the ProM framework. We report the results of trace alignment on one synthetic and two real-life event logs, and show that trace alignment has significant promise in process diagnostic efforts.     

Complexity of min-max subsequence problems

We determine the computational complexity of the problem of ordering a set of n numbers, either into a sequence or a cycle, such that the maximum sum of any k successive numbers is minimal. Both problems are easy for k=2 and strongly NP-hard for any k?3. However, the two problems allow a polynomial-time approximation scheme that is linear in n.