An extensible framework for analysing resource behaviour using event logs

Business processes depend on human resources and managers must regularly evaluate the performance of their employees based on a number of measures, some of which are subjective in nature. As modern organisations use information systems to automate their business processes and record information about processes’ executions in event logs, it now becomes possible to get objective information about resource behaviours by analysing data recorded in event logs. We present an extensible framework for extracting knowledge from event logs about the behaviour of a human resource and for analysing the dynamics of this behaviour over time. The framework is fully automated and implements a predefined set of behavioural indicators for human resources. It also provides a means for organisations to define their own behavioural indicators, using the conventional Structured Query Language, and a means to analyse the dynamics of these indicators. The framework's applicability is demonstrated using an event log from a German bank.

Polymorphisms in inflammation pathway genes and endometrial cancer risk

BACKGROUND Experimental and epidemiologic evidence have suggested that chronic inflammation may play a critical role in endometrial carcinogenesis. METHODS To investigate this hypothesis, a two-stage study was carried out to evaluate single-nucleotide polymorphisms (SNP) in inflammatory pathway genes in association with endometrial cancer risk. In stage I, 64 candidate pathway genes were identified and 4,542 directly genotyped or imputed SNPs were analyzed among 832 endometrial cancer cases and 2,049 controls, using data from the Shanghai Endometrial Cancer Genetics Study. Linkage disequilibrium of stage I SNPs significantly associated with endometrial cancer (P < 0.05) indicated that the majority of associations could be linked to one of 24 distinct loci. One SNP from each of the 24 loci was then selected for follow-up genotyping. Of these, 21 SNPs were successfully designed and genotyped in stage II, which consisted of 10 additional studies including 6,604 endometrial cancer cases and 8,511 controls. RESULTS Five of the 21 SNPs had significant allelic odds ratios (ORs) and 95% confidence intervals (CI) as follows: FABP1, 0.92 (0.85-0.99); CXCL3, 1.16 (1.05-1.29); IL6, 1.08 (1.00-1.17); MSR1, 0.90 (0.82-0.98); and MMP9, 0.91 (0.87-0.97). Two of these polymorphisms were independently significant in the replication sample (rs352038 in CXCL3 and rs3918249 in MMP9). The association for the MMP9 polymorphism remained significant after Bonferroni correction and showed a significant association with endometrial cancer in both Asian- and European-ancestry samples. CONCLUSIONS These findings lend support to the hypothesis that genetic polymorphisms in genes involved in the inflammatory pathway may contribute to genetic susceptibility to endometrial cancer. Impact statement: This study adds to the growing evidence that inflammation plays an important role in endometrial carcinogenesis.

Unraveling unstructured process models

A BPMN model is well-structured if splits and joins are always paired into single-entry-single-exit blocks. Well-structuredness is often a desirable property as it promotes readability and makes models easier to analyze. However, many process models found in practice are not well-structured, and it is not always feasible or even desirable to restrict process modelers to produce only well-structured models. Also, not all processes can be captured as well-structured process models. An alternative to forcing modelers to produce well-structured models, is to automatically transform unstructured models into well-structured ones when needed and possible. This talk reviews existing results on automatic transformation of unstructured process models into structured ones.

Connectivity of workflow nets : the foundations of stepwise verification

Behavioral models capture operational principles of real-world or designed systems. Formally, each behavioral model defines the state space of a system, i.e., its states and the principles of state transitions. Such a model is the basis for analysis of the system’s properties. In practice, state spaces of systems are immense, which results in huge computational complexity for their analysis. Behavioral models are typically described as executable graphs, whose execution semantics encodes a state space. The structure theory of behavioral models studies the relations between the structure of a model and the properties of its state space. In this article, we use the connectivity property of graphs to achieve an efficient and extensive discovery of the compositional structure of behavioral models; behavioral models get stepwise decomposed into components with clear structural characteristics and inter-component relations. At each decomposition step, the discovered compositional structure of a model is used for reasoning on properties of the whole state space of the system. The approach is exemplified by means of a concrete behavioral model and verification criterion. That is, we analyze workflow nets, a well-established tool for modeling behavior of distributed systems, with respect to the soundness property, a basic correctness property of workflow nets. Stepwise verification allows the detection of violations of the soundness property by inspecting small portions of a model, thereby considerably reducing the amount of work to be done to perform soundness checks. Besides formal results, we also report on findings from applying our approach to an industry model collection.

The biconnected verification of workflow nets

Formal representations of business processes are used for analysis of the process behavior. Workflow nets are a widely used formalism for describing the behavior of business processes. Structure theory of processes investigates the relation between the structure of a model and its behavior. In this paper, we propose to employ the connectivity property of workflow nets as an angle to their structural analysis. In particular, we show how soundness verification can be organized using biconnected components of a workflow net. This allows for efficient identification and localization of flaws in the behavior of workflow nets and for supporting process analysts with diagnostic information

Process compliance measurement based on behavioural profiles

Process compliance measurement is getting increasing attention in companies due to stricter legal requirements and market pressure for operational excellence. On the other hand, the metrics to quantify process compliance have only been defined recently. A major criticism points to the fact that existing measures appear to be unintuitive. In this paper, we trace back this problem to a more foundational question: which notion of behavioural equivalence is appropriate for discussing compliance? We present a quantification approach based on behavioural profiles, which is a process abstraction mechanism. Behavioural profiles can be regarded as weaker than existing equivalence notions like trace equivalence, and they can be calculated efficiently. As a validation, we present a respective implementation that measures compliance of logs against a normative process model. This implementation is being evaluated in a case study with an international service provider.

Process compliance analysis based on behavioural profiles

Process compliance measurement is getting increasing attention in companies due to stricter legal requirements and market pressure for operational excellence. In order to judge on compliance of the business processing, the degree of behavioural deviation of a case, i.e., an observed execution sequence, is quantified with respect to a process model (referred to as fitness, or recall). Recently, different compliance measures have been proposed. Still, nearly all of them are grounded on state-based techniques and the trace equivalence criterion, in particular. As a consequence, these approaches have to deal with the state explosion problem. In this paper, we argue that a behavioural abstraction may be leveraged to measure the compliance of a process log – a collection of cases. To this end, we utilise causal behavioural profiles that capture the behavioural characteristics of process models and cases, and can be computed efficiently. We propose different compliance measures based on these profiles, discuss the impact of noise in process logs on our measures, and show how diagnostic information on non-compliance is derived. As a validation, we report on findings of applying our approach in a case study with an international service provider.

Causal behavioural profiles : efficient computation, applications, and evaluation

Analysis of behavioural consistency is an important aspect of software engineering. In process and service management, consistency verification of behavioural models has manifold applications. For instance, a business process model used as system specification and a corresponding workflow model used as implementation have to be consistent. Another example would be the analysis to what degree a process log of executed business operations is consistent with the corresponding normative process model. Typically, existing notions of behaviour equivalence, such as bisimulation and trace equivalence, are applied as consistency notions. Still, these notions are exponential in computation and yield a Boolean result. In many cases, however, a quantification of behavioural deviation is needed along with concepts to isolate the source of deviation. In this article, we propose causal behavioural profiles as the basis for a consistency notion. These profiles capture essential behavioural information, such as order, exclusiveness, and causality between pairs of activities of a process model. Consistency based on these profiles is weaker than trace equivalence, but can be computed efficiently for a broad class of models. In this article, we introduce techniques for the computation of causal behavioural profiles using structural decomposition techniques for sound free-choice workflow systems if unstructured net fragments are acyclic or can be traced back to S- or T-nets. We also elaborate on the findings of applying our technique to three industry model collections.

Efficient computation of causal behavioural profiles using structural decomposition

Identification of behavioural contradictions is an important aspect of software engineering, in particular for checking the consistency between a business process model used as system specification and a corresponding workflow model used as implementation. In this paper, we propose causal behavioural profiles as the basis for a consistency notion, which capture essential behavioural information, such as order, exclusiveness, and causality between pairs of activities. Existing notions of behavioural equivalence, such as bisimulation and trace equivalence, might also be applied as consistency notions. Still, they are exponential in computation. Our novel concept of causal behavioural profiles provides a weaker behavioural consistency notion that can be computed efficiently using structural decomposition techniques for sound free-choice workflow systems if unstructured net fragments are acyclic or can be traced back to S- or T-nets.

Vertical alignment of process models – how can we get there?

There is a wide variety of drivers for business process modelling initiatives, reaching from business evolution and process optimisation over compliance checking and process certification to process enactment. That, in turn, results in models that differ in content due to serving different purposes. In particular, processes are modelled on different abstraction levels and assume different perspectives. Vertical alignment of process models aims at handling these deviations. While the advantages of such an alignment for inter-model analysis and change propagation are out of question, a number of challenges has still to be addressed. In this paper, we discuss three main challenges for vertical alignment in detail. Against this background, the potential application of techniques from the field of process integration is critically assessed. Based thereon, we identify specific research questions that guide the design of a framework for model alignment.

Multivariate statistical analysis of hydrochemical data to assess alluvial aquifer–stream connectivity during drought and flood : Cressbrook Creek, southeast Queensland, Australia

A catchment-scale multivariate statistical analysis of hydrochemistry enabled assessment of interactions between alluvial groundwater and Cressbrook Creek, an intermittent drainage system in southeast Queensland, Australia. Hierarchical cluster analyses and principal component analysis were applied to time-series data to evaluate the hydrochemical evolution of groundwater during periods of extreme drought and severe flooding. A simple three-dimensional geological model was developed to conceptualise the catchment morphology and the stratigraphic framework of the alluvium. The alluvium forms a two-layer system with a basal coarse-grained layer overlain by a clay-rich low-permeability unit. In the upper and middle catchment, alluvial groundwater is chemically similar to streamwater, particularly near the creek (reflected by high HCO3/Cl and K/Na ratios and low salinities), indicating a high degree of connectivity. In the lower catchment, groundwater is more saline with lower HCO3/Cl and K/Na ratios, notably during dry periods. Groundwater salinity substantially decreased following severe flooding in 2011, notably in the lower catchment, confirming that flooding is an important mechanism for both recharge and maintaining groundwater quality. The integrated approach used in this study enabled effective interpretation of hydrological processes and can be applied to a variety of hydrological settings to synthesise and evaluate large hydrochemical datasets.

Real-time monitoring of nucleation-growth cycle of carbon nanoparticles in acetylene plasmas

Quantum cascade laserabsorption spectroscopy was used to measure the absolute concentration of acetylene in situ during the nanoparticle growth in Ar + C2H2 RF plasmas. It is demonstrated that the nanoparticle growth exhibits a periodical behavior, with the growth cycle period strongly dependent on the initial acetylene concentration in the chamber. Being 300 s at 7.5% of acetylene in the gas mixture, the growth cycle period decreases with the acetylene concentration increasing; the growth eventually disappears when the acetylene concentration exceeds 32%. During the nanoparticle growth, the acetylene concentration is small and does not exceed 4.2% at radio frequency (RF) power of 4 W, and 0.5% at RF power of 20 W. An injection of a single acetylene pulse into the discharge also results in the nanoparticlenucleation and growth. The absorption spectroscopy technique was found to be very effective for the time-resolved measurement of the hydrocarbon content in nanoparticle-generatingplasmas.

Low-pressure planar magnetron discharge for surface deposition and nanofabrication

Current-voltage characteristics of the planar magnetron are studied experimentally and by numerical simulation. Based on the measured current-voltage characteristics, a model of the planar magnetron discharge is developed with the background gas pressure and magnetic field used as parameters. The discharge pressure was varied in a range of 0.7-1.7 Pa, the magnetic field of the magnetron was of 0.033-0.12 T near the cathode surface, the discharge current was from 1 to 25 A, and the magnetic field lines were tangential to the substrate surface in the region of the magnetron discharge ignition. The discharge model describes the motion of energetic secondary electrons that gain energy by passing the cathode sheath across the magnetic field, and the power required to sustain the plasma generation in the bulk. The plasma electrons, in turn, are accelerated in the electric field and ionize effectively the background gas species. The model is based on the assumption about the prevailing Bohm mechanism of electron conductivity across the magnetic field. A criterion of the self-sustained discharge ignition is used to establish the dependence of the discharge voltage on the discharge current. The dependence of the background gas density on the current is also observed from the experiment. The model is consistent with the experimental results. © 2010 American Institute of Physics.

Foreword

Foreword for the book, "INTERCHANGING: Future designs for responsive transport environments"

Urban Informatics and Public Transport

The holistic urban experience we perceive when immersed in an urban context is at the heart of urban informatics. This experience encompasses all urban elements such as architecture, people, and culture. Urban informatics explores the possibilities and opportunities created by new technologies and information for enhancing the urban experience. Public transport is an essential urban experience. Everyday, urban dwellers takes public transport to commute and move between different parts of the city. Public transport serves people from all over the city and moves them through different places in the city, using different means of transportation. The nature of public transport—involving people, places, and technologies, makes it a fitting context for urban informatics interventions. There are three main aspects of the public transport experience that can readily benefit from urban informatics interventions the: pragmatic aspect, hedonistic aspect, and social aspect. From the pragmatic perspective, these interventions can help people to be more efficient and effective in taking public transport. Hedonistic-related interventions aim to bring enjoyment and fun to our mundane commute. Finally, urban informatics can strengthen the sense of community in a socially-passive context like public transport environments through adopting socially focused interventions.