Supporting Business Process Improvement through Business Process Weakness Pattern Collections

A main task of Business Process Management is to identify weaknesses in business processes that may result in monetary or quality-related drawbacks in order to eliminate them subsequently. A common way to identify weaknesses is to analyze process models. While this has traditionally been done manually, recent work proposes using automatic model query approaches promising time and money savings. Query approaches take (weakness) patterns as input and return all process model subsections that match these patterns, hence may be subject to weaknesses. Although numerous model query approaches have been developed, collections of weaknesses do virtually not exist. To exploit the benefits of model querying, a weakness collection would be highly desirable. In this paper, we provide a first version of a weakness pattern collection, which we identified in an empirical study on several hundreds of weakness-afflicted process models and assess its usefulness through applying it to another process model collection

IDENTIFICATION, ABSTRACTION AND CLASSIFICATION OF INCONSISTENCY STRUCTURES IN DECLARATIVE PROCESS MODELS

Handling inconsistencies in declarative process models (DPMs) has been of increased interest in previous years as even a single contradiction within a constraint set makes the entire DPM unsatisfiable. To support inconsistency detection and resolution, we provide a collection of generic inconsistency structures in this work. To this aim, we (1) iteratively identify inconsistency structures, (2) generalize them by analyzing their extendibility, (3) classify them based on their characteristics and (4) provide a visual representation of each generic structure. The resulting collection of structures provides the basis for future inconsistency detection and visualization approaches

Investigating Inconsistency Understanding to Support Interactive Inconsistency Resolution in Declarative Process Models

Handling inconsistencies in business rules is an important part of corporate compliance management. This includes the resolution of inconsistencies, which currently is a fully automated process that might not always be plausible in a real-world scenario. To include human experts and develop interactive resolution approaches, an understanding of inconsistencies is crucial. Thus, we focus on investigating inconsistency understanding in declarative process models by testing the applicability of insights from declarative process model understanding to different inconsistency characteristics. In the future, this will provide the basis for a series of cognitive experiments evaluating the effects of inconsistency characteristics and representation on inconsistency understanding in declarative process models

Detecting Compliance with Business Rules in Ontology-Based Process Modeling

Extending business processes with semantic annotations has gained recent attention. This comprises relating process elements to ontology elements in order to create a shared conceptual and terminological understanding. In business process modeling, processes may have to adhere to a multitude of rules. A common way to detect compliance automatedly is studying the artifact of the process model itself. However, if an ontology exists as an additional artifact, it may prove beneficial to exploit this structure for compliance detection, as it provides a rich specification of the business process. We therefore propose an approach that models a rules-layer ontop of an ontology. Said rules-layer is implemented by a logic program and can be used to reason about the compliance of an underlying ontology. Our approach allows ad-hoc access to external ontologies, other than similar approaches that are reliant on a redundant logical representation of process model elements

Advanced Auditing of Run-Time Conflicts in Declarative Process Models using Time Series Clustering

We present a novel approach for auditing conflicts between declarative constraints that arise during process execution, i.e., relative to observed traces. As a main advantage, taking a post-execution perspective allows to consider all observed traces and their interrelations, and to assess conflicts from a global perspective. Our approach allows to classify and prioritize conflicts as a basis for re-modelling, e.g., which conflicts are an outlier, and which require an urgent change to the model. Also, our approach provides means for quantitative root-cause analysis, i.e., prioritizing which rules need to be changed. We implement our approach and show that it can be applied in settings of industrial scale by means of runtime experiments with real-life data-sets

Resolving Inconsistencies in Declarative Process Models based on Culpability Measurement

Contrary to traditional process models, declarative process models define a set of declarative constraints to specify the behavior which a process should adhere to. In the scope of process mining, declarative process discovery aims to derive such constraint sets from event logs. Here, a problem for current discovery techniques is that of inconsistency. That is, dependent of certain event log characteristics, the derived constraint set may contain contradictory constraints. This in turn however makes the discovered model unusable, as contradictory constraints make it impossible to execute declarative process models, thus hampering previous process discovery efforts. In this work, we present an approach for resolving inconsistencies in declarative process models, based on methods from the scientific field of inconsistency measurement. We introduce our approach algorithm and evaluate its feasibility with data sets of the BPI Challenge 2017

Towards Intelligent Chatbots for Customer Care - Practice-Based Requirements for a Research Agenda

Chatbots bare a great potential to save efforts and costs in customer care through service automation. Current results are however still at an early stage in functionality and not widely attainable. Here, developing a new form of intelligent chatbots is a current challenge still under review. While there have been numerous proposals for future work, virtually all agenda-setting contributions are solely based on scientific literature. This is unsatisfactory from both an academic and practical perspective, as the industrial view on the future of chatbots seems to be neglected. Therefore, this work explores how professional experts see the future of intelligent chatbots for customer care and suggests how practice can guide research based on an expert panel with 17 industrial partners. Our work identifies research opportunities based on the demands and views of key practitioners by pin-pointing expected trends. Furthermore, based on the expert opinions, we derive guidelines for organizations which state key factors that should be considered in the development or adoption of chatbots in customer care

Unified Enterprise Knowledge Representation with Conceptual Models - Capturing Corporate Language in Naming Conventions

Conceptual modeling is an established instrument in the knowledge engineering process. However, a precondition for the usability of conceptual models is not only their syntactic correctness but also their semantic comparability. Assuring comparability is quite challenging especially when models are developed by different persons. Empirical studies show that such models can vary heavily, especially in model element naming, even if they are meant to express the same issue. In contrast to most ontology-driven approaches proposing the resolution of these differences ex-post, we introduce an approach that avoids naming differences in conceptual models already during modeling. Therefore we formalize naming conventions combining domain thesauri and phrase structures based on a linguistic grammar. This allows for guiding modelers automatically during the modeling process using standardized labels for model elements, thus assuring unified enterprise knowledge representation. Our approach is generic, making it applicable for any modeling language

Effects of Quantitative Measures on Understanding Inconsistencies in Business Rules

Business Rules have matured to an important aspect in the development of organizations, encoding company knowledge as declarative constraints, aimed to ensure compliant business. The management of business rules is widely acknowledged as a challenging task. A problem here is a potential inconsistency ofbusiness rules, as business rules are often created collaboratively. To support companies in managing inconsistency, many works have suggested that a quantification of inconsistencies could provide valuable insights. However, the actual effects of quantitative insights in business rules management have not yet been evaluated. In this work, we present the results of an empirical experiment using eye-tracking and other performance measures to analyze the effects of quantitative measures on understanding inconsistencies in business rules. Our results indicate that quantitative measures are associated with better understanding accuracy, understanding efficiency and less mental effort in business rules management

Experiences in Process Oriented Reorganization through Reference Modelling in Public Administrations - The Case Study REGIO@KOMM

During the last years the optimisation of business processes has gained more and more importance in the context of modernising public administrations. In line with the concept of electronic government (eGovernment) citizens demand not only an improved design of internet sites, but also the creation of real added value to administrational services. In dimensions of benefits (from the citizen’s point of view) and in dimensions of cost reduction (from the administrations’ point of view) the added value can be generated by providing fully transactional online citizen services. The establishment of such services should be supported by reorganising the underlying business processes in terms of process organisation and enabling ICT. Approximately 13,000 German municipalities mainly have to deal with the same spectrum of tasks. The administration processes that are necessary to fulfil those tasks share strong structural analogies. Within process oriented reorganisation projects, reference models can contribute to cost reduction in the phase of to-be modelling. Aim of this paper is to present experiences in applying reference modelling within the process oriented reorganisation project Regio@KomM in public administrations. The reorganisation of the process of issuing a general debit note authorisation exemplifies the practical applicability and the value potential of reference modelling in public administration