Resolving inconsistencies and redundancies in declarative process models

Declarative process models define the behaviour of business processes as a set of constraints. Declarative process discovery aims at inferring such constraints from event logs. Existing discovery techniques verify the satisfaction of candidate constraints over the log, but completely neglect their interactions. As a result, the inferred constraints can be mutually contradicting and their interplay may lead to an inconsistent process model that does not accept any trace. In such a case, the output turns out to be unusable for enactment, simulation or verification purposes. In addition, the discovered model contains, in general, redundancies that are due to complex interactions of several constraints and that cannot be cured using existing pruning approaches. We address these problems by proposing a technique that automatically resolves conflicts within the discovered models and is more powerful than existing pruning techniques to eliminate redundancies. First, we formally define the problems of constraint redundancy and conflict resolution. Second, we introduce techniques based on the notion of automata-product monoid, which guarantees the consistency of the discovered models and, at the same time, keeps the most interesting constraints in the pruned set. The level of interestingness is dictated by user-specified prioritisation criteria. We evaluate the devised techniques on a set of real-world event logs

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

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

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

Improving the Quality of Solutions by Automated Database Design Systems with the Provision of Real World Knowledge - An Evaluation

Automated database design systems have the capability of assisting human designers in the process of database analysis and design. However, the capacity of these systems to produce quality solutions which are similar to expert human designers remains largely unresolved. Therefore, in recent years there have been a number of attempts to develop systems that are not only "knowledgeable" about database design process but also have the capability of exploiting knowledge of the real world. Although such use of real world knowledge was claimed capable of increasing the quality of design models, there is currently little, if any, formal evaluation that this claim has taken place. This paper presents such an evaluation of three existing approaches proposed to facilitate system-storage and exploitation of real world knowledge; the dictionary approach, the thesaurus approach, and the knowledge reconciliation approach. Results obtained have indicated that some of the approaches under examination in this study are capable of producing higher quality design models compared to when no such knowledge is in use. However, the ability of such representations of real world knowledge to achieve the standard quality of human generated design models remains unanswered

Living with inconsistencies in a multidatabase system

Integration of autonomous sources of information is one of the most important problems in implementation of the global information systems. This paper considers multidatabase systems as one of the typical architectures of global information services and addresses a problem of storing and processing inconsistent information in such systems. A new data model proposed in the paper separates sure from inconsistent information and introduces a system of elementary operations on the containers with sure and inconsistent information. A review of the implementation aspects in an environment of a typical relational database management system concludes the paper

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

A study of the methodologies currently available for the maintenance of the knowledge-base in an expert system

This research studies currently available maintenance methodologies for expert system knowledge bases and taxonomically classifies them according to the functions they perform. The classification falls into two broad categories. These are: (1) Methodologies for building a more maintainable expert system knowledge base. This section covers techniques applicable to the development phases. Software engineering approaches as well as other approaches are discussed. (2) Methodologies for maintaining an existing knowledge base. This section is concerned with the continued maintenance of an existing knowledge base. It is divided into three subsections. The first subsection discusses tools and techniques which aid the understanding of a knowledge base. The second looks at tools which facilitate the actual modification of the knowledge base, while the last secttion examines tools used for the verification or validation of the knowledge base. Every main methodology or tool selected for this study is analysed according to the function it was designed to perform (or its objective); the concept or principles behind the tool or methodology: and its implementation details. This is followed by a general comment at the end of the analysis. Although expert systems as a rule contain significant amount of information related to the user interface, database interface, integration with conventional software for numerical calculations, integration with other knowledge bases through black boarding systems or network interactions, this research is confined to the maintenance of the knowledge base only and does not address the maintenance of these interfaces. Also not included in this thesis are Truth Maintenance Systems. While a Truth Maintenance System (TMS) automatically updates a knowledge base during execution time, these update operations are not considered \u27maintenance\u27 in the sense as used in this thesis. Maintenance in the context of this thesis refers to perfective, adaptive, and corrective maintenance (see introduction to chapter 4). TMS on the other hand refers to a collection of techniques for doing belief revision (Martin, 1990) . That is, a TMS maintains a set of beliefs or facts in the knowledge base to ensure that they remain consistent during execution time. From this perspective, TMS is not regarded as a knowledge base maintenance tool for the purpose of this study