Search results

Automating the task of identifying process weaknesses using process models is promising, as many organizations have to manage a large amount of process models. The purpose of this paper is to introduce a pattern-based approach for automatically detecting potential process weaknesses in semantic process models, thus supporting the task of business process improvement.

Based on design research, combined with a case study, the authors explore the design, application and evaluation of a pattern-based process weakness detection approach within the setting of a real-life case study in a German bank.

Business process weakness detection can be automated to a remarkable extent using pattern matching and a semantic business process modeling language. A case study provided evidence that such an approach highly supports business process analysts.

The presented approach is limited by the fact that not every potential process weakness detected by pattern matching is really a weakness but just gives the impression to be one. Hence, after detecting a weakness, analysts still have to decide on its authenticity.

Applying weakness patterns to semantic process models via pattern matching allows organizations to automatically and efficiently identify process improvement potentials. Hence, this research helps to avoid time- and resource-consuming manual analysis of process model landscapes.

The approach is not restricted to a single modeling language. Furthermore, by applying the pattern matching approach to a semantic modeling language, the authors avoid ambiguous search results. A case study proves the usefulness of the approach.

The article aims to present a holistic approach to analysis of patterns on complex online profiles, demonstrated on profiles of European scientists.

An existing analytical framework was developed to incorporate a holistic understanding of online profiles. The framework was applied to a sample of 188 online profiles belonging to 48 European scientists. The profile data were studied on three levels (content‐unit level, profile‐instance level, and profile‐network level), using methods of the qualitative comparative analysis to derive profiling patterns.

The approach developed in this work generated profiling patterns for European scientists. The patterns exist on all three levels, forming a hierarchy. This pattern structure shows the variety of ways in which scientists can use the internet for self‐presentation.

The study was based on a holistic understanding of online self‐presentation, acknowledging that personal presentation can be spread across different platforms. The study presented shows how this understanding can be used when analysing online profiling behaviour. The profiling patterns of European scientists identified in this study supplement existing typologies. The study serves as a foundation to structure further research as well as to inform practitioners.

Financial institutions have been engaged in numerous business process reengineering (BPR) projects to make their organizations more efficient. However, the success of BPR projects in banks varies significantly and it remains a challenge to systematically discover weaknesses in business process landscapes. Based on the flow chart notation language this paper seeks to argue for the definition of weakness patterns in order to automatically identify potential process weaknesses.

The authors developed weakness patterns in the flow chart notation language based on design science principles. To systematically derive process weaknesses that can be formalized, they analyzed each element of the flow chart notation as it was used in a real‐life case. They furthermore tested the identified patterns in reality in order to evaluate their validity.

The authors identified various potential weakness patterns that helped in automatically identifying weaknesses in process models. To some extent these findings are generalizable and transferable to other process modeling languages.

The pattern‐based approach depends upon how well structural weakness patterns are defined and formalized. Identified problems remain “potential” weaknesses until a manual analysis reveals that the identified potential weaknesses are actually real weaknesses or not, e.g. due to law regulations.

Using weakness patterns allows for automatically identifying potential process weaknesses in existing flow chart models. This way, this research helps in improving the so far manual analysis of process model landscapes.

The approach is a new way of looking for process weaknesses through process weakness patterns.

Most studies of conflict handling styles in organizations analyze these styles separately. These studies assume that individuals are oriented towards the use of one of the styles of conflict management. As a result, different styles are compared one by one as if they were independent. In contrast, from a more all‐embracing perspective people are seen as adopting configurations of styles. The interest in this alternative perspective lies in exploring the relations between these styles, how they combine and form patterns of conflict styles. This article presents an exploratory study that seeks to identify empirically the specific combinations of conflict handling styles that result in differentiated patterns within groups of managers. By using hierarchical and non‐hierarchical cluster analyses of a sample of managers, different patterns of conflict management were identified. The effectiveness of each of the resulting patterns was analyzed in terms of its influence on the parties' joint substantive outcomes and their mutual relationship. Results show that patterns using multiple conflict handling styles were more effective than patterns based on a single style.

In this paper, a data mining approach is proposed for monitoring the conditions leading to a rail wheel high impact load. The proposed approach incorporates logical analysis of data (LAD) and ant colony optimization (ACO) algorithms in extracting patterns of high impact loads and normal loads from historical railway records. In addition, the patterns are employed in establishing a classification model used for classifying unseen observations. A case study representing real-world impact load data is presented to illustrate the impact of the proposed approach in improving railway services.

Application of artificial intelligence and machine learning approaches becomes an essential tool in improving the performance of railway transportation systems. By using these approaches, the knowledge extracted from historical data can be employed in railway assets monitoring to maintain the assets in a reliable state and to improve the service provided by the railway network.

Results achieved by the proposed approach provide a prognostic system used for monitoring the conditions surrounding rail wheels. Incorporating this prognostic system in surveilling the rail wheels indeed results in better railway services as trips with no-delay or no-failure can be realized. A comparative study is conducted to evaluate the performance of the proposed approach versus other classification algorithms. In addition to the highly interpretable results obtained by the generated patterns, the comparative study demonstrates that the proposed approach provides classification accuracy higher than other common machine learning classification algorithms.

The methodology followed in this research employs ACO algorithm as an artificial intelligent technique and LDA as a machine learning algorithm in analyzing wheel impact load alarm-collected datasets. This new methodology provided a promising classification model to predict future alarm and a prognostic system to guide the system while avoiding this alarm.

This paper seeks to review the industrial applications of state‐of‐the‐art additive manufacturing (AM) techniques in metal casting technology. An extensive survey of concepts, techniques, approaches and suitability of various commercialised rapid casting (RC) solutions with traditional casting methods is presented.

The tooling required for producing metal casting such as fabrication of patterns, cores and moulds with RC directly by using different approaches are presented and evaluated. Relevant case studies and examples explaining the suitability and problems of using RC solutions by various manufacturers and researchers are also presented.

Latest research to optimize the current RC solutions, and new inventions in processing techniques and materials in RC performed by researchers worldwide are also discussed. The discussion regarding the benefits of RC solutions to foundrymen, and challenges to produce accurate and cost‐effective RC amongst AM manufacturers concludes this paper.

The research related to this survey is limited to the applicability of RC solutions to sand casting and investment casting processes. There is practically no implication in industrial application of RC technology.

This review presents the information regarding potential AM application – RC, which facilitates the fabrication of patterns, cores and moulds directly using the computer‐aided design data. The information available in this paper serves the purpose of researchers and academicians to explore the new options in the field of RC and especially users, manufacturers and service industries to produce casting in relatively much shorter time and at low cost and even to cast complex design components which otherwise was impossible by using traditional casting processes and CNC technology.

The purpose of this paper is to present a new heuristic approach for finding errors and possible improvements in business process models.

First, the paper translates the information that is included in a model into a set of Prolog facts. It then searches for patterns which are related to a violation of the soundness property or bad modeling style or otherwise gives rise to the assumption that the model should be improved. To validate this approach, the paper analyzes a repository of almost 1,000 business process models. For this purpose, three different model‐checkers that explore the state space of all possible executions of a model are used. The result of these tools are compared with the results given by this heuristic approach.

The paper finds that the heuristic approach identifies violations of the soundness property almost as accurate as model‐checkers. However, other than these tools, the approach never ran into state space explosion problems. Furthermore, this heuristic approach can also detect patterns for bad modeling style which can help to improve the quality of models.

Heuristic checks can run in the background while the modeler works on the model. In this way, feedback about possible modeling errors can be provided instantly. This feedback can be used to correct possible problems immediately.

Current Prolog‐based validation tools check mainly for syntactical correctness and consistency requirements. This approach adds one more perspective by also detecting control‐flow errors (like deadlocks) and even pragmatic issues.

Behavioral pattern mining for intelligent system such as SmEs sensor data are vitally important in many applications and performance optimizations. Sensor pattern mining (SPM) is also dynamic and a hot research issue to pervasive and ubiquitous of smart technologies toward improving human life. However, in large-scale sensor data, exploring and mining pattern, which leads to detect the abnormal behavior is challenging. The paper aims to discuss these issues.

Sensor data are complex and multivariate, for example, which data captured by the sensors, how it is precise, what properties are recorded or measured, are important research issues. Therefore, the method, the authors proposed Sequential Data Mining (SDM) approach to explore pattern behaviors toward detecting abnormal patterns for smart space fault diagnosis and performance optimization in the intelligent world. Sensor data types, modeling, descriptions and SPM techniques are discussed in depth using real sensor data sets.

The outcome of the paper is measured as introducing a novel idea how SDM technique’s scale-up to sensor data pattern mining. In the paper, the approach and technicality of the sensor data pattern analyzed, and finally the pattern behaviors detected or segmented as normal and abnormal patterns.

The paper is focussed on sensor data behavioral patterns for fault diagnosis and performance optimizations. It is other ways of knowledge extraction from the anomaly of sensor data (observation records), which is pertinent to adopt in many intelligent systems applications, including safety and security, efficiency, and other advantages as the consideration of the real-world problems.

The purpose of this paper is to understand failure to change by examining patterns of coherent structure and agency characteristics in changing organizations in specific sectors and to provide specific recommendations for intervention in these patterns.

A large survey in 367 organizations engaged in different change processes and from different sectors, among employees in different positions.

The paper finds that there are five patterns among changing organizations, each with their own specific problems, characteristics, and change approaches that require different interventions.

Parsimony in research models and the study of overall relations between variables does not help to understand failure to change. More integrative approaches are needed that take variety among changing organizations into account.

Change agents should not opt for a “one best strategy” for change but choose a contingent change approach that takes into consideration the specific characteristics of their organizations, change processes, and contexts in order to make change more successful.

This paper establishes that successful change cannot be explained by one or a few variables but is contingent on an interplay of agency, structure, and contextual characteristics. Together, these characteristics form constellations that characterize different sectors. The paper provides suggestions for more successful change.

The purpose of this paper is to test the applicability and the performance of an approach called logical analysis of data (LAD) on the detection of faults in rotating machinery using vibration signals.

LAD is a supervised learning data mining technique that relies on finding patterns in a binary database to generate decision functions. The hypothesis is that a LAD‐based decision model can be used as an effective tool for automatic detection of faults in rolling element bearings. A novel Multiple Integer Linear Programming approach is used to generate patterns for the LAD decision model. Frequency and time‐based features are extracted from rotor bearing vibration signals and are pre‐processed to be suitable for use with LAD.

The results show good classification accuracy with both time and frequency features.

The diagnostic tool implemented in the form of software in a production or operations maintenance environment can be very helpful to maintenance experts as it reveals the patterns that lead to the diagnosis in interpretable terms which facilitates efforts to understand the reasons behind the components' failure.

The proposed modifications to the LAD‐based decision model which is being tested for the first time in the field of fault detection in rotating machinery lead to improved accuracy results in addition to the added value of result interpretability due to this distinctive property of LAD.