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Business process workflow is a design conceptualization to automate the sequence of activities to achieve a business goal with involved participants and a predefined set of rules. Regarding this, a formal business workflow model is a prime requisite to implement a consistent and rigorous business process. In this context, majority of the existing research works are formalized structural features and have not focused on functional and behavioral design aspects of business processes. To address this problem, this paper aims to propose a formal model of business process workflow called as business process workflow using typed attributed graph (BPWATG) enriched with structural, functional and behavioral characteristics of business processes.

Typed attributed graph (ATG) and first-order logic have been used to formalize proposed BPWATG to provide rigorous syntax and semantics towards business process workflows. This is an effort to execute a business workflow on an automated machine. Further, the proposed BPWATG is illustrated using a case study to show the expressiveness of proposed model. Besides, the proposed graph is initially validated using generic modelling environment (GME) case tool. Moreover, a comparative study is performed with existing formal approaches based on several crucial features to exhibit the effectiveness of proposed BPWATG.

The proposed model is capable of facilitating structural, functional and behavioral aspects of business process workflows using several crucial features such as dependency conceptualization, timer concepts, exception handling and deadlock detection. These features are used to handle real-world problems and ensure the consistency and correctness of business workflows.

BPWATG is proposed to formalize a business workflow that is required to make a model of business process machine-readable. Besides, formalizations of dependency conceptualization, exception handling, deadlock detection and time-out concepts are specified. Moreover, several non-functional properties (reusability, scalability, flexibility, dynamicity, reliability and robustness) are supported by the proposed model.

Knowledge- and communication-intensive domains still long for a better support of creativity that considers legal requirements, compliance rules and administrative tasks as well, because current systems focus either on knowledge representation or business process management. The purpose of this paper is to discuss our model of integrated knowledge and business process representation and its presentation to users.

The authors follow a design science approach in the environment of patent prosecution, which is characterized by a highly standardized, legally prescribed process and individual knowledge study. Thus, the research is based on knowledge study, BPM, graph-based knowledge representation and user interface design. The authors iteratively designed and built a model and a prototype. To evaluate the approach, the authors used analytical proof of concept, real-world test scenarios and case studies in real-world settings, where the authors conducted observations and open interviews.

The authors designed a model and implemented a prototype for evolving and storing static and dynamic aspects of knowledge. The proposed solution leverages the flexibility of a graph-based model to enable open and not only continuously developing user-centered processes but also pre-defined ones. The authors further propose a user interface concept which supports users to benefit from the richness of the model but provides sufficient guidance.

The balanced integration of the data and task perspectives distinguishes the model significantly from other approaches such as BPM or knowledge graphs. The authors further provide a sophisticated user interface design, which allows the users to effectively and efficiently use the graph-based knowledge representation in their daily study.

The purpose of this paper is to propose a three-dimensional (3D) assembly model retrieval method based on assembling semantic information to address semantic mismatches, poor accuracy and low efficiency in existing 3D assembly model retrieval methods.

The paper proposes an assembly model retrieval method. First, assembly information retrieval is performed, and 3D models that conform to the design intention of the assembly are found by retrieving the code. On this basis, because there are conjugate subgraphs between attributed adjacency graphs (AAG) that have an assembly relationship, the assembly model geometric retrieval is translated into a problem of finding AAGs with a conjugate subgraph. Finally, the frequent subgraph mining method is used to retrieve AAGs with conjugate subgraphs.

The method improved the efficiency and accuracy of assembly model retrieval.

The examples illustrate the specific retrieval process and verify the feasibility and reasonability of the assembly model retrieval method in practical applications.

The assembly model retrieval method in the paper is an original method. Compared with other methods, good results were obtained.

The purpose of this paper is to investigate whether analysts’ personal cognitive traits mitigate the efficacy of graphical impression management.

Three experiments are conducted wherein 525 professional accountants working as financial analysts rate a hypothetical company’s performance graph depicting its net income trend. The manipulation is the presence (absence) of impression management techniques. Hypotheses test whether different techniques are effective and whether analysts’ cognitive reflection ability mitigates manipulation efficacy.

Presentation enhancement is effective only with impulsive analysts, showing the weakness of this technique through the use of colors. Measurement distortion and selectivity techniques are effective for reflective and impulsive analysts; however, reflective analysts are more critical about graphs prepared via selectivity that emphasize profit recovery following crises.

Each impression management technique is investigated in isolation and in controlled conditions. Further research could consider how personal cognitive traits impact the efficacy of combined techniques and whether imbedding manipulated graphs with other information mitigates impression management efficacy.

Research on impression management is mostly “task-oriented;” few “people-oriented” studies focus on decision making by those using financial reports. Users’ cognitive reflection ability is shown to undermine the efficacy of some impression management techniques.

Financial analysts, auditors and regulators could develop mechanisms to avoid pervasive usage of (or enhance skepticism regarding) techniques not mitigated by users’ reflectiveness.

Evidence from financial analysts with an accounting background provides insights on individual characteristics’ influence on graphical impression management efficacy.

Graph rewriting concerns the technique of transforming a graph; it is thus natural to conceive its application in the evolution of graph databases. This paper aims to propose a two-step framework where rewriting rules formalize instance or schema changes, ensuring graph’s consistency with respect to constraints, and updates are managed by ensuring rule applicability through the generation of side effects: new updates which guarantee that rule application conditions hold.

This paper proposes Schema Evolution Through UPdates, optimized version (SetUp_OPT), a theoretical and applied framework for the management of resource description framework (RDF)/S database evolution on the basis of graph rewriting rules. The framework is an improvement of SetUp which avoids the computation of superfluous side effects and proposes, via SetUpoptND, a flexible and extensible package of solutions to deal with non-determinism.

This paper shows graph rewriting into a practical and useful application which ensures consistent evolution of RDF databases. It introduces an optimised approach for dealing with side effects and a flexible and customizable way of dealing with non-determinism. Experimental evaluation of SetUpoptND demonstrates the importance of the proposed optimisations as they significantly reduce side-effect generation and limit data degradation.

SetUp originality lies in the use of graph rewriting techniques under the closed world assumption to set an updating system which preserves database consistency. Efficiency is ensured by avoiding the generation of superfluous side effects. Flexibility is guaranteed by offering different solutions for non-determinism and allowing the integration of customized choice functions.

Missing in most of the research on selling has been an examination of the process from the point of view of the customer. When satisfaction in selling has been considered, researchers have focused on the satisfaction of the salesperson with his job and/or the impact of this job satisfaction on performance (e.g. Bluen, Barling & Burns, 1990; Churchill, Ford & Walker, 1979; Pruden & Peterson, 1971). To concentrate on salesperson performance while neglecting customers is to ignore the most important half of the relationship between buyers and sellers and entirely disregards the marketing concept and the streams of research in customer satisfaction. This research takes a different approach and examines customers’ satisfaction with salespeople.

This study examines the impact of different Balanced Scorecard (BSC) formats (table, graph without summary measure, graph with a summary measure) on various decision outcomes: performance ratings, perceived informativeness, and decision efficiency.

Using an original case developed by the researchers, a total of 135 individuals participated in the experiment and rated the performance of carwash managers in two different scenarios: one manager excelled financially but failed to meet targets for all other three BSC perspectives and the other manager had the opposite results.

The evaluators rated managerial performance significantly lower in the graph format compared to a table presentation of the BSC. Performance ratings were significantly higher for the scenario where the manager failed to meet only financial perspective targets but exceeded targets for all other nonfinancial BSC perspectives, contrary to the usual predictions based on the financial measure bias. The evaluators reported that informativeness of the BSC was highest in the table or graph without summary measure formats, and, surprisingly, adding a summary measure to the graph format significantly reduced perceived informativeness compared to the table format. Decision efficiency was better for the graph formats (with or without summary measure) than for the table format.

Ours is the first study to compare tables, graphs with and without a summary measure in the context of managerial performance evaluations and to examine their impact on ratings, informativeness, and efficiency. We developed an original case to test the boundaries of the financial measure bias.

The purpose of this paper is to present a Big Data solution as a methodological approach to the automated collection, cleaning, collation, and mapping of multimodal, longitudinal data sets from social media. The paper constructs social information landscapes (SIL).

The research presented here adopts a Big Data methodological approach for mapping user-generated contents in social media. The methodology and algorithms presented are generic, and can be applied to diverse types of social media or user-generated contents involving user interactions, such as within blogs, comments in product pages, and other forms of media, so long as a formal data structure proposed here can be constructed.

The limited presentation of the sequential nature of content listings within social media and Web 2.0 pages, as viewed on web browsers or on mobile devices, do not necessarily reveal nor make obvious an unknown nature of the medium; that every participant, from content producers, to consumers, to followers and subscribers, including the contents they produce or subscribed to, are intrinsically connected in a hidden but massive network. Such networks when mapped, could be quantitatively analysed using social network analysis (e.g. centralities), and the semantics and sentiments could equally reveal valuable information with appropriate analytics. Yet that which is difficult is the traditional approach of collecting, cleaning, collating, and mapping such data sets into a sufficiently large sample of data that could yield important insights into the community structure and the directional, and polarity of interaction on diverse topics. This research solves this particular strand of problem.

The automated mapping of extremely large networks involving hundreds of thousands to millions of nodes, encapsulating high resolution and contextual information, over a long period of time could possibly assist in the proving or even disproving of theories. The goal of this paper is to demonstrate the feasibility of using automated approaches for acquiring massive, connected data sets for academic inquiry in the social sciences.

The methods presented in this paper, together with the Big Data architecture can assist individuals and institutions with a limited budget, with practical approaches in constructing SIL. The software-hardware integrated architecture uses open source software, furthermore, the SIL mapping algorithms are easy to implement.

The majority of research in the literature uses traditional approaches for collecting social networks data. Traditional approaches can be slow and tedious; they do not yield adequate sample size to be of significant value for research. Whilst traditional approaches collect only a small percentage of data, the original methods presented here are able to collect and collate entire data sets in social media due to the automated and scalable mapping techniques.

This study aims to carry out numerical modeling to predict aerodynamic noise radiation from four different Savonius rotor blade profile.

Incompressible unsteady reynolds-averaged navier-stokes (URANS) approach using gamma–theta turbulence model is conducted to obtain the time accurate turbulent flow field. The Ffowcs Williams and Hawkings (FW-H) acoustic analogy formulation is used for noise predictions at optimal tip speed ratio (TSR).

The mean torque and power coefficients are compared with the experimental data and acceptable agreement is observed. The total and Mono+Dipole noise graphs are presented. A discrete tonal component at low frequencies in all graphs is attributed to the blade passing frequency at the given TSR. According to the noise prediction results, Bach type rotor has the lowest level of noise emission. The effect of TSR on the noise level from the Bach rotor is investigated. A direct relation between angular velocity and the noise emission is found.

The savonius rotor is a type of vertical axis wind turbines suited for mounting in the vicinity of residential areas. Also, wind turbines wherein operation are efficient sources of tonal and broadband noises and affect the inhabitable environment adversely. Therefore, the acoustic pollution assessment is essential for the installation of wind turbines in residential areas.

This study aims to investigate the radiated noise level of four common Savonius rotor blade profiles, namely, Bach type, Benesh type, semi-elliptic and conventional. As stated above, numbers of studies exploit the URANS method coupled with the FW-H analogy to predict the aeroacoustics behavior of wind turbines. Therefore, this approach is chosen in this research to deal with the aeroacoustics and aerodynamic calculation of the flow field around the aforementioned Savonius blade profiles. The effect of optimal TSR on the emitted noise and the contribution of thickness, loading and quadrupole sources are of interest in this study.

A community demonstrates the unique qualities and relationships between its members that distinguish it from other communities within a network. Network analysis relies heavily on community detection. Despite the traditional spectral clustering and statistical inference methods, deep learning techniques for community detection have grown in popularity due to their ease of processing high-dimensional network data. Graph convolutional neural networks (GCNNs) have received much attention recently and have developed into a potential and ubiquitous method for directly detecting communities on graphs. Inspired by the promising results of graph convolutional networks (GCNs) in analyzing graph structure data, a novel community graph convolutional network (CommunityGCN) as a semi-supervised node classification model has been proposed and compared with recent baseline methods graph attention network (GAT), GCN-based technique for unsupervised community detection and Markov random fields combined with graph convolutional network (MRFasGCN).

This work presents the method for identifying communities that combines the notion of node classification via message passing with the architecture of a semi-supervised graph neural network. Six benchmark datasets, namely, Cora, CiteSeer, ACM, Karate, IMDB and Facebook, have been used in the experimentation.

In the first set of experiments, the scaled normalized average matrix of all neighbor's features including the node itself was obtained, followed by obtaining the weighted average matrix of low-dimensional nodes. In the second set of experiments, the average weighted matrix was forwarded to the GCN with two layers and the activation function for predicting the node class was applied. The results demonstrate that node classification with GCN can improve the performance of identifying communities on graph datasets.

The experiment reveals that the CommunityGCN approach has given better results with accuracy, normalized mutual information, F1 and modularity scores of 91.26, 79.9, 92.58 and 70.5 per cent, respectively, for detecting communities in the graph network, which is much greater than the range of 55.7–87.07 per cent reported in previous literature. Thus, it has been concluded that the GCN with node classification models has improved the accuracy.