Search results

The purpose of this paper is to present business process point analysis (BPPA), a technique to measure business functional process size, based on function point analysis (FPA), and using business process model and notation (BPMN). This paper also discusses the assessment results of BPPA compared with FPA.

Two experimental studies with participants from academia and industry were conducted. The following aspects in the experimental studies were focused: similarity, application easiness, feasibility, and application benefits. The purpose of the experiment was to assess BPPA comparing with FPA as the BPPA design followed the FPA pattern.

Experimental results showed that both academia and industry groups highly rated similarity and application benefits for BPPA compared with FPA. However, only participants from industry highly rated BPPA for application easiness and feasibility. The results also showed that participants’ previous experiences did not influence their ratings on BPPA.

BPPA helps project managers to measure functional process size of business process management projects. As BPPA is derived from FPA, its mechanism is easily recognizable by project managers who are used to FPA. These results also show that both techniques are in most cases considered rather similar.

This review scopes evidence on the use of social robots for older adults with depressive symptoms, in the scenario of smart cities, analyzing the age-related depression specificities, investigated contexts and intervention protocols' features.

Studies retrieved from two major databases were selected against inclusion and exclusion criteria. Studies were included if used social robots, included older adults over 60, and reported depressive symptoms measurements, with any type of research design. Papers not published in English, published as an abstract or study protocol, or not peer-reviewed were excluded.

28 relevant studies were included, in which PARO was the most used robot. Most studies included very older adults with neurocognitive disorders living in long-term care facilities. The intervention protocols were heterogeneous regarding the duration, session duration and frequency. Only 35.6% of the studies had a control group. Finally, only 32.1% of the studies showed a significant improvement in depression symptoms.

Despite the potential for using social robots in mental health interventions, in the scenario of smart cities, this review showed that their usefulness and effects in improving depressive symptoms in older adults have low internal and external validity. Future studies should consider factors as planning the intervention based on well-established supported therapies, characteristics and needs of the subjects, and the context in which the subjects are inserted.

The purpose of this paper is to present the proposal of a Product Line (PL)-based approach for Business Process Management (BPM) projects that cover the entire BPM lifecycle and proposes integrating it with dynamic techniques still not used together.

The authors carried out this work using the design science research methodology. The authors assessed the proposed approach using a classification procedure created through a series of specific attributes, which enables a comparison of the proposed integrated approach with related works selected from a systematic literature review.

The comparative assessment has shown that the proposed approach presents the most comprehensive solution than any other similar one suggested for the same purpose, mainly in terms of the coverage of the entire BPM lifecycle and dynamic techniques.

Due to the high-level conceptual nature of the proposed approach, the authors could not evaluate it also in terms of some controlled experiment or a case study.

The proposed approach aims at improving the management of business processes in organizations in a systematic way using concepts and techniques that exist in other areas, but not widely used together yet, such as BPM, service-oriented computing, and Software PL.

Process mining is a research area used to discover, monitor and improve real business processes by extracting knowledge from event logs available in process-aware information systems. The purpose of this paper is to evaluate the application of artificial neural networks (ANNs) and support vector machines (SVMs) in data mining tasks in the process mining context. The goal was to understand how these computational intelligence techniques are currently being applied in process mining.

The authors conducted a systematic literature review with three research questions formulated to evaluate the use of ANNs and SVMs in process mining.

The authors identified 11 papers as primary studies according to the criteria established in the review protocol. Most of them deal with process mining enhancement, mainly using ANNs. Regarding the data mining task, the authors identified three types of tasks used: categorical prediction (or classification); numeric prediction, considering the “regression” type, and clustering analysis.

Although there is scientific interest in process mining, little attention has been specifically given to ANNs and SVM. This scenario does not reflect the general context of data mining, where these two techniques are widely used. This low use may be possibly due to a relative lack of knowledge about their potential for this type of problem, which the authors seek to reverse with the completion of this study.