More and more higher education institutions are offering specialized study programs for current and future managers of Smart Sustainable Cities (SSCs). In the process, they try to reconcile the interdisciplinary nature of such studies, covering at least the technical and social aspects of SSC management, with their own traditionally discipline-based organization. However, there is little guidance on how such interdisciplinarity should be introduced. In order to address this gap, this paper identifies 87 SSC-related study programs from around the world and analyzes their disciplinary and interdisciplinary coverage. The analysis classifies programs and competencies, the former using text mining and clustering algorithms, the latter using Bloom’s taxonomy and correlation analysis.
classification tasks. The discussion focuses on two important research hypotheses: (1) whether it is possible to
construct such an ontology from a corpus of textual document, and (2) whether it is possible and beneficial to use
inferencing from this ontology to support the process of sentiment classification. To support the first hypothesis we
present a method of extraction of hierarchy of contexts from a set of textual documents and encoding this hierarchy
into a multi-level contextual ontology. To support the second hypothesis, we present a method of reasoning from the
ontology, and results of experimental verification, which show that use of this reasoning method can increase accuracy of sentiment classification for longer text documents
This chapter presents the application of new information technology in education for the training of air traffic controllers (ATCs). Machine learning, multi-criteria decision analysis, and text analysis as the methods of artificial intelligence for ATCs training have been described. The authors have made an analysis of the International Civil Aviation Organization documents for modern principles of ATCs education. The prototype of the neural network for evaluating the timeliness and correctness of the decision making by ATCs has been developed. The new theoretical and practical tasks for simulation and pre-simulation training have been obtained using expert judgment method. The methodology for sentiment analyzing the airline customers' opinions has been proposed. In addition, the examples of artificial intelligence systems and expert systems by the authors, students and colleagues from National Aviation University, Ukraine and Gdansk University of Technology, Poland have been proposed.
There is growing interest in applying computational methods in analysing large amount of data without sacrificing rigour in Information Systems research. In this paper, we demonstrate how the use of structural and temporal topic modelling can be employed to produce insights of both theoretical and practical importance from the analysis of textual comments on the quality of services in hospitals. As a first step, we revealed the thematic structures in the comments as topics which were aligned with the SERVQUAL dimensions. Following this, we established the temporal precedence among SERVQUAL factors based on the evolution of the topics over time. Theoretically, our findings are consistent with the emerging consensus on the nature of SERVQUAL dimensions from extant quantitative research and offer new propositions on the relationships among these dimensions. From the practice perspective, we produced quantified measures of factors associated with healthcare service experience
Representing a valuable human-computer interaction interface, Sentiment Analysis (SA) is applied to a wide range of problems. In the present paper, the researchers introduce a novel concept of Business Sentiment (BS) as a measurement of a Perceived Anticipated Effort (PAE) in the context of business processes (BPs). BS is considered as an emotional component of BP task contextual complexity perceived by a process worker after reading the task text. PAE is interpreted as a business process (BP) key performance indicator predicting urgency, criticality and complexity of the BP task processing. Using qualitative evaluation, the researchers proved the workability of both BS concept and its effective application method to measure PAE. As practical contributions of the research, quantitative support in a form of statistical reports and qualitative support in a form of task prioritization recommendations and time management for a BP worker are suggested
Growing IT complexity and related problems, which are reflected in IT tickets,create a need for new qualitative approaches. The goal isto automate the extraction of main topics described in tickets in order to provide high quality support for the IT process workers and enablea smooth service delivery to the end user. Present paper proposes a method of knowledge extraction in a form of stylistic patterns in business process (BP) texts, here in incoming IT tickets texts. Hereby, the authors set an objective to predicttheir readability andperceivedcomplexityfor a process worker, what will influencefurther tasks execution. The results of experimental analysis of a data set of incoming ticket texts from anITIL-based Change Management process showed that the specificity of stylistic patterns expressing the readability of a ticket and perceived complexity could be identified with the help of proposed measures of the ticket length, parts-of-speech distributions and wording style
(PDF) Discovery of Stylistic Patterns in Business Process Textual Descriptions: IT Ticket Case. Available from: https://www.researchgate.net/publication/331843977_Discovery_of_Stylistic_Patterns_in_Business_Process_Textual_Descriptions_IT_Ticket_Case [accessed Jun 23 2019].
Logistics is one of the key sectors of the Polish economy. Its value reflects not only its own capacity, but also the role it plays in ensuring the proper functioning of the entire economy. The rapid development of the industry and the highest demands on logistics solutions bring to the fore the problem of preparing a new generation of specialists in logistics. That is why the question of compliance to learning expectations of both students and the labour logistics market is so important, as well as the problems of efficiency and adequacy of training provided to students with the knowledge and skills to achieve the desired job, and the opportunities of Polish Universities’ graduates to work abroad. The objective of the paper was to identify the expectations of students who study Logistics at the Faculty of Engineering Management, Poznan University of Technology.
Significant progress has been made in linguistic-based text analytics particularly with the increasing availability of data and deep learning computational models for more accurate opinion analysis and domain-specific entity recognition. In understanding customer service experience from texts, analysis of sentiments associated with different stages of the service lifecycle is a useful starting point. However, when richer insights into issues associated with negative sentiments and experiences are desired to inform intervention, deeper linguistic analyses such as identifying specific touchpoints and the context of the service users become important. While research in this direction is beginning to emerge in some domains, we are yet to see similar efforts in the domain of healthcare. We present in this paper the results from our construct development effort for quantifying how critical a negative patient experience is using different elements of the available textual feedback as a key basis for prioritizing interventions by service providers. This involves the identification of the different dimensions of the construct, associated linguistic markers and metrics to compute the criticality index. We also present the results of the application of our developed conceptualization to linguistic-based text analysis of a small dataset of patient experience feedback.
Growing amount of complexity and enterprise data creates a need for novel business process (BP) analysis methods to assess the process optimization
opportunities. This paper proposes a method of BP analysis while extracting the
knowledge about Decision-Making Logic (DML) in a form of taxonomy. In this
taxonomy, researchers consider the routine, semi-cognitive and cognitive DML levels as functions of BP conceptual aspects of Resources, Techniques,
Capacities, and Choices. Preliminary testing and evaluation of developed
method using data set of entry ticket texts from the IT Helpdesk domain showed
promising results in the identification and classification of the BP Decision-Making Logic.
In this paper, we present a concept of a multi-criteria knowledge-based Recommender System (RS) designed to provide decision support in complex business process (BP) scenarios. The developed approach is based on the knowledge aspects of Stylistic Patterns, Business Sentiment and Decision-Making Logic extracted from the BP unstructured texts. This knowledge serves as an input for a multi-criteria RS algorithm. The output is prediction of the BP complexity, based on which the algorithm modifies the type and the way of decision support, ranging from full to minimal automation. We show how the algorithm can be applied in the real-life scenarios by the example of the IT ticketing case study. We also evaluate the BP complexity prediction quality using both quantitative (data-based) and qualitative (interview-based) approach in the case study