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The purpose of this paper is to design a consortium-blockchain based framework for cross-organizational business process mining complying with privacy requirements.

Business process modeling in a cross-organizational setting is complicated due to privacy concerns. The process mining in this situation occurs through trusted third parties (TTPs). It uses a special class of Petri-nets called workflow nets (WF-nets) to represent the formal specifications of event logs in a blockchain-enabled cross-organization.

Using a smart contract algorithm, the proposed framework discovers the organization-specific business process models (BPM) without a TTP. The discovered BPMs are formally represented using WF-nets with a message factor to support the authors’ claim. Finally, the applicability and suitability of the proposed framework is demonstrated using a case study of multimodal transportation.

The proposed framework complies with privacy requirements. It shows how to represent the formal specifications of event logs in a blockchain using a special class of Petri-nets called WF-nets. It also presents a smart contract algorithm to discover organization-specific business process models (BPM) without a TTP.

The use of technology in Saudi Arabian higher education is constantly evolving. With the support of the 2030 Saudi vision, many research studies have started covering learning analytics and Big Data in the Saudi Arabian higher education. Examining learning analytics in higher education institutions promise transforming the learning experience to maximize students' learning potential. With the thousands of students' transactions recorded in various learning management systems (LMS) in Saudi educational institutions, the need to explore and research learning analytics in Saudi Arabia has caught the interest of scholars and researchers regionally and internationally. This chapter explores a Saudi private university in Jeddah, Saudi Arabia, and examines its rich learning analytics and discovers the knowledge behind it. More than 300,000 records of LMS analytical data were collected from a consecutive 4-year historic data. Romero, Ventura, and Garcia (2008) educational data mining process was applied to collect and analyze the analytical reports. Statistical and trend analysis were applied to examine and interpret the collected data. The study has also collected lecturers' testimonies to support the collected analytical data. The study revealed a transformative pedagogy that impact course instructional design and students' engagement.

This study explored spatial usage of a public library based on activity logs produced by real users. The purpose of this paper is to provide preliminary data for decision-making when establishing the library operation policy.

To achieve the goal, the author collected a variety of data including 274,242 seat reservations logs, 3,361,284 collection usage logs, and 96,098 user information for the four years in which the National Library of Korea, Sejong actually operated. The crawled data were analyzed statistically in terms of demography, month, day of week, time of day and room by room. The author conducted additional in-depth analysis according to the external factors such as weather or social demographic environment. Finally, the author discussed several issues and verified feasibility of the proposals to support decision-making in operating a library in conclusion with a secondary librarian interview.

The usage rate in all the spaces of the National Library of Korea, Sejong, has been increasing since its opening, and, in particular, the usage rate increases sharply in January, February, July and August. In addition, the usage rate during weekends was higher than that during weekdays, and all the four spaces had a high usage rate during the afternoon. These results seem to be related to weather, users’ life pattern, users’ age, and position of PCs and seats. Based on the circulation logs analysis of children’s collections, users in their 10s and 40s showed the same space usage pattern.

This study has significance in that it attempted to analyze logs produced by real users during the actual library operation period, which has not been frequently attempted in the previous studies on libraries. The findings will be provided as basic data to support decision-making for efficient operation of libraries.

Explains the use of learning logs as a tool for self‐development as a form of diary for recording and enhancing experiential learning, increasingly advocated owing to the limitations of traditional learning methods and pressures on training resources. Evaluates a study involving over 100 participants on a postgraduate management course who kept a log for one year, recording their self‐directed learning from workplace activities. Explores the relationship between participants’ learning styles and their experiences of using learning logs. Identifies the main benefits for learners as improved self‐awareness and access to more work opportunities, as well as professional development. Highlights managers’ perceptions of benefits in managing staff development. Pinpoints simplicity and flexibility in using logs as important, for both learners and managers. Discusses the importance of support for learners as a key issue, and concludes that self‐development is a social process.

The purpose of this paper is to explore the meaning of information literacy (IL) in food logging, the activity of recording food intake and monitoring weight and other health conditions that may be affected by diet, using applications (apps) accessed through mobile devices and personal computers.

Data were gathered from a small group of food logging app users through a focus group and interviews. Analysis was informed by practice theory and the growing interest in IL outside educational settings.

Food logging revolves around the epistemic modality of information, but it is the user who creates information and it is not textual. Food logging is associated with a discourse of focussing on data and downplaying the corporeal information associated with eating and its effect on the body. Social information was an important source for choosing an app, but data were rarely shared with others. Food loggers are very concerned with data quality at the point of data entry. They have a strong sense of learning about healthy eating. They were not well informed about the data privacy and access issues.

Food loggers need to be better informed about data risks around food logging.

This is the first study of food logging from an IL perspective.

Looks at learning logs and their relevance in the context of self‐development in organizations and in education ‐ especially within skills development programmes.

As the number of web applications is increasing day by day web mining acts as an important tool to extract useful information from weblogs and analyse them according to the attributes and predict the usage of a website. The main aim of this paper is to inspect how process mining can be used to predict the web usability of hotel booking sites based on the number of users on each page, and the time of stay of each user. Through this paper, the authors analyse the web usability of a website through process mining by finding the web usability metrics. This work proposes an approach to finding the usage of a website using the attributes available in the weblog which predicts the actual footfall on a website.

PROM (Process Mining tool) is used for the analysis of the event log of a hotel booking site. In this work, authors have used a case study to apply the PROM (process mining tool) to pre-process the event log dataset for analysis to discover better-structured process maps than without pre-processing.

This article first provided an overview of process mining, then focused on web mining and later discussed process mining techniques. It also described different target languages: system nets (i.e. Petri nets with an initial and a final state), inductive miner and heuristic miner, graphs showing the change in behaviour of the dataset and predicting the outcome, that is the webpage having the maximum number of hits.

In this work, a case study has been used to apply the PROM (process mining tool) to pre-process the event log dataset for analysis to discover better-structured process maps than without pre-processing.

Purpose — In the context of evaluating transportation and carbon emission policies, improve weekly activity and mobility scheduling survey methodology in order to enhance data quality while reducing costs and decreasing respondent burden for designing continuous self-administered surveys that are predominantly passive (or computer-assisted).

Approach — Evaluate a set of functionalities deployed in a web travel survey interface (2009) and compare with a pencil-and-paper survey (2002–2003) deployed in Quebec City that sought similar data about weekly mobility. The first used a pencil-and-paper approach complemented by interviews and telecommunications. The second used applets developed in Java, and Google Maps in order to assist geocoding of activity places and the reporting of actual trips into a relational database, while using email to recruit and support respondents.

Implications — Both of these surveys had to address specific technical and privacy challenges during deployment, making their comparison relevant for discussing some of the impacts of information technologies on spatiotemporal data quality, conviviality of survey procedure, respondents' motivation and privacy protection.

Limitations — While neither of these surveys employed movement-aware mobile devices, such as GPS loggers, some of the lessons learnt are relevant to the design issues raised by the increasing deployment of such devices in travel surveys, and by the growing need to manage complex surveys over extended observation periods.

Process mining provides a generic collection of techniques to turn event data into valuable insights, improvement ideas, predictions, and recommendations. This paper uses spreadsheets as a metaphor to introduce process mining as an essential tool for data scientists and business analysts. The purpose of this paper is to illustrate that process mining can do with events what spreadsheets can do with numbers.

The paper discusses the main concepts in both spreadsheets and process mining. Using a concrete data set as a running example, the different types of process mining are explained. Where spreadsheets work with numbers, process mining starts from event data with the aim to analyze processes.

Differences and commonalities between spreadsheets and process mining are described. Unlike process mining tools like ProM, spreadsheets programs cannot be used to discover processes, check compliance, analyze bottlenecks, animate event data, and provide operational process support. Pointers to existing process mining tools and their functionality are given.

Event logs and operational processes can be found everywhere and process mining techniques are not limited to specific application domains. Comparable to spreadsheet software widely used in finance, production, sales, education, and sports, process mining software can be used in a broad range of organizations.

The paper provides an original view on process mining by relating it to the spreadsheets. The value of spreadsheet-like technology tailored toward the analysis of behavior rather than numbers is illustrated by the over 20 commercial process mining tools available today and the growing adoption in a variety of application domains.

A major problem of operational control in the services industry is the integration of customers in the delivery process. The aim of this paper is to develop a method that allows service companies to evaluate the impact of customer integration on operational control in service processes.

The development of the proposed method follows a design science approach. Thus, the method is conceptualised on the basis of production, services and information systems research. A case study of loan processing in a bank serves to evaluate the applicability of the method.

As a result of this study, customer integration should be included into operational control following three steps: identification of the type of customer integration; quantification and characterisation of the impact of the integration; and identification of the appropriate mechanisms of operational control to deal with the customer integration better. The results of the case study show that customer integration has an impact on certain activities within a service process only but the results can be used to enhance operational control.

The method can be used by process managers of service companies to identify the impact of customer integration on operational control. Thus, decisions within operational control and consequently the overall productivity of a service process can be improved.

The paper delivers a new insight how customer integration and operational control can be linked in service processes. Thus, a theoretical gap in service operations literature is filled. Furthermore, the case study demonstrates how the method can be used in practice.