Liquid Business Process Model Collections

Many organizations realize that increasing amounts of data (“Big Data”) need to be dealt with intelligently in order to compete with other organizations in terms of efficiency, speed and services. The goal is not to collect as much data as possible, but to turn event data into valuable insights that can be used to improve business processes. However, data-oriented analysis approaches fail to relate event data to process models. At the same time, large organizations are generating piles of process models that are disconnected from the real processes and information systems. In this chapter we propose to manage large collections of process models and event data in an integrated manner. Observed and modeled behavior need to be continuously compared and aligned. This results in a “liquid” business process model collection, i.e. a collection of process models that is in sync with the actual organizational behavior. The collection should self-adapt to evolving organizational behavior and incorporate relevant execution data (e.g. process performance and resource utilization) extracted from the logs, thereby allowing insightful reports to be produced from factual organizational data.

Mentoring first-year pre-service teachers of primary science

Twenty-nine first-year pre-service teachers' perceptions of mentoring and primary science teaching were collected through a literature-based survey. Frequencies, means, and standard deviations of these responses provided data for analysis on these mentoring practices. Results indicated that even though mentors may provide feedback, the majority of mentors do not provide specific primary science mentoring in the areas of pedagogical knowledge, system requirements, and the modeling of teaching practice. It appears that the mentor's personal attributes may also influence the quality of mentoring. There were tentative conclusions that first-year pre-service teachers may not have strong beliefs about specific primary science mentoring practices, and possibly because of inexperience, may not be critical enough to analyse their mentoring in primary science teaching. Identifying specific mentoring for developing primary science teaching may assist mentors in their practices with pre-service teachers.

Inspiration sources for Australian fast fashion design: Tapping into consumer desire

Purpose The research purpose was to identify both the inspiration sources used by fast fashion designers and ways the designers sort information from the sources during the product development process. Design/methodology/approach This is a qualitative study, drawing on semi-structured interviews conducted with the members of the in-house design teams of three Australian fast fashion companies. Findings Australian fast fashion designers rely on a combination of trend data, sales data, product analysis and travel for design development ideas. The designers then use the consensus and embodiment methods to interpret and synthesise information from those inspiration sources. Research limitations/implications The empirical data used in the analysis were limited by interviewing fashion designers within only three Australian companies. Originality/value This research augments knowledge of fast fashion product development, in particular designers’ methods and approaches to product design within a volatile and competitive market.