In a state where games and their elements have been extensively used not only for pleasure but also for other purposes, gamification still has some pros and cons about its definition, which might influence people's decisions on their game-related strategies to improve their performance. This work tries to define gamification by using lexical meaning approach as the starting point and viewing it from a process viewpoint. Lexical meaning approach interprets gamification as a process or a product of the process. From this perspective, gamification can be viewed as a process that adds certain characteristics to an object that makes the object different from its previous condition and feasible to be formalized. Furthermore, the resulting definition is tested by comparing it to other existing gamification definitions and the understanding that constructs the definition is used as the foundation to explain the differences between gamification and serious games. This paper then defines gamification as a process that integrates game elements into gameless objects in order to have gameful characteristics. There will be a situation where gamification will produce a state of gameful reality: a situation in the real world where people can feel the significant presence of gamefulness in their daily life.
Abstract. This paper proposes and evaluates an efficient approach for loading models stored in a change-based format. The work builds on language-independent change-based persistence (CBP) of models conforming to object-oriented metamodelling architectures such as MOF and EMF, an approach which persists a model's editing history rather than its current state. We evaluate the performance of the proposed loading approach and assess its impact on saving change-based models. Our results show that the proposed approach significantly improves loading times compared to the baseline CBP loading approach, and has a negligible impact on saving.
Comparison of large models can be time-consuming since every element has to be visited, matched, and compared with its respective element in other models. This can result in bottlenecks in collaborative modelling environments, where identifying differences between two versions of a model is desirable. Reducing the comparison process to only the elements that have been modified since a previous known state (e.g., previous version) could significantly reduce the time required for large model comparison. This paper presents how change-based persistence can be used to localise the comparison of models so that only elements affected by recent changes are compared and to substantially reduce comparison and differencing time (up to 90% in some experiments) compared to state-based model comparison.
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