Universities seek to promote entrepreneurship through effective education approaches, which need to be in permanent evolution. Nevertheless, the literature in entrepreneurship education lacks empirical evidence. This article discusses relevant issues related to promoting entrepreneurship in the software field, based on the experience of a 15-ECTS course. This course seeks to instil in the students the recognition of the need to reconcile technical and business visions, organizational and commercial aspects, most of which never addressed previously. A series of semi-structured interviews made it possible to obtain relevant insights about the teaching-learning process underlying this course and its evolution over a 7-year period. Materials related with this course have been analysed, namely guidelines produced by the teachers and deliverables produced by the students. This article discusses the dimensions that were identified as fundamental for promoting entrepreneurship skills in the field of software, namely teamwork, project engagement, and contact with the market.
Abstract. In cloud computing environments, data storage systems often rely on optimistic replication to provide good performance and availability even in the presence of failures or network partitions. In this scenario, it is important to be able to accurately and efficiently identify updates executed concurrently. Current approaches to causality tracking in optimistic replication have problems with concurrent updates: they either (1) do not scale, as they require replicas to maintain information that grows linearly with the number of writes or unique clients; (2) lose information about causality, either by removing entries from client-id based version vectors or using server-id based version vectors, which cause false conflicts. We propose a new logical clock mechanism and a logical clock framework that together support a traditional key-value store API, while capturing causality in an accurate and scalable way, avoiding false conflicts. It maintains concise information per data replica, only linear on the number of replica servers, and allows data replicas to be compared and merged linear with the number of replica servers and versions.
Causality tracking mechanisms, such as vector clocks and version vectors, rely on mappings from globally unique identifiers to integer counters. In a system with a well known set of entities these ids can be preconfigured and given distinct positions in a vector or distinct names in a mapping. Id management is more problematic in dynamic systems, with large and highly variable number of entities, being worsened when network partitions occur. Present solutions for causality tracking are not appropriate to these increasingly common scenarios. In this paper we introduce Interval Tree Clocks, a novel causality tracking mechanism that can be used in scenarios with a dynamic number of entities, allowing a completely decentralized creation of processes/replicas without need for global identifiers or global coordination. The mechanism has a variable size representation that adapts automatically to the number of existing entities, growing or shrinking appropriately. The representation is so compact that the mechanism can even be considered for scenarios with a fixed number of entities, which makes it a general substitute for vector clocks and version vectors.
Version vectors and their variants play a central role in update tracking in optimistic distributed systems. Existing mechanisms for a variable number of participants use a mapping from identities to integers, and rely on some form of global configuration or distributed naming protocol to assign unique identifiers to each participant. These approaches are incompatible with replica creation under arbitrary partitions, a typical mode of operation in mobile or poorly connected environments. We present an update tracking mechanism that overcomes this limitation; it departs from the traditional mapping and avoids the use of integer counters, while providing all the functionality of version vectors in what concerns version tracking.
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