The partial ordering of events as defined by their causal relationships, that is, the ability of one event to directly, or transitively, affect another is defined. Its generalized and practical implementations in terms of partially ordered logical clocks are described. Such clocks can provide a decentralized definition of time for distributed computing systems, which lack a common time base. In their full generality, partially ordered logical clocks may be impractically expensive for long-lived computations. Several possible optimizations, depending on the application environment in which the clocks will be used, are described. Some applications are summarized.

Smart cities can be complemented by fusing various components and incorporating recent emerging technologies. IoT communications are crucial to smart city operations, which are designed to support the concept of a “Smart City” by utilising the most cutting-edge communication technologies to enhance city administration and resident services. Smart cities have been outfitted with numerous IoT-based gadgets; the Internet of Things is a modular method to integrate various sensors with all ICT technologies. This paper provides an overview of smart cities’ concepts, characteristics, and applications. We thoroughly investigate smart city applications, challenges, and possibilities with solutions in recent technological trends and perspectives, such as machine learning and blockchain. We discuss cloud and fog IoT ecosystems in the in capacity of IoT devices, architectures, and machine learning approaches. In addition we integrate security and privacy aspects, including blockchain applications, towards more trustworthy and resilient smart cities. We also highlight the concepts, characteristics, and applications of smart cities and provide a conceptual model of the smart city mega-events framework. Finally, we outline the impact of recent emerging technologies’ implications on challenges, applications, and solutions for futuristic smart cities.

In most business processes, several activities need to be executed by human resources and cannot be fully automated. To evaluate resource performance and identify best practices as well as opportunities for improvement, managers need objective information about resource behaviors. Companies often use information systems to support their processes, and these systems record information about process execution in event logs. We present a framework for analyzing and evaluating resource behavior through mining such event logs. The framework provides (1) a method for extracting descriptive information about resource skills, utilization, preferences, productivity, and collaboration patterns; (2) a method for analyzing relationships between different resource behaviors and outcomes; and (3) a method for evaluating the overall resource productivity, tracking its changes over time, and comparing it to the productivity of other resources. To demonstrate the applicability of our framework, we apply it to analyze employee behavior in an Australian company and evaluate its usefulness by a survey among industry managers.

This paper reports on a replication of earlier studies into a possible hierarchy of programming skills. In this study, the students from whom data was collected were at a university that had not provided data for earlier studies. Also, the students were taught the programming language Python, which had not been used in earlier studies. Thus this study serves as a test of whether the findings in the earlier studies were specific to certain institutions, student cohorts, and programming languages. Also, we used a non–parametric approach to the analysis, rather than the linear approach of earlier studies. Our results are consistent with the earlier studies. We found that students who cannot trace code usually cannot explain code, and also that students who tend to perform reasonably well at code writing tasks have also usually acquired the ability to both trace code and explain code

Parallel programs differ from sequential programs primarily in that the temporal relationships between events are only partially defined. However, for a given distributed computation, debugging utilities typically linearize the observed set of events into a total ordering, thus losing information and allowing potentially capturable temporal errors to escape detection. We explore use of the partially ordered relation “happened before” to augment both centralized and distributed parallel debuggers to ensure that such errors are always detected and that the results produced by the debugger are unaffected by the non-determinism inherent in the partial ordering. This greatly reduces the number of tests required during debugging. Assertions are based on time intervals, rather than treating events as dimensionless points.