Nowadays, we can observe that finding answers on social networks is a hard and time-consuming task. The main contribution of this article is the creation of a model and algorithm that allows users to find answers to t...
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Nowadays, we can observe that finding answers on social networks is a hard and time-consuming task. The main contribution of this article is the creation of a model and algorithm that allows users to find answers to their questions using a spontaneous social network called Mingle. This algorithm uses the Mingle ontology-based knowledge base to find expert users. To achieve this, two important steps are taken: Mingle ontology is updated to support user-oriented expertise and a detailed model is created for the given algorithm. This model was created considering other three similar applications and algorithms. Moreover, we used a semantic web framework. In the end, an evaluation using real-life scenarios is applied to evaluate if the created algorithm meets the initial goals.
Social networks provide a new way of communication that still preserves our human social interaction. Due to the widespread use of mobile devices, people tend to use their smartphone or tablet as the main way to make ...
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Social networks provide a new way of communication that still preserves our human social interaction. Due to the widespread use of mobile devices, people tend to use their smartphone or tablet as the main way to make that interaction. Furthermore, applications developed to these devices are fostering the use of contextual information, such as the location of the user. In this way, this article proposes Mingle, a model for a spontaneous social network targeted at mobile devices. In our proposal, the social network is spontaneous, i.e. involves only people that are physically present in a specific location. Besides presenting the model, we show a developed prototype using Android-based devices. We evaluated Mingle employing two strategies, a usage case and a performance evaluation. The results were encouraging and show the potential of deploying Mingle in real situations.
In parallel programs, the tasks of a given application must cooperate in order to accomplish the required computation. However, the communication time between the tasks may be different depending on which core they ar...
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In parallel programs, the tasks of a given application must cooperate in order to accomplish the required computation. However, the communication time between the tasks may be different depending on which core they ar...
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In parallel programs, the tasks of a given application must cooperate in order to accomplish the required computation. However, the communication time between the tasks may be different depending on which core they are executing and how the memory hierarchy and interconnection are used. The problem is even more important in multi-core machines with NUMA characteristics, since the remote access imposes high overhead, making them more sensitive to thread and data mapping. In this context, process mapping is a technique that provides performance gains by improving the use of resources such as interconnections, main memory and cache memory. The problem of detecting the best mapping is considered NP-Hard. Furthermore, in shared memory environments, there is an additional difficulty of finding the communication pattern, which is implicit and occurs through memory accesses. This work aims to provide a method for static mapping for NUMA architectures which does not require any prior knowledge of the application. Different metrics were adopted and an heuristic method based on the Edmonds matching algorithm was used to obtain the mapping. In order to evaluate our proposal, we use the NAS Parallel Benchmarks (NPB) and two modern multi-core NUMA machines. Results show performance gains of up to 75% compared to the native scheduler and memory allocator of the operating system.
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