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The frequent itemset mining (FIM) is one of the most important techniques to extract knowledge from data in many real-world applications. The Apriori algorithm is the widely-used algorithm for mining frequent itemsets from a transactional dataset. However, the FIM process is both data-intensive and computing-intensive. On one side, large scale data sets are(More)
As a widely-used parallel computing framework for big data processing today, the Hadoop MapReduce framework puts more emphasis on high-throughput of data than on low-latency of job execution. However, today more and more big data applications developed with MapReduce require quick response time. As a result, improving the performance of MapReduce jobs,(More)
In the era of big data, the volume of semantic data grows rapidly. The large scale semantic data contains a lot of significant but often implicit information that needs to be derived by reasoning. The semantic data reasoning is a challenging process. On one hand, the traditional single-node reasoning systems can hardly cope with such large amount of data(More)
As a new area of machine learning research, the deep learning algorithm has attracted a lot of attention from the research community. It may bring human beings to a higher cognitive level of data. Its unsupervised pre-training step allows us to find high-dimensional representations or abstract features which work much better than the principal component(More)
Artificial neural networks (ANNs) have been proved to be successfully used in a variety of pattern recognition and data mining applications. However, training ANNs on large scale datasets are both data-intensive and computation-intensive. Therefore, large scale ANNs are used with reservation for their time-consuming training to get high precision. In this(More)
In the Big Data era, the ever-increasing RDF data have reached a scale in billions of triples and brought obstacles and challenges to single-node RDF data stores. As a result, many distributed RDF stores have been emerging in the Semantic Web community recently. However, currently published ones are either not enough efficient on performance or failed to(More)
There have been many attempts to design brain-computer interfaces (BCIs) for wheelchair control based on steady state visual evoked potential (SSVEP), event-related desynchronization/synchronization (ERD/ERS) during motor imagery (MI) tasks, P300 evoked potential, and some hybrid signals. However, those BCI systems cannot implement the wheelchair navigation(More)
Hadoop MapReduce is a widely used parallel computing framework for solving data-intensive problems. To be able to process large-scale datasets, the fundamental design of the standard Hadoop places more emphasis on high-throughput of data than on job execution performance. This causes performance limitation when we use Hadoop MapReduce to execute short jobs(More)