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Clustering algorithms play a very important role in machine learning. With the development of big‐data artificial intelligence, distributed parallel algorithms have become an important research field. To reduce the computational complexity and running time of large‐scale datasets in the clustering process, this study proposes a distributed clustering algorithm DACA (distributed adaptive grid decision...
In wireless sensor networks (WSNs), numerous sensors can produce a significant portion of the big data. It remains an open issue how to timely gather and transmit such large amount of data while minimizing data latency through wireless sensor networks (WSNs). On the other hand, spatially correlated sensor observations lead to considerable data redundancy in the network. To efficiently eliminate data...
In order to remove resource barriers and smooth the learning curve for education on big data analytics in STEM disciplines, we develop an portable open source labware that is called STEM-BD for promoting education on big data analytics. STEM-BD integrates the following four critical components, big data platform, big data sets, data analytics algorithms and hands-on lab exercises in a multi-dimensional...
With the explosive growth number of services in cloud computing environment, how to accurately and rapidly discover the services that can meet user's functional and nonfunctional requirements is a challenging subject. Aiming at issues of service inefficiencies and low precision in the existing service discovery methods, a model for service discovery based on functions and QoS clustering is proposed...
Recognition of multiple moving objects is a very important task for achieving user-cared knowledge to send to the base station in wireless video-based sensor networks. However, video based sensor nodes, which have constrained resources and produce huge amount of video streams continuously, bring a challenge to segment multiple moving objects from the video stream online. Traditional efficient clustering...
Seismic exploration plays an important role in petroleum industry. It is widely admitted that there are a lot of limitations of conventional data analysis ways in oil and gas industry. Traditional methods in petroleum engineering are knowledge-driven and often neglect some underlying factors. On the contrary, data mining is to deal with mass of data and never overlook any important phenomena. Due...
With an ever-increasing number of Web services being available, finding desired Web service is crucial for service users. Current keyword search and most existing approaches are inefficient in two main aspects: poor scalability and lack of semantics. Firstly, users are overwhelmed by the huge number of irrelevant services returned. Secondly, the intentions of users and the semantics in Web services...
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