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Recently big data have become a research hotspot and been successfully exploited in a few applications such as data mining and business modeling. Although big data contain a plenty of treasures for all the fields of computer science, it is very difficult for the current computing paradigms and computer hardware to efficiently process and utilize big data to attain what are looked forward to. In this work, we explore the possibility of employing big data in recommendation systems. We have proposed a simple recommendation system framework BDRSF (Big Data Recommendation System Framework), which is based on big data with social context theories and has abilities in obtaining the Recommender based on the idea of supervised learning through big data training. Its main idea can be divided into three parts: (1) reduce the scale of the current recommendation problems according to the essence of recommending; (2) design a rational Recommender and propose a novel supervised learning algorithm to get it; (3) utilize the Recommender to deal with the later recommendation problems. Experimental results show that BDRSF outperforms conventional recommendation systems, which clearly indicates the effectiveness and efficiency of big data with social context in personalized recommendation. [ABSTRACT FROM AUTHOR]

Proactive fault management is an important problem in many areas of data management, including cloud computing, big data, vision, machine learning and especially for the cross-domain research of distributed computing and AI (Artificial Intelligence). Unfortunately, most real-world online failure prediction is facing the problem that the used data are difficult to label although the failure prediction should be a supervised learning problem. We observe that, in many cases, the large-scale unlabeled data can be classified through feature extraction and clustering for available prediction, and thus ideas from their combination can be brought to bear. Based on this, we have proposed an online failure prediction framework approach UDFP (Unlabeled Data based online Failure Prediction). It introduces the clustering analysis method based on the combination of the KNN (k-nearest neighbor) and the modularity idea to achieve prediction modeling. It is shown analytically that UDFP can mitigate a supervised learning problem for failure prediction in our situation to some extent. Experimental results demonstrate that UDFP, as a framework approach, has avoided the manual tagging workload and the huge difficulties, improved the predictive accuracy, and reduced cost of data management in safety-aware distributed cloud data centers while enhancing fault-tolerant capabilities and robustness. • Develop an efficient online failure prediction technique for safety-aware cloud centers. • Propose a novel framework approach UDFP based on k-nearest neighbor and modularity. • Put forward an unlabeled data based method with the idea of time series for failure prediction. • The performance of UDFP has been verified using Ganglia and Tensorflow in real hosts. [ABSTRACT FROM AUTHOR]