Cloud computing provides access to "infinite" storage and computing resources, offering promising perspectives for many applications, particularly e-learning. However, this new paradigm requires rethinking of database management principles in order to allow deployment on scalable, easy to access infrastructures, applying a pay-as-you-go model in which failures are not exceptions but rather the norm. The GOD project aims to provide an optimized data management system for e-learning in the cloud by rethinking traditional database management techniques, extending them to consider the specificities of this paradigm.

Background: Deep learning-based diagnosis systems are useful to identify abnormalities in medical images with the greatly increased workload of doctors. Specifically, the rate of new cases and deaths from malignancies is rising for liver diseases. Early detection of liver lesions plays an extremely important role in effective treatment and gives a higher chance of survival for patients. Therefore, automatic detection and classification of common liver lesions are essential for doctors. In fact, radiologists mainly rely on Hounsfield Units to locate liver lesions but previous studies often pay little attention to this factor. Methods: In this paper, we propose an improved method for the automatic classification of common liver lesions based on deep learning techniques and the variation of Hounsfield Unit densities on CT images with and without contrast. Hounsfield Unit is used to locate liver lesions accurately and support data labeling for classification. We construct a multi-phase classification model developed on the deep neural networks of Faster R-CNN, R-FCN, SSD, and Mask R-CNN with the transfer learning approach. Results: The experiments are conducted on six scenarios with multi-phase CT images of common liver lesions. Experimental results show that the proposed method improves the detection and classification of liver lesions compared with recent methods because its accuracy achieves up to 97.4%. Conclusion: The proposed models are very useful to assist doctors in the automatic segmentation and classification of liver lesions to solve the problem of depending on the clinician’s experience in the diagnosis and treatment of liver lesions.

Big data processing is attracting the interest of many researchers to process large-scale datasets and extract useful information for supporting and providing decisions. One of the biggest challenges is the problem of querying large datasets. It becomes even more complicated with similarity queries instead of exact match queries. A fuzzy join operation is a typical operation frequently used in similarity queries and big data analysis. Currently, there is very little research on this issue, thus it poses significant barriers to the efforts of improving query operations on big data efficiently. As a result, this study overviews the similarity algorithms for fuzzy joins, in which the data at the join key attributes may have slight differences within a fuzzy threshold. We analyze six similarity algorithms including Hamming, Levenshtein, LCS, Jaccard, Jaro, and Jaro - Winkler, to show the difference between these algorithms through the three criteria: output enrichment, false positives/negatives, and the processing time of the algorithms. Experiments of fuzzy joins algorithms are implemented in the Spark environment, a popular big data processing platform. The algorithms are divided into two groups for evaluation: group 1 (Hamming, Levenshtein, and LCS) and group 2 (Jaccard, Jaro, and Jaro - Winkler). For the former, Levenshtein has an advantage over the other two algorithms in terms of output enrichment, high accuracy in the result set (false positives/negatives), and acceptable processing time. In the letter, Jaccard is considered the worst algorithm considering all three criteria mean while Jaro - Winkler algorithm has more output richness and higher accuracy in the result set. The overview of the similarity algorithms in this study will help users to choose the most suitable algorithm for their problems.