{'fraction_non_alphanumeric': 0.021828103683492497, 'fraction_numerical': 0.0020463847203274215, 'mean_word_length': 5.378260869565217, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Social network analysis for efficient cloud data access

Social network analysis is used to extract features of human communities and providing instrumental information through a new domain of scientific extraction. Here Social Network domains provide various efficient data analysis services in very large scale social networks for serving and assisting in faster and efficient way using various dataset of social network in large cloud services. Wherein the computation is carried out on a parallel platform within the cloud, becomes a excellent option for researchers no longer skilled in parallel programming. In the cloud, a important challenge to effective information analysis is the computation and conversation skew (i.e., load imbalance) among desktops prompted through humanity's team behavior (e.g., bandwagon influence). Natural load balancing procedures either require gigantic effort to re-balance masses on the nodes, or cannot good cope with stragglers. On this paper, we recommend a general straggler-aware execution method, SAE, to aid the evaluation carrier within the cloud. It presents a novel computational decomposition procedure that causes straggling function extraction tactics into more excellent-grained sub strategies that are then allotted over clusters of computers for parallel execution. Experimental results show that SAE can pace up the evaluation by way of as much as 1.77 instances in comparison with state-of-the-art solutions.