We're the x-files : bisexual students 'messing up tidy sex files'

Recent studies are pointing to higher rates of anxiety, depression and other mental health concerns among bisexual-identifying young people in Australia as compared to homosexual and heterosexual young people (Jorm et aI., 2002). International research has found that bisexually active adolescent males report especially high levels of AIDS risk behaviour (Goodenow et aI., 2002). There appears to be a strong link between these findings and the under-representation and mis-representation of bisexuality in Australian school curricula, cultures and communities (McLean, 2001, 2003a, forthcoming 2004; Owens, 1998; Pallotta-Chiarolli, in preparation 2005) .

Sharing music files : tactics of a challenge to the industry

The sharing of music files has been the focus of a massive struggle between representatives of major record companies and artists in the music industry, on one side, and peer-to-peer (p2p) file-sharing services and their users, on the other. This struggle can be analysed in terms of tactics used by the two sides, which can be classified into five categories: cover-up versus exposure, devaluation versus validation, interpretation versus alternative interpretation, official channels versus mobilisation, and intimidation versus resistance. It is valuable to understand these tactics because similar ones are likely to be used in ongoing struggles between users of p2p services and representatives of the content industries.

A novel parallel algorithm for frequent itemsets mining in massive small files datasets

In big data analysis, frequent itemsets mining plays a key role in mining associations, correlations and causality. Since some traditional frequent itemsets mining algorithms are unable to handle massive small files datasets effectively, such as high memory cost, high I/O overhead, and low computing performance, we propose a novel parallel frequent itemsets mining algorithm based on the FP-Growth algorithm and discuss its applications in this paper. First, we introduce a small files processing strategy for massive small files datasets to compensate defects of low read-write speed and low processing efficiency in Hadoop. Moreover, we use MapReduce to redesign the FP-Growth algorithm for implementing parallel computing, thereby improving the overall performance of frequent itemsets mining. Finally, we apply the proposed algorithm to the association analysis of the data from the national college entrance examination and admission of China. The experimental results show that the proposed algorithm is feasible and valid for a good speedup and a higher mining efficiency, and can meet the actual requirements of frequent itemsets mining for massive small files datasets. © 2014 ISSN 2185-2766.

Effective methods and strategies for massive small files processing based on Hadoop

The Hadoop framework provides a powerful way to handle Big Data. Since Hadoop has inherent defects of high memory overhead and low computing performance in processing massive small files, we implement three methods and propose two strategies for solving small files problem in this paper. First, we implement three methods, i.e., Hadoop Archives (HAR), Sequence Files (SF) and CombineFileInputFormat (CFIF), to compensate the existing defects of Hadoop. Moreover, we propose two strategies for meeting the actual needs of different users. Finally, we evaluate the efficiency of the implemented methods and the validity of the proposed strategies. The experimental results show that our methods and strategies can improve the efficiency of massive small files processing, thereby enhancing the overall performance of Hadoop. © 2014 ISSN 1881-803X.