numerous seminars, and two anonymous referees for helpful comments. This project would not have been possible without the assistance of several individuals and organizations. MixMasterFlame and the FlameNap network shared P2P data with us, and BigChampagne LLC, the CMJ Network, Nathaniel Leibowitz

The sharing of different stages of manufacturing processes between countries is of major and growing importance. However, due to previous deficiencies in the Standard International Trade Classification (SITC) Revision 1 system it was not possible to differentiate between two key elements

and biomedical sciences. This article presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user

from different sources are described using shared, logically structured, controlled vocabularies such as the Gene Ontology (GO), this makes the data more easily retrievable and navigable, and it also enhances the degree to which they can be analyzed and combined to serve new purposes. Two major

is that big data becomes transfor-mative when disparate data sets can be linked at the individual person level. In contrast, big biomedical data are scattered across institutions and intentionally iso-lated to protect patient privacy. Both technical and social challenges to linking these data must

Modern biomedical data collection is generating exponentially more data in a multitude of formats. This flood of complex data poses significant opportunities to discover and understand the critical interplay among such diverse domains as genomics, proteomics, metabolomics, and phenomics, including

. It is computationally efficient, with cost linear in the amount of data transfered. Most importantly, when nodes leave the network in the middle of uploads, the algorithm minimizes the duplicate information shared by nodes with truncated downloads. Thus, any two peers with partial knowledge of a given file can almost

they are detected with a page fault. As a result, a page must be writtenmany times to amortize the cost of that fault. Second, the size of a virtual memory page is too big to serve as a unit of coherency, inducing false sharing. Mechanisms to handle false sharing can increase runtime overhead and may cause data

processing Big Data more accessible, but is still limited to data that is currently available, often only within an organization. Fine-grained control over what information is shared outside an organization is difficult to achieve with Big Data, particularly in the MapReduce model. We introduce a novel

Big data nowadays is a fashionable topic, independently of what people mean when they use this term. The challenges include how to capture, transfer, store, clean, analyze, filter, search, share, and visualize such data. But being big is just a matter of volume, although there is no clear agreement