Download e-book for iPad: Handbook of Massive Data Sets by Andrei Broder, Monika Henzinger (auth.), James Abello, Panos

By Andrei Broder, Monika Henzinger (auth.), James Abello, Panos M. Pardalos, Mauricio G. C. Resende (eds.)

The proliferation of big facts units brings with it a chain of specified computational demanding situations. This "data avalanche" arises in quite a lot of clinical and advertisement purposes. With advances in machine and knowledge applied sciences, lots of those demanding situations are commencing to be addressed by means of assorted inter-disciplinary teams, that indude computing device scientists, mathematicians, statisticians and engineers, operating in dose cooperation with program area specialists. excessive profile functions indude astrophysics, bio-technology, demographics, finance, geographi­ cal details structures, executive, drugs, telecommunications, the surroundings and the web. John R. Tucker of the Board on Mathe­ matical Seiences has said: "My curiosity during this problern (Massive facts units) isthat I see it because the rnost irnportant cross-cutting problern for the rnathernatical sciences in sensible problern fixing for the subsequent decade, since it is so pervasive. " The instruction manual of big information units is constituted of articles writ­ ten through specialists on chosen subject matters that care for a few significant point of big info units. It comprises chapters on info retrieval either within the web and within the conventional feel, internet crawlers, colossal graphs, string processing, information compression, dustering equipment, wavelets, op­ timization, exterior reminiscence algorithms and information constructions, the USA nationwide duster undertaking, excessive functionality computing, info warehouses, info cubes, semi-structured facts, facts squashing, facts caliber, billing within the huge, fraud detection, and information processing in astrophysics, pollution, biomolecular information, earth commentary and the environment.

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B. Tuma, editor, Sociological Methodology, pages 26-48. Jossey-Bass, 1986. P. Pirolli, J. Pitkow, and R. Rao. Silk from a sow's ear: Extracting usable structures from the web. In Proceedings of the Conference on Human Factors in Computing Systems (CHI 96}, pages 118-125, 1996. PubMed, 2000. http: I lncbi. nlm. nih. gov I. O. Rabin. Fingerprinting by random polynomials. Technical Report TR-15-81, Center for Research in Computing Technology, Harvard University, 1981. E. S. Jones. Relevance weighting of search terms.

Checkpointing is an important part of any long-running process such as a web crawl. By checkpointing we mean writing a representation of the crawler's state to stable storage that, in the event of a failure, is sufficient to allow the crawler to recover its state by reading the checkpoint and to resume crawling from the exact state it was in at the time of the checkpoint. By this definition, in the event of a failure, any work performed after the most recent checkpoint is lost, but none of the work up to the most recent checkpoint.

Finally, in the case of continuous crawling, the URL of the document that was just downloaded is also added back to the URL frontier. As noted earlier, a mechanism is required in the continuous crawling case for interleaving the downloading of new and old URLs. Mercator uses a randomized priority-based scheme for this purpose. A standard configuration for continuous crawling typically uses a frontier implementation that attaches priorities to URLs based on their download history, and whose dequeue method is biased towards higher priority URLs.

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