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A distributed incremental in; mation acquisition model; large-scale text data
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DOI：10.1007/s10586-017-1498-8.png)
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Abstract
En 中文
Timely discovering and acquiring in;
mation from incremental data on the Internet is a hot topic in a big data era. This paper presents a distributed incremental in;
mation acquisition model;
large-scale text data. To obtain a lower false positive rate and higher efficiency of the traditional Bloom filter, a distributed multidimensional Bloom filter is designed and proposed to cope with the deduplication of large-scale Web URL text data. Three methods related to Bloom filter were compared based on the false positive rate and response efficiency. The results show that the distributed incremental in;
mation acquisition model;
large-scale text data can achieve a high duplicate removal rate with a lower false positive rate.
Keywords:
Big data analytics
Deduplication of large-scale text data
Distributed incremental in
mation acquisition model
Distributed multidimensional bloom filter
False positive rate
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