The big data era is upon us: data are being generated, analyzed,
and used at an unprecedented scale, and data-driven decision making
is sweeping through all aspects of society. Since the value of data
explodes when it can be linked and fused with other data,
addressing the big data integration (BDI) challenge is critical to
realizing the promise of big data. BDI differs from traditional
data integration along the dimensions of volume, velocity, variety,
and veracity. First, not only can data sources contain a huge
volume of data, but also the number of data sources is now in the
millions. Second, because of the rate at which newly collected data
are made available, many of the data sources are very dynamic, and
the number of data sources is also rapidly exploding. Third, data
sources are extremely heterogeneous in their structure and content,
exhibiting considerable variety even for substantially similar
entities. Fourth, the data sources are of widely differing
qualities, with significant differences in the coverage, accuracy
and timeliness of data provided. This book explores the progress
that has been made by the data integration community on the topics
of schema alignment, record linkage and data fusion in addressing
these novel challenges faced by big data integration. Each of these
topics is covered in a systematic way: first starting with a quick
tour of the topic in the context of traditional data integration,
followed by a detailed, example-driven exposition of recent
innovative techniques that have been proposed to address the BDI
challenges of volume, velocity, variety, and veracity. Finally, it
presents merging topics and opportunities that are specific to BDI,
identifying promising directions for the data integration
community.
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