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Linked 'Big' Data: Towards a Manifold Increase in Big Data Value and Veracity

: Debattista, J.; Lange, C.; Scerri, S.; Auer, S.


Raicu, I. ; Institute of Electrical and Electronics Engineers -IEEE-; Association for Computing Machinery -ACM-:
2nd IEEE/ACM International Symposium on Big Data Computing, BDC 2015 : 7-10 December 2015, Limassol, Cyprus. Proceedings
Piscataway, NJ: IEEE, 2015
ISBN: 978-0-7695-5696-3 (Electronic)
ISBN: 978-1-5090-0340-2 (Print on demand)
International Symposium on Big Data Computing (BDC) <2, 2015, Limassol>
Fraunhofer IAIS ()

The Web of Data is an increasingly rich source of information, which makes it useful for Big Data analysis. However, there is no guarantee that this Web of Data will provide the consumer with truthful and valuable information. Most research has focused on Big Data's Volume, Velocity, and Variety dimensions. Unfortunately, Veracity and Value, often regarded as the fourth and fifth dimensions, have been largely overlooked. In this paper we discuss the potential of Linked Data methods to tackle all five V's, and particularly propose methods for addressing the last two dimensions. We draw parallels between Linked and Big Data methods, and propose the application of existing methods to improve and maintain quality and address Big Data's veracity challenge.