Publications

@inbook{Rajabi2016,

title = {Interlinking Big Data to Web of Data},

author = {Enayat Rajabi and Seyed-Mehdi-Reza Beheshti},

doi = {10.1007/978-3-319-30265-2_6},

isbn = {978-3-319-30263-8},

year  = {2016},

date = {2016-05-27},

volume = {18},

pages = {133-145},

publisher = {Springer International Publishing},

abstract = {The big data problem can be seen as a massive number of data islands, ranging from personal, shared, social to business data. The data in these islands is getting large scale, never ending, and ever changing, arriving in batches at irregular time intervals. Examples of these are social and business data. Linking and analyzing of this potentially connected data is of high and valuable interest. In this context, it will be important to investigate how the Linked Data approach can enable the Big Data optimization. In particular, the Linked Data approach has recently facilitated the accessibility, sharing, and enrichment of data on the Web. Scientists believe that Linked Data reduces Big Data variability by some of the scientifically less interesting dimensions. In particular, by applying the Linked Data techniques for exposing structured data and eventually interlinking them to useful knowledge on the Web, many syntactic issues vanish. Generally speaking, this approach improves data optimization by providing some solutions for intelligent and automatic linking among datasets. In this chapter, we aim to discuss the advantages of applying the Linked Data approach, towards the optimization of Big Data in the Linked Open Data (LOD) cloud by: (i) describing the impact of linking Big Data to LOD cloud; (ii) representing various interlinking tools for linking Big Data; and (iii) providing a practical case study: linking a very large dataset to DBpedia.

},

keywords = {Big Data, Linked Data},

pubstate = {published},

tppubtype = {inbook}

}