By Fatos Xhafa, Leonard Barolli, Admir Barolli, Petraq Papajorgji
This ebook covers the newest advances in tremendous information applied sciences and gives the readers with a finished overview of the cutting-edge in sizeable information processing, research, analytics, and different comparable themes. It provides new versions, algorithms, software program strategies and methodologies, overlaying the complete info cycle, from facts accumulating to their visualization and interplay, and features a set of case experiences and most sensible practices. New learn concerns, demanding situations and possibilities shaping the longer term schedule within the box of massive information also are pointed out and awarded during the ebook, that is meant for researchers, students, complicated scholars, software program builders and practitioners operating on the leading edge of their box.
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Xhafa et al. -I. Ciobanu, C. Dobre, and F. Xhafa cases it is not easy (nor possible sometimes) to have connectivity among a source sensor node and a destination data collector node. This happens because the animals to be monitored move freely and there is no possibility to control them in such a way to favor connectivity. Opportunistic networks may also be exploited to bridge the digital divide. In fact, they can support intermittent connectivity to the Internet for underdeveloped or isolated regions.
The designation of the Admission Server to be in charge of handling such query may be set according to, for example, the geographic region from where the query is originated. 2 which has 128-bits length in Exploring the Hamming Distance in Distributed Infrastructures 13 Figure 5. The di is the data identifier of reference for the similarity search process. In the sequence, the Admission Server reduces the di, also using the RHH function, aimed at obtaining the identifier si, which indicates the Hosting Server responsible for the reference data di.
These tests permitted us to highlight the following aspects: • The frequency distribution of the number of hops to retrieve all similar profiles in the set according to their similarity level. This evaluation shows that the Hamming DHT aggregates more than a normal ring-style DHT, such as Chord, reducing the distance between similar profiles in the number of hops; • The query recall, corresponding to the fraction of the profiles that is relevant to the query and successfully retrieved. This evaluation shows that it is possible to build a more efficient search engine on top of the Hamming DHT, at lower cost, measured in the number of hops to complete the query.