Download e-book for iPad: Data Storage for Social Networks: A Socially Aware Approach by Duc A. Tran

By Duc A. Tran

Evidenced via the luck of fb, Twitter, and LinkedIn, on-line social networks (OSNs) became ubiquitous, providing novel methods for individuals to entry details and speak with one another. because the expanding approval for social networking is indisputable, scalability is a vital factor for any OSN that wishes to serve various clients. Storing consumer info for the full community on a unmarried server can speedy result in a bottleneck, and, as a result, extra servers are had to extend garage skill and reduce facts request site visitors in keeping with server. including extra servers is only one step to handle scalability. your next step is to figure out how most sensible to shop the information throughout a number of servers. This challenge has been widely-studied within the literature of dispensed and database platforms. OSNs, despite the fact that, symbolize a unique category of information platforms. whilst a person spends time on a social community, the information as a rule asked is her personal and that of her neighbors; e.g., in fb or Twitter, those info are the prestige updates published through herself in addition to that published by means of the chums. This so-called social locality will be taken under consideration whilst deciding upon the server destinations to shop those information, in order that whilst a person matters a learn request, all its proper information will be lower back speedy and successfully. Social locality isn't really a layout think about conventional garage structures the place info requests are constantly processed independently. Even for today’s OSNs, social locality isn't but thought of of their information partition schemes. those schemes depend upon  distributed hash tables (DHT), utilizing constant hashing to assign the users’ information to the servers. The random nature of DHT results in susceptible social locality which has been proven to lead to negative functionality less than heavy request rather a lot. information garage for Social Networks: A Socially conscious method is aimed toward reviewing the present literature of information garage for on-line social networks and discussing new equipment that take into consideration social understanding in designing effective info garage.

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Additional resources for Data Storage for Social Networks: A Socially Aware Approach (SpringerBriefs in Optimization)

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Instead of relying on a combination of EA and METIS as done in S-SPUT, S-CLONE adopts a greedy algorithm. It is observed that minimizing Lread is equivalent to maximize M N X X i D1 sD1 xi s N X rj pjs ej i j D1 and so if we need to place a replica copy for a user i somewhere, the most desirable location should be the primary server of most neighbors of i , taking into their social strengths and read rates; this way, most neighbors will benefit from this replica when they issue a read query, thus helping to reduce the total read load.

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Although either can work in S-PUT for its evolution process, here we describe this process using SPEA2, which we have evaluated with some encouraging preliminary results. Using SPEA2 as EA for the partitioning problem, a population is represented by a set of individuals, each being a base-M string of length N , s1 s2 : : : sN , representing a possible partition assignment: user i is assigned to server si . For example, for a network of 1,000 nodes to be partitioned across 16 servers, an individual is an array of 1,000 integers, each having a value between 0 and 15.

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Data Storage for Social Networks: A Socially Aware Approach (SpringerBriefs in Optimization) by Duc A. Tran

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