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Books > Computing & IT > Applications of computing > Databases > Web / Internet databases
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Data Storage for Social Networks - A Socially Aware Approach (Paperback, 2013 ed.)
Loot Price: R1,231
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Data Storage for Social Networks - A Socially Aware Approach (Paperback, 2013 ed.)
Series: SpringerBriefs in Optimization
Expected to ship within 18 - 22 working days
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Evidenced by the success of Facebook, Twitter, and LinkedIn, online
social networks (OSNs) have become ubiquitous, offering novel ways
for people to access information and communicate with each other.
As the increasing popularity of social networking is undeniable,
scalability is an important issue for any OSN that wants to serve a
large number of users. Storing user data for the entire network on
a single server can quickly lead to a bottleneck, and,
consequently, more servers are needed to expand storage capacity
and lower data request traffic per server. Adding more servers is
just one step to address scalability. The next step is to determine
how best to store the data across multiple servers. This problem
has been widely-studied in the literature of distributed and
database systems. OSNs, however, represent a different class of
data systems. When a user spends time on a social network, the data
mostly requested is her own and that of her friends; e.g., in
Facebook or Twitter, these data are the status updates posted by
herself as well as that posted by the friends. This so-called
social locality should be taken into account when determining the
server locations to store these data, so that when a user issues a
read request, all its relevant data can be returned quickly and
efficiently. Social locality is not a design factor in traditional
storage systems where data requests are always processed
independently. Even for today's OSNs, social locality is not yet
considered in their data partition schemes. These schemes rely on
distributed hash tables (DHT), using consistent hashing to assign
the users' data to the servers. The random nature of DHT leads to
weak social locality which has been shown to result in poor
performance under heavy request loads. Data Storage for Social
Networks: A Socially Aware Approach is aimed at reviewing the
current literature of data storage for online social networks and
discussing new methods that take into account social awareness in
designing efficient data storage.
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