Abstract. Social Graph Analytics has become very popular these days, with companies like Zynga, Linkedin, and Facebook seeking to derive the most value from their respective social networks. It is common belief that relational databases are ill-equipped to deal with graph problems, resulting in the use of MapReduce implementations or special purpose graph analysis engines. We challenge this belief by presenting a few use-cases that Vertica has very successfully solved with simple SQL over a high-performance relational database engine.Keywords: Analytics, Graphs, Data Mining, Vertica, Social Networks, MapReduce, Hadoop, Pig, Influencers, K-core. IntroductionSocial networks have become a central feature of our online lives, both as consumers and enterprises. From Farmville to Linkedin to Groupon, many new businesses revolve around knowing your friends and leveraging their collective knowledge, behaviors, opinions and buying power. Indeed, for any data-driven enterprise seeking to provide a personalized and relevant customer experience, it is now no longer just web analytics -effective real-time social network analytics can reap significant rewards. The heart of social network analytics revolves around solving graph problems on large volumes of data at scale and with high performance. It is a common misconception that relational databases are ill-equipped to deal with graph problems, resulting in the use of custom coded implementations or special purpose graph analysis engines. We challenge this belief by presenting two use-cases that Vertica has very successfully solved with simple SQL over a high-performance relational database engine. The first use-case is to find the influencers in a social graph and to show how it can be used to do A/B testing of products. The second use-case is to solve the problem of counting triangles in a graph and comparing the solutions written in SQL, Hadoop/MapReduce and Pig.
This paper describes and fixes the problem of multi-keyword rated look for over secured reasoning information (MRSE) while preserving strict system sensible comfort in the reasoning processing model. Information proprietors are roused to delegate their convoluted data administration frameworks from neighborhood destinations to the business open thinking for extraordinary flexibility and monetary advantages. Anyway, for safeguarding data solace, delicate data must be secured before outsourcing, which obsoletes traditional data use in light of plaintext essential word and key expression search for. To better bolster clients in their long haul data missions on the Web, Google stay informed concerning their worries and mouse snaps while seeking on the web. In this paper, we think about the issue of arranging a client's conventional concerns into gatherings in an intense and electronic style. In a flash deciding inquiry gatherings is useful for various distinctive online search for motor parts and projects, for example, question recommendations, result position, question alteration, sessionization, and community explore for. In our method, we depart past strategies to depend on literary resemblance or moment points of confinement, and we suggest a more compelling technique that controls search for question logs.
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