A Survey of Data Management System for Cloud Computing: Models and Searching Methods <s> DATA MODEL AND SYSTEM ARCHITECTURE <s> RDF is a data model for schema-free structured information that is gaining momentum in the context of Semantic-Web data, life sciences, and also Web 2.0 platforms. The "pay-as-you-go" nature of RDF and the flexible pattern-matching capabilities of its query language SPARQL entail efficiency and scalability challenges for complex queries including long join paths. This paper presents the RDF-3X engine, an implementation of SPARQL that achieves excellent performance by pursuing a RISC-style architecture with streamlined indexing and query processing. The physical design is identical for all RDF-3X databases regardless of their workloads, and completely eliminates the need for index tuning by exhaustive indexes for all permutations of subject-property-object triples and their binary and unary projections. These indexes are highly compressed, and the query processor can aggressively leverage fast merge joins with excellent performance of processor caches. The query optimizer is able to choose optimal join orders even for complex queries, with a cost model that includes statistical synopses for entire join paths. Although RDF-3X is optimized for queries, it also provides good support for efficient online updates by means of a staging architecture: direct updates to the main database indexes are deferred, and instead applied to compact differential indexes which are later merged into the main indexes in a batched manner. Experimental studies with several large-scale datasets with more than 50 million RDF triples and benchmark queries that include pattern matching, manyway star-joins, and long path-joins demonstrate that RDF-3X can outperform the previously best alternatives by one or two orders of magnitude. <s> BIB001 </s> A Survey of Data Management System for Cloud Computing: Models and Searching Methods <s> DATA MODEL AND SYSTEM ARCHITECTURE <s> Cloud computing has become one of the key considerations both in academia and industry. Cheap, seemingly unlimited computing resources that can be allocated almost instantaneously and pay-as-you-go pricing schemes are some of the reasons for the success of Cloud computing. The Cloud computing landscape, however, is plagued by many issues hindering adoption. One such issue is vendor lock-in, forcing the Cloud users to adhere to one service provider in terms of data and application logic. Semantic Web has been an important research area that has seen significant attention from both academic and industrial researchers. One key property of Semantic Web is the notion of interoperability and portability through high level models. Significant work has been done in the areas of data modeling, matching, and transformations. The issues the Cloud computing community is facing now with respect to portability of data and application logic are exactly the same issue the Semantic Web community has been trying to address for some time. In this paper we present an outline of the use of well established semantic technologies to overcome the vendor lock-in issues in Cloud computing. We present a semantics-centric programming paradigm to create portable Cloud applications and discuss MobiCloud, our early attempt to implement the proposed approach. <s> BIB002
At present, one of the crucial research topics on data organization and management of the circumstance of cloud computing is how to describe the cloud data. In this section, some typical data models and system architectures are introduced. And we divide them into two kinds: the data models and system architectures of the academic community and the data models and system architectures of the industrial community. The academic community: Semantic web models: The semantic web technology which possesses the inference ability has become more and more popular in internet application domain. And it also arouses the enthusiasm of researchers in cloud computing community.  introduces the RDF data description model which aim at Map Reduce architecture. They utilize the property of RDF (such as RDF: sub Class of) to describe the cloud data. BIB001 also use RDF describe data, but different from  they put their focus on the query optimization.  state how to store the RDF data into HBase system and process them through Map Reduce approach. BIB002 employ DSL (Domain Specific Language) represent the cloud data, DSL is a programming language which utilize annotation to describe the specific domain data.