A Survey of Data Management System for Cloud Computing: Models and Searching Methods <s> SQL-like data query methods: Compared with Map <s> Companies providing cloud-scale services have an increasing need to store and analyze massive data sets such as search logs and click streams. For cost and performance reasons, processing is typically done on large clusters of shared-nothing commodity machines. It is imperative to develop a programming model that hides the complexity of the underlying system but provides flexibility by allowing users to extend functionality to meet a variety of requirements. ::: ::: In this paper, we present a new declarative and extensible scripting language, SCOPE (Structured Computations Optimized for Parallel Execution), targeted for this type of massive data analysis. The language is designed for ease of use with no explicit parallelism, while being amenable to efficient parallel execution on large clusters. SCOPE borrows several features from SQL. Data is modeled as sets of rows composed of typed columns. The select statement is retained with inner joins, outer joins, and aggregation allowed. Users can easily define their own functions and implement their own versions of operators: extractors (parsing and constructing rows from a file), processors (row-wise processing), reducers (group-wise processing), and combiners (combining rows from two inputs). SCOPE supports nesting of expressions but also allows a computation to be specified as a series of steps, in a manner often preferred by programmers. We also describe how scripts are compiled into efficient, parallel execution plans and executed on large clusters. <s> BIB001 </s> A Survey of Data Management System for Cloud Computing: Models and Searching Methods <s> SQL-like data query methods: Compared with Map <s> Cloud storage solutions promise high scalability and low cost. Existing solutions, however, differ in the degree of consistency they provide. Our experience using such systems indicates that there is a non-trivial trade-off between cost, consistency and availability. High consistency implies high cost per transaction and, in some situations, reduced availability. Low consistency is cheaper but it might result in higher operational cost because of, e.g., overselling of products in a Web shop. ::: ::: In this paper, we present a new transaction paradigm, that not only allows designers to define the consistency guarantees on the data instead at the transaction level, but also allows to automatically switch consistency guarantees at runtime. We present a number of techniques that let the system dynamically adapt the consistency level by monitoring the data and/or gathering temporal statistics of the data. We demonstrate the feasibility and potential of the ideas through extensive experiments on a first prototype implemented on Amazon's S3 and running the TPC-W benchmark. Our experiments indicate that the adaptive strategies presented in the paper result in a significant reduction in response time and costs including the cost penalties of inconsistencies. <s> BIB002 </s> A Survey of Data Management System for Cloud Computing: Models and Searching Methods <s> SQL-like data query methods: Compared with Map <s> Provisioning and maintenance of infrastructure for Web based digital library search engines such as CiteSeer$^x$ present several challenges. CiteSeer$^x$ provides autonomous citation indexing, full text indexing, and extensive document metadata from document scrawled from the web across computer and information sciences and related fields. Infrastructure virtualization and cloud computing are particularly attractive choices for CiteSeer$^x$, which is challenged by both growth in the size of the indexed document collection, new features and most prominently usage. In this paper, we discuss constraints and choices faced by information retrieval systems like CiteSeer$^x$ by exploring in detail aspects of placing CiteSeer$^x$ into current cloud infrastructure offerings. We also implement an ad-hoc virtualized storage system for experimenting with adoption of cloud infrastructure services. Our results show that a cloud implementation of CiteSeer$^x$ may be a feasible alternative for its continued operation and growth <s> BIB003
Reduce, SQL query has stronger expression ability such as join  . Therefore, some researchers and developers have developed corresponding SQL-Like data query according to their own system. The goal of Hive project is to build a data warehouse platform and use Hive QL language of SQLLike to search the data stored in HDFS. Yahoo uses Pig Latin  which is similar to SQL query to search the data by constructing a query plan to operate the corresponding query of the data stored in the cloud computing environment. Microsoft Company proposes SCOPE (Structured Computations Optimized for Parallel Execution) by assimilating the characteristics of SQL such as organizing the data in the dataset by row and column method, supporting join operating BIB001 . Some researchers have shifted some traditional searching methods to the cloud computing environment in some specific areas. BIB003 use such method to analyze its availability and flexibility in the data information management of digital library. BIB002 transfer the search and management of dataset log information to S3 cloud computing platform of Amazon and make a comparative analysis of its performance.  apply the traditional text query technology to cloud computing to find the searching performance of cloud computing.