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Extensive studies have been conducted to diagnose and predict students' academic performance by analyzing a large amount of data related to their learning behaviors in a blended learning environmen...

Hierarchical hybrid filing system is a research hotspot in the field of data storage, and also is widely accepted in the industrial circle, which is widely used from personal storage to data center. This paper introduces an energy-efficient optical storage at a lower cost to solve the energy consumption problem of large data center, with an attempt to build a hybrid filing system comprised of array, online optical disk library and off-line optical disk library, discusses the guarantee mechanism of data consistency and the elastic design of system, and focuses on the research of the file caching and prefetching mechanism of hybrid magneto-optical filing system based on the read-write features of optical medium. Through experiments, the system is proved to be capable of effectively saving energy and maintaining the timeliness of data retrieval and query.

With the ceaseless propulsion of informatization process, a great deal of data accumulated in the production system resulted in archiving demand; meanwhile, with the increasingly enriching types of data information, big data technology enhanced the function of unstructured data. This paper designs and realizes the KingCloud intelligent object archiving system, which achieves classification of text files through document classification technology and provides logic views of documents and achieves the acquisition of content elements through image identification, video keyframe and other technologies. As for the overall storage structure, document prefetching, memory buffer, data layout and strategy perception etc. can be optimized by combining the semantic studies of file system, and data can be intelligently classified, summarized, discovered, predicted and analyzed to remarkably improve the service ability, service quality and service performance of memory system.

1PB or more of the data has been the concern of the reliability of the data in large data warehouses. It strongly depends on many interdependent system parameters, such as the replica placement policy, the number of nodes and Previous work has substantially and respectively discussed the individual effects of these parameters, and rarely provide its optimum value, there is no mention of their optimal combination. In this paper, we propose a new object-based repair Markov model. The analysis of this model based on three popular replica placement strategy, we calculate the personal best value of these parameters in the first, then work out the best combination of GA. Compared with the existing models, our model is easier to solve, and to achieve a more comprehensive and practical conclusions.

Microblogging has become a major tool for people to not only share information, but also to talk about current affairs. Has become the most popular content in the analysis, interested companies and researchers. We focus on the micro-blog clustering high-dimensional, high sparse, and proposed a new algorithm based on k-means-k frequent itemsets. In addition, the development of a method to capture long-term mutual information context knowledge in microblogging and algorithms are also designed to measure the conversation Similar. In order to support the new micro-blog clustering algorithm. Experimental results show that the clustering algorithm has higher accuracy than the standard k-means and two points in k-means algorithm toward large-capacity and highly sparse microblogging also maintain good scalability.

Cloud computing has been a hot researching area of computer network technology, since it was proposed in 2007. Cloud computing also has been envisioned as the next-generation architecture of IT Enterprise [1]. Cloud computing infrastructures enable companies to cut costs by outsourcing computations on-demand. It moves the application software and database to the large data centers, where the management of the data and services may not be fully trustworthy [2]. This poses many new security challenges. In this paper, we just focus on data storage security in the cloud, which has been the most important aspect of quality of service. To ensure the confidentiality and integrity of user’s data in the cloud, and support of data dynamic operations, such as modification, insertion and deletion, we propose a framework for storage security, which includes cryptographic storage scheme and data structure. With our framework, the untrusted server cannot learn anything about the plaintext, and the dynamic operation can be finished in short time. The encryption algorithm and data storage structure is simple, and it’s easy to maintain. Hence, our framework is practical to use today.

The increasing number of applications for large data, such as Web search engines, we need to have high availability 7*24 tracking, storage and analysis of a large number of real-time user access logs. Traditional common trading application high availability solution, always not efficient enough to store this high-rate only inserted into the archive stream. This paper presents an integrated approach to save of this archive of data streams in a database cluster and rapid recovery. This method is based on a simple replication protocol and high performance data loading and query strategy. Experimental results show that our approach can reach efficient data loading and query and get shorter recovery time than the traditional database cluster recovery methods.

Due to the huge scale and the number of components, big data is difficult to work with the use of relational databases and desktop statistics and visualization package. Much database replication technology is used to increase the MTTF, but few have a large database system, the traditional method of backup is not feasible, expensive manpower costs reduce MTTR. On the basis of analyzing the characteristics of data in large databases, we propose a new method called Detaching Read-Only (DRO) mechanism and its changes DRO+. It reduces MTTR by reducing the physical change of the data in each database, by separating data node size granularity. Analysis and experimental results show that our method can not only reduce the MTTR an order of magnitude, but there is no additional hardware costs, but also reduce the high human costs.