Organization and storage model of marine information and its application in the "China Digital Ocean"

Based on the experience and achievement of the＂China Digital Ocean＂, the classification plan for Marine data elements is made, which can be classified into five, including marine point elements, marine line elements, marine polygon elements, marine grid elements and marine dynamic elements. In this paper, the technology of features and object-oriented method, a spatial-temporal data model is proposed, which can be applied in the large information system engineering like the ＂Digital Ocean＂, and this paper discusses the application of spatial data model, marine three-dimensional raster data model and relation data model in the building of Data Warehouse in ＂China Digital Ocean＂, and concludes the merits of these models.

Comparisons of three data storage models in parametric temporal databases

The parametric temporal data model captures a real world entity in a single tuple, which reduces query language complexity. Such a data model, however, is difficult to be implemented on top of conventional databases because of its unfixed attribute sizes. XML is a matured technology and can be an elegant solution for such challenge. Representing data in XML trigger a question about storage efficiency. The goal of this work is to provide a straightforward answer to such a question. To this end, we compare three different storage models for the parametric temporal data model and show that XML is not worse than any other approaches. Furthermore, XML outperforms the other storages under certain conditions. Therefore, our simulation results provide a positive indication that the myth about XML is not true in the parametric temporal data model.

Auditing the Use of DBMS Covert Storage Channels Relevant with Transactions

This paper proposes the concept of transaction-type covert storage channels, which are caused by database storage resources. It also proposes that the mode of auditing those channels be based on the transactions. Next, the paper analyzes and resolves the two problems arising from auditing the use of transaction-type covert storage channels in database systems: namely, the relationship between channel variables, which are altered (or viewed) by the transaction and satisfy integrity constraints in DBMS, and database states; and the circumvention of covert storage channel audit in DBMS.

Efficient hardware-based private information retrieval using partial reshuffle

The paper proposes a novel hardware-based private information retrieval （HWPIR） protocol. By partially reshuffling previously accessed items in each round, instead of frequently reshuffling the whole database, the scheme makes better use of shuffled data copies and achieves the computation overhead at O（/N/K）,where N and k are the sizes of the database and secure storage respectively. For securestorage with moderate size, e.g. k = O（/N）, the overhead is 0（4/N）. The result is much better than the state-of-art schemes （as compared to e.g. O（log2N））. Without increasing response time and communication cost, the proposed protocol is truly practicable regardless of the database size. The security and preformance of the protocol is formally analyzed.

Design and elementary realization of the Vision Earth System

The Vision Earth System is a interactive system by employing B/S model.The system has the function of query display and mutually displays relevant geologic information,integrating image information of one outcrop and realizing 3D geologic visualization.In this system,the basis is effective store,transmitting,display and quick query of enormous images and their properties data.From Java technology,this essay researches the elementary realization of Vision Earth System by adopting store formality of enormous images database,quick display image of website and quick image storage method.

Big Data; Definition and Challenges

In recent years, due to the widespread use of electronic services and the use of social network as well, large volumes of information are being made that this information contains various types of things such as videos, photos, texts etc. besides large volume. Due to the high volume and the lack of specificity of this information, covering them through traditional and relational databases is not possible and modem solutions should be used for processing them, so that processing speed is also covered. Data storage for processing and the way of accessing to them in memory, network communication, covering required features for distributed system in solutions that are in use for storing big data, are the items that should be covered. In this paper, a collection of advantages and challenges of big data, special features and characteristics of them has been provided and with the introduction of technologies in use, storage methods are studied and research opportunities to continue the way will be introduced.

Research on the Mass structured Data Storage and Sorting Algorithm and Methodology for SQL Database under the Big Data Environment

In this paper, we research on the research on the mass structured data storage and sorting algorithm and methodology for SQL database under the big data environment. With the data storage market development and centering on the server, the data will store model to data- centric data storage model. Storage is considered from the start, just keep a series of data, for the management system and storage device rarely consider the intrinsic value of the stored data. The prosperity of the Internet has changed the world data storage, and with the emergence of many new applications. Theoretically, the proposed algorithm has the ability of dealing with massive data and numerically, the algorithm could enhance the processing accuracy and speed which will be meaningful.

Big data storage technologies： a survey

There is a great thrust in industry toward the development of more feasible and viable tools for storing fast-growing volume, velocity, and diversity of data, termed ＇big data＇. The structural shift of the storage mechanism from traditional data management systems to NoSQL technology is due to the intention of fulfilling big data storage requirements. However, the available big data storage technologies are inefficient to provide consistent, scalable, and available solutions for continuously growing heterogeneous data. Storage is the preliminary process of big data analytics for real-world applications such as scientific experiments, healthcare, social networks, and e-business. So far, Amazon, Google, and Apache are some of the industry standards in providing big data storage solutions, yet the literature does not report an in-depth survey of storage technologies available for big data, investigating the performance and magnitude gains of these technologies. The primary objective of this paper is to conduct a comprehensive investigation of state-of-the-art storage technologies available for big data. A well-defined taxonomy of big data storage technologies is presented to assist data analysts and researchers in understanding and selecting a storage mecha- nism that better fits their needs. To evaluate the performance of different storage architectures, we compare and analyze the ex- isling approaches using Brewer＇s CAP theorem. The significance and applications of storage technologies and support to other categories are discussed. Several future research challenges are highlighted with the intention to expedite the deployment of a reliable and scalable storage system.