Data Storage Model Research Articles

The Development of Data Storage Security Issues under Cloud Computing

Internet-based super computing model has aroused great concern, while also facing a growing number of security issues. Data security issues for cloud storage system in the application process facing, this paper presents secure data storage architecture based on cloud computing. The architecture study security issues of cloud computing from the data storage and data security aspects are proposed based Cache data storage model based on third-party certification and data security mode, thus improving data availability, data transfers from storage the establishment of appropriate protective measures, to achieve effective protection of cloud data

Building pathway graphs from BioPAX data in R.

Biological pathways are increasingly available in the BioPAX format which uses an RDF model for data storage. One can retrieve the information in this data model in the scripting language R using the package rBiopaxParser, which converts the BioPAX format to one readable in R. It also has a function to build a regulatory network from the pathway information. Here we describe an extension of this function. The new function allows the user to build graphs of entire pathways, including regulated as well as non-regulated elements, and therefore provides amaximum of information. This function is available as part of the rBiopaxParser distribution from Bioconductor.

Building pathway graphs from BioPAX data in R

Biological pathways are increasingly available in the BioPAX format which uses an RDF model for data storage. One can retrieve the information in this data model in the scripting language R using the package rBiopaxParser, which converts the BioPAX format to one readable in R. It also has a function to build a regulatory network from the pathway information. Here we describe an extension of this function. The new function allows the user to build graphs of entire pathways, including regulated as well as non-regulated elements, and therefore provides a maximum of information. This function is available as part of the rBiopaxParser distribution from Bioconductor.

An Effective NoSQL-Based Vector Map Tile Management Approach

Within a digital map service environment, the rapid growth of Spatial Big-Data is driving new requirements for effective mechanisms for massive online vector map tile processing. The emergence of Not Only SQL (NoSQL) databases has resulted in a new data storage and management model for scalable spatial data deployments and fast tracking. They better suit the scenario of high-volume, low-latency network map services than traditional standalone high-performance computer (HPC) or relational databases. In this paper, we propose a flexible storage framework that provides feasible methods for tiled map data parallel clipping and retrieval operations within a distributed NoSQL database environment. We illustrate the parallel vector tile generation and querying algorithms with the MapReduce programming model. Three different processing approaches, including local caching, distributed file storage, and the NoSQL-based method, are compared by analyzing the concurrent load and calculation time. An online geological vector tile map service prototype was developed to embed our processing framework in the China Geological Survey Information Grid. Experimental results show that our NoSQL-based parallel tile management framework can support applications that process huge volumes of vector tile data and improve performance of the tiled map service.

Development of climate data storage and processing model

We present a storage and processing model for climate datasets elaborated in the framework of a virtual research environment (VRE) for climate and environmental monitoring and analysis of the impact of climate change on the socio-economic processes on local and regional scales. The model is based on a «shared nothings» distributed computing architecture and assumes using a computing network where each computing node is independent and selfsufficient. Each node holds a dedicated software for the processing and visualization of geospatial data providing programming interfaces to communicate with the other nodes. The nodes are interconnected by a local network or the Internet and exchange data and control instructions via SSH connections and web services. Geospatial data is represented by collections of netCDF files stored in a hierarchy of directories in the framework of a file system. To speed up data reading and processing, three approaches are proposed: a precalculation of intermediate products, a distribution of data across multiple storage systems (with or without redundancy), and caching and reuse of the previously obtained products. For a fast search and retrieval of the required data, according to the data storage and processing model, a metadata database is developed. It contains descriptions of the space-time features of the datasets available for processing, their locations, as well as descriptions and run options of the software components for data analysis and visualization. The model and the metadata database together will provide a reliable technological basis for development of a high- performance virtual research environment for climatic and environmental monitoring.

Building pathway graphs from BioPAX data in R

Biological pathways are increasingly available in the BioPAX format which uses an RDF model for data storage. We can retrieve the information in this data model in the scripting language R using the package rBiopaxParser, which converts the BioPAX format to one readable in R. It also has a function to build a regulatory network from the pathway information, here we describe an extension of this function. The new function will also include non-regulatory interactions in the pathway and thus allow extraction of maximum information. This function will be available as part of the rBiopaxParser distribution from Bioconductor.

The Method of Constructing an Integrated Corporate Information System

Background/Objectives: This article describes the integration method of mobile applications with Corporate Information System (CIS) through the previously proposed interaction architecture based on integration environment. Methods: The methods of data obtaining and processing and computational load distribution between the components of an integrated corporate system were used to develop a method of mobile application integration with corporate information system, implemented as an algorithm of building an integration environment for cross-platform mobile applications. Findings: Within the framework of this article, the method implementing the process of developing mobile application integrated with the CIS in accordance with the proposed architecture was discussed. The proposed method involves the use of different types of models: transforming data model, data storage model and data representation model. These models combine all necessary technologies of procurement, processing and representation of data circulating in the CIS. The structure of the integration environment, which includes new approaches to the organization of data storage, conversion and presentation, is described in detail. Also this article represents the algorithm for implementation of CIS mobile applications integration including the proposed structure of integration environment. Applications/Improvements: The proposed technology can be used to build various integrated solutions for the education sphere, transportation, security, provided the simultaneous development of mobile applications for multiple mobile platforms, or at the integration of several CIS.

Design and implementation of a secure cloud-based billing model for smart meters as an Internet of things using homomorphic cryptography

Smart grids introduce many outstanding security and privacy issues, especially when smart meters are connected to public networks, creating an Internet of things in which customer usage data is frequently exchanged and processed in large volumes. In this research, we propose a cloud-based data storage and processing model with the ability to preserve user privacy and confidentiality of smart meter data in a smart grid. This goal is achieved by encrypting smart meter data before storage on the cloud using a homomorphic asymmetric key cryptosystem. By applying the homomorphic feature of the cryptographic technique, we propose methods to allow most of the computing works of calculating customer invoices based on total electricity consumption to be done directly on encrypted data by the cloud. One of the outstanding features in our model is the aggregation of encrypted smart meter readings using fixed-point number arithmetic. To test the feasibility of our model, we conducted many experiments to estimate the number of homomorphic additions to be performed by the cloud and the computation time in different billing periods using data from the Smart project, in which smart grid readings were continuously collected from different households in every second within two months and electricity usage data collected every minute from 400 anonymous houses in one day. We also propose a parallel version of our billing algorithm to utilize the processing capability of multi-core processors in cloud servers so that computation time is reduced significantly compared to using our sequential algorithm. Our research works and experiments demonstrate clearly how cloud services can strengthen the security, privacy and efficiency of privacy-sensitive data frequently exchanged and processed in an Internet of things where smart meters communicate directly with public networks.

Multi-Tenant Data Storage Model and Performance Evaluation

Multi-tenant data storage model has multiple solutions and comparing the different storage solutions can help users improve their work efficiency. This paper proposes a query performance evaluation method based on the relational algebra. First of all, we introduce three wide table models. Secondly, we unite the format of tenant query SQL statement by analyzing structure of storage model, replace the unified format SQL with the relational algebra and evaluate the I/O cost of SQL query using relational algebra. Finally, through theoretical calculations and experimental simulations, we evaluate the performance of multi-tenant storage model according to query performance. The results show our evaluation method based on relational algebra provides new perspective for the study of performance evaluation in multi-tenant data model.

When things matter: A survey on data-centric internet of things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, but several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy and continuous. This paper reviews the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed.

Transformasi Skema Basis Data Relasional Menjadi Model Data Berorientasi Dokumen pada MongoDB

MongoDB is a database that uses document-oriented data storage models. In fact, to migrate from a relational database to NoSQL databases such as MongoDB is not an easy matter especially if the data are extremely complex. Based on the documentation that has been done by several global companies related to the use of MongoDB, it can be concluded that the process of migration from RDBMS to MongoDB require quite a long time. One process that takes quite a lot is transformation of relational database schema into a document-oriented data model on MongoDB. This research discusses the development transformation system of relational database schema to the document oriented data model in MongoDB. The process of transformation is done by utilizing the structure and relationships between tables in the scheme as the main parameters of the modeling algorithm. In the process of the modeling documents, it necessary to adjustments the specifications of MongoDB document that formed document model can be implemented in MongoDB. Document models are formed from transformation process can be a single document, embedded document, referenced document or combination of these. Document models are formed depending on the type, rules, and the value of the relationships cardinality between tables in the relational database schema.

基于要素类的多数据中心数据一体化存储模型研究

本文根据多个一体化数据存储模型，结合多数据中心、分布式异构环境等实际需求，提出了基于要素类的多数据中心数据一体化存储模型，并以国家自然资源和地理信息基础信息库项目为基础，进行了建模。建模结果显示：该模型可以满足多数据中心、分布式异构环境下的海量多源数据一体化存储与管理。 According to the multiple integrated data storage model, this paper proposed the integration of a multiple data center data storage model based on a feature class, combined with the actual needs of multiple data centers and distributed heterogeneous environment. It also built the model based on the project of the National Natural Resources and Geospatial Basic Information Database. Modeling results showed that the model could meet the demands of integrated storage and management of massive multi-source data under the multiple data centers and distributed heterogeneous storage environments.

Multiobjective Reliable Cloud Storage with Its Particle Swarm Optimization Algorithm

Information abounds in all fields of the real life, which is often recorded as digital data in computer systems and treated as a kind of increasingly important resource. Its increasing volume growth causes great difficulties in both storage and analysis. The massive data storage in cloud environments has significant impacts on the quality of service (QoS) of the systems, which is becoming an increasingly challenging problem. In this paper, we propose a multiobjective optimization model for the reliable data storage in clouds through considering both cost and reliability of the storage service simultaneously. In the proposed model, the total cost is analyzed to be composed of storage space occupation cost, data migration cost, and communication cost. According to the analysis of the storage process, the transmission reliability, equipment stability, and software reliability are taken into account in the storage reliability evaluation. To solve the proposed multiobjective model, a Constrained Multiobjective Particle Swarm Optimization (CMPSO) algorithm is designed. At last, experiments are designed to validate the proposed model and its solution PSO algorithm. In the experiments, the proposed model is tested in cooperation with 3 storage strategies. Experimental results show that the proposed model is positive and effective. The experimental results also demonstrate that the proposed model can perform much better in alliance with proper file splitting methods.

Big Cloud: a hybrid cloud model for secure data storage through cloud space

In recent years, cloud computing has proven its importance in every domain. Cloud computing brings the technological drift in banking and finance sectors. In today's computing field, cloud applications and cloud services are getting increased rapidly over the internet. Nowadays, each and every important service sectors are migrating towards the cloud computing as it offers scalable and secure online resources with the minimal time delay where there exists million numbers of transactions. In this paper, we have developed novel framework called as Big Cloud which provides faster retrieval of the data. We are rediscovering the anonymity problems through cloud space to solve the security issues in the banking database. On whole, we have explored the merits of involving cloud database in banking, to assure security and effective load balancing of multiple queries among various servers by applying existing encryption algorithms in a newer approach.

Survey of Big Data Storage Technology

Big data storage is the foundation of big data processing and analysis. By researching and summarizing main processing technology of data storage, this paper respectively investigates and analyzes the following four aspects: distributed file system, NoSQL database, database appliance and new-type data storage technology of MPP architecture. In addition, this paper gives some recommendations applicable to different environments in favor of grasping the development states of data storage technology from different angles. This paper summarizes file segmentation, appropriate scenarios and merits and faults of distributed file system, and mainly analyzes and summarizes the theories and appropriate scenarios of four data storage models of NoSql database. Furthermore, this paper investigates and concludes the developments and features of database appliance minutely. At the same time, outline MPP (Massively Parallel Processing) architecture, a new data storage technology. At last, the research trends of storage technology are prospected, providing references to the research of big data storage technology.

EXPRESS to OWL for construction industry: Towards a recommendable and usable ifcOWL ontology

Abstract An increasing number of information management and information exchange applications in construction industry is relying on semantic web technologies or tools from the Linked Open Data (LOD) domain to support data interoperability, flexible data exchange, distributed data management and the development of reusable tools. These goals tend to be overlapped with the purposes of the Industry Foundation Classes (IFC), which is a standard for the construction industry defined through an EXPRESS schema. A connecting point between semantic web technologies and the IFC standard would be represented by an agreed Web Ontology Language (OWL) ontology for IFC (termed ifcOWL) that allows to (1) keep on using the well-established IFC standard for representing construction data, (2) exploit the enablers of semantic web technologies in terms of data distribution, extensibility of the data model, querying, and reasoning, and (3) re-use general purpose software implementations for data storage, consistency checking and knowledge inference. Therefore, in this paper we will look into existing efforts in obtaining an ifcOWL ontology from the EXPRESS schemas of IFC and analyse which features would be required in a usable and recommendable ifcOWL ontology. In making this analysis, we present our implementations of an EXPRESS-to-OWL converter and the key features of the resulting ifcOWL ontology.

AN EFFECTIVE DATABASE MANAGEMENT OF THE URBAN UNDERGROUND FACILITIES AND TOPOGRAPHIC INFORMATION

In developed countries the most accurate and reliable spatial data (M 1:500–1:1000) are extensively used. The same spatial object could be inscribed into separate systems: utilities companies have some systems, municipality collected catalogues of CAD data, separately developed real estate map. The same spatial objects are repeated and reiterated, unreasonable costs are incurred for their storage, the customers find it complicated to determine and select the best and appropriate spatial data required just for their own needs. This article suggests the solution of the problem. Authors have introduced the idea to compose and develop information system specialized for topography and infrastructure subsequently to be merged and coordinated. The model for data storage, data provision to the customers and service activities is defined. The available electronic services for the spatial data are described as advantageous for implementation on topographic maps, for inspection and monitoring of construction projects, for the issue of permits of excavation works and for the other activities.

Колоночный сопроцессор баз данных для кластерных вычислительных систем

The paper is devoted to the design and implementation issues of columnar coprocessor for RDBMS. Columnar coprocessor (CCOP) is developed on the base of columnar data storage mod-el. It is designed for large computing cluster systems. CCOP can utilize CPUs as well as manycore coprocessors MIC. CCOP maintains the columnar indices with surrogate keys stored in distribut-ed main memory. The partitioning is performed on the base of domain-interval model. For the data warehouse workload, CCOP demonstrates performance much higher than row-stores do.

Development of Four-Column Data Storage Model for Data-Manipulation of Greenhouse Gases and Soil Properties

Studies on greenhouse gas emissions and climate change require huge amounts of data. Generally, these data can be inducted directly from measuring devices or from different systems like geographical information systems and other specific systems, which are used for experimental data storage and management. Database technology has come a long way over the years, however even with these advances in technology and the availability of supercomputers processing large amounts of data from different data storage methods is still a challenge. In the present study, one physical data table was generated for data-manipulation and data storage using a web-developed platform. Data storage with four-column usage assures the possibility for continuous refinement during the research processes. It is because of the determination of new variables after the entity identification process.

The Research and Application of a Big Data Storage Model

In the big data environment, there are many new demands with data storage, the traditional data storage mode which based on relational database cannot meet these needs, many application systems tend to use NoSQL to solve the problem of big data storage. However, NoSQL gave up on the relationship between operation support, so that part of existing application system is difficult to use the simple way of transplantation. This thesis with reference to some typical existing big data storage scheme, we presents a big data compatible with relational storage model storage scheme, this scheme can not only meet the big data storage requirements, but also can support the most relational operation, so that the original system based on relational database can easily be ported to new storage schemes in.