Realization of Dynamic Interface and High Performance Data Retrieval

This paper introduces a method to realize dynamic interface, and designs a database storage model based on XML field technology to realize convenient data storage, any combination condition retrieval function and how to improve the retrieval speed in this kind of storage model. Usually a business system needs to provide information entry and retrieval functions, software designers have to design the appropriate entry items, input interface and retrieval functions for each business system and spend too much time on the repetitive works. And later engineers have to maintain the changing needs of the entry project, so we can apply the dynamic interface technology to achieve the customize needs of input items by the user, reducing the time of the repetitive works. Dynamic interface technology includes the realization of database storage and high performance data retrieval. This paper explores a storage model based on XML database to realize common and efficient storage and discuss on how to improve the retrieval speed in this kind of storage model.

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The network data storage model based on the HTML

Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) ◽

10.2991/iccsee.2013.336 ◽

2013 ◽

Author(s):

Lei Li ◽

Jianping Zhou ◽

Runhua Wang ◽

Yi Tang

Keyword(s):

Data Storage ◽

Network Data ◽

Storage Model ◽

Model Based

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Data Storage, Retrieval and Management

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Large-Scale Distributed Computing and Applications ◽

10.4018/978-1-61520-703-9.ch006 ◽

2010 ◽

pp. 111-140

Author(s):

Valentin Cristea ◽

Ciprian Dobre ◽

Corina Stratan ◽

Florin Pop

Keyword(s):

Data Storage ◽

Resource Sharing ◽

High Performance ◽

Large Scale ◽

Workflow Management ◽

Large Data ◽

Data Retrieval ◽

Distributed Data Storage ◽

Processing Power ◽

Data Transfers

The latest advances in network and distributedsystem technologies now allow integration of a vast variety of services with almost unlimited processing power, using large amounts of data. Sharing of resources is often viewed as the key goal for distributed systems, and in this context the sharing of stored data appears as the most important aspect of distributed resource sharing. Scientific applications are the first to take advantage of such environments as the requirements of current and future high performance computing experiments are pressing, in terms of even higher volumes of issued data to be stored and managed. While these new environments reveal huge opportunities for large-scale distributed data storage and management, they also raise important technical challenges, which need to be addressed. The ability to support persistent storage of data on behalf of users, the consistent distribution of up-to-date data, the reliable replication of fast changing datasets or the efficient management of large data transfers are just some of these new challenges. In this chapter we discuss how the existing distributed computing infrastructure is adequate for supporting the required data storage and management functionalities. We highlight the issues raised from storing data over large distributed environments and discuss the recent research efforts dealing with challenges of data retrieval, replication and fast data transfers. Interaction of data management with other data sensitive, emerging technologies as the workflow management is also addressed.

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Analysis and Research on Spatial Data Storage Model Based on Cloud Computing Platform

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/100/1/012112 ◽

2017 ◽

Vol 100 ◽

pp. 012112 ◽

Cited By ~ 1

Author(s):

Yong Hu

Keyword(s):

Cloud Computing ◽

Data Storage ◽

Spatial Data ◽

Storage Model ◽

Computing Platform ◽

Model Based ◽

Cloud Computing Platform

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Knowledge Base Commons (KBCommons) v1.1: a universal framework for multi-omics data integration and biological discoveries

BMC Genomics ◽

10.1186/s12864-019-6287-8 ◽

2019 ◽

Vol 20 (S11) ◽

Cited By ~ 3

Author(s):

Shuai Zeng ◽

Zhen Lyu ◽

Siva Ratna Kumari Narisetti ◽

Dong Xu ◽

Trupti Joshi

Keyword(s):

Knowledge Base ◽

Data Storage ◽

High Performance ◽

Data Retrieval ◽

Omics Data ◽

Web Interface ◽

Web Resource ◽

Privilege Management ◽

Multi Level ◽

Comprehensive Framework

Abstract Background Knowledge Base Commons (KBCommons) v1.1 is a universal and all-inclusive web-based framework providing generic functionalities for storing, sharing, analyzing, exploring, integrating and visualizing multiple organisms’ genomics and integrative omics data. KBCommons is designed and developed to integrate diverse multi-level omics data and to support biological discoveries for all species via a common platform. Methods KBCommons has four modules including data storage, data processing, data accessing, and web interface for data management and retrieval. It provides a comprehensive framework for new plant-specific, animal-specific, virus-specific, bacteria-specific or human disease-specific knowledge base (KB) creation, for adding new genome versions and additional multi-omics data to existing KBs, and for exploring existing datasets within current KBs. Results KBCommons has an array of tools for data visualization and data analytics such as multiple gene/metabolite search, gene family/Pfam/Panther function annotation search, miRNA/metabolite/trait/SNP search, differential gene expression analysis, and bulk data download capacity. It contains a highly reliable data privilege management system to make users’ data publicly available easily and to share private or pre-publication data with members in their collaborative groups safely and securely. It allows users to conduct data analysis using our in-house developed workflow functionalities that are linked to XSEDE high performance computing resources. Using KBCommons’ intuitive web interface, users can easily retrieve genomic data, multi-omics data and analysis results from workflow according to their requirements and interests. Conclusions KBCommons addresses the needs of many diverse research communities to have a comprehensive multi-level OMICS web resource for data retrieval, sharing, analysis and visualization. KBCommons can be publicly accessed through a dedicated link for all organisms at http://kbcommons.org/.

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An Optimization Technique of the 3D Indoor Map Data Based on an Improved Octree Structure

Mathematical Problems in Engineering ◽

10.1155/2020/4315850 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Xiaomin Yu ◽

Huiqiang Wang ◽

Hongwu Lv ◽

Junqiang Fu

Keyword(s):

Data Storage ◽

Optimization Technique ◽

Data Retrieval ◽

Retrieval Algorithm ◽

Storage Structure ◽

Storage Model ◽

Indoor Space ◽

Close Relationship ◽

Time Requirements ◽

Map Data

The construction and retrieval of indoor maps are important for indoor positioning and navigation. It is necessary to ensure a good user experience while meeting real-time requirements. Unlike outdoor maps, indoor space is limited, and the relationship between indoor objects is complex which would result in an uneven indoor data distribution and close relationship between the data. A data storage model based on the octree scene segmentation structure was proposed in this paper initially. The traditional octree structure data storage model has been improved so that the data could be backtracked. The proposed method will solve the problem of partition lines within the range of the object data and improve the overall storage efficiency. Moreover, a data retrieval algorithm based on octree storage structure was proposed. The algorithm adopts the idea of “searching for a point, points around the searched point are within the searching range.” Combined with the octree neighbor retrieval methods, the closure constraints are added. Experimental results show that using the improved octree storage structure, the retrieval cost is 1/8 of R-tree. However, by using the neighbor retrieval, it improved the search efficiency by about 27% on average. After adding the closure constraint, the retrieval efficiency increases by 25% on average.

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