EAGER: An Efficient and Effective Distributed Information System

EAGER：高效、有效的分布式信息系统

• 批准号: 1354123
• 负责人: Haiying Shen
• 金额: $ 30万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1354123&HistoricalAwards=false
• 关键词: EAGER; Efficient; Effective; Distributed; Information

The tremendous growth of information in the data-intensive world and a new wave of big data are creating a promising future for global ultra-large-scale data sharing, where widely-scattered massive data will be pooled and shared globally. A distributed data intensive information system is a critical component for realizing this future. The system will allow users to efficiently and effectively search similar data. However, the unprecedented amount of data, along with the large-scale environment and autonomous nature of participants pose high efficiency and effectiveness challenges to the development of such a system. This research will provide collaborative research opportunities for faculty, graduate and undergraduate students, as well as K-12 students in South Carolina.A growing need persists for developing an efficient and effective information searching system, and this challenge represents one of the more formidable hurdles facing data-intensive computing. This proposal is aimed at addressing this need through the development of a distributed information system supporting efficient and effective data searching. This system achieves both high efficiency and effectiveness. Efficiency means the speed and overhead of sorting and searching date, while effectiveness means the ability to find all matching data in the system with fewer false positives and false negatives. This system translates data items to IDs, maps the data items to nodes in a distributed system and enables the similarity searching in a distributed manner. First, previous data translation methods relying on a multi-dimensional space to hash a data item to one index achieve high efficiency but suffer from low effectiveness due to the curse of dimensionality in data dimension reduction. Previous exact mapping methods that hash each keyword of a data item for data search are highly effective but inefficient. By eliminating the need of a multi-dimensional space, this system is both highly efficient and effective. Second, unlike some previous systems relying on a centralized or hierarchical structure for data searching, this system builds a distributed hash table (DHT) structure, which provides highly efficient data searching in a distributed manner. Unlike most traditional DHT-based data sharing which provides only exact matching services, this system offers similarity searching.

在数据密集型世界中，信息的巨大增长和新一波的大数据浪潮正在为全球超大规模的数据共享创造一个有希望的未来，在这些数据共享中，将在全球范围内汇总并共享广泛的大规模数据。分布式数据密集型信息系统是实现这一未来的关键组成部分。该系统将允许用户有效地搜索相似的数据。但是，大量的数据，以及参与者的大规模环境和自治性，对这种系统的开发构成了高效率和有效性的挑战。这项研究将为教职员工，研究生和本科生以及南卡罗来纳州的K-12学生提供合作研究机会。不断增长的需求仍然需要开发高效有效的信息搜索系统，而这一挑战代表了面临数据密集型计算的更艰巨的障碍之一。该建议旨在通过开发分布式信息系统来满足这一需求，以支持有效的数据搜索。该系统既可以达到高效率又有效性。效率是指分类和搜索日期的速度和开销，而有效性则意味着能够在系统中找到较少的假阳性和假否定的系统中的所有匹配数据。该系统将数据项转换为ID，将数据项映射到分布式系统中的节点，并以分布式方式启用相似性搜索。首先，以前的数据翻译方法依靠多维空间将数据项放到一个索引上可以达到高效率，但由于降低数据维度的维度诅咒而导致效率低下。以前的确切映射方法哈希数据项的每个关键字用于数据搜索是非常有效但效率低下的。通过消除对多维空间的需求，该系统既高效又有效。其次，与一些以前的系统依靠集中式或分层结构进行数据搜索不同，该系统构建了分布式哈希表（DHT）结构，该结构以分布式方式提供了高效的数据搜索。与仅提供精确匹配服务的大多数传统基于DHT的数据共享不同，该系统提供了相似性搜索。