Big data storage and retrieval technology for high-impact location derivative data

计算和网络技术的发展迎来了“大数据”时代。世界上90%的数据 在过去的两年中，企业正在寻求利用数据来获得竞争优势。 大量的数据需要大规模的处理。旧的关系数据库（RDBMS）技术是 通常无法支持高查询速率。RDBMS提供了强一致性保证和灵活的查询 以牺牲可扩展性为代价;这促使大数据用户选择NoSQL数据存储。NoSQL数据 商店通常是可扩展的，获得合理的查询响应时间可能是具有挑战性的。关键是设计数据 模型考虑到查询访问模式，应用所谓的数据去规范化技术。这 该项目将专注于自动化数据非规范化的过程：将其从耗时的手动 过程部分自动化。我们计划开发工具来分析现有的SQL查询， NoSQL数据模型设计者的反规范化算法。我们将在以下背景下处理这一问题： Global Fleet Management：一家总部位于温哥华的公司，对商用车辆进行GPS跟踪。 我们希望我们的经验将导致部分自动化数据的通用技术的发展 反规范化

Evolution of computing and networking technology ushered in an era of "Big Data". 90% of the world's data has been produced in the last two years, and businesses are looking to leverage data to gain competitive edge. Massive volumes of data call for processing at massive scale. Old relational database (RDBMS) technology is often unable to support high query rates. RDBMS provides strong consistency guarantees and flexible querying at the expense of scalability; this motivates big data users to opt for NoSQL data stores. While NoSQL data stores generally scale, getting reasonable query response times can be challenging. The key is to design the data model in consideration of the query access pattern, applying so-called data denormalization techniques. This project will focus on automating the process of data denormalization: turning it from a time-consuming manual process to partly automated. We plan to develop tools that will analyze existing SQL queries and recommend denormalization heuristics for designers of NoSQL data models. We will address this problem in the context of Global Fleet Management: a Vancouver based company that performs GPS tracking of commercial vehicles. We expect that our experience will lead to development of generalized techniques for partly automated data denormalization.