III: Small: Just in Time Datastructures

III：小：即时数据结构

• 批准号: 1617586
• 负责人: Oliver Kennedy
• 金额: $ 49.43万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-15 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1617586&HistoricalAwards=false
• 关键词: III; Small; Just; Time; Datastructures

Indexes have always been a critical component of any database. Now that "big data" is a household term, they're more crucial than ever before. Indexes keep data organized, making it faster and easier for you to get at anything from the microblogs in your stream to the money in your bank account. Of course, it's sometimes hard to figure out how to best keep data organized. For example, online social media often needs to handle over a hundred thousand new messages in a single second. That's a lot of data that needs to be organized, and it needs to be organized fast to keep up with the users' needs. The Just-in-Time Data Structures project is exploring ways to make indexes smarter, better able to prioritize their work, and better able to adapt to changing workload patterns.Specifically, we're generalizing a class of index data structures called adaptive indexes. In an adaptive index, data is organized as a side-effect of query processing. In a just-in-time data structure, user-defined policies specify how an index is to be transformed in reaction to queries and new data. Like a just-in-time compiler, the index is adaptively optimized for the workload that is currently being presented to it. In addition to laying the foundations for research on just-in-time data structures, we are exploring how to design workload-optimal policies, and how just-in-time data structures can be adapted for incremental view maintenance.

索引一直是任何数据库的关键组成部分。 现在，“大数据”是一个家庭学期，它们比以往任何时候都更为重要。 索引保持数据的井井有条，使您更容易逐渐更容易从流中的微博到银行帐户中的钱。 当然，有时很难弄清楚如何最好地保持数据组织。 例如，在线社交媒体通常需要在一秒钟内处理超过十万新消息。 这是许多需要组织的数据，需要快速组织以满足用户的需求。 即时数据结构项目正在探索使索引更聪明，能够更好地优先级工作并更好地适应不断变化的工作负载模式的方法。特别是，我们正在概括一类称为自适应索引的索引数据结构。 在自适应索引中，数据被组织为查询处理的副作用。 在即时的数据结构中，用户定义的策略指定了如何在对查询和新数据的反应中转换索引。 就像一个即时的编译器一样，该索引对于当前正在介绍的工作负载进行了自适应优化。 除了为即时数据结构进行研究基础外，我们还在探索如何设计工作负载最佳的策略，以及如何适应即时数据结构以进行增量视图维护。

项目成果

Small Data

小数据

• DOI: 10.1109/icde.2017.216
• 发表时间: 2017
• 期刊: IEEE 33rd International Conference on Data Engineering (ICDE
• 作者: Kennedy, Oliver;Hipp, D. Richard;Idreos, Stratos;Marian, Amelie;Nandi, Arnab;Troncoso, Carmela;Wu, Eugene
• 通讯作者: Wu, Eugene

Fluid Data Structures

• DOI: 10.1145/3315507.3330197
• 发表时间: 2019-01-01
• 期刊: PROCEEDINGS OF THE 17TH ACM SIGPLAN INTERNATIONAL SYMPOSIUM ON DATABASE PROGRAMMING LANGUAGES (DBPL '19)
• 作者: Balakrishnan, Darshana;Ziarek, Lukasz;Kennedy, Oliver
• 通讯作者: Kennedy, Oliver

TreeToaster: Towards an IVM-Optimized Compiler

TreeToaster：迈向 IVM 优化的编译器

• DOI: 10.1145/3448016.3459244
• 发表时间: 2021
• 期刊: SIGMOD '21: International Conference on Management of Data
• 作者: Balakrishnan, Darshana;Nuessle, Carl;Kennedy, Oliver;Ziarek, Lukasz
• 通讯作者: Ziarek, Lukasz

Not Your Father's Big Data (Lighting Talk)

不是你父亲的大数据（Lighting Talk）

• 发表时间: 2019
• 期刊: Conference on Innovative Data Systems Research (CIDR
• 作者: Carl Nuessle, Oliver Kennedy

Just-in-Time Index Compilation

• 发表时间: 2019-01
• 期刊: ArXiv
• 作者: D. Balakrishnan;Lukasz Ziarek;Oliver Kennedy