CAREER: Scalable and Flexible Indexing of Compressed Sequences

职业：压缩序列的可扩展且灵活的索引

• 批准号: 2337891
• 负责人: Dominik Kempa
• 金额: $ 59.82万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2029-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2337891&HistoricalAwards=false
• 关键词: CAREER; Scalable; Flexible; Indexing; Compressed

Lossless data compression is a classical and ubiquitous task that reduces the size of data, leading to a decreased cost of transfer or archival. Modern applications, however, require more than compression. In domains such as computational biology, terabytes of highly compressible data are generated at an increasing rate. To fully take advantage of this data, it needs to be not only stored in a small space but also accessible and searchable. Recent years have witnessed the birth of data structures called "compressed indexes" that can accomplish this task. The research so far, however, has mostly focused on static indexes, leaving out important aspects such as support for updates and efficient construction. This project's overarching goal is to develop powerful, dynamic compressed indexes capable of handling a versatile set of queries and various representations, that can moreover be efficiently constructed. This will make it significantly cheaper, faster, and more energy-efficient to store and share highly compressible datasets such as DNA collections, thereby fully unlocking the potential of advances in DNA sequencing. The advances of this project will also be integrated into research experience for underrepresented students, as well as outreach to non-computer science students.The main research goals of this project can be broadly classified into the following three directions. First, the project will lead to new efficient algorithms for constructing compressed indexes. The new approach lies in first preprocessing the input using lightly sub-optimal compression and then constructing the final index in compressed time, i.e., time proportional to the precompressed text. Second, the project aims to utilize and improve modern suffix sampling techniques to design new and powerful indexes that are both compressed and able to support powerful queries, including multi-string representations. Finally, the project will study the underdeveloped landscape of lower bounds for compressed indexes. Currently, only the most basic queries, such as random access, are well-understood, and much less is known about lower bounds on more versatile representations or lower bounds for compressed computation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

无损数据压缩是一项经典且普遍存在的任务，它可以减小数据大小，从而降低传输或归档成本。然而，现代应用程序需要的不仅仅是压缩。在计算生物学等领域，数 TB 的高度可压缩数据的生成速度越来越快。为了充分利用这些数据，它不仅需要存储在很小的空间中，而且还需要可访问和可搜索。近年来，出现了可以完成此任务的称为“压缩索引”的数据结构。然而，迄今为止的研究主要集中在静态索引上，而忽略了诸如支持更新和高效构建等重要方面。该项目的总体目标是开发强大的动态压缩索引，能够处理一组通用的查询和各种表示，而且可以有效地构建。这将使存储和共享高度可压缩的数据集（例如 DNA 集合）变得更便宜、更快、更节能，从而充分释放 DNA 测序进步的潜力。该项目的进展还将融入针对代表性不足的学生的研究经验，以及对非计算机科学专业学生的推广。该项目的主要研究目标大致可分为以下三个方向。首先，该项目将带来用于构建压缩索引的新有效算法。新方法在于首先使用轻度次优压缩对输入进行预处理，然后在压缩时间内构建最终索引，即与预压缩文本成比例的时间。其次，该项目旨在利用和改进现代后缀采样技术来设计新的强大索引，这些索引既经过压缩又能够支持强大的查询，包括多字符串表示。最后，该项目将研究压缩指数下界的欠发达景观。目前，只有最基本的查询（例如随机访问）得到了很好的理解，而对于更通用的表示形式的下限或压缩计算的下限知之甚少。该奖项反映了 NSF 的法定使命，并被认为值得支持通过使用基金会的智力优点和更广泛的影响审查标准进行评估。