In recent years, there has been a lot of interest in the field of keyword querying relational databases. A variety of systems such as DBXplorer [ACD02], Discover [HP02] and ObjectRank [BHP04] have been proposed. Another such system is BANKS, which enables data and schema browsing together with keyword-based search for relational databases. It models tuples as nodes in a graph, connected by links induced by foreign key and other relationships. The size of the database graph that BANKS uses is proportional to the sum of the number of nodes and edges in the graph. Systems such as SPIN, which search on Personal Information Networks and use BANKS as the backend, maintain a lot of information about the users' data. Since these systems run on the user workstation which have other demands of memory, such a heavy use of memory is unreasonable and if possible, should be avoided. In order to alleviate this problem, we introduce EMBANKS (acronym for External Memory BANKS), a framework for an optimized disk-based BANKS system. The complexity of this framework poses many questions, some of which we try to answer in this thesis. We demonstrate that the cluster representation proposed in EMBANKS enables in-memory processing of very large database graphs. We also present detailed experiments that show that EMBANKS can significantly reduce database load time and query execution times when compared to the original BANKS algorithms.
Text-to-SQL based on Large Language Models and Database Keyword Search
Instituto Tecgraf, PUC-Rio（里约天主教大学 Tecgraf 研究所）；Petrobras（巴西石油公司）；Departamento de Informática, PUC-Rio（里约天主教大学 信息学系）；Departamento de Computação, UFC（塞阿拉联邦大学 计算机科学系）
Benchmarking Top-K Keyword and Top-K Document Processing with T${}^2$K${}^2$ and T${}^2$K${}^2$D${}^2$
Top-k关键字和top-k文档抽取是非常流行的文本分析技术。Top-k关键字和文档通常是动态计算的，但它们利用了构建成本高昂的加权词汇表。为了比较相互竞争的加权方案和数据库实现，通常使用基准测试。据我们所知，目前没有基准可以解决这些问题。因此，在本文中，我们提出了T ${}^2$ K ${}^2$，top-k关键字和文档基准，及其面向决策支持的进化T ${}^2$ K ${}^2$ D ${}^2$。这两个基准测试都有一个真实的tweet数据集和具有各种复杂性和选择性的查询。它们有助于评估加权方案和数据库实现的计算性能。为了说明我们的基准测试的相关性和通用性，我们一方面成功地对TF-IDF和Okapi BM 25加权方案进行了性能测试，另一方面对不同的关系型（Oracle，PostgreSQL）和面向文档的（MongoDB）数据库实现进行了性能测试。
T${}^2$K${}^2$: The Twitter Top-K Keywords Benchmark
从文本数据中进行信息检索的重点是构建包含加权词元组的词汇表。这样的词汇表然后可以被各种文本分析算法利用来提取新的知识，例如，前k个关键字、前k个文档等。前k个关键字被随意地用于各种目的，经常被即时计算，因此必须被有效地计算。为了比较相互竞争的加权方案和数据库实现，通常使用基准测试。据我们所知，目前没有基准解决这些问题。因此，在本文中，我们提出了一个top-k关键字基准测试，T ${}^2$ K ${}^2$，它具有一个真正的鸣叫数据集和各种复杂性和选择性的查询。T ${}^2$ K ${}^2$帮助评估加权方案和数据库实现的计算性能。为了说明T ${}^2$ K ${}^2$的相关性和通用性，我们一方面成功地对TF-IDF和Okapi BM 25加权方案进行了测试，另一方面成功地对不同的关系（Oracle，PostgreSQL）和面向文档（MongoDB）数据库实现进行了测试。
