A previously proposed keyword search paradigm produces, as a query result, a ranked list of Object Summaries (OSs). An OS is a tree structure of related tuples that summarizes all data held in a relational database about a particular Data Subject (DS). However, some of these OSs are very large in size and therefore unfriendly to users that initially prefer synoptic information before proceeding to more comprehensive information about a particular DS. In this paper, we investigate the effective and efficient retrieval of concise and informative OSs. We argue that a good size-l OS should be a stand-alone and meaningful synopsis of the most important information about the particular DS. More precisely, we define a size-l OS as a partial OS composed of l important tuples. We propose three algorithms for the efficient generation of size-l OSs (in addition to the optimal approach which requires exponential time). Experimental evaluation on DBLP and TPC-H databases verifies the effectiveness and efficiency of our approach.
Relational Keyword Search (R-KwS) systems enable naive/informal users to explore and retrieve information from relational databases without knowing schema details or query languages. These systems take the keywords from the input query, locate the elements of the target database that correspond to these keywords, and look for ways to "connect" these elements using information on referential integrity constraints, i.e., key/foreign key pairs. Although several such systems have been proposed in the literature, most of them only support queries whose keywords refer to the contents of the target d…
We present a novel natural language query interface, the AggChecker, aimed at text summaries of relational data sets. The tool focuses on natural language claims that translate into an SQL query and a claimed query result. Similar in spirit to a spell checker, the AggChecker marks up text passages that seem to be inconsistent with the actual data. At the heart of the system is a probabilistic model that reasons about the input document in a holistic fashion. Based on claim keywords and the document structure, it maps each text claim to a probability distribution over associated query translati…
Diversification of DB applications highlighted the limitations of relational database management system (RDBMS) particularly on the modeling plan. In fact, in the real world, we are increasingly faced with the situation where applications need to handle imprecise data and to offer a flexible querying to their users. Several theoretical solutions have been proposed. However, the impact of this work in practice remained negligible with the exception of a few research prototypes based on the formal model GEFRED. In this chapter, the authors propose a new approach for exploitation of fuzzy relatio…
Using structural informations to summarize graph-structured RDF data is helpful in tackling query performance issues. However, leveraging structural indexes needs to revise or even redesign the internal of RDF systems. Given an RDF dataset that have already been bulk loaded into a relational RDF system, we aim at improving the query performance on such systems. We do so by summarizing neighborhood structures and encoding them into triples which can be managed along side the exist instance data. At query time, we optimally select the effective structural patterns, and adding these patterns to t…
Translating Natural Language Queries (NLQs) to Structured Query Language (SQL) in interfaces deployed in relational databases is a challenging task, which has been widely studied in database community recently. Conventional rule based systems utilize series of solutions as a pipeline to deal with each step of this task, namely stop word filtering, tokenization, stemming/lemmatization, parsing, tagging, and translation. Recent works have mostly focused on the translation step overlooking the earlier steps by using ad-hoc solutions. In the pipeline, one of the most critical and challenging probl…
Being declarative, SQL stands a better chance at being the programming language for conceptual computing next to natural language programming. We examine the possibility of using SQL as a back-end for natural language database programming. Distinctly from keyword based SQL querying, keyword dependence and SQL's table structure constraints are significantly less pronounced in our approach. We present a mobile device voice query interface, called Cyrus, to arbitrary relational databases. Cyrus supports a large type of query classes, sufficient for an entry level database class. Cyrus is also app…
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）
Top-k keyword and top-k document extraction are very popular text analysis techniques. Top-k keywords and documents are often computed on-the-fly, but they exploit weighted vocabularies that are costly to build. To compare competing weighting schemes and database implementations, benchmarking is customary. To the best of our knowledge, no benchmark currently addresses these problems. Hence, in this paper, we present T${}^2$K${}^2$, a top-k keywords and documents benchmark, and its decision support-oriented evolution T${}^2$K${}^2$D${}^2$. Both benchmarks feature a real tweet dataset and querie…
University of Waterloo（滑铁卢大学）；The Central Bank of the Republic of Türkiye（土耳其共和国中央银行）；Bilkent University（比尔肯特大学）
Translating natural language queries (NLQ) into structured query language (SQL) in interfaces to relational databases is a challenging task that has been widely studied by researchers from both the database and natural language processing communities. Numerous works have been proposed to attack the natural language interfaces to databases (NLIDB) problem either as a conventional pipeline-based or an end-to-end deep-learning-based solution. Nevertheless, regardless of the approach preferred, such solutions exhibit black-box nature, which makes it difficult for potential users targeted by these …
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。
We provide methods for in-database support of decision making under uncertainty. Many important decision problems correspond to selecting a package (bag of tuples in a relational database) that jointly satisfy a set of constraints while minimizing some overall cost function; in most real-world problems, the data is uncertain. We provide methods for specifying -- via a SQL extension -- and processing stochastic package queries (SPQs), in order to solve optimization problems over uncertain data, right where the data resides. Prior work in stochastic programming uses Monte Carlo methods where the…
