Topic
Null (SQL)
About: Null (SQL) is a research topic. Over the lifetime, 1015 publications have been published within this topic receiving 24634 citations. The topic is also known as: null & SQL null.
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IBM1
TL;DR: System R as mentioned in this paper is an experimental database management system developed to carry out research on the relational model of data, which chooses access paths for both simple (single relation) and complex queries (such as joins), given a user specification of desired data as a boolean expression of predicates.
Abstract: In a high level query and data manipulation language such as SQL, requests are stated non-procedurally, without reference to access paths. This paper describes how System R chooses access paths for both simple (single relation) and complex queries (such as joins), given a user specification of desired data as a boolean expression of predicates. System R is an experimental database management system developed to carry out research on the relational model of data. System R was designed and built by members of the IBM San Jose Research Laboratory.
2,082 citations
Book•
01 Jan 2005
TL;DR: This chapter explains the development of a Relational Model for SQL and some examples show how the model changed over time from simple to complex to elegant and efficient.
Abstract: Preface About the Authors 1 What's in a Database? 2 Relational Model 3 Relational Calculus 4 Relational Algebra 5 SQL 6 SQL and Programming Languages 7 Entity-Relationship Model 8 Normalisation 9 Conclusion References Index
1,367 citations
Posted Content•
TL;DR: This work proposes Seq2 SQL, a deep neural network for translating natural language questions to corresponding SQL queries, and releases WikiSQL, a dataset of 80654 hand-annotated examples of questions and SQL queries distributed across 24241 tables fromWikipedia that is an order of magnitude larger than comparable datasets.
Abstract: A significant amount of the world's knowledge is stored in relational databases. However, the ability for users to retrieve facts from a database is limited due to a lack of understanding of query languages such as SQL. We propose Seq2SQL, a deep neural network for translating natural language questions to corresponding SQL queries. Our model leverages the structure of SQL queries to significantly reduce the output space of generated queries. Moreover, we use rewards from in-the-loop query execution over the database to learn a policy to generate unordered parts of the query, which we show are less suitable for optimization via cross entropy loss. In addition, we will publish WikiSQL, a dataset of 80654 hand-annotated examples of questions and SQL queries distributed across 24241 tables from Wikipedia. This dataset is required to train our model and is an order of magnitude larger than comparable datasets. By applying policy-based reinforcement learning with a query execution environment to WikiSQL, our model Seq2SQL outperforms attentional sequence to sequence models, improving execution accuracy from 35.9% to 59.4% and logical form accuracy from 23.4% to 48.3%.
830 citations
TL;DR: This paper deals with imprecise querying of regular relational databases by extending an existing query language, namely SQL, which concerns the integration in the extended language of many propositions that have been made elsewhere.
Abstract: An important issue in extending database management systems functionalities is to allow the expression of imprecise queries to enable these systems to satisfy the user needs more closely. This paper deals with imprecise querying of regular relational databases. The basic idea is to extend an existing query language, namely SQL. In this context, two important points must be considered: one concerns the integration in the extended language of many propositions that have been made elsewhere, in particular those concerning fuzzy aggregation operators; and the second point is to know whether the equivalences which are valid in SQL still hold in the extended language. Both these topics are investigated in this paper. >
518 citations
TL;DR: The spatial query language has been designed as a minimal extension to the interrogative part of SQL and distinguishes from previously designed SQL extensions by: the preservation of SQL concepts; the high-level treatment of spatial objects; and the incorporation of spatial operations and relationships.
Abstract: Recently, attention has been focused on spatial databases, which combine conventional and spatially related data, such as geographic information systems, CAD/CAM, or VLSI. A language has been developed to query such spatial databases. It recognizes the significantly different requirements of spatial data handling and overcomes the inherent problems of the application of conventional database query languages. The spatial query language has been designed as a minimal extension to the interrogative part of SQL and distinguishes from previously designed SQL extensions by: the preservation of SQL concepts; the high-level treatment of spatial objects; and the incorporation of spatial operations and relationships. It consists of two components, a query language to describe what information to retrieve and a presentation language to specify how to display query results. Users can ask standard SQL queries to retrieve nonspatial data based on nonspatial constraints, use Spatial SQL commands to inquire about situations involving spatial data, and give instructions in the Graphical Presentation Language, GPL to manipulate or examine the graphical presentation. >
440 citations