Generalization of strategies for fuzzy query translation in classical relational databases

Users of information systems would like to express flexible queries over the data possibly retrieving imperfect items when the perfect ones, which exactly match the selection conditions, are not available. Most commercial DBMSs are still based on the SQL for querying. Therefore, providing some flexibility to SQL can help users to improve their interaction with the systems without requiring them to learn a completely novel language. Based on the fuzzy set theory and the α-cut operation of fuzzy number, this paper presents the generic fuzzy queries against classical relational databases and develops the translation of the fuzzy queries. The generic fuzzy queries mean that the query condition consists of complex fuzzy terms as the operands and complex fuzzy relations as the operators in a fuzzy query. With different thresholds that the user chooses for the fuzzy query, the user’s fuzzy queries can be translated into precise queries for classical relational databases.     

基于SQL Server 2000的通用模糊查询工具设计

本文设计并实现了一个基于SQL Server 2000的通用模糊查询工具,该工具可以把带权重的模糊查询转换为标准的SQL语句。用户可以对SQL Server中建立的任何数据库表进行模糊查询。系统提供了以下功能：定义模糊谓词及其隶属函数;定义模糊算子;构造带权重的模糊的、精确的或混合的查询语句,权重和阈值可以由用户给出。满足查询条件的记录将根据匹配度的降序输出。本系统的设计方法也可以推广到其他的数据库系统,如Oracle,Access等。     

多关系子句相关的查询优化

关系数据库通过SQL语言进行查询操作.由于SQL语言是一种非过程化语言,因此,用户所编写的SQL语句一般不是最优的,于是,对SQL查询进行优化就成为数据库管理系统的任务.本文以应用实例为基础,结合数据库理论,介绍查询优化技术在多关系相关子查询中的运用.     

Modelling mobile app requirements for semantic traceability

The paper presents a modelling method aimed to support the definition and elicitation of requirements for mobile apps through an approach that enables semantic traceability for the requirements representation. Business process-centricity is employed in order to capture requirements in a knowledge structure that retains procedural knowledge from stakeholders and can be traversed by semantic queries in order to trace domain-specific contextual information for the modelled requirements. Consequently, instead of having requirements represented as natural language items that are documented by diagrammatic models, the communication channels are switched: semantically interlinked conceptual models become the requirements representation, while free text can be used for requirements annotations/metadata. Thus, the method establishes a knowledge externalization channel between business stakeholders and app developers, also tackling the Twin Peaks bridging challenge (between requirements and early designs). The method is presented using its modelling procedure as a guiding thread, with each step illustrated by case-based samples of the modelling language and auxiliary functionality. The design work is encompassed by an existing metamodelling framework and introduces a taxonomy for modelling relations, since the metamodel is the key enabler for the goal of semantic traceability. The research was driven by the ComVantage EU research project, concerned with mobile app support for collaborative business process execution. Therefore, the project provides context for the illustrating examples; however, generalization possibilities beyond the project scope will also be discussed, with respect to both motivation and outcome.     

Extending the UML concepts to transform natural language queries with fuzzy semantics into SQL

Database applications tend toward getting more versatile and broader to comply with the expansion of various organizations. However, naïve users usually suffer from accessing data arbitrarily by using formal query languages. Therefore, we believe that accessing databases using natural language constructs will become a popular interface in the future. The concept of object-oriented modeling makes the real world to be well represented or expressed in some kinds of logical form. Since the class diagram in UML is used to model the static relationships of databases, in this paper, we intend to study how to extend the UML class diagram representations to capture natural language queries with fuzzy semantics. By referring to the conceptual schema throughout the class diagram representation, we propose a methodology to map natural language constructs into the corresponding class diagram and employ Structured Object Model (SOM) methodology to transform the natural language queries into SQL statements for query executions. Moreover, our approach can handle queries containing vague terms specified in fuzzy modifiers, like ‘good’ or ‘bad’. By our approach, users obtain not only the query answers but also the corresponding degree of vagueness, which can be regarded as the same way we are thinking.     

Semantic interpretation of a database query language

In this paper, we will discuss a system that semantically interprets a formal database accessing language and generates natural language from this interpretation. In the past, the major way of communication between a user and a database was by means of a formal language. One such language is the SQL query language. Even though constructed as a user friendly language, SQL exemplifies the same difficulties for users as do other formal languages, namely a fairly rigid syntax, the necessity of variable binding, the lack of pronouns, and in the case of erroneous queries error messages that do not provide much insight. To alleviate some of the formal language problems, yet utilize the power of the formal language, we set out to build a natural language ‘umbrella’ for the SQL user. Our goal was not to build a natural language query system, but rather to use semantic knowledge and natural language for paraphrasing the formal language (SQL) and producing error messages as a feedback mechanism. In this way we build a genuine help facility, which would not only aid the user in dealing with SQL, but also trap erroneous queries.     

Semantics preserving SPARQL-to-SQL translation

Most existing RDF stores, which serve as metadata repositories on the Semantic Web, use an RDBMS as a backend to manage RDF data. This motivates us to study the problem of translating SPARQL queries into equivalent SQL queries, which further can be optimized and evaluated by the relational query engine and their results can be returned as SPARQL query solutions. The main contributions of our research are: (i) We formalize a relational algebra based semantics of SPARQL, which bridges the gap between SPARQL and SQL query languages, and prove that our semantics is equivalent to the mapping-based semantics of SPARQL; (ii) Based on this semantics, we propose the first provably semantics preserving SPARQL-to-SQL translation for SPARQL triple patterns, basic graph patterns, optional graph patterns, alternative graph patterns, and value constraints; (iii) Our translation algorithm is generic and can be directly applied to existing RDBMS-based RDF stores; and (iv) We outline a number of simplifications for the SPARQL-to-SQL translation to generate simpler and more efficient SQL queries and extend our defined semantics and translation to support the bag semantics of a SPARQL query solution. The experimental study showed that our proposed generic translation can serve as a good alternative to existing schema dependent translations in terms of efficient query evaluation and/or ensured query result correctness.     

Preventing injection attacks with syntax embeddings

Software written in one language often needs to construct sentences in another language, such as SQL queries, XML output, or shell command invocations. This is almost always done using unhygienic string manipulation, the concatenation of constants and client-supplied strings. A client can then supply specially crafted input that causes the constructed sentence to be interpreted in an unintended way, leading to an injection attack. We describe a more natural style of programming that yields code that is impervious to injections by construction. Our approach embeds the grammars of the guest languages (e.g. SQL) into that of the host language (e.g. Java) and automatically generates code that maps the embedded language to constructs in the host language that reconstruct the embedded sentences, adding escaping functions where appropriate. This approach is generic, meaning that it can be applied with relative ease to any combination of context-free host and guest languages.     

Ultrawrap: SPARQL execution on relational data

The Semantic Web’s promise of web-wide data integration requires the inclusion of legacy relational databases,¹ i.e. the execution of SPARQL queries on RDF representation of the legacy relational data. We explore a hypothesis: existing commercial relational databases already subsume the algorithms and optimizations needed to support effective SPARQL execution on existing relationally stored data. The experiment is embodied in a system, Ultrawrap, that encodes a logical representation of the database as an RDF graph using SQL views and a simple syntactic translation of SPARQL queries to SQL queries on those views. Thus, in the course of executing a SPARQL query, the SQL optimizer uses the SQL views that represent a mapping of relational data to RDF, and optimizes its execution. In contrast, related research is predicated on incorporating optimizing transforms as part of the SPARQL to SQL translation, and/or executing some of the queries outside the underlying SQL environment.Ultrawrap is evaluated using two existing benchmark suites that derive their RDF data from relational data through a Relational Database to RDF (RDB2RDF) Direct Mapping and repeated for each of the three major relational database management systems. Empirical analysis reveals two existing relational query optimizations that, if applied to the SQL produced from a simple syntactic translations of SPARQL queries (with bound predicate arguments) to SQL, consistently yield query execution time that is comparable to that of SQL queries written directly for the relational representation of the data. The analysis further reveals the two optimizations are not uniquely required to achieve a successful wrapper system. The evidence suggests effective wrappers will be those that are designed to complement the optimizer of the target database.     

A spatial model for complex objects with a broad boundary supporting queries on uncertain data

This paper proposes a new data model for spatial databases that supports spatial entities with a broad boundary. The model is proposed as a solution to the problem of handling uncertainty in spatial data. Broad boundaries absorb all the uncertainty by which spatial data are commonly affected and allow computations in presence of uncertainty without rough simplifications of the reality. A specific model for topological relations between regions with a broad boundary is then developed and hierarchically structured topological operators are proposed. Such operators allow users to progressively refine their queries and constitute the basis for a spatial extension to SQL. The model proposed in the paper can be easily integrated into existing data models for spatial databases.     

基于自裁剪异构图的NL2SQL模型

自然语言转换为结构化查询语言（NL2SQL）是语义解析领域的重要任务,其核心为对数据库模式和自然语言问句进行联合学习。现有研究通过将整个数据库模式和自然语言问句联合编码构建异构图,使得异构图中引入大量无用信息,并且忽略了数据库模式中不同信息的重要性。为提高NL2SQL模型的逻辑与执行准确率,提出一种基于自裁剪异构图与相对位置注意力机制的NL2SQL模型（SPRELA）。采用序列到序列的框架,使用ELECTRA预训练语言模型作为骨干网络。引入专家知识,对数据库模式和自然语言问句构建初步异构图。基于自然语言问句对初步异构图进行自裁剪,并使用多头相对位置注意力机制编码自裁剪后的数据库模式与自然语言问句。利用树型解码器和预定义的SQL语法,解码生成SQL语句。在Spider数据集上的实验结果表明,SPRELA模型执行准确率达到71.1%,相比于相同参数量级别的RaSaP模型提升了1.1个百分点,能够更好地将数据库模式与自然语言问句对齐,从而理解自然语言查询中的语义信息。     

Extending ER models to capture database transformations to build data sets for data mining

In a data mining project developed on a relational database, a significant effort is required to build a data set for analysis. The main reason is that, in general, the database has a collection of normalized tables that must be joined, aggregated and transformed in order to build the required data set. Such scenario results in many complex SQL queries that are written independently from each other, in a disorganized manner. Therefore, the database grows with many tables and views that are not present as entities in the ER model and similar SQL queries are written multiple times, creating problems in database evolution and software maintenance. In this paper, we classify potential database transformations, we extend an ER diagram with entities capturing database transformations and we introduce an algorithm which automates the creation of such extended ER model. We present a case study with a public database illustrating database transformations to build a data set to compute a typical data mining model.     

基于树状模型的复杂自然语言查询转SQL技术研究

自然语言查询转SQL （NL2SQL）是指将自然语言表达的查询文本自动转化成数据库系统可以理解并执行的结构化查询语言SQL表达式的技术.NL2SQL可以为普通用户提供数据库查询访问的自然交互界面,从而实现基于数据库的自然问答.复杂查询的NL2SQL是当前数据库学术界的研究热点,主流方法采用序列到序列（Seq2seq）的编解码方式对问题进行建模.然而,已有的工作大多基于英文场景,面向中文领域实际应用时,中文特殊的口语化表达导致复杂查询转化困难;此外,现有工作难以正确输出包含复杂计算表达式的查询子句.针对上述问题,提出一种树状模型取代序列表示,将复杂查询自顶向下分解为多叉树,树结点代表SQL的各组成元素,采用深度优先搜索来预测生成SQL语句.在DuSQL中文NL2SQL竞赛的两个官方测试集中,该方法分别取得了第1名和第2名的成绩,验证了其有效性.     

嵌套查询的非嵌套化处理研究

嵌套查询是ＳＱＬ查询语言的重要特色，传统的数据为系统处理嵌套查询的方法是ＴＩＳ．ＴＩＳ方法处理效率很低。目前高嵌套查询处理效率的有效方法是非嵌套化处理方法。     

Time Automaton: A visual mechanism for temporal querying

Available visual temporal querying tools do not provide the means for formulating complex temporal queries. For these queries users have to adopt text-based querying languages, such as SQL. The problem, however, is that using text-based languages is less comfortable than using visual tools and, most important, in some cases temporal queries can be extremely difficult to formulate for users that do not possess programming competences. In this paper we propose the Time Automaton, a highly flexible visual mechanism that enables the formulation of a large set of different types of temporal queries, ranging from the simple to the most complex ones. To prove its practical application we created a tool that implements the mechanism and used it to analyze a real dataset. Time Automaton was validated against a representative sample of temporal queries extracted from the matured OWL-Time Ontology. In order to understand if users, with or without programming competences, could understand and use the Time Automaton we conducted a usability experiment.     

结合数据库结构及内容的问句理解方法研究

问句理解是模型将自然语言问句转换成SQL的重要基础。目前多数利用深度学习的模型仅是通过数据库结构,未结合数据库内容充分理解问句生成SQL查询。在SQLova模型的基础上,提出一种基于表结构和内容的问句理解方法。利用表结构和表内容关注机制获得问句更准确的语义表达式,通过子类分类任务填充SQL草图完成SQL查询。在阿里云首届中文NL2SQL挑战赛发布的中文数据集上进行测试,结果表明,结合数据库结构与内容的问句理解方法取得78%的准确率,比不结合表内容的模型高出1.8%,在WikiSQL数据集上比SQLova准确率高出1.4%,可以有效提高生成SQL查询的准确率。     

Object-oriented query language access to relational databases: A semantic framework for query translation

This research investigates and approach to query processing in a multidatabase system that uses an objectoriented model to capture the semantics of other data models. The object-oriented model is used to construct a global schema, defining an integrated view of the different schemas in the environment. The model is also used as a self-describing model to build a meta-database for storing information about the global schema. A unique aspect of this work is that the object-oriented model is used to describe the different data models of the multidatabase environment, thereby extending the meta database with semantic information about the local schemas. With the global and local schemas all represented in an object-oriented form, structural mappings between the global schema and each local schema are then easily supported. An object algebra then provides a query language for expressing global queries, using the structural mappings to translate object algebra queries into SQL queries over local relational schema. The advantage of using an object algebra is that the object-oriented database can be viewed as a blackboard for temporary storage of local data and for establishing relationships between different databases. The object algebra can be used to directly retrieve temporarily-stored data from the object-oriented database or to transparently retrieve data from local sources using the translation process described in this paper.     

网页搜索中查询时效性的实时计算模型

网页搜索中的查询时效性是指查询对新闻网页的需求。这种时间相关的因素,在网页排序过程中用于平衡其他非时间性因素,使排序更好地满足用户体验。为此该文提出了一种查询时效性的实时计算模型从用户搜索和媒体报道两个角度,分别对时效性建模,然后这两种不同来源的时效性相互补充,综合计算某个时刻用户搜索某个查询时,其综合时效性得分。这个量化得分在网页排序阶段用于提高或抑制新闻网页的露出;同时也为网页搜索结果中展现新闻直达区提供依据。在人工评测以及用户点击通过率统计上,该模型均取得了不错的实际效果。     

Conversational querying

The traditional interaction mechanism with a database system is through the use of a query language, the most widely used one being SQL. However, when one is facing a situation where he or she has to make a minor modification to a previously issued SQL query, either the whole query has to be written from scratch, or one has to invoke an editor to edit the query. This, however, is not the way we converse with each other as humans. During the course of a conversation, the preceding interaction is used as a context within which many incomplete and/or incremental phrases are uniquely and unambiguously interpreted, sparing the need to repeat the same things again and again. In this paper, we present an effective mechanism that allows a user to interact with a database system in a way similar to the way humans converse. More specifically, incomplete SQL queries are accepted as input which are then matched to identified parts of previously issued queries. Disambiguation is achieved by using various types of semantic information. The overall method works independently of the domain under which it is used (i.e., independently of the database schema). Several algorithms that are variations of the same basic mechanism are proposed. They are mutually compared with respect to efficiency and accuracy through a limited set of experiments on human subjects. The results have been encouraging, especially when semantic knowledge from the schema is exploited, laying a potential foundation for conversational querying in databases.     

一种基于命名实体识别的需求跟踪方法

针对基于文本的需求跟踪方法严重依赖文本质量的问题,提出了一种利用命名实体识别技术标注制品文档关键词的需求跟踪方法。该方法通过代码实体上下文构建命名实体识别模型,解决了抽象语法树和正则表达式无法解析非源代码形式的软件制品的问题。利用命名实体识别模型标识出软件制品中的代码实体之后,方法将软件制品转换为文档集合并进行语义聚类,最后再通过映射算法创建制品间的需求跟踪关系。实验结果表明,与基于所有词项和基于高权重词项的需求跟踪方法相比,该方法能够有效提高需求跟踪结果的质量。