Relational Query Languages Research Articles

TraNCE

Nested relational query languages have long been seen as an attractive tool for scenarios involving large hierarchical datasets. There has been a resurgence of interest in nested relational languages. One driver has been the affinity of these languages for large-scale processing platforms such as Spark and Flink. This demonstration gives a tour of TraNCE, a new system for processing nested data on top of distributed processing systems. The core innovation of the system is a compiler that processes nested relational queries in a series of transformations; these include variants of two prior techniques, shredding and unnesting, as well as a materialization transformation that customizes the way levels of the nested output are generated. The TraNCE platform builds on these techniques by adding components for users to create and visualize queries, as well as data exploration and notebook execution targets to facilitate the construction of large-scale data science applications. The demonstration will both showcase the system from the viewpoint of usability by data scientists and illustrate the data management techniques employed.

Technical Perspective

The challenge of extracting structured information from text, or sequential data in general, is prevalent across a multitude of data-science domains. This challenge, known as Information Extraction (IE), instantiates to core components in text analytics, and a plethora of IE paradigms have been developed over the past decades. Rules and rule systems have consistently been key components in such paradigms, yet their roles have varied and evolved over time. Analytics engines such as IBM's SystemT use IE rules for materializing relations inside relational query languages. Machinelearning classifiers and probabilistic graphical models (e.g., Conditional Random Fields) use rules for feature generation. They also serve as weak constraints in Markov Logic Networks (and extensions such as DeepDive), and generators of noisy training data in the state-of-the-art Snorkel system.

StreamQRE: Modular Specification and Efficient Evaluation of Quantitative Queries over Streaming Data.

Real-time decision making in emerging IoT applications typically relies on computing quantitative summaries of large data streams in an efficient and incremental manner. To simplify the task of programming the desired logic, we propose StreamQRE, which provides natural and high-level constructs for processing streaming data. Our language has a novel integration of linguistic constructs from two distinct programming paradigms: streaming extensions of relational query languages and quantitative extensions of regular expressions. The former allows the programmer to employ relational constructs to partition the input data by keys and to integrate data streams from different sources, while the latter can be used to exploit the logical hierarchy in the input stream for modular specifications. We first present the core language with a small set of combinators, formal semantics, and a decidable type system. We then show how to express a number of common patterns with illustrative examples. Our compilation algorithm translates the high-level query into a streaming algorithm with precise complexity bounds on per-item processing time and total memory footprint. We also show how to integrate approximation algorithms into our framework. We report on an implementation in Java, and evaluate it with respect to existing high-performance engines for processing streaming data. Our experimental evaluation shows that (1) StreamQRE allows more natural and succinct specification of queries compared to existing frameworks, (2) the throughput of our implementation is higher than comparable systems (for example, two-to-four times greater than RxJava), and (3) the approximation algorithms supported by our implementation can lead to substantial memory savings.

Implementing GraphQL as a Query Language for Deductive Databases in SWI-Prolog Using DCGs, Quasi Quotations, and Dicts

The methods to access large relational databases in a distributed system are well established: the relational query language SQL often serves as a language for data access and manipulation, and in addition public interfaces are exposed using communication protocols like REST. Similarly to REST, GraphQL is the query protocol of an application layer developed by Facebook. It provides a unified interface between the client and the server for data fetching and manipulation. Using GraphQL's type system, it is possible to specify data handling of various sources and to combine, e.g., relational with NoSQL databases. In contrast to REST, GraphQL provides a single API endpoint and supports flexible queries over linked data. GraphQL can also be used as an interface for deductive databases. In this paper, we give an introduction of GraphQL and a comparison to REST. Using language features recently added to SWI-Prolog 7, we have developed the Prolog library GraphQL.pl, which implements the GraphQL type system and query syntax as a domain-specific language with the help of definite clause grammars (DCG), quasi quotations, and dicts. Using our library, the type system created for a deductive database can be validated, while the query system provides a unified interface for data access and introspection.

The complexity of higher-order queries

Higher-order transformations are ubiquitous within data management. In relational databases, higher-order queries appear in numerous aspects including query rewriting and query specification. This work investigates languages that combine higher-order transformations with ordinary relational database query languages. We study the two most basic computational problems associated with these query languages – the evaluation problem and the containment problem. We isolate the complexity of evaluation at every order, in an analysis similar to that for that standard typed lambda calculus. We show that the containment problem (and hence, the equivalence problem) is decidable in several important subcases, particularly in the case where query constants and variables range over the positive relational operators. The main decidability result relies on techniques that differ from those used in classical query containment. We also show that the analysis of higher-order queries is closely connected to the evaluation and containment problems for non-recursive Datalog.

SQL-based semantics for path expressions over hierarchical data in relational databases

Hierarchical part-of relationships/aggregation structures and related queries are essential parts of information systems. However, relational database query languages do not explicitly support hierarchical relationships and queries. A hierarchical query may require a great number of join operations, which increases the effort in query formulation. Therefore, we propose path expressions in formulating hierarchical views over relational data because path expressions are a conventional and compact way to represent hierarchical relationships. We embed path expressions within SQL queries and compile them to standard SQL. This ensures that the path expressions can straightforwardly be implemented on the top of standard relational database systems. The compilation of a path expression is given by an attribute grammar, a conventional formalism to define the semantics of a language.

Efficient processing of substring match queries with inverted variable-length gram indexes

With the widespread use of the internet, text-based data sources have become ubiquitous and the demand for effective support of string matching queries continues to increase. The relational query language SQL supports LIKE clauses for string data to handle substring matching queries. Due to the popularity of such substring matching queries, there have been many studies on designing efficient indexes to support the LIKE clause in SQL. Among them, the indexes based on q-grams with fixed or variable sizes have been studied extensively.In this paper, we show that the optimal processing of substring matching queries with inverted variable-length gram indexes should be decided judiciously. However, the search space of finding the optimal variable-length gram set among those available is exponential with the number of grams. To reduce the search space, we present effective pruning strategies which do not sacrifice optimality. Based on the cost estimation, we propose the optimal algorithms to find the best plan with the minimum cost for substring matching queries using these pruning strategies. We also provide the approximate algorithms to overcome the exponential nature of search space to find an optimal query plan. Our performance study confirms that the proposed techniques improve query execution time for substring matching significantly compared to the running time of the traditional algorithms.

Efficient implementation of generalized quantification in relational query languages

We present research aimed at improving our understanding of the use and implementation of quantification in relational query languages in general and SQL in particular. In order to make our results as general as possible, we use the framework of Generalized Quantification. Generalized Quantifiers (GQs) are high-level, declarative logical operators that in the past have been studied from a theoretical perspective. In this paper we focus on their practical use, showing how to incorporate a dynamic set of GQs in relational query languages, how to implement them efficiently and use them in the context of SQL. We present experimental evidence of the performance of the approach, showing that it improves over traditional (relational) approaches.

Semantic query in a relational database using a local ontology construction

Semantic Web refers to a Web of linked data in which data can be shared and reused, allowing more uses than the traditional 'Web of documents'. However, most of the information on the Web is stored in relational databases and such databases cannot be used by the Semantic Web. Relational databases can, however, be used to construct an ontology as the core of the Semantic Web. We propose a new approach which enables Semantic Web applications to access data actually stored in relational databases using a corresponding ontology. In our approach, domain ontologies can be used to formulate relational database queries in order to simplify the data access of the underlying data sources. The method we propose involves two main phases: the construction of a local ontology from a relational database and a semantic query in a relational database using relational database query language (RDQL). In the first phase, we construct a Web ontology language ontology from data in a relational database. In the second phase, we propose a technique to automatically extract the semantics of relational databases and transform this information into a representation that can be processed and understood by a machine. The method proposed is simulated and implemented using Jena and the simulation results show the effectiveness of the proposed approach. Therefore, we propose RDQL as a real alternative to the commonly used structured query language access to relational databases.

Relational languages and data models for continuous queries on sequences and data streams

Most data stream management systems are based on extensions of the relational data model and query languages, but rigorous analyses of the problems and limitations of this approach, and how to overcome them, are still wanting. In this article, we elucidate the interaction between stream-oriented extensions of the relational model and continuous query language constructs, and show that the resulting expressive power problems are even more serious for data streams than for databases. In particular, we study the loss of expressive power caused by the loss of blocking query operators, and characterize nonblocking queries as monotonic functions on the database. Thus we introduce the notion of NB -completeness to assure that a query language is as suitable for continuous queries as it is for traditional database queries. We show that neither RA nor SQL are NB -complete on unordered sets of tuples, and the problem is even more serious when the data model is extended to support order—a sine-qua-non in data stream applications. The new limitations of SQL, compounded with well-known problems in applications such as sequence queries and data mining, motivate our proposal of extending the language with user-defined aggregates (UDAs). These can be natively coded in SQL, according to simple syntactic rules that set nonblocking aggregates apart from blocking ones. We first prove that SQL with UDAs is Turing complete. We then prove that SQL with monotonic UDAs and union operators can express all monotonic set functions computable by a Turing machine ( NB -completeness) and finally extend this result to queries on sequences ordered by their timestamps. The proposed approach supports data stream models that are more sophisticated than append-only relations, along with data mining queries, and other complex applications.

A query language for selecting, harmonizing, and aggregating heterogeneous XML data

PurposeThe purpose of this paper is to introduce an expressive query language, called relational XML query language (RXQL), capable of dealing with heterogeneous Extensible Markup Language (XML) documents in data‐centric applications. In RXQL, data harmonization (i.e. the removal of heterogeneous factors from XML data) is integrated with typical data‐centric features (e.g. grouping, ordering, and aggregation).Design/methodology/approachRXQL is based on the XML relation representation, developed in the authors' previous work. This is a novel approach to unambiguously represent semistructured data relationally, which makes it possible in RXQL to manipulate XML data in a tuple‐oriented way, while XML data are typically manipulated in a path‐oriented way.FindingsThe user is able to describe the result of an RXQL query straightforwardly based on non‐XML syntax. The analysis of this description, through the mechanism developed in this paper, affords the automatic construction of the query result. This feature increases significantly the declarativeness of RXQL compared to the path‐oriented XML languages where the user needs to control the construction of the result extensively.Practical implicationsThe authors' formal specification of the construction of the query result can be considered as an abstract implementation of RXQL.Originality/valueRXQL is a declarative query language capable of integrating data harmonization seamlessly with other data‐centric features in the manipulation of heterogeneous XML data. So far, these kinds of XML query languages have been missing. Obviously, the expressive power of RXQL can be achieved by computationally complete XML languages, such as XQuery. However, these are not actual query languages, and the query formulation in them usually presupposes programming skills that are beyond the ordinary end‐user.

From XQuery to relational logics

Predicate logic has long been seen as a good foundation for querying relational data. This is embodied in the correspondence between relational calculus and first-order logic, and can also be seen in mappings from fragments of the standard relational query language SQL to extensions of first-order logic (e.g. with counting). A key question is what is the analog to this correspondence for querying tree-structured data, as seen, for example, in XML documents. We formalize this as the question of the appropriate logical query language for defining transformations on tree-structured data. The predominant practitioner paradigm for defining such transformations is top-down tree building . This is embodied by the XQuery query language, which builds the output tree in parallel starting at the root, based on variable bindings and nodeset queries in the XPath language. The goal of this article is to compare the expressiveness of top-down tree-building languages based on a benchmark of predicate logic. We start by giving a formalized XQuery XQ that can serve as a representative of the top-down approach. We show that all queries in XQ with only atomic equality are equivalent to first-order interpretations, an analog to first-order logic (FO) in the setting of transformations of tree-structured data. We then consider fragments of atomic XQ . We identify a fragment that maps efficiently into first-order, a fragment that maps into existential first-order logic, and a fragment that maps into the navigationally two-variable fragment of first-order logic—an analog of two-variable logic in the setting where data values are unbounded. When XQ is considered with deep equality, we find that queries can be translated into FO with counting ( FO (Cnt)). Translations from XQ to logical languages on relations have a number of consequences. We use them to derive complexity bounds for XQ fragments, and to bound the Boolean expressiveness of XQ fragments.

Using the Relational Model to Capture Topological Information of Spaces

Motivated by research on how topology may be a helpful foundation for building information modelling, a relational database version of the notions of chain complex and chain complex morphism is defined and used for storing cw-complexes and their morphisms, hence instances of building projects and different views upon them, into relational databases. In many cases, this can be done without loss of topological information. The equivalence of categories between sets with binary relations and Alexandrov spaces is proven and used to incorporate the relational complexes into the more general setting of topological databases. For the latter, a topological version of a relational query language is defined by transferring the usual relational algebra operators into topological constructions. In the end, it is proven that such a topological version of relational algebra in general must be able to compute the transitive closure of a relation.

A quantum logic-based query processing approach for extending relational query languages.

Evaluating a traditional database query against a data tuple returns a true on match and a false on mismatch. Unfortunately, there are many application scenarios where such an evaluation is not possible or does not adequately meet users needs. A further problematic application area is text retrieval where in general finding a complete match is impossible. Thus, there is a need for incorporating impreciseness and proximity into a logic-based query language: Objects fulfill such a similarity condition to a certain degree which is expressed by a result value out of the interval [0, 1]. In this work we will sketch a quantum logic-based approach, which provides the combination of classical Boolean predicates and similarity conditions into one integrating formalism.

Time-Stamp Management and Query Execution in Data Stream Management Systems

Relational query languages can effectively express continuous queries on data streams after modest extensions. However, implementing such queries efficiently in data stream management systems requires major changes in execution models and optimization techniques. In particular, finer-granularity execution models that are conducive to effective time-stamp management and response-time optimization must replace databases' relational algebra schemes. This article introduces such a model and uses it to solve the idle-waiting problems of data stream operators, such as unions, joins, and aggregates over windows with slides.

Expressiveness and complexity of XML publishing transducers

A number of languages have been developed for specifying XML publishing, that is, transformations of relational data into XML trees. These languages generally describe the behaviors of a middleware controller that builds an output tree iteratively, issuing queries to a relational source and expanding the tree with the query results at each step. To study the complexity and expressive power of XML publishing languages, this article proposes a notion of publishing transducers , which generate XML trees from relational data. We study a variety of publishing transducers based on what relational queries a transducer can issue, what temporary stores a transducer can use during tree generation, and whether or not some tree nodes are allowed to be virtual, that is, excluded from the output tree. We first show how existing XML publishing languages can be characterized by such transducers, and thus provide a synergy between theory and practice. We then study the membership, emptiness, and equivalence problems for various classes of transducers. We establish lower and upper bounds, all matching, ranging from PTIME to undecidable. Finally, we investigate the expressive power of these transducers and existing languages. We show that when treated as relational query languages, different classes of transducers capture either complexity classes (e.g., PSPACE) or fragments of datalog (e.g., linear datalog). For tree generation, we establish connections between publishing transducers and logical transductions, among other things.

Extending a Natural Language Interface with Geospatial Queries

In this installation of architectural perspectives, we describe an extension of a menu-based natural language interface (MBNLI) to support geospatial queries. Our extension makes it easier for application analysts and even inexperienced users to phrase complex queries without knowing the relational database query language SQL, database schemas (table structures), spatial operators, or spatial indexes.

A light-weight software engineering approach to developing small-scale information systems using .NET 2.0

Applied database management systems (DBMS) courses that require information systems projects present formidable challenges to both students and instructors. Students assume the twin responsibilities of (1) learning major theoretical concepts (e.g. the relational model, data modeling, database design, and standard structured query language (SQL)) while (2) mastering the syntax and semantics of a specific complex technology (e.g. MySQL, Oracle, MSSQL). Dealing with the project problem domain also adds to demands on student time and understanding. Consequently, instructors are increasingly challenged to organize course content, technology instruction, and project resources so that students can complete non-trivial course projects in a reasonable time frame. This tutorial will introduce a light-weight software engineering approach to guiding student project development in an introductory database management systems course. This iterative and incremental approach to database programming projects is predicated on the database application principle of firm separation of logic via the N-tier model and the software engineering tenet of well-specified requirements. Students enrolled in an introductory DBMS course follow this methodology to complete GUI client/server team projects. Database and software engineering skills and understanding developed in this course are then applied to more substantive 2- and 3-tier individual projects in an advanced DBMS course and team projects in a capstone software engineering course.

Relational languages for metadata integration

In this article, we develop a relational algebra for metadata integration, Federated Interoperable Relational Algebra (FIRA). FIRA has many desirable properties such as compositionality, closure, a deterministic semantics, a modest complexity, support for nested queries, a subalgebra equivalent to canonical Relational Algebra (RA), and robustness under certain classes of schema evolution. Beyond this, FIRA queries are capable of producing fully dynamic output schemas, where the number of relations and/or the number of columns in relations of the output varies dynamically with the input instance. Among existing query languages for relational metadata integration, only FIRA provides generalized dynamic output schemas, where the values in any (fixed) number of input columns can determine output schemas.Further contributions of this article include development of an extended relational model for metadata integration, the Federated Relational Data Model , which is strictly downward compatible with the relational model. Additionally, we define the notion of Transformational Completeness for relational query languages and postulate FIRA as a canonical transformationally complete language. We also give a declarative, SQL-like query language that is equivalent to FIRA, called Federated Interoperable Structured Query Language (FISQL).While our main contributions are conceptual, the federated model, FISQL/FIRA, and the notion of transformational completeness nevertheless have important applications to data integration and OLAP. In addition to summarizing these applications, we illustrate the use of FIRA to optimize FISQL queries using rule-based transformations that directly parallel their canonical relational counterparts. We conclude the article with an extended discussion of related work as well as an indication of current and future work on FISQL/FIRA.

TQL: a query language for semistructured data based on the ambient logic

The ambient logic is a modal logic that was proposed for the description of the structural and computational properties of distributed and mobile computation. The structural part of the ambient logic is, essentially, a logic of labelled trees, hence it turns out to be a good foundation for query languages for semistructured data, much in the same way as first-order logic is a fitting foundation for relational query languages. We define here a query language for semistructured data that is based on the ambient logic, and we outline an execution model for this language. The language turns out to be quite expressive. Its strong foundations and the equivalences that hold in the ambient logic are helpful in the definition of the language semantics and execution model.