Query Language For Semistructured Data Research Articles

Ephemeral data handling in microservices with Tquery.

The adoption of edge and fog systems, along with the introduction of privacy-preserving regulations, compel the usage of tools for expressing complex data queries in an ephemeral way. That is, queried data should not persist. Database engines partially address this need, as they provide domain-specific languages for querying data. Unfortunately, using a database in an ephemeral setting has inessential issues related to throughput bottlenecks, scalability, dependency management, and security (e.g., query injection). Moreover, databases can impose specific data structures and data formats, which can hinder the development of microservice architectures that integrate heterogeneous systems and handle semi-structured data. In this article, we present Jolie/Tquery, the first query framework designed for ephemeral data handling in microservices. Jolie/Tquery joins the benefits of a technology-agnostic, microservice-oriented programming language, Jolie, and of one of the most widely-used query languages for semi-structured data in microservices, the MongoDB aggregation framework. To make Jolie/Tquery reliable for the users, we follow a cleanroom software engineering process. First, we define Tquery, a theory for querying semi-structured data compatible with Jolie and inspired by a consistent variant of the key operators of the MongoDB aggregation framework. Then, we describe how we implemented Jolie/Tquery following Tquery and how the Jolie type system naturally captures the syntax of Tquery and helps to preserve its invariants. To both illustrate Tquery and Jolie/Tquery, we present the use case of a medical algorithm and build our way to a microservice that implements it using Jolie/Tquery. Finally, we report microbenchmarks that validate the expectation that, in the ephemeral case, using Jolie/Tquery outperforms using an external database (MongoDB, specifically).

On Regular Paths with Counting and Data Tests

Regular path expressions represent the navigation core of the XPath query language for semi-structured data (XML), and it has been characterized as the First Order Logic with Two Variables (FO2). Data tests refers to (dis)equality comparisons on data tree models, which are unranked trees with two kinds of labels, propositions from a finite alphabet, and data values from a possibly infinite alphabet. Node occurrences on tree models can be constrained by counting/arithmetic constructors. In this paper, we identify an EXPTIME extension of regular paths with data tests and counting operators. This extension is characterized in terms of a closed under negation Presburger tree logic. As a consequence, the EXPTIME bound also applies for standard query reasoning (emptiness, containment and equivalence).

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.

Modelling the history of semi-structured geographical entities

Geographical entities are characterized by rather complex structures. They involve space and thematic information, which is subject to change in time, while history should be maintained. On the other hand, these structures may be irregular (i.e. they do not necessarily conform to a fixed schema), because associated data is usually collected based on different specifications and multiple resolutions. Hence, the representation of geographical entities in traditional data models, such as the relational or object-oriented, is not always feasible. In this respect, this paper investigates the use of semi-structured data (SSD) models—an innovative approach recently developed in Information Technology—for modelling dynamic geographical entities. A framework for the representation of geographic entities in Object Exchange Model (OEM), a popular model for semi-structured data, is introduced. Additionally, it is shown how useful information can be extracted from such a representation using the LOREL query language for SSD. A simplified case study in the application domain of cadastre involving SSD is examined closely.

UnQL: a query language and algebra for semistructured data based on structural recursion

This paper presents structural recursion as the basis of the syntax and semantics of query languages for semistructured data and XML. We describe a simple and powerful query language based on pattern matching and show that it can be expressed using structural recursion, which is introduced as a top-down, recursive function, similar to the way XSL is defined on XML trees. On cyclic data, structural recursion can be defined in two equivalent ways: as a recursive function which evaluates the data top-down and remembers all its calls to avoid infinite loops, or as a bulk evaluation which processes the entire data in parallel using only traditional relational algebra operators. The latter makes it possible for optimization techniques in relational queries to be applied to structural recursion. We show that the composition of two structural recursion queries can be expressed as a single such query, and this is used as the basis of an optimization method for mediator systems. Several other formal properties are established: structural recursion can be expressed in first-order logic extended with transitive closure; its data complexity is PTIME; and over relational data it is a conservative extension of the relational calculus. The underlying data model is based on value equality, formally defined with bisimulation. Structural recursion is shown to be invariant with respect to value equality.

The Lorel query language for semistructured data

language, designed for querying semistructured data. Semistructured data is becoming more and more prevalent, e.g., in structured documents such as HTML and when performing simple integration of data from multiple sources. Traditional data models and query languages are inappropriate, since semistructured data often is irregular: some data is missing, similar concepts are represented using different types, heterogeneous sets are present, or object structure is not fully known. Lorel is a user-friendly language in the SQL/OQL style for querying such data effectively. For wide applicability, the simple object model underlying Lorel can be viewed as an extension of the ODMG data model and the Lorel language as an extension of OQL. The main novelties of the Lorel language are: (i) the extensive use of coercion to relieve the user from the strict typing of OQL, which is inappropriate for semistructured data; and (ii) powerful path expressions, which permit a flexible form of declarative navigational access and are particularly suitable when the details of the structure are not known to the user. Lorel also includes a declarative update language. Lorel is implemented as the query language of the Lore prototype database management system at Stanford. Information about Lore can be found at http://www-db.stanford.edu/lore. In addition to presenting the Lorel language in full, this paper briefly describes the Lore system and query processor. We also briefly discuss a second implementation of Lorel on top of a conventional object-oriented database management system, the O2 system.