An Expressive Functional Data Model and Query Language for Bioinformatics Data Integration

International audience

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.

In recent years, applications aimed at exploring and analyzing spatial data have emerged, powered by the increasing need of software that integrates Geographic Information Systems (GIS) and On-Line Analytical Processing (OLAP). These applications have been called SOLAP (Spatial OLAP). In previous work, the authors have introduced Piet, a system based on a formal data model that integrates in a single framework GIS, OLAP (On-Line Analytical Processing), and Moving Object data. Real-world problems are inherently spatio-temporal. Thus, in this paper we present a data model that extends Piet, allowing tracking the history of spatial data in the GIS layers. We present a formal study of the two typical ways of introducing time into Piet: timestamping the thematic layers in the GIS, and timestamping the spatial objects in each layer. We denote these strategies snapshot-based and timestamp-based representations, respectively, following well-known terminology borrowed from temporal databases. We present and discuss the formal model for both alternatives. Based on the timestamp-based representation, we introduce a formal First-Order spatio-temporal query language, which we denote $\mathcal{L}_t,$ able to express spatio-temporal queries over GIS, OLAP, and trajectory data. Finally, we discuss implementation issues, the update operators that must be supported by the model, and sketch a temporal extension to Piet-QL, the SQL-like query language that supports Piet.

For bridging the gap between information retrieval (IR) and databases (DB), this article focuses on the logical view. We claim that IR should adopt three major concepts from DB, namely inference, vague predicates and expressive query languages. By regarding IR as uncertain inference, probabilistic versions of relational algebra and Datalog yield very powerful inference mechanisms for IR as well as allowing for more flexible systems. For dealing with various media and data types, vague predicates form a natural extension of text retrieval methods to attribute values, thus switching from propositional to predicate logic. A more expressive IR query language should support joins, be able to compute aggregated results, and allow for restructuring of the result objects.

The development of an Electronic Health Record (EHR) system requires a well-defined data set and a robust data model to support complex clinical workflows, interoperability, and data quality. Many existing EHR implementations lack standardized and integrated data models, resulting in redundancy, semantic inconsistency, and limited support for secondary data use. This study aims to develop a comprehensive EHR data model based on a Minimum Data Set (MDS) that systematically represents medical service processes across emergency, outpatient, inpatient, and surgical care. The proposed approach involves the design of a conceptual, logical, and physical data model. Business processes were first analysed to identify relationships among clinical and administrative data classes, which were then transformed into a conceptual data model. The logical data model was developed through database normalization and schema definition, while the physical data model was constructed to represent the actual database structure for implementation. The results demonstrate a structured EHR data model comprising 21 interrelated data classes that reflect real-world medical service workflows. The conceptual data model establishes clear entity relationships, supporting data consistency and interoperability. The logical data model provides a normalized SQL-based schema that minimizes redundancy and enhances data integrity, while the physical data model enables efficient data storage and access. A prototype EHR system was implemented to evaluate the feasibility of the proposed data model, showing stable performance, usability, and alignment with functional requirements. This study concludes that a standardized EHR data model derived from an MDS can effectively support comprehensive clinical documentation, interoperability, and future scalability. The proposed model provides a strong foundation for developing national or institutional EHR systems and can serve as a reference for further EHR data standardization efforts.

In this paper, a new moving objects database model, Route-based model for Moving Objects on Dynamic Transportation Networks (RMODTN), is proposed which is suited for dealing with the interrelationship between moving objects and the underlying transportation networks. The data model is given as a collection of data types and operations which can be plugged into a DBMS to obtain a complete data model and query language. Besides, an index framework for network-constrained moving objects is provided, which can deal with the full dynamic trajectories of network-constrained moving objects so that the queries on the whole life span of the moving objects can be efficiently supported. To evaluate the performance of the proposed methods, we have implemented RMODTN on PostgreSQL and conducted a series of experiments. The experimental results show quite satisfying query processing performances.

This paper presents DEDALE, a spatial database system intended to overcome some limitations of current systems by providing an abstract and non-specialized data model and query language for the representation and manipulation of spatial objects. DEDALE relies on a logical model based on linear constraints, which generalizes the constraint database model of [KKR90]. While in the classical constraint model, spatial data is always decomposed into its convex components, in DEDALE holes are allowed to fit the need of practical applications. The logical representation of spatial data although slightly more costly in memory, has the advantage of simplifying the algorithms. DEDALE relies on nested relations, in which all sorts of data (thematic, spatial, etc.) are stored in a uniform fashion. This new data model supports declarative query languages, which allow an intuitive and efficient manipulation of spatial objects. Their formal foundation constitutes a basis for practical query optimization. We describe several evaluation rules tailored for geometric data and give the specification of an optimizer module for spatial queries. Except for the latter module, the system has been fully implemented upon the O 2 DBMS, thus proving the effectiveness of a constraint-based approach for the design of spatial database systems.

We demonstrate a system for querying probabilistic XML documents with simple XPath queries. A user chooses between a variety of query answering techniques, both exact and approximate, and observes the running behavior, pros, and cons, of each method, in terms of efficiency, precision of the result, and data model and query language supported.

Nowadays, a huge amount of biomedical data of different biological entities is provided by many online databases and services, each with its own data model, user interface and query language. However, typical bioinformatics scenarios require the use of more than one resource. Therefore, the availability of a single bioinformatics platform that integrates many biological resources and services is a fundamental issue. Some attempts to go beyond the drawbacks of a handcrafted combination of different resources have already been made. Regardless of focus and considered biological data sources, existing tools lack one or more high-level functional features, such as web interface availability, dynamic data visualization, custom queries support, analytics, and expandability. Here, we present BioGraph, a web application that allows to query, visualize and analyze biological data belonging to several online available resources. BioGraph is built on top of our previously developed graph database called BioGraphDB, which collects and integrates heterogeneous biological data and makes them available through a common structure and Gremlin as unique query language. BioGraph makes use of state-of-the-art technologies and provides some pre-compiled bioinformatics scenarios, as well as the possibility to perform custom queries and obtaining an interactive and dynamic visualization of results.

The conference calendar of 1994 showed a coincidence of two international meetings with an overlap in their field of interest. These were the international symposium on Spatial Data Handling (SDH) in Edinburgh and the symposium of ISPRS Commission III in Munich, both in the week from 5 to 9 september. Because of the fact that omnipresence is still not one of the faculties of men, not even of GIS specialists, the Subcommission on GIS of the Netherlands Geodetic Commission took the initiative to organise a workshop where participants of both meetings could meet. This initiative was made in cooperation with members of the ISPRS Intercommission Working Group III/IV on "The Conceptual Aspect of GIS", but due to ISPRS regulations the meeting could not be put of the ISPRS calendar. A organising committee was formed in which the department for "Surveying and Remote Sensing" of the Wageningen Agricultural University took a major responsibility.

Semistructured data has attracted much attention in database research lately. New data models, query languages and systems have been developed. However, very little research has been done on formally defined semantics for the new data models and query languages. As a consequence, some aspects of the meaning of expressions in the database and answers to queries are not completely understood. In this paper we propose a semantics for semistructured data based on a modal logic framework used to give semantics to feature structures. A semistructured database is seen as inducing a certain model, which is then used to evaluate queries posed to the database in a first-order language. The main advantage of this approach is to give a formal basis for analyzing some possibly conflicting views of semistructured data as a model for partial information.KeywordsModal LogicQuery LanguageComplex ObjectOutgoing EdgeHybrid LogicThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Cyber-Physical Production Systems (CPPS) and Smart Products are considered key features in the development of the fourth industrial revolution. To create a connected environment in manufacturing based on CPPS, components must be able to store and exchange data with machines, and with other components and assemblies along the entire production system. At the same time, Smart Product features require that products and their components be able to store and exchange data throughout their entire lifecycle. Therefore, the aim of this paper is to present a preliminary integrated component data model based on Unified Modeling Language (UML) for the implementation of CPPS and Smart Product features. The development of the data model is based on requirements gathered both from the literature review and from corporate interviews with potential users. The results are still preliminary since the research results are part of a bigger research effort under an international collaboration network.

The amount of data has increased exponentially as a consequence of the availability of new data sources and the advances in data collection and storage. This data explosion was accompanied by the popularization of the Big Data term, addressing large volumes of data, with several degrees of complexity, often without structure and organization, which cannot be processed or analyzed using traditional processes or tools. Moving towards Big Data Warehouses (BDWs) brings new problems and implies the adoption of new logical data models and tools to query them. Hive is a DW system for Big Data contexts that organizes the data into tables, partitions and buckets. Several studies have been conducted to understand ways of optimizing its performance in data storage and processing, but few of them explore whether the way data is structured has any influence on how quickly Hive responds to queries. This paper investigates the role of data organization and modelling in the processing times of BDWs implemented in Hive, benchmarking multidimensional star schemas and fully denormalized tables with different Scale Factors (SFs), and analyzing the impact of adequate data partitioning in these two data modelling strategies.

A stream validation system called SVALI is developed in order to continuously validate correct behavior of industrial equipment. A functional data model allows the user to define meta-data, analyses, and queries about the monitored equipment in terms of types and functions. Two different approaches to validate that sensor readings in a data stream indicate correct equipment behavior are supported: with the model-and-validate approach anomalies are detected based on a physical model, while with learn-and-validate anomalies are detected by comparing streaming data with a model of normal behavior learnt during a training period. Both models are expressed on a high level using the functional data model and query language. The experiments show that parallel stream processing enables SVALI to scale very well with respect to system throughput and response time. The paper is based on a real world application for wheel loader slippage detection at Volvo Construction Equipment implemented in SVALI.

Building a constraint-based spatial database system: model, languages, and implementation