The DEDALE system for complex spatial queries

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

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.

Spatial big data architecture: From Data Warehouses and Data Lakes to the LakeHouse

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.

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.

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.

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.

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.

Interaction with geographic information systems via spatial queries

The use of Spatial Access Methods (SAMs) in spatial database systems, such as Geographical Information Systems, is necessary to achieve efficient retrieval of data items according to their spatial properties. Existing SAMs organizing minimum bounding rectangle (MBR) approximations of spatial objects can be classified into four groups. Each group is characterized by the special technique adopted for managing MBRs: (a) Ordering, (b) Transformation, (c) Clipping, and (d) Overlapping. This paper introduces a new technique. The basic idea of this technique is to represent all spatial objects by their MBRs and further reduce them into points of the same dimensionality, so that any multidimensional Point Access Method (PAM) may be used to support access. Essential for the functionality of the new method is the query window extension. The results of both analytical and experimental work show that SAMs using the new technique clearly outperform popular SAMs, such as the R- and R*-trees for data sets consisting of equal-sized MBRs. As for data sets of varying MBR sizes a competitive performance can be obtained.

Efficient spatial query processing in Graph Database Management Systems (GDBMSs) has become increasingly important owing to the prevalence of spatial graph data. However, current GDBMSs lack effective spatial indexing, causing performance issues with complex spatial graph queries. This study proposes a spatial index called Subgraph Integrated R-Tree (SGIR-Tree) for efficient spatial query processing in GDBMSs. The SGIR-Tree integrates the hierarchical R-Tree structure with the graph structure of GDBMSs by converting R-Tree elements into graph components like nodes and edges. The Minimum Bounding Rectangle (MBR) information of spatial objects and R-Tree nodes is stored as properties of these graph elements, and the leaf nodes are directly connected to the spatial nodes. This approach combines the efficiency of spatial indexing with the flexibility of graph databases, thereby allowing spatial query results to be directly utilized in graph traversal. Experiments using OpenStreetMap datasets demonstrate that the SGIR-Tree outperforms the previous approaches in terms of query overhead and index overhead. The results are expected to improve spatial graph data processing in various fields, including location-based service and urban planning, significantly advancing spatial data management in GDBMSs.

An emerging topic in the field of spatial data management is the handling of location uncertainty of spatial objects, mainly due to inaccurate measurements. The literature on location uncertainty so far has focused on modifying traditional spatial search algorithms in order to handle the impact of objects' location uncertainty in query results. In this paper, we present the first, to the best of our knowledge, theoretical analysis that estimates the average number of false hits introduced in the results of rectangular range queries in the case of data points uniformly distributed in 2D space. Then, we relax the original distribution assumptions showing how to deal with arbitrarily distributed data points and more realistic location uncertainty distributions. The accuracy of the results of our analytical approach is demonstrated through an extensive experimental study using various synthetic and real datasets. Our proposal can be directly employed in spatial database systems in order to provide users with the accuracy of spatial query results based only on known dataset and query parameters.

Relational data modeling can often be restrictive as it provides no direct facility for modeling polymorphic types, reified relations, multi-valued attributes, and other common high-level structures in data. This creates many challenges in data modeling and engineering tasks, and has led to the rise of more flexible NoSQL databases, such as graph and document databases. In the absence of structured schemas, however, we can neither express nor validate the intention of data models, making long-term maintenance of databases substantially more difficult. To resolve this dilemma, we argue that, parallel to the role of classical predicate logic for relational algebra, contemporary foundations of mathematics rooted in type theory can guide us in the development of powerful new high-level data models and query languages. To this end, we introduce a new polymorphic entity-relation-attribute (PERA) data model, grounded in type-theoretic principles and accessible through classical conceptual modeling, with a near-natural query language: TypeQL. We illustrate the syntax of TypeQL as well as its denotation in the PERA model, formalize our model as an algebraic theory with dependent types, and describe its stratified semantics.

Topological relationships between spatial objects have been a focus of research on spatial data handling and reasoning for a long time. Especially as predicates they support the design of suitable query languages for spatial data retrieval and analysis in databases. Unfortunately, they are so far only applicable to simplified abstractions of spatial objects like single points, continuous lines, and simple regions, as they occur in systems like current geographical information systems and spatial database systems. Since these abstractions are usually not sufficient to cope with the complexity of geographic reality, their generalization is needed which especially has influence on the nature, definition, and number of their topological relationships. This paper partially closes this gap and first introduces very general spatial data types for complex points and complex regions. It then defines the corresponding complete sets of mutually exclusive, topological relationships.

Many geographical applications have to deal with spatial objects that reveal an intrinsically vague or fuzzy nature. A spatial object is fuzzy if locations exist that cannot be assigned completely to the object or to its complement. Spatial database systems and Geographical Information Systems are currently unable to cope with this kind of data. Based on an available abstract data model of fuzzy spatial data types for fuzzy points, fuzzy lines, and fuzzy regions, this paper proposes the Spatial Plateau Algebra that provides spatial plateau data types as an executable type system for fuzzy spatial data types. Each spatial plateau object consists of a finite number of crisp counterparts that are all adjacent or disjoint to each other, are associated with different membership values, and hence form different plateaus. The formal framework and the implementation are based on well known, exact models and available implementations of crisp spatial data types. Spatial plateau set and metric operations as well as spatial plateau topological predicates on spatial plateau objects are expressed as a combination of geometric methods on the underlying crisp spatial objects. This paper offers a conceptually clean specification for implementing a database extension for fuzzy spatial objects and their operations and predicates. Further, we demonstrate the embedding of these new data types as attribute data types in a database schema as well as the incorporation of fuzzy spatial operations and predicates into a database query language.