What's special about spatial?

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.

These proceedings contain the papers from the 29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2021), held online November 2-5, 2021. The conference started as a series of workshops and symposia back in 1993 with the aim of promoting interdisciplinary discussions among researchers, developers, users, and practitioners and fostering research in all aspects of geographic information systems, especially in relation to novel systems based on geospatial data and knowledge. It continues to provide a forum for original research contributions covering all conceptual, design and implementation aspects of geospatial data ranging from applications, user interfaces and visualization, to data storage, query processing, indexing and data mining. The conference is the premier annual event of the ACM Special Interest Group on Spatial Information (ACM SIGSPATIAL).

Geographical Information Systems (GIS) are an advanced technology framework for acquiring, organizing and analyzing spatial data. In contrast to static maps, GIS enables to develop dynamic digital models using a layered architecture that integrates many thematic components, including infrastructure, topography and soil types, to show complex geographic relationships. This chapter focuses into the fundamentals of GIS, including hardware, software and the crucial role of skilled professionals in transforming raw geographical data into usable insights for urban planning, environmental modeling and disaster response. GIS efficacy is dependent on the Spatial Data Infrastructure (SDI), which offers the standardized guidelines and norms required for smooth data exchange and compatibility. The chapter describes the technological procedures involved in processing geographical data, from basic data collection by GPS and remote sensing to sophisticated analysis through Database Management Systems (DBMS). Spatial DBMS (SDBMS), which are designed to handle geographic coordinates and shapes, are given particular attention. The description also emphasizes the need for rigorous data quality standards, metadata documentation and transformation procedures like georeferencing and reprojection. These processes ensure the accuracy, dependability and scientific soundness of geospatial datasets for advanced agricultural and environmental management decision-making.

1 - An Introduction to Spatial Databases

Apache Calcite is a foundational software framework that provides query processing, optimization, and query language support to many popular open-source data processing systems such as Apache Hive, Apache Storm, Apache Flink, Druid, and MapD. Calcite's architecture consists of a modular and extensible query optimizer with hundreds of built-in optimization rules, a query processor capable of processing a variety of query languages, an adapter architecture designed for extensibility, and support for heterogeneous data models and stores (relational, semi-structured, streaming, and geospatial). This flexible, embeddable, and extensible architecture is what makes Calcite an attractive choice for adoption in big-data frameworks. It is an active project that continues to introduce support for the new types of data sources, query languages, and approaches to query processing and optimization.

Caching is important for any system attempting to achieve high performance. The semantic caching is an approach trying to benefit from the certain knowledge of data semantics. The authors expect that this information might enable reuse of semantically close data rather than exactly equal to cached data in the traditional system. However, the major obstacle for extensive application of semantic caching for any data model or query language is the computational complexity of the query containment problem, which is, in general, undecidable. In this article the authors introduce and compare three approximate conservative query matching algorithms for semantic caching of semi-structured queries. The authors then analyze their applicability for distributed query processing. Based on this analysis, the authors outline few scenarios where semantic caching can be beneficial for query processing in a distributed system of heterogeneous semi-structured information resources.

Many databases contain temporal, or time-referenced, data and use intervals to capture the temporal aspect. While SQL-based database management systems (DBMSs) are capable of supporting the management of interval data, the support they offer can be improved considerably. A range of proposed temporal data models and query languages offer ample evidence to this effect. Natural queries that are very difficult to formulate in SQL are easy to formulate in these temporal query languages. The increased focus on analytics over historical data where queries are generally more complex exacerbates the difficulties and thus the potential benefits of a temporal query language. Commercial DBMSs have recently started to offer limited temporal functionality in a step-by-step manner, focusing on the representation of intervals and neglecting the implementation of the query evaluation engine. This article demonstrates how it is possible to extend the relational database engine to achieve a full-fledged, industrial-strength implementation of sequenced temporal queries, which intuitively are queries that are evaluated at each time point. Our approach reduces temporal queries to nontemporal queries over data with adjusted intervals, and it leaves the processing of nontemporal queries unaffected. Specifically, the approach hinges on three concepts: interval adjustment , timestamp propagation , and attribute scaling . Interval adjustment is enabled by introducing two new relational operators, a temporal normalizer and a temporal aligner, and the latter two concepts are enabled by the replication of timestamp attributes and the use of so-called scaling functions. By providing a set of reduction rules, we can transform any temporal query, expressed in terms of temporal relational operators, to a query expressed in terms of relational operators and the two new operators. We prove that the size of a transformed query is linear in the number of temporal operators in the original query. An integration of the new operators and the transformation rules, along with query optimization rules, into the kernel of PostgreSQL is reported. Empirical studies with the resulting temporal DBMS are covered that offer insights into pertinent design properties of the article's proposal. The new system is available as open-source software.

One can distinguish between two ways of incorporating database management system (DBMS) in geographic information systems (GIS). The first way is that GIS vendors use DBMS mainly to store thematic data. The second way to integrate DBMS within GIS is DBMS offering support of designers spatial data types. Most GIS and computer aided design system users would be concerned if there was no multi-user access possible on data during design processes. There is no question that the results of their work being constructed on shared data should be automatically stored within the database periodically. Many DBMSs have evolved to offer spatial functionality, which brings further benefits for GIS and construction applications. The spatial functionality concerns the support of spatial data types in the data model, the implementation of spatial access methods, and the execution of spatial database queries. Geometry and topology are often used in Geo-DBMS terminology as synonyms to denote the different ways of describing spatial data types.

The architecture of Geographic Information Systems (GISs) is changing: more and more systems are based on the integrated architecture, i.e. storing geometric data in the Data Base Management System (DBMS) together with administrative data. The first step in building a Geo-DBMS is by having data types and operators for simple features (i.e. geometric primitives): point, line and polygon. This has reached a level of standardisation and is now implemented in several commercial DBMSs. The next step is to have support for the topologically structured features in the DBMS, i.e. complex features. The DBMS can then check and guarantee consistency. In addition, complex operations can be executed within the DBMS. Despite the fact that topologically structured models are well known and that it is not difficult to store the topological references, it still remains an unresolved issue as to how to effectively implement these models within a relational DBMS. In this paper, we describe the design and implementation of a topologically structured management at the DBMS level. Our focus is to translate topological structures into geometric primitives. It is then possible to define a DBMS view on a topological primitive, which makes this appear as a geometric primitive. This process supports the best of both worlds: on the one hand there are advantages of the topological structure (no redundancy) and on the other hand the ease of explicit geometric primitives in querying, analysis and presentation is available.

This is the conference report of the 23 rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2015), held in Seattle, Washington, USA, November 3-6, 2015. This conference was the twenty-third edition in a series of symposia and workshops that began in 1993 with the aim of promoting interdisciplinary discussions among researchers, developers, users, and practitioners and fostering research in all aspects of geographic information systems, especially in relation to novel systems based on geospatial data and knowledge. The conference is the premier annual event of the ACM Special Interest Group on Spatial Information (ACM SIGSPATIAL) and provides a forum for original research contributions covering all conceptual, design, and implementation aspects of geospatial data ranging from applications, user interfaces, and visualization to data storage, query processing, indexing and data mining.

The ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2025 (ACM SIGSPATIAL 2025), the thirty-second edition, will be held in Minneapolis, MN, USA, from November 3 to November 6, 2025 (https://sigspatial2025.sigspatial.org/org/). The conference began as a series of symposia and workshops starting in 1993 with the aim of bringing together researchers, developers, users, and practitioners in relation to novel systems based on geospatial data and knowledge, and fostering interdisciplinary discussions and research in all aspects of geographic information systems. The conference provides a forum for original research contributions covering all conceptual, design, and implementation aspects of geospatial data ranging from applications, user interfaces, and visualization to data storage and query processing and indexing. The conference is the premier annual event of the ACM Special Interest Group on Spatial Information (ACM SIGSPATIAL).

Nowadays, with the advancement of database information technology, databases has led to large-scale distributed databases. According to this study, database management systems are improved and optimized so that they provide responses to customer questions with lower cost. Query processing in database management systems is one of the important topics that grabs attentions. Until now, many techniques have been implemented for query processing in database system. The purpose of these methods is to optimize query processing in the database. The main topics that is interested in query processing in the database makes run-time adjustments of processing or summarizing topics by using the new approaches. The aim of this research is to optimize processing in the database by using adaptive methods. Ant Colony Algorithm (ACO) is used for solving optimization problems. ACO relies on the created pheromone to select the optimal solution. In this article, in order to make adaptive hybrid query processing. The proposed algorithm is fundamentally divided into three parts: separator, replacement policy, and query similarity detector. In order to improve the optimization and frequent adaption and correct selection in queries, the Ant Colony Algorithm has been applied in this research. In this algorithm, based on Versatility (adaptability) scheduling, Queries sent to the database have been attempted be collected. The simulation results of this method demonstrate that reduce spending time in the database. According to the proposed algorithm, one of the advantages of this method is to identify frequent queries in high traffic times and minimize the time and the execution time. This optimization method reduces the system load during high traffic load times for adaptive query Processing and generally reduces the execution runtime and aiming to minimize cost. The rate of reduction of query cost in the database with this method is 2.7%. Due to the versatility of high-cost queries, this improvement is manifested in high traffic times. In the future Studies, by adapting new system development methods, distributed databases can be optimized.

AQL (acronym for: A Query Language) is a Data Base management system based on n-ary relational model. It has been designed to help the nonspecialist in data processing in solving interactively unstructured problems, in building applications, in running simulations. A rich pattern of default options, synonyms, attribute definitions, inference and a menu facility provide an easy-to-use interface which combines data extraction facility and computational capabilities of the host programming language (i.e. APL). The extension of such a system to cover the geographic data requires extensions in both the conceptual schema and the language to represent and express concepts pertaining to the geographic nature of data. Also, definition mechanism are found to be extremely useful to represent aggregate concepts in geographic data processing. The general characteristics of AQL are widely discussed in (ANT78): here they will be reviewed briefly, then the geographic extension will be discussed.

A bi-level object-oriented data model together with a user query language called OFQL is designed which can support applications like GIS (geographic information systems). The data model is divided into two layers, the higher-level data model and the lower-level data model. The higher-level data model or the geographic object data model primarily consists of the geographic objects and a set of semantic spatial functions through which the topological relationships of the geographic objects are defined or derived. The lower-level data model or the geometric object data model has geometric objects which are the actual spatial representations of the geographic objects in the higher-level data model. It also has a set of geometric functions that retrieve, manipulate, and compute for geometric objects. The general architecture of a GIS system using this data modeling approach consists of two modules: the query processor and the function implementor. With the OFQL user-interface, the user is able to pose queries of a geographic nature without knowing the details of the spatial representation and computation of the geographic objects.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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.