Hierarchical Metadata Driven View Selection for Spatial Query Evaluations

Spatial Data Warehouses (SDW) and Spatial OLAP (SOLAP) systems are well-known Business Intelligence technologies that aim to support multidimensional and online analysis of huge volumes of datasets with spatial reference. Spatial vagueness is one of the most neglected imperfections of spatial data. Although several works propose new ad-hoc models for handling spatial vagueness in information systems, the implementation of those models in Spatial DBMS and SDW is still in an embryonic state. Thus, in this paper, we present a new design method for SOLAP datacubes that allows handling vague spatial data analysis issues. This method relies on a risk management method applied to the potential risks of data misinterpretation and decision-makers’ tolerance levels to those risks. We also present a system implementing our method.

In this paper we present a Spatial Data Warehouse system that we use for aggregation and analysis of huge amounts of spatial data. The data is generated by utilities meters communicating via radio. In order to provide sufficient efficiency for our system we propose data and workload distribution as well as advanced indexing techniques. The system is based on a cascaded star model, which is a spatial development of a standard star schema and contains interconnected and often nested star schemas. The cascaded star allows efficient storage and analysis of spatial data, whose range extends from meter measurement values to weather information. The indexing tree structure and operation is tightly integrated with the spatial character of the data. Thanks to an available memory evaluating mechanism the system is very flexible in the field of aggregates accuracy. We also implemented indexing structure updating mechanism. The system is written in Java; for the data base we use Oracle 9i. Basing on the wide variety of tests results, we prove that a distributed system significantly surpasses the centralized version in terms of efficiency. We also show that a selective materialization of indexing structure fragments strongly increases system efficiency.

With the increasing development of the application of location services, massive check-in data is produced by social media applications on the mobile appliances, which includes characteristics of spatio-temporal information, user-emotion information, and etc. Traditional analysis techniques cannot handle check-in data well because of the complexity of spatio-temporal information. Spatial data warehouse provided a good architecture for spatial data’s storage and analysis. In this research, we designed a spatial data warehouse to store and manage the check-in data, used OLAP analysis technology to analyze it, and found many interesting results. It showed spatial data warehouse and OLAP provided a good frame to analyze check-in data.

Spatial data generated by an Internet of Things (IoT) network is important to assist the spatial analytics process in issues related to smart cities. In these cities, IoT devices generate spatial data constantly. Thus, data can get increasingly voluminous very fast. In this paper, we investigate the challenge of managing these data through the use of a spatial data warehouse designed over a parallel and distributed data processing framework extended with a spatial analytics system. We propose an architecture aimed to assist a smart cities manager in the decision-making process. This architecture integrates a cloud layer where these technologies are located with a fog computing layer for extracting, transforming and loading the data into the spatial data warehouse. Furthermore, we introduce a set of guidelines to aid smart cities managers to implement the proposed architecture. These guidelines describe and discuss important issues that should be faced by the managers. We validate our architecture with a case study that uses real data collected by IoT devices in a smart city. This case study encompasses the execution of three different categories of spatial queries, demonstrating the architecture's efficacy and effectiveness to support spatial analytics in the context of smart cities.

With a huge amount of data stored in spatial databases and the introduction of spatial components to many relational or object-relational databases, it is important to study the methods for spatial data warehousing and OLAP of spatial data. In this paper, we study methods for spatial OLAP, by integrating nonspatial OLAP methods with spatial database implementation techniques. A spatial data warehouse model, which consists of both spatial and nonspatial dimensions and measures, is proposed. Methods for the computation of spatial data cubes and analytical processing on such spatial data cubes are studied, with several strategies being proposed, including approximation and selective materialization of the spatial objects resulting from spatial OLAP operations. The focus of our study is on a method for spatial cube construction, called object-based selective materialization, which is different from cuboid-based selective materialization (proposed in previous studies of nonspatial data cube construction). Rather than using a cuboid as an atomic structure during the selective materialization, we explore granularity on a much finer level: that of a single cell of a cuboid. Several algorithms are proposed for object-based selective materialization of spatial data cubes, and a performance study has demonstrated the effectiveness of these techniques.

GIS is a geo-referenced data based system for manipulating digital spatial and thematic data. Because of its unique properties of spatial data (spatial dependence and spatial heterogeneity), there is an urgent need to investigate the mechanisms of spatial processes through exploring spatial data. The techniques of spatial analysis can be categorized into six groups: (1) generation of spatial data and its preliminary treatment; (2) spatialization of attribute data and scale transformation of spatial data; (3) visualization and exploration of spatial data; (4) geo-statistics; (5) analysis of lattice data; and (6) reversing information from complicated spatial data. To meet the real world solutions, a procedural diagram of spatial data analysis is suggested as below: (1) preliminary data treatment; (2) modeling; (3) solution; and (4) interpretation. Various techniques are being employed to investigate spatial processes, the identical study objective and some common characteristics of the study object may lead to the integration and convergence of the various approaches, and further the introduction of a general science: spatial mathematics. A same important issue is to develop robust spatial analysis computer packages for public uses.

In order to study nowcasting operations using spatial data mining, a spatial data warehouse must be built via ETL process to store radar spatial data reflects strong convection weather. Also, the quality control of radar spatial data in the ETL process, which directed at weather station observed data and radar spatial data. based on dynamic incremental rule engine(DIRE) and ISTDW method. The experimental result demonstrates that DIRE can effectively uncover the abnormal value in radar data and that ISTDW can estimate the absent values in radar data via interpolation with the tolerance of errors.

Although many real world phenomena are vague and characterized by having uncertain location or vague shape, existing spatial data warehouse models do not support spatial vagueness and then cannot properly represent these phenomena. In this paper, we propose the VSCube conceptual model to represent and manipulate shape vagueness in spatial data warehouses, allowing the analysis of business scores related to vague spatial data, and therefore improving the decision-making process. Our VSCube conceptual model is based on the cube metaphor and supports geometric shapes and the corresponding membership values, thus providing more expressiveness to represent vague spatial data. We also define vague spatial aggregation functions (e.g. vague spatial union) and vague spatial predicates to enable vague SOLAP queries (e.g. intersection range queries). Finally, we introduce the concept of vague SOLAP and its operations (e.g. drill-down and roll-up). We demonstrate the applicability of our model by describing an application concerning pest control in agriculture and by discussing the reuse of existing models in the VSCube conceptual model.

Data warehouses and On-Line Analytical Processing (OLAP) provide an analysis framework supporting the decision making process. In many application domains, complex analysis tasks often require to take geographical information into account. Several proposals exist for integrating OLAP and Geographic Information Systems (GIS). However, there are very few attempts to support continuous fields, i.e., phenomena that are perceived as having a value at each point in space and/or time. Examples of such phenomena include temperature, altitude, or land use. In this paper, we extend a conceptual multidimensional model with continuous fields, showing that this can be achieved by defining an appropriate data type that encapsulates the different operations needed for manipulating such fields. We also define a query language based on relational calculus that allows expressing spatial OLAP queries involving continuous fields, and use this language to formally characterize this class of queries.

professional database especially the data center management model has achieved the integration, share of data and the separation between data and application in oil exploration and development, but the issues for data analysis and data mining, especially the professional data mining, are not well solved. In this paper, the necessity of spatial data warehouse used in oil and gas exploration and development is analyzed, data management framework based on the spatial data warehouse for oil and gas exploration and development is proposed, the construction steps of spatial data warehouse are provided, the differences between spatial data warehouse and other data management model are given, the key issues the construction of spatial data warehouse faces are analyzed.

The design of Spatial OLAP (SOLAP) applications consists of (i) Spatial Data Warehouse (SDW) model design and (ii) SOLAP visualization definition because a specific set of understandable and readable cartographic visualizations corresponds to a particular type of SOLAP query. Unfortunately few works investigate geovisualization issues in SOLAP systems and propose new methodologies to visualize spatio-temporal data, and no works investigate tools for readable SOLAP cartographic displays. Moreover, some works propose ad-hoc methodologies for DWs and SDWs exclusively based on data and user analysis requirements. Therefore, we present in this paper (i) a new geovisualization methodology for SOLAP queries that yields readable maps and (ii) a new prototyping design methodology for SOLAP applications that accounts for geovisualization requirements.

This article describes how Spatial On-Line Analytical Processing, such a decisional technology, offers the possibilities of spatial and multidimensional analysis of data stored in multidimensional structure namely spatial data warehouse. However, this technology is limited in the quality aspect of the decision related to the multicriteria consideration. In the current article, the objective is to propose a Spatial Decision Support System namely “Silvicultura” for facilitating decision making in complex situations. This approach is based on integrating multicriteria analysis with SOLAP in order to enrich the spatial and multidimensional analysis with the contribution of MCA tools for mitigating conflict situations. The authors have based their proposal modeling on Unified Modeling language, since it is a well-known standard modeling language and can be easily extended for multidimensional modeling. Finally, in order to validate their proposal, the authors present a case study to show how to use it in the agroforestry management.

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.

Modern location-based applications rely extensively on the efficient processing of spatial data and queries. Spatial query engines are commonly engineered as an extension to a relational database or a cluster-computing framework. Large parts of the spatial processing runtime is spent on evaluating spatial predicates and traversing spatial indexing structures. Typical high-level implementations of these spatial structures incur significant interpretive overhead, which increases latency and lowers throughput. A promising idea to improve the performance of spatial workloads is to leverage native code generation techniques that have become popular in relational query engines. However, architecting a spatial query compiler is challenging since spatial processing has fundamentally different execution characteristics from relational workloads in terms of data dimensionality, indexing structures, and predicate evaluation. In this paper, we discuss the underlying reasons why standard query compilation techniques are not fully effective when applied to spatial workloads, and we demonstrate how a particular style of query compilation based on techniques borrowed from partial evaluation and generative programming manages to avoid most of these difficulties by extending the scope of custom code generation into the data structures layer. We extend the LB2 main-memory query compiler, a relational engine developed in this style, with spatial data types, predicates, indexing structures, and operators. We show that the spatial extension matches the performance of specialized library code and outperforms relational and map-reduce extensions.

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