Architecture of space-time record system based on location-leading

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

In order to efficiently store, utilize, manage and analyze the huge global spatial data and location based service, the management architecture based on global subdivision grid (GSG), which supports the huge data processing and the global direct manipulation based on spheroid or ellipsoid surface, is proposed in this paper. Taking advantage of computer cluster theory, the space-time integration technology and the virtual reality technology, the GSG management architecture for global spatial data is composed of four-level protocol framework and three-layer data management pattern, designed in detail in this paper. Four-lever protocol framework mainly includes protocols of data representation level, protocols of network and cluster level, protocols of management level and protocols of application level and three-layer data management pattern includes the data layer, the middle layer and the client layer. The GSG management architecture provides logic framework for storage, management, analysis of spatial data, which will be important development direction of Global GIS.

The recent developments in spatial data collection, management and software require the availability of proper geodetic infrastructures for integrating different types and sources of coordinates without causing effective changes in positions. Nowadays, positions are mostly collected by GNSS data collectors based on WGS84/ITRF reference systems. The data are then subjected to transformations and projections to a locally used system. Another possibility is direct data collection based on the local coordinate system by classical surveys using land surveying, photogrammetry, laser scanning, etc. The spatial data management is commonly operated using Geographic Information Systems (GIS) software for mapping, analysis, planning, and other services. The conversions between different coordinate systems should be well defined to guarantee the consistency of the coordinates on all systems and tools. In Palestine, the classical and local surveys are all based on the local coordinate system Pal1923Grid for engineering, cadastral and planning applications. The different GNSS RTK-service providers use different definitions and transformation methods between WGS84 or the International Terrestrial Reference Frames (ITRF) and the local Palestine1923Grid, whereas the Land authority has adopted a group of parameters to be implemented on the Global Navigation Satellite Systems (GNSS) data collectors, which do not fit with Palestine1923Grid properties. Additionally, different transformation methods are used in GIS applications for converting the coordinates between the different systems using WGS84 as an intermediate system. Here, the coordinates of a group of the geodetic network in the West Bank of Palestine are used to assess the accuracy of the different transformations and systems by comparing the transformed coordinates using the GNSS system and the originally registered coordinates. Furthermore, a grid of points covering the coordinate system extents is used to describe the differences between the transformations and systems. It was found that the parameters provided by GNSS service providers have results that are consistent with each other and the geodetic network in the West Bank of Palestine compared to GIS-software parameters. By contrast, all systems have extremely deteriorated coordinates in the Gaza strip and the further parts of the Pal1923Grid extents.

Integrating single-cell and spatial multi-omics data is essential for resolving cellular identity, regulatory programs and tissue organization, yet remains challenging under realistic experimental designs. In practice, data are often incomplete, unpaired and partially overlapping across batches and platforms, resulting in mosaic settings where missingness, batch effects and limited correspondence are tightly coupled. Existing methods typically rely on shared cells, explicit anchors or post hoc mapping, and can become unstable as overlap diminishes or spatial structure is not explicitly modeled. Here we present GAMMI (Graph-guided Adversarial Mosaic Multi-omics Integration), a unified graph learning framework for mosaic integration of heterogeneous single-cell and spatial multi-omics data. Rather than performing direct cell or sample alignment, GAMMI learns biologically meaningful relational structure by jointly embedding cells and molecular features in a shared latent space using heterogeneous graphs that encode cell-feature, feature-feature and spatial adjacency relationships. An edge-based contrastive objective with missingness-aware negative sampling avoids false-negative supervision under unobserved modalities, while adversarial domain adaptation suppresses batch-associated variation at the embedding level. Spatial data are incorporated as structurally informative constraints during learning, enabling systematic enrichment of molecular representations across spatial locations. Across diverse mosaic single-cell benchmarks and spatial datasets, GAMMI consistently outperforms state-of-the-art methods in biological conservation, batch correction and spatial reconstruction, including in low-overlap and fully unpaired regimes.

Spatial data is generally managed by multiple organizations, and there are significant heterogeneities in these spatial data management nodes within each organization. In order to share spatial data over multiple organizations efficiently, we propose a comprehensive framework of federated spatial data grid based on super-peer network model. Concerning the high overlap of the bounding boxes in the directory of indexing information about spatial data, a spatial indexing method which extends X-tree for the framework is proposed. We introduce a dynamic maintenance mechanism to achieve excellent load balance, and an algorithm for spatial range queries is presented. Both the results of theoretic analysis and experiments show that the management of spatial data in the framework is efficient.

The management of the magnanimous spatial data needs the distributed cooperative GIS platform. This paper proposed the Peer To Peer and Cooperating Computing Hybrid Discovering Model of Distributed Geospatial Information. Based on the (Spatial Resource Discovery Agent (SRDA) and ordinary neighbor node, Combined with Spatial data model of MAPGIS7.0, this article brings up the geographic structure query language in P2P network (P2PGSQL), and has designed the interpreter of P2PGSQL. In the Peer To Peer and Cooperating Computing Hybrid Discovering Model, this paper has designed the algorithms that construct the distributed hash table and decrease the delay. By discovering and performing the potential swaps that are beneficial to the match between overlay and physical network, it can reduce the average latency and improve the performance of the system. Based on the P2P Cooperating Computing Hybrid Discovering Model, this article has given the method of the P2PGSQL in grid environment and built the mapping model of the global spatial query sentence to the local query sentence. With constituting a global spatial data resource catalog to record the distributing information of all spatial database on each node, P2PGSQL has backed up effective expression of spatial database management mission in grid environment. Hence, P2PGSQL can sustain the query of distribute spatial data in grid environment. This article has extended the grid spatial relation operation (Intersection, Union, Difference) and synthesized operation (Length, Buffer, intersecting region) and so on. The P2PGSQL can reduce the quantity of spatial data transmitting among nodes and enhance the capacity of parallel operation. At the same time, P2PGSQL can also sustain multi-spatial data model by the extension of structure data on the data type. At the end, an experimental instance of P2PGSQL has been carried out based on mapgis7.0 platform.

Extraction of interesting knowledge from large spatial databases is an important task in the development of spatial database systems. Spatial data mining is the branch of data mining that deals with spatial (location) data. Analyzing the huge amount (usually tera-bytes) of spatial data obtained from large databases such as credit card payments, telephone calls, environmental records, census demographics etc. is, however, a very difficult task. Visual data mining applies human visual perception to the exploration of large data sets. Presenting data in an interactive, graphical form often fosters new insights, encouraging the formation and validation of new hypotheses to the end of better problem-solving and gaining deeper domain knowledge. In this paper we give a short overview of visual data mining techniques, especially the area of analyzing spatial data. We provide some examples for effective visualizations of spatial data in important application areas such as consumer analysis, e-mail traffic analysis, and census demographics.

The impetus for the development of GIS technologies is derived primarily from the fact that traditional manual techniques for the derivation, organization, storage and use of spatial data are no longer sufficient to meet the demands and expectations placed on such activities. The GIS technology is a service technology used to assist spatial data management, evaluations and analyses. There are three basic approaches currently developed that organize spatial data in a format such that the computer can perform previously time-consuming analytical and display processes. These are: (1)the rectangular grid approach; (2)the fine or coarse polygon approach; and (3)the triangulated irregular network approach. There is a wide range of applications that can be supported by GIS. These can be reasonably divided into two groups: those that depend on qualitative analysis and those that depend on quantitative analysis. The GIS design requirements are much more demanding to support quantitative analyses.

With the advancement of GIS technology since its inception in 1960’s, many educational institutions, government departments, public/ private sectors and individuals have started its use for the production and management of spatial data. Spatial Data Infrastructure (SDI) concept was introduced in the early1990’s and provides a set of technologies, standards, protocols, policies and guidelines on the whole cycle of geospatial data production and distributions, i.e., from data capture to storage and to sharing. SDI initiative at national level, termed as National Spatial Data Infrastructure (NSDI), has been taken by different countries including India. Geospatial community is facing various challenges like handling of large volumes of geospatial data, requirement of high computing resources to process geospatial data, scalability and interoperability. Therefore, need of advanced technologies in the form of SDI and cloud computing is realized to resolve the above challenges. Cloud computing has several characteristics like scalability, elasticity and self-provisioning that offers high-performance computing resources to perform geoprocessing efficiently. The main aim of the present paper is to study SDI and its components along with analysis and comparison of NSDI of various countries as well as to conceptualize and discuss service oriented architecture of cloud enabled SDI. Several challenges of the spatial data handling and processing that occurred due to the high intensity of data and lack of processing capability can be solved by adopting proposed cloud enabled SDI architecture. This research will help geospatial community and SDI developers in various perspectives including data sharing and management, interoperability, security and reliability, fault tolerance, mass market solution, upfront cost and global collaboration.

This passage puts forward a kind of simple and convenient three-dimensional laser measurement technology for a small volume and complex shape space data acquisition of the object. This technology mainly uses the camera self calibration method for construction of three-dimensional model algorithm and creates the measurement environment of the measuring technique in machinery and hardware camp. The result of the experiment shows that the desktop 3D laser scanning measuring instrument is convenient, and can acquire spatial location ideal data with low cost.

HARMO-DATA is an ongoing project, funded by EU in the framework of the INTERREG V-A Italy-Slovenia 2014-2020 Programme. It involves different stakeholders, target groups and end-users in three regions: Friuli-Venezia-Giulia (Italy), Veneto (Italy) and Slovenia. The main purpose of this project is to develop common solutions for more efficient cross-border spatial data management – by harmonizing the existing spatial data, implementing a cross-border spatial data platform, and developing a common protocol for the harmonization of territorial data. It will provide an instrument to define the specific obligations and rights of the involved parts – in terms of data harmonization, exchange, use and maintenance. Five pilot case studies were identified by the project partners – in cooperation with public and private end-users, and additional stakeholders. The core use cases of the project relate to spatial data search, view and download, and the harmonization model for spatial datasets applies the INSPIRE data specifications. A joined common spatial data platform was established as an extension of the existing search-view-download platforms (metadata systems), upgraded and improved to better enable open data access by users from both Italy and Slovenia. The common spatial HARMO-DATA data platform, as well as, a joint protocol for cross-border spatial data harmonization, have been formalized in an official bilateral agreement.

This paper aims at reevaluating the role of Spatial Data Infrastructure (SDI) as an initiative which can facilitate disaster management by providing a better way for managing spatial data. SDI contains technological and non-technological aspects for sharing and exploiting geo-information resources. The results of the research have shown that a prototype emergency spatial data sharing system using an SDI framework can assist the disaster management in such a way that they can improve the efficiency and quality of data for the decision making and increase the effectiveness in data collection and sharing activities during the management of emergency spatial data.

In response to user requirements, the author has developed the management system for the geopark and outlined its functionalities in this paper. The information management system for Dunhuang geopark is structured on the C/S architecture, employing SuperMap as the secondary development platform and utilizing SQL Server to establish the database. The seamless integration of multisource spatial data, coupled with spatial data management driven by metadata, facilitates the storage and updating management of geoheritage spatial data. This approach effectively meets the construction requirements for the geopark information system, which deals with extensive spatial data.

Efficient secondary memory processing of window queries on spatial data

Developments in production, sharing and use of spatial data and information involve revisiting the role and scope of cartography. Cartography is a core discipline for spatially modelling, investigating and mapping natural and cultural environments, developing location-aware applications, establishing spatial data infrastructures and forming spatially enabled societies. In this context, spatial data handling provides key tools for creating spatial databases, integrating spatial data, producing geographic information and maps and so on. Although cartography plays a key role in many phases of such activities, it tends to be introduced only as visualization phase of spatial data handling. On the other hand, it is sometimes regarded to encompass entire phases of spatial data handling. So this article investigates the relationships between cartography and spatial data handling, management and use, and on this basis, proposes a modern and comprehensive framework for cartography to contribute to restoring the full conceptual breadth of the discipline.