PROVIDING R-TREE SUPPORT FOR MONGODB

Supporting large amounts of spatial data is a significant characteristic of modern databases. However, unlike some mature relational databases, such as Oracle and PostgreSQL, most of current burgeoning NoSQL databases are not well designed for storing geospatial data, which is becoming increasingly important in various fields. In this paper, we propose a novel method to provide R-tree index, as well as corresponding spatial range query and nearest neighbour query functions, for MongoDB, one of the most prevalent NoSQL databases. First, after in-depth analysis of MongoDB’s features, we devise an efficient tabular document structure which flattens R-tree index into MongoDB collections. Further, relevant mechanisms of R-tree operations are issued, and then we discuss in detail how to integrate R-tree into MongoDB. Finally, we present the experimental results which show that our proposed method out-performs the built-in spatial index of MongoDB. Our research will greatly facilitate big data management issues with MongoDB in a variety of geospatial information applications.

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PROVIDING R-TREE SUPPORT FOR MONGODB

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b4-545-2016 ◽

2016 ◽

Vol XLI-B4 ◽

pp. 545-549

Author(s):

Longgang Xiang ◽

Xiaotian Shao ◽

Dehao Wang

Keyword(s):

Spatial Data ◽

Relational Databases ◽

Spatial Index ◽

Geospatial Information ◽

Nosql Databases ◽

Document Structure ◽

Depth Analysis ◽

Novel Method ◽

Tree Index ◽

Management Issues

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NoSQL Databases

10.31219/osf.io/u5q3y ◽

2019 ◽

Author(s):

Ziqi Li

Keyword(s):

Social Media ◽

Big Data ◽

Spatial Data ◽

High Performance ◽

Relational Databases ◽

Unstructured Data ◽

Social Media Data ◽

Nosql Databases ◽

Column Store ◽

Media Data

NoSQL databases are open-source, schema-less, horizontally scalable and high-performance databases. These characteristics make them very different from relational databases, the traditional choice for spatial data. The four types of data stores in NoSQL databases (key-value store, document store, column store, and graph store) contribute to significant flexibility for a range of applications. NoSQL databases are well suited to handle typical challenges of big data, including volume, variety, and velocity. For these reasons, they are increasingly adopted by private industries and used in research. They have gained tremendous popularity in the last decade due to their ability to manage unstructured data (e.g. social media data).

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State-of-the-Art Geospatial Information Processing in NoSQL Databases

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9050331 ◽

2020 ◽

Vol 9 (5) ◽

pp. 331

Author(s):

Dongming Guo ◽

Erling Onstein

Keyword(s):

Data Processing ◽

Data Storage ◽

Relational Databases ◽

Response Times ◽

State Of The Art ◽

Query Languages ◽

Geospatial Data ◽

Geospatial Information ◽

Nosql Databases ◽

Data Formats

Geospatial information has been indispensable for many application fields, including traffic planning, urban planning, and energy management. Geospatial data are mainly stored in relational databases that have been developed over several decades, and most geographic information applications are desktop applications. With the arrival of big data, geospatial information applications are also being modified into, e.g., mobile platforms and Geospatial Web Services, which require changeable data schemas, faster query response times, and more flexible scalability than traditional spatial relational databases currently have. To respond to these new requirements, NoSQL (Not only SQL) databases are now being adopted for geospatial data storage, management, and queries. This paper reviews state-of-the-art geospatial data processing in the 10 most popular NoSQL databases. We summarize the supported geometry objects, main geometry functions, spatial indexes, query languages, and data formats of these 10 NoSQL databases. Moreover, the pros and cons of these NoSQL databases are analyzed in terms of geospatial data processing. A literature review and analysis showed that current document databases may be more suitable for massive geospatial data processing than are other NoSQL databases due to their comprehensive support for geometry objects and data formats and their performance, geospatial functions, index methods, and academic development. However, depending on the application scenarios, graph databases, key-value, and wide column databases have their own advantages.

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LAZY R-tree: The R-tree with lazy splitting algorithm

Journal of Information Science ◽

10.1177/0165551519828616 ◽

2019 ◽

Vol 46 (2) ◽

pp. 243-257

Author(s):

Yang Yang ◽

Pengwei Bai ◽

Ningling Ge ◽

Zhipeng Gao ◽

Xuesong Qiu

Keyword(s):

Data Structure ◽

Spatial Data ◽

Splitting Method ◽

Hash Table ◽

Spatial Index ◽

Index Structure ◽

Splitting Algorithm ◽

Tree Construction ◽

Tree Index

The spatial index is a data structure formed according to the position and shape of the spatial object or the relationship between the spatial objects according to certain rules, and the spatial data is managed by an effective spatial data structure. The quality of a spatial index directly affects the performance of spatial queries. The R-tree index structure is a highly efficient spatial index. According to the R-tree query rule, when performing spatial query, most data that is not related to the query condition can be filtered out, and finally, a few leaf nodes can be accessed to query the data satisfying the condition. Its query performance is affected by factors such as non-leaf node overlap and node space utilisation. This article proposes a lazy splitting method to improve the R-tree construction process. The scheme works as follows: (1) When a node overflows, it creates an overflow node for that node and all overflow nodes are saved in a hash table. (2) If the node continues to insert data, the data are added to its overflow node. (3) When an overflow node is saturated, the node and its overflow node are split into two saturated nodes. We use both simulated and actual data to perform experiments. The experimental results show that an R-tree constructed by the lazy algorithm is superior to an R-tree constructed using the original R-tree PM algorithm or the corner-based splitting (CBS) algorithm based on the number of splits created, the node space used and the efficiency of region queries and k-nearest neighbour (kNN) queries.

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A Two-Tier Spatial Index for Non-flat Spatial Data Broadcasting on Air

IEICE Transactions on Communications ◽

10.1587/transcom.e97.b.2809 ◽

2014 ◽

Vol E97.B (12) ◽

pp. 2809-2818 ◽

Cited By ~ 2

Author(s):

SeokJin IM ◽

HeeJoung HWANG

Keyword(s):

Spatial Data ◽

Spatial Index ◽

Data Broadcasting

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Geospatial information informs bonobo conservation efforts

10.1093/oso/9780198728511.003.0017 ◽

2018 ◽

Author(s):

Janet Nackoney ◽

Jena Hickey ◽

David Williams ◽

Charly Facheux ◽

Takeshi Furuichi ◽

...

Keyword(s):

Spatial Data ◽

Pan Paniscus ◽

Democratic Republic Of Congo ◽

Conservation Strategies ◽

Suitable Habitat ◽

Community Based ◽

Geospatial Information ◽

République Démocratique Du Congo ◽

Nous Montrons ◽

Dynamic Conservation

The endangered bonobo (Pan paniscus), endemic to the Democratic Republic of Congo (DRC), is threatened by hunting and habitat loss. Two recent wars and ongoing conflicts in the DRC greatly challenge conservation efforts. This chapter demonstrates how spatial data and maps are used for monitoring threats and prioritizing locations to safeguard bonobo habitat, including identifying areas of highest conservation value to bonobos and collaboratively mapping community-based natural resource management (CBNRM) zones for reducing deforestation in key corridor areas. We also highlight the development of a range-wide model that analysed a variety of biotic and abiotic variables in conjunction with bonobo nest data to map suitable habitat. Approximately 28 per cent of the range was predicted suitable; of that, about 27.5 per cent was located in official protected areas. These examples highlight the importance of employing spatial data and models to support the development of dynamic conservation strategies that will help strengthen bonobo protection. Le bonobo en voie de disparition (Pan paniscus), endémique à la République Démocratique du Congo (DRC), est menacé par la chasse et la perte de l’habitat. Deux guerres récentes et les conflits en cours dans le DRC menacent les efforts de conservation. Ici, nous montrons comment les données spatiales et les cartes sont utilisées pour surveiller les menaces et prioriser les espaces pour protéger l’habitat bonobo, inclut identifier les zones de plus haute valeur de conservation aux bonobos. En plus, la déforestation est réduite par une cartographie collaborative communale de gestion de ressources dans les zones de couloirs essentiels. Nous soulignons le développement d’un modèle de toute la gamme qui a analysé un variété de variables biotiques et abiotiques en conjonction avec les données de nid bonobo pour tracer la carte d’un habitat adéquat. Environ 28 per cent de la gamme est prédit adéquat; de cela, environ 27.5 per cent est dans une zone officiellement protégée. Ces exemples soulignent l’importance d’utiliser les données spatiales et les modèles pour soutenir le développement de stratégies de conservations dynamiques qui aideront à renforcer la protection des bonobos.

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Factors influencing integrated information management: Spatial data infrastructure in Pakistan

Information Development ◽

10.1177/02666669211048483 ◽

2021 ◽

pp. 026666692110484

Author(s):

Asmat Ali ◽

Muhammad Imran ◽

Munazza Jabeen ◽

Zahir Ali ◽

Syed Amer Mahmood

Keyword(s):

Information Management ◽

Spatial Data ◽

Structural Equation ◽

Explanatory Power ◽

Primary Data ◽

Equation Modeling ◽

Geospatial Information ◽

Data Infrastructure ◽

Significance Level ◽

Retrieval Processes

Spatial data is one of the core components in all information retrieval processes for decision-making. Spatial data acquisition consumes enormous monetary resources and time. The Integrated Geospatial Information Framework (IGIF) provides a basis and guide for developing, integrating, strengthening, and maximizing geospatial information management and related resources in all countries. To this, governments all over the world are establishing national spatial data infrastructures (SDIs). However, such initiatives face a considerable amount of resistance as organizations often do not want to share their data assets. The present study investigates these barriers in the establishment of national SDI in Pakistan. The constraints studied through the IGIF pathways and past studies were adapted via a pilot study and conceptualized in a hypothesized model. We collected primary data via the administration of 520 questionnaire surveys to 280 public and private organizations. Partial least squares structural equation modeling (PLS-SEM) was applied to statistically confirm the conceptual model of the barriers to disseminating spatial data. The results indicate institutional barriers from the absence of national data policy, lack of specified roles of stakeholders, poor inter-organizational coordination, missing data-sharing policy, and weak organizational partnerships, with coefficients 0.26, 1.555, 1.305, 8.288, and 0.136, respectively, at the p < 0.001 significance level. The PLS-SEM R2 0.65 indicates a good explanatory power of the model. The methodology developed in the present study will allow devising more sustainable policies for spatial data management and dissemination in Pakistan and beyond.

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Formalizing the Mapping of UML Conceptual Schemas to Column-Oriented Databases

International Journal of Data Warehousing and Mining ◽

10.4018/ijdwm.2018070103 ◽

2018 ◽

Vol 14 (3) ◽

pp. 44-68 ◽

Cited By ~ 1

Author(s):

Fatma Abdelhedi ◽

Amal Ait Brahim ◽

Gilles Zurfluh

Keyword(s):

Big Data ◽

Data Warehouse ◽

Relational Databases ◽

Traditional Approach ◽

Physical Models ◽

Decision Making Process ◽

Nosql Databases ◽

Care Field ◽

Sufficient Degree

Nowadays, most organizations need to improve their decision-making process using Big Data. To achieve this, they have to store Big Data, perform an analysis, and transform the results into useful and valuable information. To perform this, it's necessary to deal with new challenges in designing and creating data warehouse. Traditionally, creating a data warehouse followed well-governed process based on relational databases. The influence of Big Data challenged this traditional approach primarily due to the changing nature of data. As a result, using NoSQL databases has become a necessity to handle Big Data challenges. In this article, the authors show how to create a data warehouse on NoSQL systems. They propose the Object2NoSQL process that generates column-oriented physical models starting from a UML conceptual model. To ensure efficient automatic transformation, they propose a logical model that exhibits a sufficient degree of independence so as to enable its mapping to one or more column-oriented platforms. The authors provide experiments of their approach using a case study in the health care field.

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USING PARTICIPATORY APPROACH TO IMPROVE AVAILABILITY OF SPATIAL DATA FOR LOCAL GOVERNMENT

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iii-4-w1-139-2016 ◽

2016 ◽

Vol III-4/W1 ◽

pp. 139-144

Author(s):

T. Kliment ◽

V. Cetl ◽

H. Tomič ◽

J. Lisiak ◽

M. Kliment

Keyword(s):

Spatial Data ◽

Local Governments ◽

Local Level ◽

Property Tax ◽

Open Data ◽

Quality Level ◽

Tax Rates ◽

Geospatial Information ◽

Data Infrastructure ◽

Participatory Gis

Nowadays, the availability of authoritative geospatial features of various data themes is becoming wider on global, regional and national levels. The reason is existence of legislative frameworks for public sector information and related spatial data infrastructure implementations, emergence of support for initiatives as open data, big data ensuring that online geospatial information are made available to digital single market, entrepreneurs and public bodies on both national and local level. However, the availability of authoritative reference spatial data linking the geographic representation of the properties and their owners are still missing in an appropriate quantity and quality level, even though this data represent fundamental input for local governments regarding the register of buildings used for property tax calculations, identification of illegal buildings, etc. We propose a methodology to improve this situation by applying the principles of participatory GIS and VGI used to collect observations, update authoritative datasets and verify the newly developed datasets of areas of buildings used to calculate property tax rates issued to their owners. The case study was performed within the district of the City of Požega in eastern Croatia in the summer 2015 and resulted in a total number of 16072 updated and newly identified objects made available online for quality verification by citizens using open source geospatial technologies.

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From Surveying to Geomatics

Landscape & Environment ◽

10.21120/le/10/3-4/8 ◽

2016 ◽

Vol 10 (3-4) ◽

pp. 153-160 ◽

Cited By ~ 1

Author(s):

Besim Ajvazi ◽

Fisnik Loshi ◽

Béla Márkus

Keyword(s):

Information And Communication Technologies ◽

Spatial Data ◽

Professional Education ◽

Spatial Databases ◽

Unmanned Aerial Systems ◽

Geospatial Information ◽

Automatic Data ◽

Data Infrastructure ◽

Building Information ◽

Concept Building

In the land surveying profession fast changes have been taking place in the last fifty years. Technological changes are generated by the Information and Communication Technologies; the analogue – digital trends; the automatic data acquisition methods replace manual ones; instead of two-dimensional base maps we use dynamic spatial databases more and more integrated into a global data infrastructure. However, these changes cause impacts also on scientific level. The traditional top-down approach substituted by bottom-up methodologies; in many cases the point-by-point measurement is changed by 3D laserscanning or Unmanned Aerial Systems, which produces huge amount of data, but it needs new algorithms for information extraction; instead of a simple data provision land surveyors support complex spatial decisions. The paper is dealing with some aspects of these changes. In the first chapter the authors would like to highlight the “data-information-knowledge” relations and the importance of changes in professional education. The second chapter gives an example of the benefits of a Global Spatial Data Infrastructure in spatial decision support. Finally we introduce a new concept (Building Information Modelling) in modelling the real world. However, until now BIM is used in building construction industry, it can can be a paradigm shift in geospatial information management in general.

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