Implementation of Multidimensional Databases with Document-Oriented NoSQL

Chevalier, Max; Malki, Mohammed El; Kopliku, Arlind; Teste, Olivier; Tournier, Ronan

doi:10.1007/978-3-319-22729-0_29

Cited by 29 publications

(15 citation statements)

References 12 publications

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“…MongoDB utilizes the document-oriented data model to store data as collections in documents [38]. Different from RDBMS, there is no fixed schema in MongoDB.…”

Section: Key-value Data Modelmentioning

confidence: 99%

Evaluating the Open Source Data Containers for Handling Big Geospatial Raster Data

Yang

et al. 2018

IJGI

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Big geospatial raster data pose a grand challenge to data management technologies for effective big data query and processing. To address these challenges, various big data container solutions have been developed or enhanced to facilitate data storage, retrieval, and analysis. Data containers were also developed or enhanced to handle geospatial data. For example, Rasdaman was developed to handle raster data and GeoSpark/SpatialHadoop were enhanced from Spark/Hadoop to handle vector data. However, there are few studies to systematically compare and evaluate the features and performances of these popular data containers. This paper provides a comprehensive evaluation of six popular data containers (i.e., Rasdaman, SciDB, Spark, ClimateSpark, Hive, and MongoDB) for handling multi-dimensional, array-based geospatial raster datasets. Their architectures, technologies, capabilities, and performance are compared and evaluated from two perspectives: (a) system design and architecture (distributed architecture, logical data model, physical data model, and data operations); and (b) practical use experience and performance (data preprocessing, data uploading, query speed, and resource consumption). Four major conclusions are offered: (1) no data containers, except ClimateSpark, have good support for the HDF data format used in this paper, requiring time-and resource-consuming data preprocessing to load data; (2) SciDB, Rasdaman, and MongoDB handle small/mediate volumes of data query well, whereas Spark and ClimateSpark can handle large volumes of data with stable resource consumption; (3) SciDB and Rasdaman provide mature array-based data operation and analytical functions, while the others lack these functions for users; and (4) SciDB, Spark, and Hive have better support of user defined functions (UDFs) to extend the system capability.

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“…MongoDB utilizes the document-oriented data model to store data as collections in documents [38]. Different from RDBMS, there is no fixed schema in MongoDB.…”

Section: Key-value Data Modelmentioning

confidence: 99%

Evaluating the Open Source Data Containers for Handling Big Geospatial Raster Data

Yang

et al. 2018

IJGI

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“…We relate document-oriented databases to NoSQL databases, which are schema-less. Collections in this database comprising unique documents which are being used to store heterogeneous, unstructured and complex structures documents [21]. The document-oriented data model for the database is as shown in the Fig.…”

Section: Document Oriented Modelmentioning

confidence: 99%

Context Storage Using Cloud-Based Mongodb With Rule-Based Rete Algorithm

Shivakumar¹

2021

ITII

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In this Context-aware computing era, everything is being automated and because of this, smart system’s count been incrementing day by day. The smart system is all about context awareness, which is a synergy with the objects in the system. The result of the interaction between the users and the sensors is nothing but the repository of the vast amount of context data. Now the challenging task is to represent, store, and retrieve context data. So, in this research work, we have provided solutions to context storage. Since the data generated from the sensor network is dynamic, we have represented data using Context dimension tree, stored the data in cloud-based ‘MongoDB’, which is a NoSQL. It provides dynamic schema and reasoning data using If-Then rules with RETE algorithm. The Novel research work is the integration of NoSQL cloud-based MongoDB, rule-based RETE algorithm and CLIPS tool architecture. This integration helps us to represent, store, retrieve and derive inferences from the context data efficiently..

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“…Although several authors have observed that there is a need for data-model approaches to the design and management of NoSQL databases [9,10,11] [33] and in a column-oriented NoSQL database [34].…”

Section: Related Workmentioning

confidence: 99%

Data modeling in the NoSQL world

Atzeni

Bugiotti

Cabibbo

et al. 2020

Computer Standards & Interfaces

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NoSQL systems have gained their popularity for many reasons, including the flexibility they provide in organizing data, as they relax the rigidity provided by the relational model and by the other structured models. This flexibility and the heterogeneity that has emerged in the area have led to a little use of traditional modeling techniques, as opposed to what has happened with databases for decades. In this paper, we argue how traditional notions related to data modeling can be useful in this context as well. Specifically, we propose NoAM (NoSQL Abstract Model), a novel abstract data model for NoSQL databases, which exploits the commonalities of various NoSQL systems. We also propose a database design methodology for NoSQL systems based on NoAM, with initial activities that are independent of the specific target system. NoAM is used to specify a system-independent representation of the application data and, then, this intermediate representation can be implemented in target NoSQL databases, taking into account their specific features. Overall, the methodology aims at supporting scalability, performance, and consistency, as needed by next-generation web applications.

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Implementation of Multidimensional Databases with Document-Oriented NoSQL

Cited by 29 publications

References 12 publications

Evaluating the Open Source Data Containers for Handling Big Geospatial Raster Data

Evaluating the Open Source Data Containers for Handling Big Geospatial Raster Data

Context Storage Using Cloud-Based Mongodb With Rule-Based Rete Algorithm

Data modeling in the NoSQL world

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