Towards NoSQL Graph Data Warehouse for Big Social Data Analysis

Akid, Hajer; Ayed, Mounir Ben

doi:10.1007/978-3-319-53480-0_95

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…Graph data models have arisen since the eighties, but their popularity gradually decreased with the emergence of other data models, especially the geographical, spatial, semi-structured and XML [51]. Recently, graph databases have regained the attention of both academics and business entities due to the ever-increasing need to store, process, manage and analyze graph-like structures such as social networks [52], [53], biological networks [54]- [56], and document networks [57], [58]. Indeed, graph databases are considered as one of the most useful structures and natural ways for modeling interactions between the objects of a network [9].…”

Section: B Property Graph Data Modelmentioning

confidence: 99%

Performance of NoSQL Graph Implementations of Star vs. Snowflake Schemas

et al. 2022

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Nowadays, the data used for decision-making come from a wide variety of sources which are difficult to manage using relational databases. To address this problem, many researchers have turned to Not only SQL (NoSQL) databases to provide scalability and flexibility for On-Line Analytical Processing (OLAP) systems. In this paper, we propose a set of formal rules to convert a multidimensional data model into a graph data model (MDM2G). These rules allow conventional star and snowflake schemas to fit into NoSQL graph databases. We apply the proposed rules to implement star-like and snowflake-like graph data warehouses. We compare their performances to similar relational ones focusing on the data model, dimensionality, and size. The experimental results show large differences between relational and graph implementations of a data warehouse. A relational implementation performs better for queries on a couple of tables, but conversely, a graph implementation is better when queries involve many tables. Surprisingly the performances of a star-like and snowflake-like graph data warehouses are very close. Hence a snowflake schema could be used in order to easily consider new sub-dimensions in a graph data warehouse.

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Section: B Property Graph Data Modelmentioning

confidence: 99%

Performance of NoSQL Graph Implementations of Star vs. Snowflake Schemas

et al. 2022

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“…Bicevska and Oditis [25] demonstrated that NoSQL technology is suitable for processing high volumes of data from various settings. Akid and Ayed [26] extended the NoSQL technology and proposed an entirely new architecture of modern DW to construct a graph document-oriented NoSQL database for storing and analyzing big social data. However, both [25] and [26] ignored the ETL process requirement under the NoSQL DW, thereby limiting the creation of these DWs and hindering the transfer of required data.…”

Section: ) Modern Data Warehousesmentioning

confidence: 99%

Review on Integrating Geospatial Big Datasets and Open Research Issues

et al. 2021

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Big data and geographic information systems (GIS) are two technologies that have increasingly influenced many areas in the last 10 years and will continue to improve and help solve serious global problems, such as consequences of climate change or global pandemics. A wide spectrum of GIS applications interacts with the continuous growth of geospatial big data sources to drive precise and informed decisions. Geospatial big data integration is designed to accomplish the compatibility of distinct geospatial datasets regardless of their spatial coverage. The large number of geospatial big data sources demand effective data integration for storing and handling such datasets, which will be used for geospatial data analysis and visualization. For instance, risk management datasets related to healthcare and the environment are heterogeneous and disparate. Obtaining a unified view of such geospatial big datasets is complicated and challenging, especially if we consider problems related to healthcare pandemics and environmental disasters. Hence, before we can attempt to predict and mitigate processes occurring in these domains, we must realize that geospatial big data integration is crucial in consolidating datasets. We explore and discuss issues involved in integrating geospatial big datasets in this study. We then classify big data integration processes into three categories, namely, data warehousing, data transformation and integration methods. Furthermore, several research challenges focused on geospatial big data, big earth data, data warehousing, data transformation and linked data are presented. Lastly, open research issues and emerging trends that require in-depth investigations in the near future are highlighted in this study.INDEX TERMS Big data integration, geographic information system (GIS), geospatial big data.

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Graph Databases: An Alternative to Relational Databases in an Interconnected Big Data Environment

Pavliš

2024

2024 47th MIPRO ICT and Electronics Convention (MIPRO)

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Towards NoSQL Graph Data Warehouse for Big Social Data Analysis

Cited by 6 publications

References 13 publications

Performance of NoSQL Graph Implementations of Star vs. Snowflake Schemas

Performance of NoSQL Graph Implementations of Star vs. Snowflake Schemas

Review on Integrating Geospatial Big Datasets and Open Research Issues

Graph Databases: An Alternative to Relational Databases in an Interconnected Big Data Environment

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