Data variety, come as you are in multi-model data warehouses

Bimonte, Sandro; Gallinucci, Enrico; Marcel, Patrick; Rizzi, Stefano

doi:10.1016/j.is.2021.101734

Cited by 18 publications

(15 citation statements)

References 31 publications

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“…We have identified key characteristics for a successful ETL solution based on a survey conducted by several researchers and online sources [25]. Big enterprises are looking for capabilities like data transformation (T), real-time integration (R), real-time analytics (R), GUI support (G), cloud support (C), horizontal scalability (S), non-RDBMS connections (N), parallel processing (P), metadata management (M), and automation (A).…”

Section: Feature Comparisonmentioning

confidence: 99%

Comparative Analysis of ETL Tools in Big Data Analytics

Qaiser

Farooq

Mustafa

et al. 2023

PakJET

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Massive amounts of data are being generated these days all around the world. The majority of social networking platforms, ecommerce sites, the public and private sectors, healthcare institutions, cloud networks, and various servers are all generating immense amount of data. Collected data from various sites could be in a structured or unstructured format. Extract Transform Load (ETL) is a crucial component of the growing demand for quicker business decisions aimed at many contemporary applications. Due to the volume and speed of data, real-time ETL is built on the foundation of multi-source, unstructured data stream extraction and transformation employing disc data in dispersed environments. The entire procedure is pipelined while processing so that the final resultant data can provide some essential and useful conclusions to work on. Some analytical findings are once again helpful in making decisions. However, the produced results may differ in some numbers, graphs, and figures in many circumstances. This occurs as a result of the usage of some unrealistic tools for big data processing. This paper proposed several methods and appropriate ETL (extract, transform, and load) tools for the big data processing, which may result in appropriate analytics and conclusions from the data.

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Section: Feature Comparisonmentioning

confidence: 99%

Comparative Analysis of ETL Tools in Big Data Analytics

Qaiser

Farooq

Mustafa

et al. 2023

PakJET

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“…Bimonte et al [2] investigate the effectiveness and efficiency of MMDWs to store multidimensional data. Though one multi-model solution coping with variety is proposed with reference to a case study, there is no discussion and evaluation of all the single-and multimodel solutions made available for the different types of multidimensional elements when relying on a MMDBMS.…”

Section: Nosql Olapmentioning

confidence: 99%

Logical design of multi-model data warehouses

et al. 2022

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Multi-model DBMSs, which support different data models with a fully integrated backend, have been shown to be beneficial to data warehouses and OLAP systems. Indeed, they can store data according to the multidimensional model and, at the same time, let each of its elements be represented through the most appropriate model. An open challenge in this context is the lack of methods for logical design. Indeed, in a multi-model context, several alternatives emerge for the logical representation of dimensions and facts. The goal of this paper is to devise a set of guidelines for the logical design of multi-model data warehouses so that the designer can achieve the best trade-off between features such as querying, storage, and ETL. To this end, for each model considered (relational, document-based, and graph-based) and for each type of multidimensional element (e.g., non-strict hierarchy) we propose some solutions and carry out a set of intra-model and inter-model comparisons. The resulting guidelines are then tested on a case study that shows all types of multidimensional elements.

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“…The pivot table acts as a 2D spreadsheet which analyses and supports the multi‐dimensional data on multiple pages. Bimonte et al 25 analyzed the feasibility of the multi‐model method for DW with reference to the star schema and dimensions and facts on the schema less data. Multi‐modal DBMS (Database Management Systems) performance was described as ensuring better extensibility, evaluability and flexibility to minimize the ETL cost.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated heuristic based context dependent ETL process to generate multi‐dimensional model for tabular data

Hira¹,

Deshpande

2022

Concurrency and Computation

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Over the past decade, enterprises have broadly adopted data warehousing in various activities. Today, abundant information is available on websites in the form of tables or spreadsheets. This huge amount of data cannot be processed directly because of its complexity, heterogeneity, and gap between user requirements. In this work, an automatic approach is proposed to build the multi-dimensional structure (MDS) of heterogeneous tabular data format for intelligent decision-making. The proposed MDS is generated by identifying components such as dimensions and hierarchies. It automatically extracts measures based on the spatial characteristics of data dimensions like region, time as well as their hierarchies. This proposed approach automatically generates a multi-dimensional model for BI tools without complicated ETL (Extraction, Transformation and Loading) process and helps to solve several business queries, like "Top 5 states in India based on Irrigated area in 2009". Moreover, the proposed method reduces the time and cost of building multi-dimensional models to a very large extent. The correctness of proposed method is tested with the synthetic and economic datasets of Government websites where information is stored in tabular formats and various heterogeneous setups where the proposed method saved approximately 4000 to 5000 computing hours of the ETL process.

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Data variety, come as you are in multi-model data warehouses

Cited by 18 publications

References 31 publications

Comparative Analysis of ETL Tools in Big Data Analytics

Comparative Analysis of ETL Tools in Big Data Analytics

Logical design of multi-model data warehouses

Automated heuristic based context dependent ETL process to generate multi‐dimensional model for tabular data

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