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Table 2 Comparison of different BDVC models

From: Big data monetization throughout Big Data Value Chain: a comprehensive review


BDVC model steps & techniques




Data discovery: collect/annotate-prepare-organize

Creating value from disparate data and informing the enterprise decision-making

Storage is performed at the end of step “Data discovery”

Data integration: integrate

Data exploitation: analyze-visualize-make decisions


Understanding of the application domain

Developing a Big Data processes understanding would enable business managers to improve business operations and ensure sustainable competitiveness

Analytical techniques were limited to data mining. The model targets business managers

Selecting data and building input dataset

Preprocessing and cleansing

Transforming data

Data mining

Evaluating and interpreting patterns

Visualization and feedback


Data acquisition/extraction

Study of the possibilities of Big Data pipeline deployment in public, private or hybrid cloud

Data processing is performed after data storage

Data transformation

Data storage

Data processing

Data analysis

Data visualization


Data acquisition

Generate value, model the high-level activities of an information system and be able to integrate an ecosystem

Data analysis is performed before the data pre-processing and storage

Data analysis

Data curation

Data storage

Data usage


Data generation

BDVC is presented as a framework that shows the evolution of data processing following a life cycle

Data visualization is considered as a support method for data analysis

Data acquisition

Data storage

Data analysis


Data collection

Achieve a complete quality assessment of Big Data value chain trough a hybrid model

The quality evaluation is applied in parallel with BDVC, in particular in pre-processing, processing and analysis phases

Pre Big Data quality evaluation

Pre-processing quality evaluation

Post Big Data quality evaluation

Processing and analytics quality evaluation.



Data sources, types and accessibility

It is based on BDVC to achieve knowledge and wisdom

Phases too nested and strong coupling

Preprocessing and storing

Processing and visualization



BDVC model to realize goals in Big Data system

The datasets storage after preprocessing is not emphasized






Data extraction

Rely on BDVC to improve metadata management for Big Data systems

Repeated data loading presents a latency for real or near real time processing

Data loading and preprocessing

Data processing

Data storage

Data analysis

Data loading and transformation

Data interfacing and visualization



Theory-driven Big Data Analysis guidance process

Storage levels is not clear to assert the construction of the datasets for monetization





Creation: data capture

Rely on BDVC to realize data monetization, which is part of a global strategy, including different stakeholders

Monetization is the last phase of the chain, added after consumption

Storage: warehousing

Processing: data mining, fusion, and analytics

Consumption: sharing



Data generation

Extracting value from Big Data systems in smart grids environment

The preprocessing step is not detailed in spite of its importance in a Big Data lifecycle

Data acquisition

Data storing

Data processing

Data querying

Data analytics



Data acquisition

Discover ideas and knowledge about the governance sector

The approach adopted is based more on Big Data Analytics than BDVC

Data aggregation

Data processing

Data delivery


Data sources

Big Data architecture of smart grid to enable monitoring energy systems

Smart grid data are often unstructured. Preprocessing phase must be specified

Data integration

Data storage

Data analytics

Data visualization


Data ingestion

Application of Big Data metrics based on a BDVC adapted to sentiment analysis

This model cannot be generalized on different Big Data environments

Data analysis

Data visualization