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Table 2 Comparison of different BDVC models

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

ReferencesBDVC model steps & techniquesContextObservations
[65]Data discovery: collect/annotate-prepare-organizeCreating value from disparate data and informing the enterprise decision-makingStorage is performed at the end of step “Data discovery”
Data integration: integrate
Data exploitation: analyze-visualize-make decisions
[40]Understanding of the application domainDeveloping a Big Data processes understanding would enable business managers to improve business operations and ensure sustainable competitivenessAnalytical 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
[5]Data acquisition/extractionStudy of the possibilities of Big Data pipeline deployment in public, private or hybrid cloudData processing is performed after data storage
Data transformation
Data storage
Data processing
Data analysis
Data visualization
[60]Data acquisitionGenerate value, model the high-level activities of an information system and be able to integrate an ecosystemData analysis is performed before the data pre-processing and storage
Data analysis
Data curation
Data storage
Data usage
[34]Data generationBDVC is presented as a framework that shows the evolution of data processing following a life cycleData visualization is considered as a support method for data analysis
Data acquisition
Data storage
Data analysis
[74]Data collectionAchieve a complete quality assessment of Big Data value chain trough a hybrid modelThe 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.
[66]Data sources, types and accessibilityIt is based on BDVC to achieve knowledge and wisdomPhases too nested and strong coupling
Preprocessing and storing
Processing and visualization
[68]Acquisition-recordingBDVC model to realize goals in Big Data systemThe datasets storage after preprocessing is not emphasized
[75]Data extractionRely on BDVC to improve metadata management for Big Data systemsRepeated 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
[77]AcquisitionTheory-driven Big Data Analysis guidance processStorage levels is not clear to assert the construction of the datasets for monetization
[61]Creation: data captureRely on BDVC to realize data monetization, which is part of a global strategy, including different stakeholdersMonetization is the last phase of the chain, added after consumption
Storage: warehousing
Processing: data mining, fusion, and analytics
Consumption: sharing
[71]Data generationExtracting value from Big Data systems in smart grids environmentThe 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
[69]Data acquisitionDiscover ideas and knowledge about the governance sectorThe approach adopted is based more on Big Data Analytics than BDVC
Data aggregation
Data processing
Data delivery
[70]Data sourcesBig Data architecture of smart grid to enable monitoring energy systemsSmart grid data are often unstructured. Preprocessing phase must be specified
Data integration
Data storage
Data analytics
Data visualization
[73]Data ingestionApplication of Big Data metrics based on a BDVC adapted to sentiment analysisThis model cannot be generalized on different Big Data environments
Data analysis
Data visualization