From: Big data monetization throughout Big Data Value Chain: a comprehensive review
References | BDVC model steps & techniques | Context | Observations |
---|---|---|---|
[65] | 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 | |||
[40] | 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 | |||
[5] | 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 | |||
[60] | 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 | |||
[34] | 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 | |||
[74] | 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. | |||
Visualization | |||
[66] | 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 | |||
[68] | Acquisition-recording | BDVC model to realize goals in Big Data system | The datasets storage after preprocessing is not emphasized |
Extraction-cleaning-annotation | |||
Integration-aggregation-representation | |||
Analysis-modeling | |||
Interpretation | |||
[75] | 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 | |||
[77] | Acquisition | Theory-driven Big Data Analysis guidance process | Storage levels is not clear to assert the construction of the datasets for monetization |
Pre-processing | |||
Analytics | |||
Interpretation | |||
[61] | 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 | |||
Monetization | |||
[71] | 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 | |||
Monitoring | |||
[69] | 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 | |||
[70] | 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 | |||
[73] | 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 |