# A Framework to Build a Big Data Ecosystem Oriented to the Collaborative Networked Organization
## 3 Proposed Framework
### Approaches Dimension  
The approaches dimension has two objectives: (1) to preprocess local data of each CNO participant before being transferred to the higher level and (2) support the intelligent processing of big data. Thus, it reduces the use of resources. This dimension considers five approaches, which are: data alignment, compression, centralized, decentralized and inclusive. The approaches are used in the components of the programmable modules dimension. Each approach uses different techniques according to CNO's architecture levels; see the data fusion dimension. Table 2 shows the relationship between the approaches with the levels mentioned before.  
* Data alignment: This reduces errors in the data to be processed in the upper level. Each CNO's participant must transform their data from its specific orientation to collaborative orientation before data fusion. This challenge can be overcome by using methods for data alignments, such as the primary key, indexes, heuristic matching, Monte Carlo simulations or cross-correlation analysis methods.
* Compression: This reduces the use of resources in the CNO, such as data transfer in the network, disk space, DPC processing and power consumption. The CNO may use lossy compression and lossless compression methods.
* Centralized: This sub-dimension combines a CNO's global data sources in the DPC. Thus, a participant may request information from the DPC when: (1) local information is not enough or (2) the middle level does not satisfy the request. This approach processes data in two ways: streaming processing to obtain results in real time and batch processing for extended processing.
* Decentralized: This allows the participant to take advantage of the local and/or the most popular information of the CNO; the latter is replicated from the level immediately superior to the participant. Thus, the participant can make decisions without consulting their superior level. Therefore, when all participants need to send requests to the DPC (centralized), the decentralized approach can consume fewer resources, including during networking and processing.
* Inclusive: The centralized approach avoids robust local infrastructure for each participant because it moves computation to the top level. However, big data generated in the CNO can overwhelm this fully centralized approach. The CNO-BD framework extends this dimension through the inclusive sub-dimension to adaptively choose the processing, as follows: (1) local in each participant (decentralized), (2) distributed between local and other participants, (3) global in the DPC (centralized) and/or (4) simultaneous processing through the three previous types. This approach considers the content of the data and their relationships. Thus, the data processing is distributed among the participants which may balance the workload with many requests for large datasets. The distribution of data processing has two characteristics: (1) the data can be transferred to a participant from both DPC or requesting node and (2) data transfer can be avoided by using the most popular CNO information. As a result, the DPC only processes information that is not accessible at the two lower levels. The inclusive approach uses techniques and components of the data fusion dimension, the big data assurance dimension and the programmable modules dimension. This synergy allows the intelligent processing of big data.