# A Framework to Build a Big Data Ecosystem Oriented to the Collaborative Networked Organization
## 3 Proposed Framework
### Programmable Modules Dimension
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technique** & **Description and Models/Algorithms** & **Example CNO Applications** \\ \hline \multirow{4}{*}{Cluster analysis} & This classifies input data into different small groups & * Management of user profile in an EE. \\  & according to the degree of similarity. It is based on & * Marketing through the segmentation of customers in a DVO. \\ \hline \multirow{4}{*}{Data mining} & This integrates statistical and machine learning & * Analysis of text documents for scientific purposes in a PVC. \\  & Models/algorithms a priori. Pattern & * Processing of food images to determine their quality in a SC. \\ \hline \multirow{4}{*}{Genetic algorithms} & These find true or approximate solutions to & * Computer-aided molecular design for the treatment of diseases (e-Science). \\  & the process of natural evolution or survival of the fittest. & * Generation of products and sales campaigns for specific clients (VT oriented to marketing). \\  & Models/algorithms: Selection. Mutation. Crossover. & * Determination of the polarity (+, – or neutral) of \\ \hline \multirow{4}{*}{Natural language processing} & This is based on artificial intelligence to analyze & some aspect in the VG according to social networks. \\  & human’s natural language. It uses soft data. & * Analysis of contracts in an insurance agency (DVO) to \\  & Models/algorithms: Part-of-speech tagging. Sentiment & * dataset fraud. \\  & analysis. Speech recognition. & * Analysis of audio calls of a customer service center of \\  & & an EE. \\ \hline \multirow{4}{*}{Machine learning} & This predicts a dependent variable (target) based on & * Analysis of financial feelings using social networks of \\  & large known datasets. It creates artificial intelligence & * Analysis of the closed circuit (video) of a VG for \\  & using statistical methods and inductive learning. & * perimeter security. \\  & Models/algorithms: Expectation maximization. Deep & * Prediction of readmission rates of patients in a \\  & learning. Principal component analysis. & hospital (VO). \\ \hline \multirow{4}{*}{Crowdsourcing} & People, in general, contribute to the accomplishment of & * Creation of a Kaizen system based on the report of \\  & a task. Therefore, it is a massive collaboration. It uses & the participants in a VE. \\ \cline{1-1}  & multiple formats and types of data. & * Open call from Disaster Rescue Networks (VBE) to \\ \cline{1-1}  & Models/algorithms: Knowledge discovery and & help in a region impacted by a natural disaster. \\ \cline{1-1}  & management. Distributed human intelligence tasking. & * A massive collaboration of a VL to solve \\ \cline{1-1}  & Broadcast search. & * empirical problems. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._generated: centralized or inclusive; see the approaches dimension. Regardless of the type of processing approach, the resulting information is replicated and given to the requesting participant. * Cache Module (CM): This module reduces the number of requests of the next higher level and/or of the DPC by using cache. Cache allows each CNO's participant to register and use both: (1) the most popular valuable information of the CNO and (2) metadata embedded in big data fragments that describe their attributes and characteristics. The acquired cache is quickly classified, compressed and adapted; this maximizes CM's capabilities for big data and CNO's dynamism. CM can substitute information based on cold data for information based on hot data by means of a data structure that orders the information based on its temperature. As a result, CM has the most popular information of the CNO. Additionally, the CM considers cache in real time, which allows information to be obtained on the fly through interoperability between participants. Thus, CM can answer some participant's requests without consulting the superior level. Hence, CM can reduce the consumption of resources of the CNO.