How can Data Mining Help in Data Processing and Storage?

Large companies collect and store a lot of data, so much of it that most of it is lost and hard to find without proper organization. For a few years there have been several solutions to save and store this information. The most common term used for this process is called “Backup” of the information.

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Over the years, companies are growing more and more, and consequently, it is more and more difficult to ensure that the Backups data are made by saving the necessary information. Today, Data Mining is a concept that has been growing and has recently begun to be applied in the appropriate storage of data. This process of “Data mining” or “Data Mining” began to be used initially to find repetitive patterns in large databases and thus be able to apply this information in different processes. 

Data mining is the process of finding anomalies, patterns, and correlations in large data sets, in order to predict outcomes. In order to arrive at this result, it is first necessary to perform data preprocessing.

Data preprocessing is a stage of the KKD (information discovery process) that is responsible for: cleaning, integrating, transforming and reducing the data to be taken to data mining. The main objective of this stage is to reduce or eliminate non-useful patterns. It also has the function of reducing the data set to improve the efficiency of the next step (data mining). It is worth noting that one advantage of carrying out this preprocessing is that quality data can be obtained or incomplete information can be recovered. 

Data storage or generation of Backups is a technological area that is increasingly benefiting from “Data Mining”. The latter has been beginning to be implemented at this time of processes in order to be able to reduce the amount of data stored and organize it properly. 

In order to perform data preprocessing, the following steps or stages must be met:

1. Data set: collect the data that the company has accumulated.

2. Clean data: select the relevant, necessary and useful data.

3. Feature Engineering: Sort the selected data. After this step the data can be for training or to form a new data. 

    3.1 training data: is the data used to train a model. It can be done through:

  • Algorithm Learning
  • model train

   3.2 New data: Through the already existing data, new useful data can be generated.

4. scoring model. It is to know how the new information is going to be interpreted and to validate its quality, for this special techniques are used (example: formulas).

5. Model Evaluation: This is the final step, where the model is evaluated to check that it works correctly before being taken to data mining.

Knowing the advantages that data processing and data mining entails, we offer services of Backup, cloud data storage y IT consulting that provide you with personalized support in these processes. 

¡Contáct Usanos to find out how we can help you!

 

Bibliography:

¿Qué es la minería de datos? (n.d.). Recuperado el  04 de mayo 2020, de https://www.sas.com/es_mx/insights/analytics/data-mining.html

Herrera, F., Riquelme, J., & Ruiz, R. (2004, Mayo 7). Presentación-Data-preparation – lsi.us.es. Recuperado el  04 de mayo 2020, de http://www.lsi.us.es/redmidas/IIreunion/trans/prepro_roberto.ppt

García, S., Ramírez-Gallego,S., Luengo, J., Herrera, F. (2016, Octubre). Big Data: Preprocesamiento y calidad de datos. Recuperado el  04 de mayo 2020, de https://sci2s.ugr.es/sites/default/files/ficherosPublicaciones/2133_Nv237-Digital-sramirez.pdf