Data cleanup, or data rubbing, is the process of detecting and correcting or removing data or records that are incorrect from a database. It also includes correcting or removing unformatted or duplicate data or records. The data removed in this process is often referred to as “dirty data”. Data cleaning is a necessary process to protect data quality. Large businesses with extensive datasets or assets typically use automated tools and algorithms to detect such records and correct common errors (such as missing zip codes in customer records).
The most powerful big data circles have rigorous data cleanup tools and processes to ensure that data quality is protected and trust in datasets is high for all types of users.
Latent Dirichlet Allocation (LDA), büyük miktardaki metin verisi üzerinde gizli konu yapılarının keşfedilmesine olanak tanıyan bir konu modelleme tekniğidir.
A Business Continuity Plan (BCP) is a detailed document that shows how a business will continue to operate in the event of an unplanned interruption in service.
Generative Adversarial Networks (GANs), iki sinir ağını (jeneratör ve ayırt edici) birbiriyle yarışan bir öğrenme mekanizmasında eğiterek gerçekçi veriler üreten yapay zeka modelleridir. Bu teknolojinin farklı kullanım alanlarına yönelik birçok türevi geliştirilmiştir
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