Network Terms to Learn by Kalai Selvi Arivalagan (top android ebook reader TXT) 📕

segment for information technology (IT) vendors who specialize in machine learning and artificial intelligence as a service (MLaaS and AIaaS).

 

Data Wrangling

 

Data wrangling is a process that data scientists and data engineers use to locate new data sources and convert the acquired information from its raw data format to one that is compatible with automated and semi-automated analytics tools.  Data wrangling, which is sometimes referred to as data munging, is arguably the most time-consuming and tedious aspect of data analytics.

The exact tasks required in data wrangling depend on what transformations the analyst requires to make a dataset useable. The basic steps involved in data wranging include:

Discovery -- learn what information is contained in a data source and decide if the information has value. Structuring -- standardize the data format for disparate types of data so it can be used for downstream processes. Cleaning -- remove incomplete and redundant data that could skew analysis. Enriching -- decide if you have enough data or need to seek out additional internal and/or 3rd-party sources. Validating -- conduct tests to expose data quality and consistency issues. Publishing -- make wrangled data available to stakeholders in downstream projects.

In the past, wrangling required the analyst to have a strong background in scripting languages such as Python or R. Today, an increasing number of data wrangling tools use machine learning (ML) algorithms to carry out wrangling tasks with very little human intervention.

 

Digital Twin
A digital twin is a digital version of an entity or system that exists in the physical world. They allow users to learn how a change will impact a physical object or system by testing the change first on a virtual model.

Digital twins can be either static or dynamic. Static twins, which are also referred to as simulations, represent an entity or system at a specific point in time. Dynamic twins are linked to the physical entity or system they represent in order to accurately depict the state of the entity or system in real time.

Digital twins play an important role in research and development (R&D), system integration, change management and enterprise risk management. Popular uses for digital twins include:

Forecasting the health of an entity or system under specific conditions. Training staff how to use/manage a physical entity or system. Capturing requirements for a new entity or sytem. Predicting how a change will affect a real-world entity or system. Comparing two different lifecycle plans for an entity or system. Understanding an entity or system's dependencies prior to building it in the real world.

 

Data Purging
Data purging is the process of permanently removing obsolete data from a specific storage location when it is no longer required.

Common criteria for data purges include the advanced age of the data or the type of data in question. When a copy of the purged data is saved in another storage location, the copy is referred to as an archive.

The purging process allows an administrator to permanently remove data from its primary storage location, yet still retrieve and restore the data from the archive copy should there ever be a need. In contrast, the delete process also removes data permanently from a storage location, but doesn’t keep a backup.

In enterprise IT, the compound term purging and archiving is used to describe the removal of large amounts of data, while the term delete is used to refer to the permanent removal of small, insignificant amounts of data. In this context, the term deletion is often associated with data quality and data hygiene, whereas the term purging is associated with freeing up storage space for other uses.

Strategies for data purging are often based on specific industry and legal requirements. When carried out automatically through business rules, purging policies can help an organization run more efficiently and reduce the total cost of data storage both on-premises and in the cloud.

 

Graphics Processing Unit (GPU)
A graphics processing unit (GPU) is a parallel processor that allows repetitive calculations within an application to run simultaneously. GPUs were introduced towards the end of the last century to help central processing units (CPUs) keep up with the huge number of calculations required by animated video games. The GPU carried out repetitive calculations concurrently, while the rest of the application continued to run on the CPU.

As the demand for graphic applications increased towards the end of the last century, GPUs became more popular. Eventually, they became not just an enhancement but a necessity for optimum performance of a PC.  Today, GPUs are powerful enough to perform rapid mathematical calculations in parallel for deep learning algorithms and are used in just about every type of computing device, including mobile phones, tablets, display adapters, workstations and game consoles.

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Publication Date: 06-22-2017

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Latest Terms in Information Technology for Networking