Network Terms to Learn, Kalai Selvi Arivalagan [books to read in a lifetime .TXT] 📗
- Author: Kalai Selvi Arivalagan
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Predictive Maintenance
Predictive maintenance (PdM) is an approach to asset management that relies on operational data to determine when a physical asset requires service. An important goal of PdM is to minimize maintenance costs by preventing equipment failures before they occur. Predictive maintenance plays an important role in industries that requires high availability (HA) for machine parts. PdM can be contrasted with reactive maintenance and preventive maintenance.
Reactive Maintenance – Run equipment until it breaks and then fix or replace it.
Preventive Maintenance – Replace parts and schedule equipment repairs on a time- or machine-run-based schedule.
Predictive Maintenance – Use intelligent sensors to monitor machine parts in operations and machine learning to determine when data has deviated from desired parameters.
Predictive maintenance software uses data produced by Internet of Things (IoT) and Industry 4.0 edge nodes to monitor the condition of mechanical assets as they are operating. Consumer-grade predictive maintenance software apps will typically issue an alert when data suggests a replacement part or maintenance appointment is needed.
In contrast, some enterprise-level PdM software applications are able to connect to other business systems and actually order replacement parts and set up appointments so repairs can be made. Popular enterprise PdM vendors include Fxix, UpKeep and eMaint.
Term of the day - 34
Ubiquitous Computing
Ubiquitous computing is a paradigm in which the processing of information is linked with each activity or object as encountered. It involves connecting electronic devices, including embedding microprocessors to communicate information. Devices that use ubiquitous computing have constant availability and are completely connected. Ubiquitous computing focuses on learning by removing the complexity of computing and increases efficiency while using computing for different daily activities. Ubiquitous computing is also known as pervasive computing, everyware and ambient intelligence.
Integrated Analytics Platform
An integrated analytics platform is an integrated solution that brings together performance management, analytics and business intelligence tools in a single package. It provides an end-to-end solution for delivering business intelligence from multiple fronts and gives the user a clear visual representation of data as well as providing services such as revenue calculation, forecasting and developing marketing strategy models and algorithms all on the same system, allowing for interoperability.
Edge Analytics
Edge analytics refers to the analysis of data from some non-central point in a system, such as a network switch, peripheral node or connected device or sensor. As an emerging term, “edge analytics” defines the attempt to collect data in decentralized environments.
Analytics of Things
Analytics of Things is the term used to describe the analysis of the data generated by the Internet of Things devices. In other words, analytics of the Internet of Things is Analytics of Things. Analytics of Things is required so as to make the connected devices smart and to give the devices the ability to make intelligent decisions.
Intelligent Edge
Intelligent edge is a term describing a process where data is analyzed and aggregated in a spot close to where it is captured in a network. The intelligent edge, also described as “intelligence at the edge,” has important ramifications for distributed networks including the internet of things (IoT).
Ubicomp
Ubicomp is an abbreviation for the term "ubiquitous computing." Ubiquitous computing is an idea related to expanding an interface to make it seem "pervasive" in a given environment. Ubicomp is also the name of an annual conference on ubiquitous computing.
Smart Device
A smart device, as the name suggests, is an electronic gadget that is able to connect, share and interact with its user and other smart devices. Although usually small in size, smart devices typically have the computing power of a few gigabytes.
Empowered Edge
Empowered edge is a term in IT that is used to talk about empowering computing centralization that is distributed toward the edge of a network, toward the end user and the end user device. It is a key concept in device management in the cloud and big data age. Empowered edge is also known as device democracy.
Cybercrime
In general, cybercrime is defined as either a crime involving computing against a digital target or a crime in which a computing system is used to commit criminal offenses. As a broad category of crime, cybercrime includes such disparate sorts of activities as illegal access of data, use of computer communications to commit fraud, or the ransoming of systems via digital means. Cybercrime may also be referred to as computer crime.
Cyber Defense
Cyber defense is a computer network defense mechanism which includes response to actions and critical infrastructure protection and information assurance for organizations, government entities and other possible networks. Cyber defense focuses on preventing, detecting and providing timely responses to attacks or threats so that no infrastructure or information is tampered with. With the growth in volume as well as complexity of cyber attacks, cyber defense is essential for most entities in order to protect sensitive information as well as to safeguard assets.
Security Architecture
Security architecture is a unified security design that addresses the necessities and potential risks involved in a certain scenario or environment. It also specifies when and where to apply security controls. The design process is generally reproducible. In security architecture, the design principles are reported clearly, and in-depth security control specifications are generally documented in independent documents. System architecture can be considered a design that includes a structure and addresses the connection between the components of that structure.
Data Modeling
Data modeling is a representation of the data structures in a table for a company’s database and is a very powerful expression of the company's business requirements. This data model is the guide used by functional and technical analysts in the design and implementation of a database. Data models are used for many purposes, from high-level conceptual models to physical data models.
Predictive Model Markup Language (PMML)
Predictive Model Markup Language (PMML) is an XML-based markup language designed to provide a method of defining application models related to predictive analytics and data mining. PMML attempts to eliminate proprietary issues and incompatibility from application exchange models.
Predictive Modeling
Predictive modeling is a process through which a future outcome or behavior is predicted based on the past and current data at hand.
It is a statistical analysis technique that enables the evaluation and calculation of the probability of certain results related to software, systems or an entire IT environment.
Bi-Directional Predictive Frame (B-Frame)
A bi-directional predictive frame (B-Frame) is part of an MPEG video compression standard. In this method, groups of sequential pictures are aggregated to form a group of pictures (GOP), which are displayed in sequence to provide video. A single bi-directional predictive frame relates to other frames directly preceding or following it. By recording just the information that differs from a preceding picture or a following picture, the data storage requirements for each individual picture become much lower than in a technique that would store each successive image completely. A bi-directional predictive frame may also be known as a bi-directional frame.
Predictive Analytics Tools
Predictive analytics tools provide business owners a way to predict how their customers and potential audiences are reacting to promotions and other strategies or campaigns that the businesses might be running. Predictive analytics tools work by providing well-tracked historical data and real-time insights.
Business Analytics (BA)
Business analytics (BA) refers to all the methods and techniques that are used by an organization to measure performance. Business analytics are made up of statistical methods that can be applied to a specific project, process or product. Business analytics can also be used to evaluate an entire company. Business analytics are performed in order to identify weaknesses in existing processes and highlight meaningful data that will help an organization prepare for future growth and challenges. The need for good business analytics has spurred the creation of business analytics software and enterprise platforms that mine an organization’s data in order to automate some of these measures and pick out meaningful insights.
Customer Relationship Management Analytics (CRM Analytics)
Customer relationship management analytics (CRM analytics) refers to applications used to evaluate an organization’s customer data to facilitate and streamline business choices. CRM analytics also may be used for online analytical processing (OLAP) through the use of data mining. CRM analytical tools use a variety of applications that help measure the effectiveness of customer related processes and ultimately provide customer categorization, such as profitability analysis, event monitoring, what-if scenarios and predictive modeling.
Customer Analytics
Customer analytics is an activity within of e-commerce whereby customers' online shopping and internet search behavior is examined by software with the results used by teams of marketing professionals looking to increase revenues for online merchants. Customer analytics uses data collection and subsequent software analysis to zero-in on customers' online order transactions for the purpose of sorting out specific customer demographics, shopping patterns, internet usage and applying predictive analyses to allow marketers to take measures to increase online business profit margins. Other commonly used terms for customer analytics include Customer Relationship Management analytics, or CRM analytics.
Real-Time Customer Analytics
Real-time customer analytics is a type of analytics which concentrates on real-time data captured from customers as they are performing their actions rather than on older historical data like traditional analytics, which use historical data to predict future trends. Real-time customer analytics also gives more emphasis to customer interaction and usage data rather than page views and other similar statistics, giving a more customer-centric view rather than a demographic one.
Real-Time Web Analytics
Real-time Web analytics is a technology in which the owner/manager of a website has the ability to monitor a website's users and activities in an instantaneous (or almost instant) fashion. The term encompasses all facets of a website, from visitors, pageviews, clicks, sales and other metrics, and generally implies immediate updating of dashboards and reporting.
Analytical Engine
Analytical engine most often refers to a computing machine engineered by Charles Babbage in the early 1800s. It is considered an early and very important step toward modern computer design. This term can also be used to refer to any comprehensive internal system for analytics.
Big Data Mining
Big data mining is referred to the collective data mining or extraction techniques that are performed on large sets /volume of data or the big data. Big data mining is primarily done to extract and retrieve desired information or pattern from humongous quantity of data.
Real-Time Predictive Analytics
Real-time predictive analytics is the process of extracting useful information from data sets in real time. This is done to determine and predict future outcomes. Real-time predictive analytics does not precisely predict what will happen in the future; instead, it forecasts what might happen on the basis of certain "if" scenarios.
Predictive Alerting
Predictive alerting is technology that is able to provide predictions of certain events or inputs. It is related to machine learning because the technology is able to learn from the data it is regularly processing and based on its learning, is able to make predictions which are actionable. The technology is used in many industries such as telecommunications, banking and finance, and defense.
Distributed File System
A distributed file system (DFS) is a file system with data stored on a server. The data is accessed and processed as if it
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