Knowledge of terms to know
What is Agile Manifesto?
The Agile Manifesto is a document that identifies four key values and 12 principles that the Agile Alliance believe software developers should use to guide their work. The four core values of Agile software development as stated by the Agile Manifesto are:
- Value individuals and interactions over processes and tools.
- Value working software over comprehensive documentation.
- Value customer collaboration over contract negotiation.
- Value responding to change over following a plan.
The 12 principles articulated in the Agile Manifesto are:
- Satisfy customers through early and continuous delivery of valuable work.
- Break big work down into smaller tasks that can be completed quickly.
- Recognize that the best work emerges from self-organized teams.
- Provide motivated individuals with the environment and support they need and trust them to get the job done.
- Create processes that promote sustainable efforts.
- Maintain a constant pace for completed work.
- Welcome changing requirements, even late in a project.
- Assemble the project team and business owners on a daily basis throughout the project.
- Have the team reflect at regular intervals on how to become more effective, then tune and adjust behaviour accordingly.
- Measure progress by the amount of completed work.
- Continually seek excellence.
- Harness change for a competitive advantage.
What is Remote Attack?
A remote attack is a malicious action that targets one or a network of computers. The remote attack does not affect the computer the attacker is using. Instead, the attacker will find vulnerable points in a computer or network’s security software to access the machine or system. The main reasons for remote attacks are to view or steal data illegally, introduce viruses or other malicious software to another computer or network or system, and cause damage to the targeted computer or network.
A remote attack is also known as a remote exploit.
What is Structured Prediction?
Structured prediction is a particular discipline applied to machine learning in which machine learning techniques predict structured objects. Typically, the structured prediction makes use of supervised machine learning programs with labels that can apply in order to produce outcomes.
One of the simplest and easiest ways to talk about structured prediction is that it uses training problems to solve a classification task. A resource available from NeurIPS quoted by Sasha Rush in July of 2010 describes it as: “a framework for solving problems of classification or regression in which the output variables are mutually dependent or constrained.”
Specifically, when a prediction cannot be solved by direct observation of all possible values, the structured prediction takes inputs, and uses them to predict the results.
What is Markov Chain?
A Markov chain is a mathematical process that transitions from one state to another within a finite number of possible states. It is a collection of different states and probabilities of a variable, where its future condition or state is substantially dependent on its immediate previous state.
A Markov chain is also known as a discrete time Markov chain (DTMC) or Markov process.
Markov chains are primarily used to predict the future state of a variable or any object based on its past state. It applies probabilistic approaches in predicting the next state. Markov chains are exhibited using directed graphs, which define the current and past state and the probability of transitioning from one state to another.
Markov chains have several implementations in computing and Internet technologies. For example, the PageRank(r) formula employed by Google search uses a Markov chain to calculate the PageRank of a particular Web page. It is also used to predict user behaviour on a website based on users’ previous preferences or interactions with it.
What is Predictive Alerting?
Predictive alerting is a 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 defence.
Predictive alerting is the output of a computing system able to learn from the data it has been processing over a period of time. For example, the system stores and processes financial data in a large bank and a certain number of users are given conditional access to the system and each user has certain ways to access the system. If a user tries to access the system in a different way, say, with a new device, the system sets off a predictive alert.
Predictive alerting is highly useful in applications such as preventing fraudulent transactions in the banking sector or predicting customer churn in companies. Predictive alerting is different from other alerting systems such as rule-based alerting because it adapts and learns from new data sets which can be varied and complex in nature. Rule-based alerts, on the other hand, can work only based on preconfigured rules and may not be suitable for real-life scenarios.
What is Employee poaching (talent poaching)?
Employee poaching (talent poaching) or job poaching is the recruiting of employees who work at competing companies. The term “poaching” is associated with illegal hunting, but job poaching isn’t, for the most part, unethical or illegal and can help to ensure a competitive job marketplace.
Increasing sophistication of recruitment management systems may be enabling employee poaching. Resume databases and social media tools make it easier to identify specific people. The more advanced tools will filter and rank prospects and automate the initial contact. If this door knocking works, a recruiter may call.
The ultimate defense against job poaching is a solid employee retention plan that ensures that pay rates are competitive and seeks a high level of employee engagement. But these aren’t the only steps some employers will take to retain employees.
What is Predictive Maintenance?
Predictive maintenance is a maintenance strategy driven by predictive analytics. The solutions are used for detecting failure patterns or anomalies, but are only deployed when there is high probability of imminent failure. This helps in deploying limited resources, maximizing device or equipment uptime, enhancing quality and supply chain processes, and thus improving the overall satisfaction for all the stakeholders involved.
Predictive maintenance focuses on predicting when device failure will occur and preventing that occurrence of failure with the help of maintenance monitoring so that maintenance can be planned before an issue manifests. One of the distinct features of predictive maintenance is that the maintenance frequency is a bare minimum, and it helps in preventing unplanned reactive maintenance and the expenses associated with preventive maintenance. The failure prediction in cases of predictive maintenance can be done with the help of one of many techniques. Based on the expert opinion and device in use, the proper technique is selected.
There are many benefits associated with predictive maintenance when used as a maintenance strategy. As maintenance is performed when failure is likely to occur, there is high cost savings related to the production hours lost to maintenance, expenses related to parts and supplies and the time for the equipment to be fixed. Predictive maintenance can minimize issues with reliability or quality. It can help in preventing expensive failures from occurring. Overstock can also be reduced in inventory thanks to predictive maintenance.
However, there are drawbacks associated with predictive maintenance. The expertise and skill level required for the data analysis in predictive maintenance needs to be highly specialized. The monitoring techniques used are also quite expensive.
Predictive maintenance is well suited for devices or equipment which perform critical functions or which have failure modes that can be cost-effectively predicted with the help of periodic monitoring.
tcping.exe is a console application like ‘ping’ that is instead directed toward a TCP port.
Feature of OneNote: OCR. Copy and paste any image with text into OneNote, right-click and select “Copy text from image.”
Syslog: The Complete System Administrator Guide is a great tutorial for learning about Syslog and Linux logging in general. While Syslog is involved in most everything on a Linux system, it can also be used on Windows instances and other operating systems that implement the protocol.
Posterbooking is a fast, easy platform that can turn any screen into smart digital signage. This cloud-based solution allows you to upload images & video to screens in one or more locations within minutes. Content can be scheduled, screens can be synced and you can get reports on what’s being displayed. Works with your favourite media player, and the first 10 screens are free.
Slashdot shares the latest news on science, technology and politics. Stories are submitted and evaluated by site users and editors and every story includes a comments section where users can add their thoughts.
VDesk is an open-source solution for launching programs on new virtual desktops in Windows 10. The program is command line but ships with an option to integrate with File Explorer.
Knowledge of terms to know
What is 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.
Real-time predictive analytics is based on a predictive model which is deployed to run in order to enable decision-making processes in real time. A predictive model is built on the basis of large amounts of data. The predictive model could be built in two ways: by a data scientist or through a streaming operational data analytics platform. The whole process involves rigorous experimentation, historical data and other similar processes. This whole process is iterative by nature. This model is then made to predict run time when a continuous stream of data is fed to it. Therefore, real-time predictive analytics enhances the customer experience and in turn helps businesses to increase earnings.
What is Corporate Culture Model?
A corporate culture model is the organizing principle behind the values, beliefs and attitudes that characterize a company and guide its practices.
Corporate cultures are often categorized by the degree of emphasis placed on various defining elements such as hierarchy, process, innovation, collaboration, competition, community involvement and social engagement. To some extent, a company’s culture model, if not articulated outright, may be categorized by details contained in the company’s mission statement or vision statement.
What is 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 the humongous quantity of data.
This is usually performed on a large quantity of unstructured data that is stored over time by an organization. Typically, big data mining works on data searching, refinement, extraction and comparison algorithms. Big data mining also requires support from underlying computing devices, specifically their processors and memory, for performing operations/queries on a large amount of data.
Big data mining techniques and processes are also used within big data analytics and business intelligence to deliver summarized targeted and relevant information, patterns and/or relationships between data, systems, processes and more.
What is Data Analytics?
Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioural data and patterns, and techniques vary according to organizational requirements.
Data analytics is also known as data analysis.
Data analytics is primarily conducted in business-to-consumer (B2C) applications. Global organizations collect and analyze data associated with customers, business processes, market economics or practical experience. Data is categorized, stored and analyzed to study purchasing trends and patterns.
Evolving data facilitates thorough decision-making. For example, a social networking website collects data related to user preferences, community interests and segment according to specified criteria such as demographics, age or gender. Proper analysis reveals key user and customer trends and facilitates the social network’s alignment of content, layout and overall strategy.
