Microsoft is releasing Windows 10 Insider Preview Build 21286 (RS_PRERELEASE) to Windows Insiders in the Dev Channel introducing news and interests on the taskbar, modernized storage spaces settings, run commands on startup in the Windows Subsystem for Linux (WSL), improve for transitioning between time zones, Winter 2020 Update for Windows File Recovery, and various changes and improvements. Read more in Windows Insider Blog > Announcing Windows 10 Insider Preview Build 21286
Knowledge of terms to know
What is Automated Treatment Plan?
An automated treatment plan is composed of a series of electronic forms and software specifically designed to help medical professionals and health care providers in treating their patients. These forms are usually customized to meet the various needs and demands of individual practitioners particularly those who are in the behavioral health care practices. Patient data is usually captured and stored for further retrieval and report generation with relation to their corresponding medical treatment plans. An IT professional is usually needed to assist in the process of developing the automated treatment plan. On most cases, vendors and OEMs are hired to implement automated treatment plans for organizations that do not have their own IT staff.
Automated health treatment plans are designed for one purpose – that is to make the entire documentation process easier and faster for behavioral practitioners. A typical automated treatment plan software can include data management features, customizable forms, and reporting capabilities. They can help in meeting specific clinical needs, patient health initiatives, and the treatment goals of nurses, physicians and other caregivers. Having an automated treatment plan around will also reduce the possibility of human error and will improve the quality of care given to patients by providing clinicians easy access to basic standards of care as well as the details of the treatment. Database extraction can provide further information such as comparisons on how care standards are accessed and applied in individual practices, thus increasing successful patient outcomes. Electronic health records can also be incorporated into the software.
What is Function as a service (FaaS)?
Function as a service (FaaS) is a cloud computing model that enables users to develop applications and deploy functionalities without maintaining a server, increasing process efficiency. The concept behind FaaS is serverless computing and architecture, meaning the developer does not have to take server operations into consideration, as they are hosted externally. This is typically utilized when creating microservices such as web applications, data processors, chatbots and IT automation.
FaaS provides developers with the ability to run a single function, piece of logic or part of an application. Code is written into the developer end that triggers remote servers to execute the intended action. Unlike other cloud computing models that run on at least one server at all times, FaaS only runs when a function is conducted and then shuts down.
Advantages of FaaS:
- Developers can spend more time writing app-specific code and less time handling server logistics.
- Allows applications to be scalable and independent rather than integrated within a larger platform.
- Customers are billed solely based on amount of executed functionality, meaning money is never spent on inactive resources.
- Features such as support, availability and fault tolerance are inherently included.
What is Edge Computing?
Edge computing is a distributed information technology (IT) architecture in which client data is processed at the periphery of the network, as close to the originating source as possible. The move toward edge computing is driven by mobile computing, the decreasing cost of computer components and the sheer number of networked devices in the internet of things (IoT).
The name “edge” in edge computing is derived from network diagrams; typically, the edge in a network diagram signifies the point at which traffic enters or exits the network. The edge is also the point at which the underlying protocol for transporting data may change. For example, a smart sensor might use a low-latency protocol like MQTT to transmit data to a message broker located on the network edge, and the broker would use the hypertext transfer protocol (HTTP) to transmit valuable data from the sensor to a remote server over the Internet.
The OpenFog consortium uses the term fog computing to describe edge computing. The word “fog” is meant to convey the idea that the advantages of cloud computing should be brought closer to the data source. (In meteorology, fog is simply a cloud that is close to the ground.) Consortium members include Cisco, ARM, Microsoft, Dell, Intel and Princeton University.
What is Interpreter?
An interpreter is a computer program that is used to directly execute program instructions written using one of the many high-level programming languages.
The interpreter transforms the high-level program into an intermediate language that it then executes, or it could parse the high-level source code and then performs the commands directly, which is done line by line or statement by statement.
Humans can only understand high-level languages, which are called source code. Computers, on the other hand, can only understand programs written in binary languages, so either an interpreter or compiler is required.
Programming languages are implemented in two ways: interpretation and compilation. As the name suggests, an interpreter transforms or interprets a high-level programming code into code that can be understood by the machine (machine code) or into an intermediate language that can be easily executed as well.
The interpreter reads each statement of code and then converts or executes it directly. In contrast, an assembler or a compiler converts a high-level source code into native (compiled) code that can be executed directly by the operating system (e.g. by creating a .exe program).
Both compilers and interpreters have their advantages and disadvantages and are not mutually exclusive; this means that they can be used in conjunction as most integrated development environments employ both compilation and translation for some high-level languages.
In most cases, a compiler is preferable since its output runs much faster compared to a line-by-line interpretation. Rather than scanning the whole program and translating it into machine code like a compiler does, the interpreter translates code one statement at a time.
While the time to analyze source code is reduced, especially a particularly large one, execution time for an interpreter is comparatively slower than a compiler. On top of that, since interpretation happens per line or statement, it can be stopped in the middle of execution to allow for either code modification or debugging.
Compilers must generate intermediate object code that requires more memory to be linked, contrarily to interpreters which tend to use memory more efficiently.
Since an interpreter reads and then executes code in a single process, it very useful for scripting and other small programs. As such, it is commonly installed on Web servers, which run a lot of executable scripts. It is also used during the development stage of a program to test small chunks of code one by one rather than having to compile the whole program every time.
Every source statement will be executed line by line during execution, which is particularly appreciated for debugging reasons to immediately recognize errors. Interpreters are also used for educational purposes since they can be used to show students how to program one script at a time.
What is Data Lineage?
Data lineage is the history of data, including where the data has traveled through-out its existence within an organization. Data lineage is a required part of corporate and government data policy compliance. Tracking the history of data is achieved through data lineage documentation and software. Without a way to identify where data errors are introduced into the environment, it is difficult for data stewards to identify and fix data quality issues.
With effective tools, data governance can be eased through the documentation of data’s entire journey through the organization. The documentation of data lineage helps simplify two of the main data governance concerns in for the effects of changes in data: root cause analysis and business impact analysis (BIA). The clear understanding of the root causes and impacts of issues with data is aided by knowing everything that happened to the data since it came to be.
In software development, the tracking of data lineage can help with reconciling the difficulties between Agile development best practices, data governance regulations, and company data policy. Data lineage tools and procedures help track where data flaws were introduced, which can ease diagnoses and correction. Implementing the tracking of data lineage can be difficult and often seen as a low priority, however, earlier correction means less error propagation, which means the implementation of data lineage tools early in the process often proves worth the effort.
What is Production Environment?
Production environment is a term used mostly by developers to describe the setting where software and other products are actually put into operation for their intended uses by end-users.
A production environment can be thought of as a real-time setting where programs are run and hardware setups are installed and relied on for organization or commercial daily operations.
One way to define a production environment is by contrasting it with a testing or development environment. In a testing environment, a product is still being used theoretically. Users (typically engineers) look for bugs or design flaws. In the production environment, the product has been delivered and needs to work flawlessly.
The production environment is different from the development environment since it’s the place where the application is actually available for business use. It allows enterprises to show clients a “live” service.
While developers need their own version to work on, clients and end-users must have a distributable version they can use. Distinct builds are created to allow developers to test new functionalities, hunt for bugs to squash, and add new code without affecting the customer’s version. The purpose of this difference is to allow any test to be performed without impacting the operativity of the live product.
Each developer might work in his or her own specific development environment with distinct differences, and different development versions might have unique features such as showing contextual data that is normally hidden. There’s a single production environment, instead, that is used to avoid confusion with customers as well as to prevent security issues.
A third environment is sometimes present, and it’s called a staging or preproduction environment. Here the best candidate version for release is tested, and it’s usually a mirror of the production environment.
Preproduction is usually short-lived, and only serves the purpose of performing the final stress testing the “next” version of the product before it goes live. When a certain feature is sufficiently checked to reach approval, it can be moved from the test to the preproduction environment before it’s launched in the production environment.
Deploying to production is a particularly sensitive matter, as the clients or users might not be lenient if bugs or errors are found in the final version, or if a new feature does not work as intended. Because of this, sometimes the product is run in quality control (QC) environments which can or cannot be the same thing as preproduction.
For example, a video game patch change could be play-tested by hand-picked gamers on a QC server to ask for their feedback. Unwanted or unexpected changes could be rolled back to avoid negative reactions from the community.
A related term, production code, refers to code that is being used by end-users in a real-time situation, or code that is useful for end-user operations. A debate over what constitutes production code shows that there is a lot of ambiguity about the formal application of either term to a specific scenario because of the many stages that code and tech products go through in their respective life cycles.
What is Data Redundancy?
Data redundancy is a condition created within a database or data storage technology in which the same piece of data is held in two separate places.
This can mean two different fields within a single database or two different spots in multiple software environments or platforms. Whenever data is repeated, it basically constitutes data redundancy.
Data redundancy can occur by accident but is also done deliberately for backup and recovery purposes.
What is Secondary Storage Device?
A secondary storage device refers to any non-volatile storage device that is internal or external to the computer. It can be any storage device beyond the primary storage that enables permanent data storage.
A secondary storage device is also known as an auxiliary storage device, backup storage device, tier 2 storage, or external storage.
What is Data Science?
Data science is the field of applying advanced analytics techniques and scientific principles to extract valuable information from data for business decision-making, strategic planning, and other uses. It’s increasingly critical to businesses: The insights that data science generates help organizations increase operational efficiency, identify new business opportunities, and improve marketing and sales programs, among other benefits. Ultimately, they can lead to competitive advantages over business rivals.
Data science incorporates various disciplines — for example, data engineering, data preparation, data mining, predictive analytics, machine learning, and data visualization, as well as statistics, mathematics, and software programming. It’s primarily done by skilled data scientists, although lower-level data analysts may also be involved. In addition, many organizations now rely partly on citizen data scientists, a group that can include business intelligence (BI) professionals, business analysts, data-savvy business users, data engineers, and other workers who don’t have a formal data science background.
The rest of our definition for data science provides a comprehensive guide to data science and further explains what it is, why it’s important to organizations, how it works, the business benefits it provides, and the challenges it poses. You’ll also find an overview of data science applications, tools, and techniques, plus information on what data scientists do and the skills they need. Throughout the guide, there are hyperlinks to related TechTarget articles that delve more deeply into the topics covered here and offer insight and expert advice on data science initiatives.
What is Remote Direct Memory Access (RDMA)?
Remote direct memory access (RDMA) is a term used in IT to describe systems that allow different networked computers to send one another data without impacting the operating system of either machine.
IT pros talking about RDMA talk about zero-copy networking where data gets read directly from the main memory of the original computer and inserted into the main memory of another networked machine. These types of processes are used to improve performance and maintain more efficient data transfer. Sometimes, they can speed up data transfer or accommodate better throughput. Device manufacturers may talk about RDMA as a feature of components that will allow this kind of data transfer. Experts may talk about how strategies like RDMA can help to make local area networks or other kinds of small networks faster and more efficient.
Some disadvantages of RDMA may include inconsistent updating of information between the computers in question. Without a practice called pinning, elements of memory systems can get corrupted in RDMA setups. Today’s networking technicians need to consider many different options for routing ever-more complex data transfers.
What is Wireless Access Point (wireless AP)?
A wireless access point (wireless AP) is a network device that transmits and receives data over a wireless local area network (WLAN). The wireless access point serves as the interconnection point between the WLAN and a fixed wire network.
Typically, wireless routers are used in homes and small businesses where all users can be supported by one combined AP and router. Wireless APs are used in larger businesses and venues where many APs are required to provide services that support thousands of users.
Conceptually, an AP is like an Ethernet hub, but instead of relaying LAN frames only to other 802.3 stations, an AP relays 802.11 frames to all other 802.11 or 802.3 stations in the same subnet. When a wireless device moves beyond the range of one AP, it is handed over to the next AP.
The number of access points needed will increase as a function of the number of network users and the physical size of the network.
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