Data management in the context of Transclusion

In software development, front end refers to the presentation layer that users interact with, while back end refers to the data management and processing behind the scenes. "Full stack" refers to both together. In the client–server model, the client is usually considered the front end, handling most user-facing tasks, and the server is the back end, mainly managing data and logic.

A staging area (otherwise staging base, staging facility, staging ground, staging point, or staging post) is a location in which organisms, people, vehicles, equipment, or material are assembled before use. It may refer to:

• In aviation, a designated area where equipment can be staged prior to the arrival or departure of an aircraft.
• In construction, a designated area in which vehicles, supplies, and construction equipment are positioned for access and use to a construction site.
• In ecology, the resting and feeding places of migratory birds.
• In entertainment, places designated for setting up parades and other elaborate presentations.
• In real estate, the use of furniture to stage an area of one's home to prepare it for sale.
• In media, designated places for news conferences placed near locations of high media interest.
• In space exploration, an area where final assembly is done on space vehicles before they are moved out to their launch pad.
• In data management, an intermediate storage area between the sources of information and the data warehouse (DW) or data mart (DM). It is usually of temporary nature, and its contents can be erased after the DW/DM has been loaded successfully (see data staging).
• In software development, an environment for testing that exactly resembles a production environment. It seeks to mirror an actual production environment as closely as possible and may connect to other production services and data, such as databases.

In military usage, a staging area is a place where troops or equipment in transit are assembled or processed. The US Department of Defense uses these definitions:

Computational science, also known as scientific computing, technical computing or scientific computation (SC), is a division of science, and more specifically the computer sciences, which uses advanced computing capabilities to understand and solve complex physical problems in science. While this typically extends into computational specializations, this field of study includes:

• Algorithms (numerical and non-numerical): mathematical models, computational models, and computer simulations developed to solve sciences (e.g, physical, biological, and social), engineering, and humanities problems
• Computer hardware that develops and optimizes the advanced system hardware, firmware, networking, and data management components needed to solve computationally demanding problems
• The computing infrastructure that supports both the science and engineering problem solving and the developmental computer and information science

In practical use, it is typically the application of computer simulation and other forms of computation from numerical analysis and theoretical computer science to solve problems in various scientific disciplines. The field is different from theory and laboratory experiments, which are the traditional forms of science and engineering. The scientific computing approach is to gain understanding through the analysis of mathematical models implemented on computers. Scientists and engineers develop computer programs and application software that model systems being studied and run these programs with various sets of input parameters. The essence of computational science is the application of numerical algorithms and computational mathematics. In some cases, these models require massive amounts of calculations (usually floating-point) and are often executed on supercomputers or distributed computing platforms.

Data mining is the process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.

The term "data mining" is a misnomer because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support systems, including artificial intelligence (e.g., machine learning) and business intelligence. Often the more general terms (large scale) data analysis and analytics—or, when referring to actual methods, artificial intelligence and machine learning—are more appropriate.

In computing, data transformation is the process of converting data from one format or structure into another format or structure. It is a fundamental aspect of most data integration and data management tasks such as data wrangling, data warehousing, data integration and application integration.

Data transformation can be simple or complex based on the required changes to the data between the source (initial) data and the target (final) data. Data transformation is typically performed via a mixture of manual and automated steps. Tools and technologies used for data transformation can vary widely based on the format, structure, complexity, and volume of the data being transformed.

Strategic communication is the purposeful use of communication by an organization to reach a specific goal. Organizations like governments, corporations, NGOs and militaries seeking to communicate a concept, process, or data to satisfy their organizational or strategic goals will use strategic communication. The modern process features advanced planning, international telecommunications, and dedicated global network assets. Targeted organizational goals can include commercial, non-commercial, military business, combat, political warfare and logistic goals. Strategic communication can either be internal or external to the organization. The interdisciplinary study of strategic communications includes organizational communication, management, military history, mass communication, PR, advertising and marketing.

In computing and data management, data mapping is the process of creating data element mappings between two distinct data models. Data mapping is used as a first step for a wide variety of data integration tasks, including:

• Data transformation or data mediation between a data source and a destination
• Identification of data relationships as part of data lineage analysis
• Discovery of hidden sensitive data such as the last four digits of a social security number hidden in another user id as part of a data masking or de-identification project
• Consolidation of multiple databases into a single database and identifying redundant columns of data for consolidation or elimination

For example, a company that would like to transmit and receive purchases and invoices with other companies might use data mapping to create data maps from a company's data to standardized ANSI ASC X12 messages for items such as purchase orders and invoices.

In computer science and data management, a commit is a behavior that marks the end of a transaction and provides Atomicity, Consistency, Isolation, and Durability (ACID) in transactions. The submission records are stored in the submission log for recovery and consistency in case of failure. In terms of transactions, the opposite of committing is giving up tentative changes to the transaction, which is rolled back.

Due to the rise of distributed computing and the need to ensure data consistency across multiple systems, commit protocols have been evolving since their emergence in the 1970s. The main developments include the Two-Phase Commit (2PC) first proposed by Jim Gray, which is the fundamental core of distributed transaction management. Subsequently, the Three-phase Commit (3PC), Hypothesis Commit (PC), Hypothesis Abort (PA), and Optimistic Commit protocols gradually emerged, solving the problems of blocking and fault recovery.

United States federal government agencies use the term cyberinfrastructure to describe research environments that support advanced data acquisition, data storage, data management, data integration, data mining, data visualization and other computing and information processing services distributed over the Internet beyond the scope of a single institution. In scientific usage, cyberinfrastructure is a technological and sociological solution to the problem of efficiently connecting federal laboratories, large scales of data, processing power, and scientists with the goal of enabling novel scientific discoveries and advancements in human knowledge.

A taxonomic database is a database created to hold information on biological taxa – for example groups of organisms organized by species name or other taxonomic identifier – for efficient data management and information retrieval. Taxonomic databases are routinely used for the automated construction of biological checklists such as floras and faunas, both for print publication and online; to underpin the operation of web-based species information systems; as a part of biological collection management (for example in museums and herbaria); as well as providing, in some cases, the taxon management component of broader science or biology information systems. They are also a fundamental contribution to the discipline of biodiversity informatics.