Keith "Youngin" George II is a former mixtape DJ, music executive, manager, producer, and technology app director. He has collaborated with Maino, T-Pain, Nas and Soulja Boy, among others. He was instrumental in the launch of social media app and website, Kandiid in 2021 and served as Fliiks App Director of Regional Development. == Career == Keith Anthony George II was born in Upper Heyford, Oxfordshire, England. His father was in the Air Force which exposed him to different cultures and music. He graduated from Allen High School and attended San Antonio College. George's music career began in 2006 as a mixtape DJ working as DJ Youngin Beatz. He performed at various shows and worked with a variety of artists, managers, and music executives. In 2007, George released the mixtape, Untapped market Vol. 1 (Da Underdogz), which featured tracks from artists including Kanye West, Lil Wayne, 50 Cent, Yung Berg, and Nelly. In 2008, he began working with Def Jam executive Sarah Alminawi who was managing Maino at the time. George played a key role in the marketing and promotional success of Maino's single, Hi Hater, which peaked at #8 on Billboard's US Bubbling Under Hot 100 chart. In 2021, George was an advisor and infrastructure head at Kandiid, a social media app which won a W3 Award in 2022. In 2023, he became involved with Fliiks App as Director of Regional Development which earned a Telly Award, two Muse Awards, and a W3 Award in 2025. In 2025, George was a composer and producer on two singles on Sekou Andrews's album, Koumami; The Chosen One: ACT 1 (featuring Lion Babe) and Love Don't Care (featuring Jordin Sparks and Omari Hardwick). In 2025, he was awarded an Atlanta City Proclamation for Philanthropy and Community Leadership for his partnership with Women's International Grail, a nonprofit organization that assists women, single mothers, and low-income families. He also collaborates with local youth programs, creative networks, and minority-owned startups, providing access to mentorship and industry knowledge. == Awards ==
Layers (digital image editing)
Layers are used in digital image editing to separate different elements of an image. A layer can be compared to a transparency on which imaging effects or images are applied and placed over or under an image. Today they are an integral feature of image editors. In the early days of computing, memory was at a premium and the idea of using multi-layered images was considered infeasible in personal computer applications as the tradeoffs were image size and color depth. As the price of memory fell it became feasible to apply the concept of layering to raster images. The first software known to apply the concept of layers was LALF, which was released in 1989 for the NEC PC-9801. LALF's terminology for layers is "cells", after the concept of drawing animation frames over-top of a stencil. Layers were introduced in Western markets by Fauve Matisse (later Macromedia xRes), and then available in Adobe Photoshop 3.0, in 1994, which lead to widespread adoption. In vector image editors that support animation, layers are used to further enable manipulation along a common timeline for the animation; in SVG images, the equivalent to layers are "groups". == Layer types == There are different kinds of layers, and not all of them exist in all programs. They represent a part of a picture, either as pixels or as modification instructions. They are stacked on top of each other, and depending on the order, determine the appearance of the final picture. In graphics software, layers are the different levels at which one can place an object or image file. In the program, layers can be stacked, merged, or defined when creating a digital image. Layers can be partially obscured allowing portions of images within a layer to be hidden or shown in a translucent manner within another image. Layers can also be used to combine two or more images into a single digital image. For the purpose of editing, working with layers allows for applying changes to just one specific layer. == Layer (basic) == The standard layer available to most programs consists of a rectangular, semitransparent picture which may be superimposed over other layers. Some programs require that layers cover the same area as the final canvas, but others offer layers of multiple sizes. Each layer may bear individual settings, such as opacity, blending modes, dynamic filters, and potentially hundreds of other properties. == Layer mask == A layer mask is linked to a layer and hides part of the layer from the picture. What is painted black on the layer mask will not be visible in the final picture. What is grey will be more or less transparent depending on the shade of grey. As the layer mask can be both edited and moved around independently of both the background layer and the layer it applies to, it gives the user the ability to test a lot of different combinations of overlay. == Adjustment layer == An adjustment layer typically applies a common effect like brightness or saturation to other layers. However, as the effect is stored in a separate layer, it is easy to try it out and switch between different alternatives, without changing the original layer. In addition, an adjustment layer can easily be edited, just like a layer mask, so an effect can be applied to just part of the image.
Metadata
Metadata (or metainformation) is data (or information) that defines and describes the characteristics of other data. It often helps to describe, explain, locate, or otherwise make data easier to retrieve, use, or manage. For example, the title, author, and publication date of a book are metadata about the book. But, while a data asset is finite, its metadata is infinite. As such, efforts to define, classify types, or structure metadata are expressed as examples in the context of its use. The term "metadata" has a history dating to the 1960s where it occurred in computer science and in popular culture. Different types of metadata serve different functions. For example, descriptive metadata for a document might include the author, creation date, file size and keywords. Metadata has various purposes. It can help users find relevant information and discover resources. It can also help organize electronic resources, provide digital identification, and archive and preserve resources. Metadata allows users to access resources by "allowing resources to be found by relevant criteria, identifying resources, bringing similar resources together, distinguishing dissimilar resources, and giving location information". Metadata of telecommunication activities including Internet traffic is very widely collected by various national governmental organizations. This data is used for the purposes of traffic analysis and can be used for mass surveillance. Unique metadata standards exist for different disciplines (e.g., museum collections, digital audio files, websites, etc.). Describing the contents and context of data or data files increases its usefulness. For example, a web page may include metadata specifying what software language the page is written in (e.g., HTML), what tools were used to create it, what subjects the page is about, and where to find more information about the subject. This metadata can automatically improve the reader's experience and make it easier for users to find the web page online. A CD may include metadata providing information about the musicians, singers, and songwriters whose work appears on the disc. In many countries, government organizations routinely store metadata about emails, telephone calls, web pages, video traffic, IP connections, and cell phone locations. == Types == There are many distinct types of metadata, including: Descriptive metadata – the descriptive information about a resource. It is used for discovery and identification. It includes elements such as title, abstract, author, and keywords. Structural metadata – metadata about containers of data and indicates how compound objects are put together, for example, how pages are ordered to form chapters. It describes the types, versions, relationships, and other characteristics of digital materials. Administrative metadata – the information to help manage a resource, like resource type, and permissions, and when and how it was created. Reference metadata – the information about the contents and quality of statistical data. Statistical metadata – also called process data, may describe processes that collect, process, or produce statistical data. Legal metadata – provides information about the creator, copyright holder, and public licensing, if provided. Metadata is not strictly bound to one of these categories, as it can describe a piece of data in many other ways. While the metadata application is manifold, covering a large variety of fields, there are specialized and well-accepted models to specify types of metadata. Bretherton & Singley (1994) distinguish between two distinct classes: structural/control metadata and guide metadata. Structural metadata describes the structure of database objects such as tables, columns, keys and indexes. Guide metadata helps humans find specific items and is usually expressed as a set of keywords in a natural language. According to Ralph Kimball, metadata can be divided into three categories: technical metadata (or internal metadata), business metadata (or external metadata), and process metadata. Dan Linstedt, creator of the data vault methodology, says business metadata "...provide[s] definition of the functionality, definition of the data, definition of the elements, and definition of how the data is used within business...business metadata includes business requirements, time-lines, business metrics, business process flows, and business terminology." Business metadata is important because it can greatly facilitate the usefulness of the data to business people. A simple example of business metadata is a glossary entry. Hover functionality in an application or web form can enable a glossary definition to be shown when cursor is on a field or term. Other examples of business metadata include annotation ability within applications. For example, a business user may be viewing a business intelligence (BI) report and notice a trend in the data. The user may have background knowledge as to why this trend occurs. Some business intelligence tools enable the user to create an annotation within the report that explains the trend. Such an annotation can enhance other users' understanding of the data. This example is especially powerful because it is created by a business user for the use of other business people. NISO distinguishes three types of metadata: descriptive, structural, and administrative. Descriptive metadata is typically used for discovery and identification, as information to search and locate an object, such as title, authors, subjects, keywords, and publisher. Structural metadata describes how the components of an object are organized. An example of structural metadata would be how pages are ordered to form chapters of a book. Finally, administrative metadata gives information to help manage the source. Administrative metadata refers to the technical information, such as file type, or when and how the file was created. Two sub-types of administrative metadata are rights management metadata and preservation metadata. Rights management metadata explains intellectual property rights, while preservation metadata contains information to preserve and save a resource. Statistical data repositories have their own requirements for metadata in order to describe not only the source and quality of the data but also what statistical processes were used to create the data, which is of particular importance to the statistical community in order to both validate and improve the process of statistical data production. An additional type of metadata beginning to be more developed is accessibility metadata. Accessibility metadata is not a new concept to libraries; however, advances in universal design have raised its profile. Projects like Cloud4All and GPII identified the lack of common terminologies and models to describe the needs and preferences of users and information that fits those needs as a major gap in providing universal access solutions. Those types of information are accessibility metadata. The Schema.org website has incorporated several accessibility properties based on IMS Global Access for All Information Model Data Element Specification. While the efforts to describe and standardize the varied accessibility needs of information seekers are beginning to become more robust, their adoption into established metadata schemas has not been as developed. For example, while Dublin Core (DC)'s "audience" and MARC 21's "reading level" could be used to identify resources suitable for users with dyslexia and DC's "format" could be used to identify resources available in braille, audio, or large print formats, there is more work to be done. == History == Metadata was traditionally used in the card catalogs of libraries until the 1980s when libraries converted their catalog data to digital databases. In the 2000s, as data and information were increasingly stored digitally, this digital data was described using metadata standards. An early description of "meta data" for computer systems was written by David Griffel and Stuart McIntosh at the MIT Center for International Studies in 1967: "In summary then, we have statements in an object language about subject descriptions of data and token codes for the data. We also have statements in a meta language describing the data relationships and transformations, and ought/is relations between norm and data." == Definition == Metadata means "data about data". Metadata is defined as the data providing information about one or more aspects of the data; it is used to summarize basic information about data that can make tracking and working with specific data easier. Some examples include: Means of creation of the data Source of the data Time and date of creation Creator or author of the data Location on a computer network where the data was created Standards used Data quality For example, a digital image may include metadata that describes the size of the image, its color depth, resolution,
Information behavior
Information behavior is a field of information science research that seeks to understand the way people search for and use information in various contexts. It can include information seeking and information retrieval, but it also aims to understand why people seek information and how they use it. The term 'information behavior' was coined by Thomas D. Wilson in 1982 and sparked controversy upon its introduction. The term has now been adopted and Wilson's model of information behavior is widely cited in information behavior literature. In 2000, Wilson defined information behavior as "the totality of human behavior in relation to sources and channels of information". A variety of theories of information behavior seek to understand the processes that surround information seeking. An analysis of the most cited publications on information behavior during the early 21st century shows its theoretical nature. Information behavior research can employ various research methodologies grounded in broader research paradigms from psychology, sociology and education. In 2003, a framework for information-seeking studies was introduced that aims to guide the production of clear, structured descriptions of research objects and positions information-seeking as a concept within information behavior. == Concepts of information behavior == === Information need === Information need is a concept introduced by Wilson. Understanding the information need of an individual involved three elements: Why the individual decides to look for information, What purpose the information they find will serve, and How the information is used once it is retrieved === Information-seeking behavior === Information-seeking behavior is a more specific concept of information behavior. It specifically focuses on searching, finding, and retrieving information. Information-seeking behavior research can focus on improving information systems or, if it includes information need, can also focus on why the user behaves the way they do. A review study on information search behavior of users highlighted that behavioral factors, personal factors, product/service factors and situational factors affect information search behavior. Information-seeking behavior can be more or less explicit on the part of users: users might seek to solve some task or to establish some piece of knowledge which can be found in the data in question, or alternatively the search process itself is part of the objective of the user, in use cases for exploring visual content or for familiarising oneself with the content of an information service. In the general case, information-seeking needs to be understood and analysed as a session rather than as a one-off transaction with a search engine, and in a broader context which includes user high-level intentions in addition to the immediate information need. === Information use === An information need is the recognition that a gap exists in one’s knowledge, prompting a desire to seek information to fill that gap. It often arises when a person encounters a problem or question they cannot resolve with their current understanding. === Information poverty and barriers === Introduced by Elfreda Chatman in 1987, information poverty is informed by the understanding that information is not equally accessible to all people. Information poverty does not describe a lack of information, but rather a worldview in which one's own experiences inside their own small world may create a distrust in the information provided by those outside their own lived experiences. == Metatheories == In Library and Information Science (LIS), a metatheory is described "a set of assumptions that orient and direct theorizing about a given phenomenon". Library and information science researchers have adopted a number of different metatheories in their research. A common concern among LIS researchers, and a prominent discussion in the field, is the broad spectrum of theories that inform the study of information behavior, information users, or information use. This variation has been noted as a cause of concern because it makes individual studies difficult to compare or synthesize if they are not guided by the same theory. This sentiment has been expressed in studies of information behavior literature from the early 1980s and more recent literature reviews have declared it necessary to refine their reviews to specific contexts or situations due to the sheer breadth of information behavior research available. Below are descriptions of some, but not all, metatheories that have guided LIS research. === Cognitivist approach === A cognitive approach to understanding information behavior is grounded in psychology. It holds the assumption that a person's thinking influences how they seek, retrieve, and use information. Researchers that approach information behavior with the assumption that it is influenced by cognition, seek to understand what someone is thinking while they engage in information behavior and how those thoughts influence their behavior. Wilson's attempt to understand information-seeking behavior by defining information need includes a cognitive approach. Wilson theorizes that information behavior is influenced by the cognitive need of an individual. By understanding the cognitive information need of an individual, we may gain insight into their information behavior. Nigel Ford takes a cognitive approach to information-seeking, focusing on the intellectual processes of information-seeking. In 2004, Ford proposed an information-seeking model using a cognitive approach that focuses on how to improve information retrieval systems and serves to establish information-seeking and information behavior as concepts in and of themselves, rather than synonymous terms. === Constructionist approach === The constructionist approach to information behavior has roots in the humanities and social sciences. It relies on social constructionism, which assumes that a person's information behavior is influenced by their experiences in society. In order to understand information behavior, constructionist researchers must first understand the social discourse that surrounds the behavior. The most popular thinker referenced in constructionist information behavior research is Michel Foucault, who famously rejected the concept of a universal human nature. The constructionist approach to information behavior research creates space for contextualizing the behavior based on the social experiences of the individual. One study that approaches information behavior research through the social constructionist approach is a study of the information behavior of a public library knitting group. The authors use a collectivist theory to frame their research, which denies the universality of information behavior and focuses on "understanding the ways that discourse communities collectively construct information needs, seeking, sources, and uses". === Constructivist approach === The constructivist approach is born out of education and sociology in which, "individuals are seen as actively constructing an understanding of their worlds, heavily influenced by the social world(s) in which they are operating". Constructivist approaches to information behavior research generally treat the individual's reality as constructed within their own mind rather than built by the society in which they live. The constructivist metatheory makes space for the influence of society and culture with social constructivism, "which argues that, while the mind constructs reality in its relationship to the world, this mental process is significantly informed by influences received from societal conventions, history and interaction with significant others". == Theories == A common concern among LIS researchers, and a prominent discussion in the field, is the broad spectrum of theories that inform LIS research. This variation has been noted as a cause of concern because it makes individual studies difficult to compare if they are not guided by the same theory. Recent studies have shown that the impact of these theories and theoretical models is very limited. LIS researchers have applied concepts and theories from many disciplines, including sociology, psychology, communication, organizational behavior, and computer science. === Wilson's theory of information behavior (1981) === The term was coined by Thomas D. Wilson in his 1981 paper, on the grounds that the current term, 'information needs' was unhelpful since 'need' could not be directly observed, while how people behaved in seeking information could be observed and investigated. However, there is increasing work in the information-searching field that is relating behaviors to underlying needs. In 2000, Wilson described information behavior as the totality of human behavior in relation to sources and channels of information, including both active and passive information-seeking, and information use. He described info
Hybrid algorithm
A hybrid algorithm is an algorithm that combines two or more other algorithms that solve the same problem, either choosing one based on some characteristic of the data, or switching between them over the course of the algorithm. This is generally done to combine desired features of each, so that the overall algorithm is better than the individual components. "Hybrid algorithm" does not refer to simply combining multiple algorithms to solve a different problem – many algorithms can be considered as combinations of simpler pieces – but only to combining algorithms that solve the same problem, but differ in other characteristics, notably performance. == Examples == In computer science, hybrid algorithms are very common in optimized real-world implementations of recursive algorithms, particularly implementations of divide-and-conquer or decrease-and-conquer algorithms, where the size of the data decreases as one moves deeper in the recursion. In this case, one algorithm is used for the overall approach (on large data), but deep in the recursion, it switches to a different algorithm, which is more efficient on small data. A common example is in sorting algorithms, where the insertion sort, which is inefficient on large data, but very efficient on small data (say, five to ten elements), is used as the final step, after primarily applying another algorithm, such as merge sort or quicksort. Merge sort and quicksort are asymptotically optimal on large data, but the overhead becomes significant if applying them to small data, hence the use of a different algorithm at the end of the recursion. A highly optimized hybrid sorting algorithm is Timsort, which combines merge sort, insertion sort, together with additional logic (including binary search) in the merging logic. A general procedure for a simple hybrid recursive algorithm is short-circuiting the base case, also known as arm's-length recursion. In this case whether the next step will result in the base case is checked before the function call, avoiding an unnecessary function call. For example, in a tree, rather than recursing to a child node and then checking if it is null, checking null before recursing. This is useful for efficiency when the algorithm usually encounters the base case many times, as in many tree algorithms, but is otherwise considered poor style, particularly in academia, due to the added complexity. Another example of hybrid algorithms for performance reasons are introsort and introselect, which combine one algorithm for fast average performance, falling back on another algorithm to ensure (asymptotically) optimal worst-case performance. Introsort begins with a quicksort, but switches to a heap sort if quicksort is not progressing well; analogously introselect begins with quickselect, but switches to median of medians if quickselect is not progressing well. Centralized distributed algorithms can often be considered as hybrid algorithms, consisting of an individual algorithm (run on each distributed processor), and a combining algorithm (run on a centralized distributor) – these correspond respectively to running the entire algorithm on one processor, or running the entire computation on the distributor, combining trivial results (a one-element data set from each processor). A basic example of these algorithms are distribution sorts, particularly used for external sorting, which divide the data into separate subsets, sort the subsets, and then combine the subsets into totally sorted data; examples include bucket sort and flashsort. However, in general distributed algorithms need not be hybrid algorithms, as individual algorithms or combining or communication algorithms may be solving different problems. For example, in models such as MapReduce, the Map and Reduce step solve different problems, and are combined to solve a different, third problem.
Summify
Summify was a social news aggregator founded by Mircea Paşoi and Cristian Strat, two former Google and Microsoft interns from Romania. The service emailed its users a periodic summary of news articles shared from their social networks based on their relevance and importance. The platform supported Twitter, Facebook, and Google Reader accounts. == History == In 2009, Paşoi and Strat created ReadFu, a plugin that provided a contextual summary and statistics of the target page of a hyperlink. In January 2010, ReadFu was accepted into the Vancouver-based start-up incubator Bootup Labs. On March 20, 2010 the service was renamed to Summify and a private beta began. On August 11, 2010 Paşoi and Strat announced a new direction for the service. It would become a real-time social news reader that aggregates incoming news from social networks and displays articles by importance using social reactions. After some feedback that the users preferred article digests by email more than the real-time news reader version, Summify discontinued the news reader version. In March 2011, Summify completed a Seed round, with investors including Rob Glaser, Accel Partners, and Stewart Butterfield. Summify received coverage from various news and media outlets such as TechCrunch. It was also featured in various news platforms, such as Time, The Globe and Mail, Mashable, VentureBeat, Gizmodo, Lifehacker, and The Next Web. Summify released a free app on the Apple App Store on July 8, 2011. The app allowed users to read their web summaries from iOS mobile devices. Summify was acquired by Twitter on January 19, 2012. The service shut down soon after, on June 22, 2012.
ArchiMate
ArchiMate ( AR-ki-mayt) is an open and independent enterprise architecture modeling language to support the description, analysis and visualization of architecture within and across business domains in an unambiguous way. ArchiMate is a technical standard from The Open Group and is based on concepts from the now superseded IEEE 1471 standard. It is supported by various tool vendors and consulting firms. ArchiMate is also a registered trademark of The Open Group. The Open Group has a certification program for ArchiMate users, software tools and courses. ArchiMate distinguishes itself from other languages such as Unified Modeling Language (UML) and Business Process Modeling and Notation (BPMN) by its enterprise modelling scope. Also, UML and BPMN are meant for a specific use and they are quite heavy – containing about 150 (UML) and 250 (BPMN) modeling concepts whereas ArchiMate works with just about 50 (in version 2.0). The goal of ArchiMate is to be ”as small as possible”, not to cover every edge scenario imaginable. To be easy to learn and apply, ArchiMate was intentionally restricted “to the concepts that suffice for modeling the proverbial 80% of practical cases". == Overview == ArchiMate offers a common language for describing the construction and operation of business processes, organizational structures, information flows, IT systems, and technical infrastructure. This insight helps the different stakeholders to design, assess, and communicate the consequences of decisions and changes within and between these business domains. The main concepts and relationships of the ArchiMate language can be seen as a framework, the so-called Archimate Framework: It divides the enterprise architecture into a business, application and technology layer. In each layer, three aspects are considered: active elements, an internal structure and elements that define use or communicate information. One of the objectives of the ArchiMate language is to define the relationships between concepts in different architecture domains. The concepts of this language therefore hold the middle between the detailed concepts, which are used for modeling individual domains (for example, the Unified Modeling Language (UML) for modeling software products), and Business Process Model and Notation (BPMN), which is used for business process modeling. == History == ArchiMate is partly based on the now superseded IEEE 1471 standard. It was developed in the Netherlands by a project team from the Telematica Instituut in cooperation with several Dutch partners from government, industry and academia. Among the partners were Ordina NV, Radboud Universiteit Nijmegen, the Leiden Institute for Advanced Computer Science (LIACS) and the Centrum Wiskunde & Informatica (CWI). Later, tests were performed in organizations such as ABN AMRO, the Dutch Tax and Customs Administration and the ABP. The development process lasted from July 2002 to December 2004, and took about 35 person years and approximately 4 million euros. The development was funded by the Dutch government (Dutch Tax and Customs Administration), and business partners, including ABN AMRO and the ABP Pension Fund. In 2008 the ownership and stewardship of ArchiMate was transferred to The Open Group. It is now managed by the ArchiMate Forum within The Open Group. In February 2009 The Open Group published the ArchiMate 1.0 standard as a formal technical standard. In January 2012 the ArchiMate 2.0 standard, and in 2013 the ArchiMate 2.1 standard was released. In June 2016, the Open Group released version 3.0 of the ArchiMate Specification. An update to Archimate 3.0.1 came out in August 2017. Archimate 3.1 was published 5 November 2019. The latest version of the ArchiMate Specification is version 3.2 released October 2022. Version 3.0 adds enhanced support for capability-oriented strategic modelling, new entities representing physical resources (for modelling the ingredients, equipment and transport resources used in the physical world) and a generic metamodel showing the entity types and the relationships between them. == ArchiMate framework == === Core framework === The main concepts and elements of the ArchiMate language are being presented as ArchiMate core framework. It consists of three layers and three aspects. This creates a matrix of combinations. Every layer has its passive structure, behavior and active structure aspects. ==== Layers ==== ArchiMate has a layered and service-oriented look on architectural models. The higher layers make use of services that are provided by the lower layers. Although, at an abstract level, the concepts that are used within each layer are similar, we define more concrete concepts that are specific for a certain layer. In this context, we distinguish three main layers: The business layer is about business processes, services, functions and events of business units. This layer "offers products and services to external customers, which are realized in the organization by business processes performed by business actors and roles". The application layer is about software applications that "support the components in the business with application services". The technology layer deals "with the hardware and communication infrastructure to support the application layer. This layer offers infrastructural services needed to run applications, realized by computer and communication hardware and system software". Each of these main layers can be further divided in sub-layers. For example, in the business layer, the primary business processes realising the products of a company may make use of a layer of secondary (supporting) business processes; in the application layer, the end-user applications may make use of generic services offered by supporting applications. On top of the business layer, a separate environment layer may be added, modelling the external customers that make use of the services of the organisation (although these may also be considered part of the business layer). In line with service orientation, the most important relation between layers is formed by use relations, which show how the higher layers make use of the services of lower layers. However, a second type of link is formed by realisation relations: elements in lower layers may realise comparable elements in higher layers; e.g., a ‘data object’ (application layer) may realise a ‘business object’ (business layer); or an ‘artifact’ (technology layer) may realise either a ‘data object’ or an ‘application component’ (application layer). ==== Aspects ==== Passive structure is the set of entities on which actions are conducted. In the business layer the example would be information objects, in the application layer data objects and in the technology layer, they could include physical objects. Behavior refers to the processes and functions performed by the actors. "Structural elements are assigned to behavioral elements, to show who or what displays the behavior". Active structure is the set of entities that display some behavior, e.g. business actors, devices, or application components. === Full framework === The Full ArchiMate framework is enriched by the physical layer, which was added to allow modeling of “physical equipment, materials, and distribution networks” and was not present in the previous version. The implementation and migration layer adds elements that allow architects to model a state of transition, to mark parts of the architecture that are temporary for the purpose, as the name says, of implementation and migration. Strategy layer adds three elements: resource, capability and course of action. These elements help to incorporate strategic dimension to the ArchiMate language by allowing it to depict the usage of resources and capabilities in order to achieve some strategic goals. Finally, there is a motivation aspect that allows different stakeholders to describe the motivation of specific actors or domains, which can be quite important when looking at one thing from several different angles. It adds several elements like stakeholder, value, driver, goal, meaning etc. == ArchiMate language == The ArchiMate language is formed as a top-level and is hierarchical. On the top, there is a model. A model is a collection of concepts. A concept can be either an element or a relationship. An element can be either of behavior type, structure, motivation or a so-called composite element (which means that it does not fit just one aspect of the framework, but two or more). The functionality of all concepts without a dependency on a specific layer is described by the generic metamodel. This layer-independent description of concepts is useful when trying to understand the mechanics of the Archimate language. === Concepts === ==== Elements ==== The generic elements are distributed into the same categories as the layers: Active structure elements Behavior elements Passive structure elements Motivation elements Active structure e