Enterprise architecture (EA) is a business function concerned with the structures and behaviours of a business, especially business roles and processes that create and use business data. The international definition according to the Federation of Enterprise Architecture Professional Organizations is "a well-defined practice for conducting enterprise analysis, design, planning, and implementation, using a comprehensive approach at all times, for the successful development and execution of strategy. Enterprise architecture applies architecture principles and practices to guide organizations through the business, information, process, and technology changes necessary to execute their strategies. These practices utilize the various aspects of an enterprise to identify, motivate, and achieve these changes." The United States Federal Government is an example of an organization that practices EA, in this case with its Capital Planning and Investment Control processes. Companies such as Independence Blue Cross, Intel, Volkswagen AG, and InterContinental Hotels Group also use EA to improve their business architectures as well as to improve business performance and productivity. Additionally, the Federal Enterprise Architecture's reference guide aids federal agencies in the development of their architectures. == Introduction == As a discipline, EA "proactively and holistically lead[s] enterprise responses to disruptive forces by identifying and analyzing the execution of change" towards organizational goals. EA gives business and IT leaders recommendations for policy adjustments and provides best strategies to support and enable business development and change within the information systems the business depends on. EA provides a guide for decision making towards these objectives. The National Computing Centre's EA best practice guidance states that an EA typically "takes the form of a comprehensive set of cohesive models that describe the structure and functions of an enterprise. The individual models in an EA are arranged in a logical manner that provides an ever-increasing level of detail about the enterprise." Important players within EA include enterprise architects and solutions architects. Enterprise architects are at the top level of the architect hierarchy, meaning they have more responsibilities than solutions architects. While solutions architects focus on their own relevant solutions, enterprise architects focus on solutions for and the impact on the whole organization. Enterprise architects oversee many solution architects and business functions. As practitioners of EA, enterprise architects support an organization's strategic vision by acting to align people, process, and technology decisions with actionable goals and objectives that result in quantifiable improvements toward achieving that vision. The practice of EA "analyzes areas of common activity within or between organizations, where information and other resources are exchanged to guide future states from an integrated viewpoint of strategy, business, and technology." === Definitions === The term enterprise can be defined as an organizational unit, organization, or collection of organizations that share a set of common goals and collaborate to provide specific products or services to customers. In that sense, the term enterprise covers various types of organizations, regardless of their size, ownership model, operational model, or geographical distribution. It includes those organizations' complete sociotechnical system, including people, information, processes, and technologies. Enterprise as a sociotechnical system defines the scope of EA. The term architecture refers to fundamental concepts or properties of a system in its environment; and embodied in its elements, relationships, and in the principles of its design and evolution. A methodology for developing and using architecture to guide the transformation of a business from a baseline state to a target state, sometimes through several transition states, is usually known as an enterprise architecture framework. A framework provides a structured collection of processes, techniques, artifact descriptions, reference models, and guidance for the production and use of an enterprise-specific architecture description. Open-source tools supporting EA practice, such as the Essential Project, have also been evaluated for suitability in academic and commercial training contexts. Paramount to changing the EA is the identification of a sponsor. Their mission, vision, strategy, and the governance framework define all roles, responsibilities, and relationships involved in the anticipated transformation. Changes considered by enterprise architects typically include innovations in the structure or processes of an organization; innovations in the use of information systems or technologies; the integration and/or standardization of business processes; and improvement of the quality and timeliness of business information. According to the standard ISO/IEC/IEEE 42010, the product used to describe the architecture of a system is called an architectural description. In practice, an architectural description contains a variety of lists, tables, and diagrams. These are models known as views. In the case of EA, these models describe the logical business functions or capabilities, business processes, human roles and actors, the physical organization structure, data flows and data stores, business applications and platform applications, hardware, and communications infrastructure. The first use of the term "enterprise architecture" is often incorrectly attributed to John Zachman's 1987 A framework for information systems architecture. The first publication to use it was instead a National Institute of Standards (NIST) Special Publication on the challenges of information system integration. The NIST article describes EA as consisting of several levels. Business unit architecture is the top level and might be a total corporate entity or a sub-unit. It establishes for the whole organization necessary frameworks for "satisfying both internal information needs" as well as the needs of external entities, which include cooperating organizations, customers, and federal agencies. The lower levels of the EA that provide information to higher levels are more attentive to detail on behalf of their superiors. In addition to this structure, business unit architecture establishes standards, policies, and procedures that either enhance or stymie the organization's mission. The main difference between these two definitions is that Zachman's concept was the creation of individual information systems optimized for business, while NIST's described the management of all information systems within a business unit. The definitions in both publications, however, agreed that due to the "increasing size and complexity of the [i]mplementations of [i]nformation systems... logical construct[s] (or architecture) for defining and controlling the interfaces and... [i]ntegration of all the components of a system" is necessary. Zachman in particular urged for a "strategic planning methodology." == Overview == === Schools of thought === Within the field of enterprise architecture, there are three overarching schools: Enterprise IT Design, Enterprise Integrating, and Enterprise Ecosystem Adaption. Which school one subscribes to will impact how they see the EA's purpose and scope, as well as the means of achieving it, the skills needed to conduct it, and the locus of responsibility for conducting it. Under Enterprise IT Design, the main purpose of EA is to guide the process of planning and designing an enterprise's IT/IS capabilities to meet the desired organizational objectives, often by greater alignment between IT/IS and business concerns. Architecture proposals and decisions are limited to the IT/IS aspects of the enterprise and other aspects service only as inputs. The Enterprise Integrating school believes that the purpose of EA is to create a greater coherency between the various concerns of an enterprise (HR, IT, Operations, etc.), including the link between strategy formulation and execution. Architecture proposals and decisions here encompass all aspects of the enterprise. The Enterprise Ecosystem Adaption school states that the purpose of EA is to foster and maintain the learning capabilities of enterprises so they may be sustainable. Consequently, a great deal of emphasis is put on improving the capabilities of the enterprise to improve itself, to innovate, and to coevolve with its environment. Typically, proposals and decisions encompass both the enterprise and its environment. === Benefits, challenges, and criticisms === The benefits of EA are achieved through its direct and indirect contributions to organizational goals. Notable benefits include support in the areas related to design and re-design of the organizational structures during mergers, acquisitions, or
GNU Binutils
The GNU Binary Utilities, or binutils, is a collection of programming tools maintained by the GNU Project for working with executable code including assembly, linking and many other development operations. The tools are originally from Cygnus Solutions. The tools are typically used along with other GNU tools such as GNU Compiler Collection, and the GNU Debugger. == Tools == The tools include: == elfutils == Ulrich Drepper wrote elfutils, to partially replace GNU Binutils, purely for Linux and with support only for ELF and DWARF. It distributes three libraries with it for programmatic access.
SMBGhost
SMBGhost (or SMBleedingGhost or CoronaBlue) is a type of security vulnerability, with wormlike features, that affects Windows 10 computers and was first reported publicly on 10 March 2020. == Security vulnerability == A proof of concept (PoC) exploit code was published 1 June 2020 on GitHub by a security researcher. The code could possibly spread to millions of unpatched computers, resulting in as much as tens of billions of dollars in losses. Microsoft recommends all users of Windows 10 versions 1903 and 1909 and Windows Server versions 1903 and 1909 to install patches, and states, "We recommend customers install updates as soon as possible as publicly disclosed vulnerabilities have the potential to be leveraged by bad actors ... An update for this vulnerability was released in March [2020], and customers who have installed the updates, or have automatic updates enabled, are already protected." Workarounds, according to Microsoft, such as disabling SMB compression and blocking port 445, may help but may not be sufficient. According to the advisory division of Homeland Security, "Malicious cyber actors are targeting unpatched systems with the new [threat], ... [and] strongly recommends using a firewall to block server message block ports from the internet and to apply patches to critical- and high-severity vulnerabilities as soon as possible."
Score bug
A score bug is a digital on-screen graphic which is displayed in a broadcast of a sporting event, displaying the current score and other statistics. It is similar in function to a scoreboard, and is usually placed at either the top or lower third of the television screen. == History == The concept of a persistent score bug was devised by Sky Sports head David Hill, who was dissatisfied over having to wait to see what the score was after tuning into a football match in-progress. The score bug was introduced when Sky launched its coverage of the then newly-formed English Premier League in August 1992. Hill's boss repeatedly demanded that the graphic be removed, describing it as the "stupidest thing [he] had ever seen". Hill defied the boss's demands and kept the graphic in place. ITV introduced a score bug at the start of the 1993–94 football season, and the BBC introduced a score bug towards the end of 1993. The concept was introduced to the United States by ABC Sports and ESPN during coverage of the 1994 FIFA World Cup. Their justification for the graphic was to provide a location for a rotating series of sponsor logos, in order to allow matches to air without commercial interruption. With the acquisition of rights to the National Football League (NFL) by BSkyB's American sibling Fox (a fellow venture of Rupert Murdoch), Hill became the first president of Fox Sports. Under Hill's leadership, Fox introduced a version of the score bug branded as the "Fox Box", which was part of its inaugural season of NFL coverage in 1994. Variety criticized it as an "annoying see-through clock and score graphic" and expressed concern for people "who actually watched the beginning of the game and would rather have their screen clear of graphics". Hill even received a death threat from an irate viewer, with a specific emphasis on him being a "foreigner", but the score bug soon became a ubiquitous feature for American football broadcasts, along with almost all American sports broadcasts in the years that followed. Dick Ebersol of NBC Sports initially opposed the idea of a score bug, as he thought that fans would dislike seeing more graphics on the screen and would change the channel from blowout games if the score was constantly being displayed. Since the 2010s, the on-air design and positioning of some score bugs have been influenced by the needs of Internet video (especially when viewing an event on devices with smaller screens), including bugs noticeably larger than prior iterations designed with television viewing in mind, or designs primarily kept towards the bottom-center of the screen (easing the ability for the bug to remain visible when highlights are cropped for square videos posted on social media). == Details == Score bugs used in team sports typically include the names of both teams, an abbreviation of the team's name, and/or the team's logo; for individual sports, they include the names of individual competitors. In sports where a game clock or playing periods are used, those are generally also displayed as part of the score bug. Some broadcasts also include teams' win-loss records. In 2024, ESPN experimented with adding a persistent win probability meter to its bug in Major League Baseball, which was based on input from its statisticians. === Variations === In addition to the above information, score bugs in some sports include additional information: In baseball, score bugs display the current inning, number of outs, the pitch clock if applicable, and a graphic displaying which bases are occupied; and usually include names of the current pitcher and batter, the pitcher's pitch count, and the number of balls and strikes accrued by the batter. In basketball, score bugs generally include the shot clock, the number of fouls accrued by each team, and whether a team is in the bonus. In cricket, score bugs often take the form of larger dashboards across the bottom of the screen, displaying the current team up and their number of runs, wickets, and overs, a display showing the runs scored and number of balls faced by the current batting partnership, and statistics for the opposing team's bowler (including the number of wickets scored and runs given up). In American football, score bugs usually include the play clock and the down and distance of the current play; they also incorporate graphics indicating when a penalty flag has been thrown. In ice hockey, score bugs display when a penalty or power play is in effect, and often include the number of shots on goal accrued by each team. In golf, Fox popularized the display of a persistent leaderboard graphic in the bottom-right of the screen, usually displaying the top 5. ==== Racing ==== Telecasts of automobile races often include a score bug with the current positions of participants, statistics such as distance behind the leader, and the remaining distance or number of laps. In the mid-2010s, NASCAR broadcasters such as Fox began to transition from horizontal tickers to vertical leaderboards (also referred to as "pylons", in reference to the physical scoring pylons at). The CW differentiated itself by using a horizontal display that divides the field into multiple columns along the bottom of the screen.
Graphics address remapping table
The graphics address remapping table (GART), also known as the graphics aperture remapping table, or graphics translation table (GTT), is an I/O memory management unit (IOMMU) used by Accelerated Graphics Port (AGP) and PCI Express (PCIe) graphics cards. The GART allows the graphics card direct memory access (DMA) to the host system memory, through which buffers of textures, polygon meshes and other data are loaded. AMD later reused the same mechanism for I/O virtualization with other peripherals including disk controllers and network adapters. A GART is used as a means of data exchange between the main memory and video memory through which buffers (i.e. paging/swapping) of textures, polygon meshes and other data are loaded, but can also be used to expand the amount of video memory available for systems with only integrated or shared graphics (i.e. no discrete or inbuilt graphics processor), such as Intel HD Graphics processors. However, this type of memory (expansion) remapping has a caveat that affects the entire system: specifically, any GART, pre-allocated memory becomes pooled and cannot be utilised for any other purposes but graphics memory and display rendering. Since PCI Express, the GART is extended to the GTT (Graphics Translation Table), which act as a buffer or cache between system memory and graphics card, and in PCI Express, the GTT buffer size is changeable by the GPU driver. == Operating system support == === Windows === Support for AGP GART was added since Windows 95 OSR2. Later, support for GTT was added since Windows XP SP2 and Windows Vista. === Linux === Jeff Hartmann served as the primary maintainer of the Linux kernel's agpgart driver, which began as part of Brian Paul's Utah GLX accelerated Mesa 3D driver project. The developers primarily targeted Linux 2.4.x kernels, but made patches available against older 2.2.x kernels. Dave Jones heavily reworked agpgart for the Linux 2.6.x kernels, along with more contributions from Jeff Hartmann. === FreeBSD === In FreeBSD, the agpgart driver appeared in its 4.1 release. === Solaris === AGPgart support was introduced into Solaris Express Developer Edition as of its 7/05 release.
Geometric hashing
In computer science, geometric hashing is a method for efficiently finding two-dimensional objects represented by discrete points that have undergone an affine transformation, though extensions exist to other object representations and transformations. In an off-line step, the objects are encoded by treating each pair of points as a geometric basis. The remaining points can be represented in an invariant fashion with respect to this basis using two parameters. For each point, its quantized transformed coordinates are stored in the hash table as a key, and indices of the basis points as a value. Then a new pair of basis points is selected, and the process is repeated. In the on-line (recognition) step, randomly selected pairs of data points are considered as candidate bases. For each candidate basis, the remaining data points are encoded according to the basis and possible correspondences from the object are found in the previously constructed table. The candidate basis is accepted if a sufficiently large number of the data points index a consistent object basis. Geometric hashing was originally suggested in computer vision for object recognition in 2D and 3D, but later was applied to different problems such as structural alignment of proteins. == Geometric hashing in computer vision == Geometric hashing is a method used for object recognition. Let’s say that we want to check if a model image can be seen in an input image. This can be accomplished with geometric hashing. The method could be used to recognize one of the multiple objects in a base, in this case the hash table should store not only the pose information but also the index of object model in the base. === Example === For simplicity, this example will not use too many point features and assume that their descriptors are given by their coordinates only (in practice local descriptors such as SIFT could be used for indexing). ==== Training Phase ==== Find the model's feature points. Assume that 5 feature points are found in the model image with the coordinates ( 12 , 17 ) ; {\displaystyle (12,17);} ( 45 , 13 ) ; {\displaystyle (45,13);} ( 40 , 46 ) ; {\displaystyle (40,46);} ( 20 , 35 ) ; {\displaystyle (20,35);} ( 35 , 25 ) {\displaystyle (35,25)} , see the picture. Introduce a basis to describe the locations of the feature points. For 2D space and similarity transformation the basis is defined by a pair of points. The point of origin is placed in the middle of the segment connecting the two points (P2, P4 in our example), the x ′ {\displaystyle x'} axis is directed towards one of them, the y ′ {\displaystyle y'} is orthogonal and goes through the origin. The scale is selected such that absolute value of x ′ {\displaystyle x'} for both basis points is 1. Describe feature locations with respect to that basis, i.e. compute the projections to the new coordinate axes. The coordinates should be discretised to make recognition robust to noise, we take the bin size 0.25. We thus get the coordinates ( − 0.75 , − 1.25 ) ; {\displaystyle (-0.75,-1.25);} ( 1.00 , 0.00 ) ; {\displaystyle (1.00,0.00);} ( − 0.50 , 1.25 ) ; {\displaystyle (-0.50,1.25);} ( − 1.00 , 0.00 ) ; {\displaystyle (-1.00,0.00);} ( 0.00 , 0.25 ) {\displaystyle (0.00,0.25)} Store the basis in a hash table indexed by the features (only transformed coordinates in this case). If there were more objects to match with, we should also store the object number along with the basis pair. Repeat the process for a different basis pair (Step 2). It is needed to handle occlusions. Ideally, all the non-colinear pairs should be enumerated. We provide the hash table after two iterations, the pair (P1, P3) is selected for the second one. Hash Table: Most hash tables cannot have identical keys mapped to different values. So in real life one won’t encode basis keys (1.0, 0.0) and (-1.0, 0.0) in a hash table. ==== Recognition Phase ==== Find interesting feature points in the input image. Choose an arbitrary basis. If there isn't a suitable arbitrary basis, then it is likely that the input image does not contain the target object. Describe coordinates of the feature points in the new basis. Quantize obtained coordinates as it was done before. Compare all the transformed point features in the input image with the hash table. If the point features are identical or similar, then increase the count for the corresponding basis (and the type of object, if any). For each basis such that the count exceeds a certain threshold, verify the hypothesis that it corresponds to an image basis chosen in Step 2. Transfer the image coordinate system to the model one (for the supposed object) and try to match them. If successful, the object is found. Otherwise, go back to Step 2. === Finding mirrored pattern === It seems that this method is only capable of handling scaling, translation, and rotation. However, the input image may contain the object in mirror transform. Therefore, geometric hashing should be able to find the object, too. There are two ways to detect mirrored objects. For the vector graph, make the left side positive, and the right side negative. Multiplying the x position by -1 will give the same result. Use 3 points for the basis. This allows detecting mirror images (or objects). Actually, using 3 points for the basis is another approach for geometric hashing. === Geometric hashing in higher-dimensions === Similar to the example above, hashing applies to higher-dimensional data. For three-dimensional data points, three points are also needed for the basis. The first two points define the x-axis, and the third point defines the y-axis (with the first point). The z-axis is perpendicular to the created axis using the right-hand rule. Notice that the order of the points affects the resulting basis
Depth peeling
In computer graphics, depth peeling is an exact multipass method of order-independent transparency that extracts transparent fragments into depth layers and composites those layers in depth order. Depth peeling has the advantage of being able to generate correct results even for complex images containing intersecting transparent objects. == Method == Depth peeling works by rendering the image multiple times. Depth peeling uses two Z buffers, one that works conventionally, and one that is not modified, and sets the minimum distance at which a fragment can be drawn without being discarded. For each pass, the previous pass' conventional Z-buffer is used as the minimal Z-buffer, so each pass removes already-captured nearer fragments and draws the next depth layer behind them. The resulting images can then be composited in depth order to form a single image. A major drawback of classical depth peeling is performance: it requires one geometry pass per peeled layer, so scenes with high depth complexity require many passes that each re-rasterize the transparent geometry. Later variants reduce the number of passes by peeling multiple layers or both front and back layers in a pass. Dual depth peeling reduces the geometry-pass count from N to N/2+1 by peeling one layer from the front and one from the back in each pass, while multi-layer depth peeling peels several layers per pass and reported up to an 8x speed-up in RGBA8 settings.