Pyramid, or pyramid representation, is a type of multi-scale signal representation developed by the computer vision, image processing and signal processing communities, in which a signal or an image is subject to repeated smoothing and subsampling. Pyramid representation is a predecessor to scale-space representation and multiresolution analysis. == Pyramid generation == There are two main types of pyramids: lowpass and bandpass. A lowpass pyramid is made by smoothing the image with an appropriate smoothing filter and then subsampling the smoothed image, usually by a factor of 2 along each coordinate direction. The resulting image is then subjected to the same procedure, and the cycle is repeated multiple times. Each cycle of this process results in a smaller image with increased smoothing, but with decreased spatial sampling density (that is, decreased image resolution). If illustrated graphically, the entire multi-scale representation will look like a pyramid, with the original image on the bottom and each cycle's resulting smaller image stacked one atop the other. A bandpass pyramid is made by forming the difference between images at adjacent levels in the pyramid and performing image interpolation between adjacent levels of resolution, to enable computation of pixelwise differences. == Pyramid generation kernels == A variety of different smoothing kernels have been proposed for generating pyramids. Among the suggestions that have been given, the binomial kernels arising from the binomial coefficients stand out as a particularly useful and theoretically well-founded class. Thus, given a two-dimensional image, we may apply the (normalized) binomial filter (1/4, 1/2, 1/4) typically twice or more along each spatial dimension and then subsample the image by a factor of two. This operation may then proceed as many times as desired, leading to a compact and efficient multi-scale representation. If motivated by specific requirements, intermediate scale levels may also be generated where the subsampling stage is sometimes left out, leading to an oversampled or hybrid pyramid. With the increasing computational efficiency of CPUs available today, it is in some situations also feasible to use wider supported Gaussian filters as smoothing kernels in the pyramid generation steps. === Gaussian pyramid === In a Gaussian pyramid, subsequent images are weighted down using a Gaussian average (Gaussian blur) and scaled down. Each pixel containing a local average corresponds to a neighborhood pixel on a lower level of the pyramid. This technique is used especially in texture synthesis. === Laplacian pyramid === A Laplacian pyramid is very similar to a Gaussian pyramid but saves the difference image of the blurred versions between each levels. Only the smallest level is not a difference image to enable reconstruction of the high resolution image using the difference images on higher levels. This technique can be used in image compression. === Steerable pyramid === A steerable pyramid, developed by Simoncelli and others, is an implementation of a multi-scale, multi-orientation band-pass filter bank used for applications including image compression, texture synthesis, and object recognition. It can be thought of as an orientation selective version of a Laplacian pyramid, in which a bank of steerable filters are used at each level of the pyramid instead of a single Laplacian or Gaussian filter. == Applications of pyramids == === Alternative representation === In the early days of computer vision, pyramids were used as the main type of multi-scale representation for computing multi-scale image features from real-world image data. More recent techniques include scale-space representation, which has been popular among some researchers due to its theoretical foundation, the ability to decouple the subsampling stage from the multi-scale representation, the more powerful tools for theoretical analysis as well as the ability to compute a representation at any desired scale, thus avoiding the algorithmic problems of relating image representations at different resolution. Nevertheless, pyramids are still frequently used for expressing computationally efficient approximations to scale-space representation. === Detail manipulation === Levels of a Laplacian pyramid can be added to or removed from the original image to amplify or reduce detail at different scales. However, detail manipulation of this form is known to produce halo artifacts in many cases, leading to the development of alternatives such as the bilateral filter. Some image compression file formats use the Adam7 algorithm or some other interlacing technique. These can be seen as a kind of image pyramid. Because those file format store the "large-scale" features first, and fine-grain details later in the file, a particular viewer displaying a small "thumbnail" or on a small screen can quickly download just enough of the image to display it in the available pixels—so one file can support many viewer resolutions, rather than having to store or generate a different file for each resolution.
Agent-assisted automation
Agent-assisted automation is a type of call center technology that automates elements of what the call center agent 1) does with his/her desktop tools and/or 2) says to customers during the call using pre-recorded audio. It is a relatively new category of call center technology that shows promise in improving call center productivity and compliance. == Types of agent-assisted automation == === Pre-recorded audio === Pre-recorded audio (sometimes referred to as soundboard (computer program) or as soundboard technology) is another form of agent-assisted automation. The purpose of using pre-recorded messages is to increase the probability (and in some cases error-proof the process so) that the right information is provided to customers at the right time. The required disclosures are pre-recorded to ensure accuracy and understandability. By integrating the recordings with the customer relationship management software, the right combination of disclosures can be played based on the combination of goods and services the customer purchased. The integration with the customer relationship management software also ensures that the order cannot be submitted until the disclosures are played, essentially error-proofing (poka-yoke) the process of ensuring the customer gets all the required consumer protection information. Phone surveys are ideal applications of this technology. Whether surveying market preferences or political views, the pre-recorded audio with an agent listening allows the questions to be asked in the same way every time, uninfluenced by the agents' fatigue levels, accents, or their own views. === Fraud prevention === Fraud prevention is a specialized type of agent-assisted automation focused on reducing ID theft and credit card fraud. ID theft and credit card fraud are huge threats for call centers and their customers and few good solutions exist, but new agent-assisted automation solutions are producing promising results. The technology allows the agents to remain on the phone while the customers use their phone key pads to enter the information. The tones are masked and the information passes directly into the customer relationship management system or payment gateway in the case of credit card transactions. The automation essentially makes it impossible for call center agents and also call center personnel that might be monitoring the calls to steal the credit card number, social security number, or other personally identifiable information. === Outbound telemarketing === Another specialized application space of agent-assisted automation is in outbound telemarketing, which goes under numerous headings including outbound prospecting, cold calling, solicitation, fund-raising, etc. Turnover is high among agents engaged in this kind of work because the task is tedious and emotionally difficult. It is tedious because the agent spends the bulk of their day, not talking to qualified leads, but in getting wrong numbers and answering machines. == Benefits == Just as automation has benefited manufacturing by reducing the mental and physical effort required of workers while simultaneously improving throughput, quality, and safety, agent-assisted automation is improving call center results while reducing the tiring aspects of the job for agents. In some cases, the agent-assisted automation streamlines the process and allows calls to be handled more quickly. By eliminating cutting and pasting from one application to another, by auto-navigating applications, and by providing a single view of the customer, agent-assisted automation can reduce call handle time and increase agent productivity. Second, in theory, the more steps that can be automated and the more logic that can be built into the call flow (e.g., if the customer buys items 2 and 9, then disclosures a, c, and f are read by the pre-recorded audio), then companies may be able to reduce the amount of training that is required of the agents while at the same time ensuring more consistency and accuracy. However, no published studies have reported this result yet. But an even larger problem in call centers is between-agent variation in behavior and results. Agents differ in the amount of training and coaching they receive, they differ in the amount of experience they have, their jobs are repetitious and tiring, and the process and procedures the agents are supposed to follow constantly change. Moreover, there are significant individual differences between agents in their intelligence, personality, motivations, etc. which all affect performance. Despite the large amount of money call centers have spent over decades trying to reduce between-agent variation, the problem is still so prevalent that one large study of customer interactions with call centers found that a customer's experience was completely a function of the quality of the agent who happened to answer the phone. Therefore, the most significant benefit of agent-assisted automation may prove to be in how the automation error-proofs or poka-yoke the process and ensures that something that needs to be done or said happens every time. Properly implemented, the between-agent variation for whatever step of the process the automation is applied to may be able to be reduced to near zero. This is especially important in a collection agency whose processes and procedures are closely regulated by the Fair Debt Collection Practices Act.
Collaborative diffusion
Collaborative Diffusion is a type of pathfinding algorithm which uses the concept of antiobjects, objects within a computer program that function opposite to what would be conventionally expected. Collaborative Diffusion is typically used in video games, when multiple agents must path towards a single target agent. For example, the ghosts in Pac-Man. In this case, the background tiles serve as antiobjects, carrying out the necessary calculations for creating a path and having the foreground objects react accordingly, whereas having foreground objects be responsible for their own pathing would be conventionally expected. Collaborative Diffusion is favored for its efficiency over other pathfinding algorithms, such as A, when handling multiple agents. Also, this method allows elements of competition and teamwork to easily be incorporated between tracking agents. Notably, the time taken to calculate paths remains constant as the number of agents increases.
Data management plan
A data management plan or DMP is a formal document that outlines how data are to be handled both during a research project, and after the project is completed. The goal of a data management plan is to consider the many aspects of data management, metadata generation, data preservation, and analysis before the project begins; this may lead to data being well-managed in the present, and prepared for preservation in the future. DMPs were originally used in 1966 to manage aeronautical and engineering projects' data collection and analysis, and expanded across engineering and scientific disciplines in the 1970s and 1980s. Up until the early 2000s, DMPs were used "for projects of great technical complexity, and for limited mid-study data collection and processing purposes". In the 2000s and later, E-research and economic policies drove the development and uptake of DMPs. == Importance == Preparing a data management plan before data are collected is claimed to ensure that data are in the correct format, organized well, and better annotated. This could arguably save time in the long term because there is no need to re-organize, re-format, or try to remember details about data. It is also claimed to increase research efficiency since both the data collector and other researchers might be able to understand and use well-annotated data in the future. One component of a data management plan is data archiving and preservation. By deciding on an archive ahead of time, the data collector can format data during collection to make its future submission to a database easier. If data are preserved, they are more relevant since they can be re-used by other researchers. It also allows the data collector to direct requests for data to the database, rather than address requests individually. A frequent argument in favor of preservation is that data that are preserved have the potential to lead to new, unanticipated discoveries, and they prevent duplication of scientific studies that have already been conducted. Data archiving also provides insurance against loss by the data collector. In the 2010s, funding agencies increasingly required data management plans as part of the proposal and evaluation process, despite little or no evidence of their efficacy. == Major components == "There is no general and definitive list of topics that should be covered in a DMP for a research project", and researchers are often left to their own devices as to how to fill out a DMP. === Information about data and data format === A description of data to be produced by the project. This might include (but is not limited to) data that are: Experimental Observational Raw or derived Physical collections Models Simulations Curriculum materials Software Images How will the data be acquired? When and where will they be acquired? After collection, how will the data be processed? Include information about Software used Algorithms Scientific workflows File formats that will be used, justify those formats, and describe the naming conventions used. Quality assurance & quality control measures that will be taken during sample collection, analysis, and processing. If existing data are used, what are their origins? How will the data collected be combined with existing data? What is the relationship between the data collected and existing data? How will the data be managed in the short-term? Consider the following: Version control for files Backing up data and data products Security & protection of data and data products Who will be responsible for management === Metadata content and format === Metadata are the contextual details, including any information important for using data. This may include descriptions of temporal and spatial details, instruments, parameters, units, files, etc. Metadata is commonly referred to as "data about data". Issues to be considered include: How detailed has the metadata to be in order to make the data meaningful? How will the metadata be created and/or captured? Examples include lab notebooks, GPS hand-held units, Auto-saved files on instruments, etc. What format will be used for the metadata? What are the metadata standards commonly used in the respective scientific discipline? There should be justification for the format chosen. === Policies for access, sharing, and re-use === Describe any obligations that exist for sharing data collected. These may include obligations from funding agencies, institutions, other professional organizations, and legal requirements. Include information about how data will be shared, including when the data will be accessible, how long the data will be available, how access can be gained, and any rights that the data collector reserves for using data. Address any ethical or privacy issues with data sharing Address intellectual property & copyright issues. Who owns the copyright? What are the institutional, publisher, and/or funding agency policies associated with intellectual property? Are there embargoes for political, commercial, or patent reasons? Describe the intended future uses/users for the data Indicate how the data should be cited by others. How will the issue of persistent citation be addressed? For example, if the data will be deposited in a public archive, will the dataset have a persistent identifier (e.g., ARK, DOI, Handle, PURL, URN) assigned to it? === Long-term storage and data management === Researchers should identify an appropriate archive for the long-term preservation of their data. By identifying the archive early in the project, the data can be formatted, transformed, and documented appropriately to meet the requirements of the archive. Researchers should consult colleagues and professional societies in their discipline to determine the most appropriate database, and include a backup archive in their data management plan in case their first choice goes out of existence. Early in the project, the primary researcher should identify what data will be preserved in an archive. Usually, preserving the data in its most raw form is desirable, although data derivatives and products can also be preserved. An individual should be identified as the primary contact person for archived data, and ensure contact information is always kept up-to-date in case there are requests for data or information about data. === Budget === Data management and preservation costs may be considerable, depending on the nature of the project. By anticipating costs ahead of time, researchers ensure that the data will be properly managed and archived. Potential expenses that should be considered are Human resources and staff as they handle data preparation, management, documentation, and preservation Hardware and/or software needed for data management, backing up, security, documentation, and preservation Costs associated with submitting the data to an archive The data management plan should include how these costs will be paid. == NSF Data Management Plan == All grant proposals submitted to National Science Foundation (NSF) must include a Data Management Plan that is no more than two pages. This is a supplement (not part of the 15-page proposal) and should describe how the proposal will conform to the Award and Administration Guide policy (see below). It may include the following: The types of data The standards to be used for data and metadata format and content Policies for access and sharing Policies and provisions for re-use Plans for archiving data Policy summarized from the NSF Award and Administration Guide, Section 4 (Dissemination and Sharing of Research Results): Promptly publish with appropriate authorship Share data, samples, physical collections, and supporting materials with others, within a reasonable time frame Share software and inventions Investigators can keep their legal rights over their intellectual property, but they still have to make their results, data, and collections available to others Policies will be implemented via Proposal review Award negotiations and conditions Support/incentives == ESRC Data Management Plan == Since 1995, the UK's Economic and Social Research Council (ESRC) have had a research data policy in place. The current ESRC Research Data Policy states that research data created as a result of ESRC-funded research should be openly available to the scientific community to the maximum extent possible, through long-term preservation and high-quality data management. ESRC requires a data management plan for all research award applications where new data are being created. Such plans are designed to promote a structured approach to data management throughout the data lifecycle, resulting in better quality data that is ready to archive for sharing and re-use. The UK Data Service, the ESRC's flagship data service, provides practical guidance on research data management planning suitable for social science researchers in the UK and around the world. ESRC has a longstanding arrangement with the UK Data A
Master data management
Master data management (MDM) is a discipline in which business and information technology collaborate to ensure the uniformity, accuracy, stewardship, semantic consistency, and accountability of the enterprise's official shared master data assets. == Reasons for master data management == Data consistency and accuracy: MDM ensures that the organization's critical data is consistent and accurate across all systems, reducing discrepancies and errors caused by multiple, siloed copies of the same data. Improved decision-making: By providing a single version of the truth (SVOT), MDM enables organizations to deliver the right data to decision makers, allowing them to clearly understand business performance and make informed, data-driven decisions. Operational efficiency: With the consistent and accurate data provided by an MDM, operational processes such as reporting and inventory management can be automated to improve efficiency. Employee learning, onboarding, and customer service also become more efficient, as MDM data facilitates rapid, accurate, and thorough information retrieval, permitting more employee time to be spent on work. Regulatory compliance: MDM tries to help organizations comply with industry standards and regulations by ensuring that master data is accurately recorded, maintained, and audited. However, issues with data quality, classification, and reconciliation may require data transformation. As with other Extract, Transform, Load-based data movements, these processes are expensive and inefficient, reducing return on investment for a project. == Business unit and product line segmentation == As a result of business unit and product line segmentation, the same entity (whether a customer, supplier, or product) will be included in different product lines. This leads to data redundancy and even confusion. For example, a customer takes out a mortgage at a bank. If the marketing and customer service departments have separate databases, advertisements might still be sent to the customer, even though they've already signed up. The two parts of the bank are unaware, and the customer is sent irrelevant communications. Record linkage can associate different records corresponding to the same entity, mitigating this issue. == Mergers and acquisitions == One of the most common problems for master data management is company growth through mergers or acquisitions. Reconciling these separate master data systems can present difficulties, as existing applications have dependencies on the master databases. Ideally, database administrators resolve this problem through deduplication of the master data as part of the merger. Over time, as further mergers and acquisitions occur, the problem can multiply. Data reconciliation processes can become extremely complex or even unreliable. Some organizations end up with 10, 15, or even 100 separate and poorly integrated master databases. This can cause serious problems in customer satisfaction, operational efficiency, decision support, and regulatory compliance. Another problem involves determining the proper degrees of detail and normalization to include in the master data schema. For example, in a federated Human Resources environment, the enterprise software may focus on storing people's data as current status, adding a few fields to identify the date of hire, date of last promotion, etc. However, this simplification can introduce business-impacting errors into dependent systems for planning and forecasting. The stakeholders of such systems may be forced to build a parallel network of new interfaces to track the onboarding of new hires, planned retirements, and divestment, which works against one of the aims of master data management. == People, processes and technology == Master data management is enabled by technology, but is more than the technologies that enable it. An organization's master data management capability will also include people and processes in its definition. === People === Several roles should be staffed within MDM. Most prominently, the Data Owner and the Data Steward. Several people would likely be allocated to each role and each person responsible for a subset of Master Data (e.g. one data owner for employee master data, another for customer master data). The Data Owner is responsible for the requirements for data definition, data quality, data security, etc. as well as for compliance with data governance and data management procedures. The Data Owner should also be funding improvement projects in case of deviations from the requirements. The Data Steward is running the master data management on behalf of the data owner and probably also being an advisor to the Data Owner. === Processes === Master data management can be viewed as a "discipline for specialized quality improvement" defined by the policies and procedures put in place by a data governance organization. It has the objective of providing processes for collecting, aggregating, matching, consolidating, quality-assuring, persisting and distributing master data throughout an organization to ensure a common understanding, consistency, accuracy and control, in the ongoing maintenance and application use of that data. Processes commonly seen in master data management include source identification, data collection, data transformation, normalization, rule administration, error detection and correction, data consolidation, data storage, data distribution, data classification, taxonomy services, item master creation, schema mapping, product codification, data enrichment, hierarchy management, business semantics management and data governance. === Technology === A master data management tool can be used to support master data management by removing duplicates, standardizing data (mass maintaining), and incorporating rules to eliminate incorrect data from entering the system to create an authoritative source of master data. Master data are the products, accounts, and parties for which the business transactions are completed. Where the technology approach produces a "golden record" or relies on a "source of record" or "system of record", it is common to talk of where the data is "mastered". This is accepted terminology in the information technology industry, but care should be taken, both with specialists and with the wider stakeholder community, to avoid confusing the concept of "master data" with that of "mastering data". ==== Implementation models ==== There are several models for implementing a technology solution for master data management. These depend on an organization's core business, its corporate structure, and its goals. These include: Source of record Registry Consolidation Coexistence Transaction/centralized ===== Source of record ===== This model identifies a single application, database, or simpler source (e.g. a spreadsheet) as being the "source of record" (or "system of record" where solely application databases are relied on). The benefit of this model is its conceptual simplicity, but it may not fit with the realities of complex master data distribution in large organizations. The source of record can be federated, for example by groups of attributes (so that different attributes of a master data entity may have different sources of record) or geographically (so that different parts of an organization may have different master sources). Federation is only applicable in certain use cases, where there is a clear delineation of which subsets of records will be found in which sources. The source of record model can be applied more widely than simply to master data, for example to reference data. ==== Transmission of master data ==== There are several ways in which master data may be collated and distributed to other systems. This includes: Data consolidation – The process of capturing master data from multiple sources and integrating it into a single hub (operational data store) for replication to other destination systems. Data federation – The process of providing a single virtual view of master data from one or more sources to one or more destination systems. Data propagation – The process of copying master data from one system to another, typically through point-to-point interfaces in legacy systems. == Change management in implementation == Challenges in adopting master data management within large organizations often arise when stakeholders disagree on a "single version of the truth" concept is not affirmed by stakeholders, who believe that their local definition of the master data is necessary. For example, the product hierarchy used to manage inventory may be entirely different from the product hierarchies used to support marketing efforts or pay sales representatives. It is above all necessary to identify if different master data is genuinely required. If it is required, then the solution implemented (technology and process) must be able to allow multiple versions of the truth to exist but will prov
Example-based machine translation
Example-based machine translation (EBMT) is a method of machine translation often characterized by its use of a bilingual corpus with parallel texts as its main knowledge base at run-time. It is essentially a translation by analogy and can be viewed as an implementation of a case-based reasoning approach to machine learning. == Translation by analogy == At the foundation of example-based machine translation is the idea of translation by analogy. When applied to the process of human translation, the idea that translation takes place by analogy is a rejection of the idea that people translate sentences by doing deep linguistic analysis. Instead, it is founded on the belief that people translate by first decomposing a sentence into certain phrases, then by translating these phrases, and finally by properly composing these fragments into one long sentence. Phrasal translations are translated by analogy to previous translations. The principle of translation by analogy is encoded to example-based machine translation through the example translations that are used to train such a system. Other approaches to machine translation, including statistical machine translation, also use bilingual corpora to learn the process of translation. == History == Example-based machine translation was first suggested by Makoto Nagao in 1984. He pointed out that it is especially adapted to translation between two totally different languages, such as English and Japanese. In this case, one sentence can be translated into several well-structured sentences in another language, therefore, it is no use to do the deep linguistic analysis characteristic of rule-based machine translation. == Example == Example-based machine translation systems are trained from bilingual parallel corpora containing sentence pairs like the example shown in the table above. Sentence pairs contain sentences in one language with their translations into another. The particular example shows an example of a minimal pair, meaning that the sentences vary by just one element. These sentences make it simple to learn translations of portions of a sentence. For example, an example-based machine translation system would learn three units of translation from the above example: How much is that X ? corresponds to Ano X wa ikura desu ka. red umbrella corresponds to akai kasa small camera corresponds to chiisai kamera Composing these units can be used to produce novel translations in the future. For example, if we have been trained using some text containing the sentences: President Kennedy was shot dead during the parade. and The convict escaped on July 15th., then we could translate the sentence The convict was shot dead during the parade. by substituting the appropriate parts of the sentences. == Phrasal verbs == Example-based machine translation is best suited for sub-language phenomena like phrasal verbs. Phrasal verbs have highly context-dependent meanings. They are common in English, where they comprise a verb followed by an adverb and/or a preposition, which are called the particle to the verb. Phrasal verbs produce specialized context-specific meanings that may not be derived from the meaning of the constituents. There is almost always an ambiguity during word-to-word translation from source to the target language. As an example, consider the phrasal verb "put on" and its Hindustani translation. It may be used in any of the following ways: Ram put on the lights. (Switched on) (Hindustani translation: Jalana) Ram put on a cap. (Wear) (Hindustani translation: Pahenna)
Storage area network
A storage area network (SAN) or storage network is a computer network which provides access to consolidated, block-level data storage. SANs are primarily used to access data storage devices, such as disk arrays and tape libraries from servers so that the devices appear to the operating system as direct-attached storage. A SAN typically is a dedicated network of storage devices not accessible through the local area network (LAN). Although a SAN provides only block-level access, file systems built on top of SANs do provide file-level access and are known as shared-disk file systems. Newer SAN configurations enable hybrid SAN and allow traditional block storage that appears as local storage but also object storage for web services through APIs. == Storage architectures == Storage area networks (SANs) are sometimes referred to as network behind the servers and historically developed out of a centralized data storage model, but with its own data network. A SAN is, at its simplest, a dedicated network for data storage. In addition to storing data, SANs allow for the automatic backup of data, and the monitoring of the storage as well as the backup process. A SAN is a combination of hardware and software. It grew out of data-centric mainframe architectures, where clients in a network can connect to several servers that store different types of data. To scale storage capacities as the volumes of data grew, direct-attached storage (DAS) was developed, where disk arrays or just a bunch of disks (JBODs) were attached to servers. In this architecture, storage devices can be added to increase storage capacity. However, the server through which the storage devices are accessed is a single point of failure, and a large part of the LAN network bandwidth is used for accessing, storing and backing up data. To solve the single point of failure issue, a direct-attached shared storage architecture was implemented, where several servers could access the same storage device. DAS was the first network storage system and is still widely used where data storage requirements are not very high. Out of it developed the network-attached storage (NAS) architecture, where one or more dedicated file server or storage devices are made available in a LAN. Therefore, the transfer of data, particularly for backup, still takes place over the existing LAN. If more than a terabyte of data was stored at any one time, LAN bandwidth became a bottleneck. Therefore, SANs were developed, where a dedicated storage network was attached to the LAN, and terabytes of data are transferred over a dedicated high speed and bandwidth network. Within the SAN, storage devices are interconnected. Transfer of data between storage devices, such as for backup, happens behind the servers and is meant to be transparent. In a NAS architecture data is transferred using the TCP and IP protocols over Ethernet. Distinct protocols were developed for SANs, such as Fibre Channel, iSCSI, Infiniband. Therefore, SANs often have their own network and storage devices, which have to be bought, installed, and configured. This makes SANs inherently more expensive than NAS architectures. == Components == SANs have their own networking devices, such as SAN switches. To access the SAN, so-called SAN servers are used, which in turn connect to SAN host adapters. Within the SAN, a range of data storage devices may be interconnected, such as SAN-capable disk arrays, JBODs and tape libraries. === Host layer === Servers that allow access to the SAN and its storage devices are said to form the host layer of the SAN. Such servers have host adapters, which are cards that attach to slots on the server motherboard (usually PCI slots) and run with a corresponding firmware and device driver. Through the host adapters the operating system of the server can communicate with the storage devices in the SAN. In Fibre channel deployments, a cable connects to the host adapter through the gigabit interface converter (GBIC). GBICs are also used on switches and storage devices within the SAN, and they convert digital bits into light impulses that can then be transmitted over the Fibre Channel cables. Conversely, the GBIC converts incoming light impulses back into digital bits. The predecessor of the GBIC was called gigabit link module (GLM). === Fabric layer === The fabric layer consists of SAN networking devices that include SAN switches, routers, protocol bridges, gateway devices, and cables. SAN network devices move data within the SAN, or between an initiator, such as an HBA port of a server, and a target, such as the port of a storage device. When SANs were first built, hubs were the only devices that were Fibre Channel capable, but Fibre Channel switches were developed and hubs are now rarely found in SANs. Switches have the advantage over hubs that they allow all attached devices to communicate simultaneously, as a switch provides a dedicated link to connect all its ports with one another. When SANs were first built, Fibre Channel had to be implemented over copper cables, these days multimode optical fibre cables are used in SANs. SANs are usually built with redundancy, so SAN switches are connected with redundant links. SAN switches connect the servers with the storage devices and are typically non-blocking allowing transmission of data across all attached wires at the same time. SAN switches are for redundancy purposes set up in a meshed topology. A single SAN switch can have as few as 8 ports and up to 32 ports with modular extensions. So-called director-class switches can have as many as 128 ports. In switched SANs, the Fibre Channel switched fabric protocol FC-SW-6 is used under which every device in the SAN has a hardcoded World Wide Name (WWN) address in the host bus adapter (HBA). If a device is connected to the SAN its WWN is registered in the SAN switch name server. In place of a WWN, or worldwide port name (WWPN), SAN Fibre Channel storage device vendors may also hardcode a worldwide node name (WWNN). The ports of storage devices often have a WWN starting with 5, while the bus adapters of servers start with 10 or 21. === Storage layer === The serialized Small Computer Systems Interface (SCSI) protocol is often used on top of the Fibre Channel switched fabric protocol in servers and SAN storage devices. The Internet Small Computer Systems Interface (iSCSI) over Ethernet and the Infiniband protocols may also be found implemented in SANs, but are often bridged into the Fibre Channel SAN. However, Infiniband and iSCSI storage devices, in particular, disk arrays, are available. The various storage devices in a SAN are said to form the storage layer. It can include a variety of hard disk and magnetic tape devices that store data. In SANs, disk arrays are joined through a RAID which makes a lot of hard disks look and perform like one big storage device. Every storage device, or even partition on that storage device, has a logical unit number (LUN) assigned to it. This is a unique number within the SAN. Every node in the SAN, be it a server or another storage device, can access the storage by referencing the LUN. The LUNs allow for the storage capacity of a SAN to be segmented and for the implementation of access controls. A particular server, or a group of servers, may, for example, be only given access to a particular part of the SAN storage layer, in the form of LUNs. When a storage device receives a request to read or write data, it will check its access list to establish whether the node, identified by its LUN, is allowed to access the storage area, also identified by a LUN. LUN masking is a technique whereby the host bus adapter and the SAN software of a server restrict the LUNs for which commands are accepted. In doing so LUNs that should never be accessed by the server are masked. Another method to restrict server access to particular SAN storage devices is fabric-based access control, or zoning, which is enforced by the SAN networking devices and servers. Under zoning, server access is restricted to storage devices that are in a particular SAN zone. == Network protocols == A mapping layer to other protocols is used to form a network: ATA over Ethernet (AoE), mapping of AT Attachment (ATA) over Ethernet Fibre Channel Protocol (FCP), a mapping of SCSI over Fibre Channel Fibre Channel over Ethernet (FCoE) ESCON over Fibre Channel (FICON), used by mainframe computers HyperSCSI, mapping of SCSI over Ethernet iFCP or SANoIP mapping of FCP over IP iSCSI, mapping of SCSI over TCP/IP iSCSI Extensions for RDMA (iSER), mapping of iSCSI over InfiniBand Network block device, mapping device node requests on UNIX-like systems over stream sockets like TCP/IP SCSI RDMA Protocol (SRP), another SCSI implementation for remote direct memory access (RDMA) transports Storage networks may also be built using Serial Attached SCSI (SAS) and Serial ATA (SATA) technologies. SAS evolved from SCSI direct-attached storage. SATA evolved from Para