In the automata theory, a tagged deterministic finite automaton (TDFA) is an extension of deterministic finite automaton (DFA). In addition to solving the recognition problem for regular languages, TDFA is also capable of submatch extraction and parsing. While canonical DFA can find out if a string belongs to the language defined by a regular expression, TDFA can also extract substrings that match specific subexpressions. More generally, TDFA can identify positions in the input string that match tagged positions in a regular expression (tags are meta-symbols similar to capturing parentheses, but without the pairing requirement). == History == TDFA were first described by Ville Laurikari in 2000. Prior to that it was unknown whether it is possible to perform submatch extraction in one pass on a deterministic finite-state automaton, so this paper was an important advancement. Laurikari described TDFA construction and gave a proof that the determinization process terminates, however the algorithm did not handle disambiguation correctly. In 2007 Chris Kuklewicz implemented TDFA in a Haskell library Regex-TDFA with POSIX longest-match semantics. Kuklewicz gave an informal description of the algorithm and answered the principal question whether TDFA are capable of POSIX longest-match disambiguation, which was doubted by other researchers. In 2017 Ulya Trafimovich described TDFA with one-symbol lookahead. The use of a lookahead symbol reduces the number of registers and register operations in a TDFA, which makes it faster and often smaller than Laurikari TDFA. Trafimovich called TDFA variants with and without lookahead TDFA(1) and TDFA(0) by analogy with LR parsers LR(1) and LR(0). The algorithm was implemented in the open-source lexer generator RE2C. Trafimovich formalized Kuklewicz disambiguation algorithm. In 2018 Angelo Borsotti worked on an experimental Java implementation of TDFA; it was published later in 2021. In 2019 Borsotti and Trafimovich adapted POSIX disambiguation algorithm by Okui and Suzuki to TDFA. They gave a formal proof of correctness of the new algorithm and showed that it is faster than Kuklewicz algorithm in practice. In 2020 Trafimovich published an article about TDFA implementation in RE2C. In 2022 Borsotti and Trafimovich published a paper with a detailed description of TDFA construction. The paper incorporated their past research and presented multi-pass TDFA that are better suited to just-in-time determinization. They also compared TDFA against other algorithms and provided benchmarks. == Formal definition == TDFA have the same basic structure as ordinary DFA: a finite set of states linked by transitions. In addition to that, TDFA have a fixed set of registers that hold tag values, and register operations on transitions that set or copy register values. The values may be scalar offsets, or offset lists for tags that match repeatedly (the latter can be represented efficiently using a trie structure). There is no one-to-one mapping between tags in a regular expression and registers in a TDFA: a single tag may need many registers, and the same register may hold values of different tags. The following definition is according to Trafimovich and Borsotti. The original definition by Laurikari is slightly different. A tagged deterministic finite automaton F {\displaystyle F} is a tuple ( Σ , T , S , S f , s 0 , R , R f , δ , φ ) {\displaystyle (\Sigma ,T,S,S_{f},s_{0},R,R_{f},\delta ,\varphi )} , where: Σ {\displaystyle \Sigma } is a finite set of symbols (alphabet) T {\displaystyle T} is a finite set of tags S {\displaystyle S} is a finite set of states with initial state s 0 {\displaystyle s_{0}} and a subset of final states S f ⊆ S {\displaystyle S_{f}\subseteq S} R {\displaystyle R} is a finite set of registers with a subset of final registers R f {\displaystyle R_{f}} (one per tag) δ : S × Σ → S × O ∗ {\displaystyle \delta :S\times \Sigma \rightarrow S\times O^{}} is a transition function φ : S f → O ∗ {\displaystyle \varphi :S_{f}\rightarrow O^{}} is a final function, where O {\displaystyle O} is a set of register operations of the following types: set register i {\displaystyle i} to nil or to the current position: i ← v {\displaystyle i\leftarrow v} , where v ∈ { n , p } {\displaystyle v\in \{\mathbf {n} ,\mathbf {p} \}} copy register j {\displaystyle j} to register i {\displaystyle i} : i ← j {\displaystyle i\leftarrow j} copy register j {\displaystyle j} to register i {\displaystyle i} and append history: i ← j ⋅ h {\displaystyle i\leftarrow j\cdot h} , where h {\displaystyle h} is a string over { n , p } {\displaystyle \{\mathbf {n} ,\mathbf {p} \}} === Example === Figure 0 shows an example TDFA for regular expression ( 1 a 2 ) ∗ 3 ( a | 4 b ) 5 b ∗ {\displaystyle (1a2)^{}3(a|4b)5b^{}} with alphabet Σ = { a , b } {\displaystyle \Sigma =\{a,b\}} and a set of tags T = { 1 , 2 , 3 , 4 , 5 } {\displaystyle T=\{1,2,3,4,5\}} that matches strings of the form a … a b … b {\displaystyle a\dots ab\dots b} with at least one symbol. TDFA has four states S = { 0 , 1 , 2 , 3 } {\displaystyle S=\{0,1,2,3\}} three of which are final S f = { 1 , 2 , 3 } {\displaystyle S_{f}=\{1,2,3\}} . The set of registers is R = { r 1 , r 2 , r 3 , r 4 , r 5 } {\displaystyle R=\{r_{1},r_{2},r_{3},r_{4},r_{5}\}} with a subset of final registers R f = { r 1 , r 2 , r 3 , r 4 , r 5 } {\displaystyle R_{f}=\{r_{1},r_{2},r_{3},r_{4},r_{5}\}} where register r i {\displaystyle r_{i}} corresponds to i {\displaystyle i} -th tag. Transitions have operations defined by the δ {\displaystyle \delta } function, and final states have operations defined by the φ {\displaystyle \varphi } function (marked with wide-tipped arrow). For example, to match string a a b {\displaystyle aab} , one starts in state 0, matches the first a {\displaystyle a} and moves to state 1 (setting registers r 1 , r 2 {\displaystyle r_{1},r_{2}} to undefined and r 3 {\displaystyle r_{3}} to the current position 0), matches the second a {\displaystyle a} and loops to state 1 (register values are now r 1 = 0 , r 2 = r 3 = 1 {\displaystyle r_{1}=0,r_{2}=r_{3}=1} ), matches b {\displaystyle b} and moves to state 2 (register values are now r 1 = 1 , r 2 = r 3 = r 4 = 2 {\displaystyle r_{1}=1,r_{2}=r_{3}=r_{4}=2} ), executes the final operations in state 2 (register values are now r 1 = 1 , r 2 = r 3 = r 4 = 2 , r 5 = 3 {\displaystyle r_{1}=1,r_{2}=r_{3}=r_{4}=2,r_{5}=3} ) and finally exits TDFA. == Complexity == Canonical DFA solve the recognition problem in linear time. The same holds for TDFA, since the number of registers and register operations is fixed and depends only on the regular expression, but not on the length of input. The overhead on submatch extraction depends on tag density in a regular expression and nondeterminism degree of each tag (the maximum number of registers needed to track all possible values of the tag in a single TDFA state). On one extreme, if there are no tags, a TDFA is identical to a canonical DFA. On the other extreme, if every subexpression is tagged, a TDFA effectively performs full parsing and has many operations on every transition. In practice for real-world regular expressions with a few submatch groups the overhead is negligible compared to matching with canonical DFA. == TDFA construction == TDFA construction is performed in a few steps. First, a regular expression is converted to a tagged nondeterministic finite automaton (TNFA). Second, a TNFA is converted to a TDFA using a determinization procedure; this step also includes disambiguation that resolves conflicts between ambiguous TNFA paths. After that, a TDFA can optionally go through a number of optimizations that reduce the number of registers and operations, including minimization that reduces the number of states. Algorithms for all steps of TDFA construction with pseudocode are given in the paper by Borsotti and Trafimovich. This section explains TDFA construction on the example of a regular expression a ∗ t b ∗ | a b {\displaystyle a^{}tb^{}|ab} , where t {\displaystyle t} is a tag and { a , b } {\displaystyle \{a,b\}} are alphabet symbols. === Tagged NFA === TNFA is a nondeterministic finite automaton with tagged ε-transitions. It was first described by Laurikari, although similar constructions were known much earlier as Mealy machines and nondeterministic finite-state transducers. TNFA construction is very similar to Thompson's construction: it mirrors the structure of a regular expression. Importantly, TNFA preserves ambiguity in a regular expression: if it is possible to match a string in two different ways, then TNFA for this regular expression has two different accepting paths for this string. TNFA definition by Borsotti and Trafimovich differs from the original one by Laurikari in that TNFA can have negative tags on transitions: they are needed to make the absence of match explicit in cases when there is a bypass for a tagged transition. Figure 1 shows TNFA for the example regu
Solomonoff's theory of inductive inference
Solomonoff's theory of inductive inference proves that, under its common sense assumptions (axioms), the best possible scientific model is the shortest algorithm that generates the empirical data under consideration. In addition to the choice of data, other assumptions are that, to avoid the post-hoc fallacy, the programming language must be chosen prior to the data and that the environment being observed is generated by an unknown algorithm. This is also called a theory of induction. Due to its basis in the dynamical (state-space model) character of Algorithmic Information Theory, it encompasses statistical as well as dynamical information criteria for model selection. It was introduced by Ray Solomonoff, based on probability theory and theoretical computer science. In essence, Solomonoff's induction derives the posterior probability of any computable theory, given a sequence of observed data. This posterior probability is derived from Bayes' rule and some universal prior, that is, a prior that assigns a positive probability to any computable theory. Solomonoff proved that this induction is incomputable (or more precisely, lower semi-computable), but noted that "this incomputability is of a very benign kind", and that it "in no way inhibits its use for practical prediction" (as it can be approximated from below more accurately with more computational resources). It is only "incomputable" in the benign sense that no scientific consensus is able to prove that the best current scientific theory is the best of all possible theories. However, Solomonoff's theory does provide an objective criterion for deciding among the current scientific theories explaining a given set of observations. Solomonoff's induction naturally formalizes Occam's razor by assigning larger prior credences to theories that require a shorter algorithmic description. == Origin == === Philosophical === The theory is based in philosophical foundations, and was founded by Ray Solomonoff around 1960. It is a mathematically formalized combination of Occam's razor and the Principle of Multiple Explanations. All computable theories which perfectly describe previous observations are used to calculate the probability of the next observation, with more weight put on the shorter computable theories. Marcus Hutter's universal artificial intelligence builds upon this to calculate the expected value of an action. === Principle === Solomonoff's induction has been argued to be the computational formalization of pure Bayesianism. To understand, recall that Bayesianism derives the posterior probability P [ T | D ] {\displaystyle \mathbb {P} [T|D]} of a theory T {\displaystyle T} given data D {\displaystyle D} by applying Bayes rule, which yields P [ T | D ] = P [ D | T ] P [ T ] P [ D | T ] P [ T ] + ∑ A ≠ T P [ D | A ] P [ A ] {\displaystyle \mathbb {P} [T|D]={\frac {\mathbb {P} [D|T]\mathbb {P} [T]}{\mathbb {P} [D|T]\mathbb {P} [T]+\sum _{A\neq T}\mathbb {P} [D|A]\mathbb {P} [A]}}} where theories A {\displaystyle A} are alternatives to theory T {\displaystyle T} . For this equation to make sense, the quantities P [ D | T ] {\displaystyle \mathbb {P} [D|T]} and P [ D | A ] {\displaystyle \mathbb {P} [D|A]} must be well-defined for all theories T {\displaystyle T} and A {\displaystyle A} . In other words, any theory must define a probability distribution over observable data D {\displaystyle D} . Solomonoff's induction essentially boils down to demanding that all such probability distributions be computable. Interestingly, the set of computable probability distributions is a subset of the set of all programs, which is countable. Similarly, the sets of observable data considered by Solomonoff were finite. Without loss of generality, we can thus consider that any observable data is a finite bit string. As a result, Solomonoff's induction can be defined by only invoking discrete probability distributions. Solomonoff's induction then allows to make probabilistic predictions of future data F {\displaystyle F} , by simply obeying the laws of probability. Namely, we have P [ F | D ] = E T [ P [ F | T , D ] ] = ∑ T P [ F | T , D ] P [ T | D ] {\displaystyle \mathbb {P} [F|D]=\mathbb {E} _{T}[\mathbb {P} [F|T,D]]=\sum _{T}\mathbb {P} [F|T,D]\mathbb {P} [T|D]} . This quantity can be interpreted as the average predictions P [ F | T , D ] {\displaystyle \mathbb {P} [F|T,D]} of all theories T {\displaystyle T} given past data D {\displaystyle D} , weighted by their posterior credences P [ T | D ] {\displaystyle \mathbb {P} [T|D]} . === Mathematical === The proof of the "razor" is based on the known mathematical properties of a probability distribution over a countable set. These properties are relevant because the infinite set of all programs is a denumerable set. The sum S of the probabilities of all programs must be exactly equal to one (as per the definition of probability) thus the probabilities must roughly decrease as we enumerate the infinite set of all programs, otherwise S will be strictly greater than one. To be more precise, for every ϵ {\displaystyle \epsilon } > 0, there is some length l such that the probability of all programs longer than l is at most ϵ {\displaystyle \epsilon } . This does not, however, preclude very long programs from having very high probability. Fundamental ingredients of the theory are the concepts of algorithmic probability and Kolmogorov complexity. The universal prior probability of any prefix p of a computable sequence x is the sum of the probabilities of all programs (for a universal computer) that compute something starting with p. Given some p and any computable but unknown probability distribution from which x is sampled, the universal prior and Bayes' theorem can be used to predict the yet unseen parts of x in optimal fashion. == Mathematical guarantees == === Solomonoff's completeness === The remarkable property of Solomonoff's induction is its completeness. In essence, the completeness theorem guarantees that the expected cumulative errors made by the predictions based on Solomonoff's induction are upper-bounded by the Kolmogorov complexity of the (stochastic) data generating process. The errors can be measured using the Kullback–Leibler divergence or the square of the difference between the induction's prediction and the probability assigned by the (stochastic) data generating process. === Solomonoff's uncomputability === Unfortunately, Solomonoff also proved that Solomonoff's induction is uncomputable. In fact, he showed that computability and completeness are mutually exclusive: any complete theory must be uncomputable. The proof of this is derived from a game between the induction and the environment. Essentially, any computable induction can be tricked by a computable environment, by choosing the computable environment that negates the computable induction's prediction. This fact can be regarded as an instance of the no free lunch theorem. == Modern applications == === Artificial intelligence === Though Solomonoff's inductive inference is not computable, several AIXI-derived algorithms approximate it in order to make it run on a modern computer. The more computing power they are given, the closer their predictions are to the predictions of inductive inference (their mathematical limit is Solomonoff's inductive inference). Another direction of inductive inference is based on E. Mark Gold's model of learning in the limit from 1967 and has developed since then more and more models of learning. The general scenario is the following: Given a class S of computable functions, is there a learner (that is, recursive functional) which for any input of the form (f(0),f(1),...,f(n)) outputs a hypothesis (an index e with respect to a previously agreed on acceptable numbering of all computable functions; the indexed function may be required consistent with the given values of f). A learner M learns a function f if almost all its hypotheses are the same index e, which generates the function f; M learns S if M learns every f in S. Basic results are that all recursively enumerable classes of functions are learnable while the class REC of all computable functions is not learnable. Many related models have been considered and also the learning of classes of recursively enumerable sets from positive data is a topic studied from Gold's pioneering paper in 1967 onwards. A far reaching extension of the Gold’s approach is developed by Schmidhuber's theory of generalized Kolmogorov complexities, which are kinds of super-recursive algorithms.
Software-defined mobile network
Software-defined mobile networking (SDMN) is an approach to the design of mobile networks where all protocol-specific features are implemented in software, maximizing the use of generic and commodity hardware and software in both the core network and radio access network (RAN). == History == Through the 20th century, telecommunications technology was driven by hardware development, with most functions implemented in special-purpose equipment. In the early 2000s, generally available CPUs became cheap enough to enable commercial software-defined radio (SDR) technology and softswitches. SDMN extends these trends into the design of mobile networks, moving nearly all network functions into software. The term "software-defined mobile network" first appeared in public literature in early 2014, used independently by Lime Microsystems and researchers from University of Oulu, Finland. == Limitations of hardware-based mobile networks == Mobile networks based on special-purpose hardware suffer from the following limitations: They have limited provisions for upgrades and usually must be replaced entirely when new standards are introduced. The individual components are not scalable in terms of performance and capacity, because the capacity of a component is fixed by the hardware implementation. Specialized equipment and its associated specialized software require vendor-specific training for the mobile operator's staff. Specialized hardware systems are usually supported and serviced by a single vendor, resulting in vendor lock-in. == Characteristics of SDMN designs == === Use of software-defined radio === SDR is an important element of SDMN, because it replaces protocol-specific radio hardware with protocol-agnostic digital transceivers. While many earlier digital radio systems used field-programmable gate arrays (FPGAs) or special-purposed digital signal processors (DSPs) for calculations on baseband radio waveforms, the SDMN approach moves all of the baseband processing into general-purpose CPUs. SDMN radio systems also use hardware with publicly-documented interfaces that is designed to be readily reproducible by multiple manufacturers. === Commodity components === SDMN designs avoid the use of components that are specialized as to their functions or that are available from only a single vendor. This is true of both the hardware and software elements of the network. === Software switching and transcoding === The telephony switches of SDMN networks are software-based, including software transcoding for speech codecs. === Centralized, distributed, or hybrid? === A new SDN architecture for wireless distribution systems (WDSs) is explored that eliminates the need for multi-hop flooding of route information and therefore enables WDNs to easily expand. The key idea is to split network control and data forwarding by using two separate frequency bands. The forwarding nodes and the SDN controller exchange link-state information and other network control signaling in one of the bands, while actual data forwarding takes place in the other band. == Advantages of SDMN == The SDMN approach has many advantages over hardware-based mobile network designs. Because SDMN hardware is protocol-agnostic, upgrades are software-only, even across technology generations. In the radio network, these changes can even be made on a site-by-site basis. Because SDMN hardware is designed to be easily sourced and reproduced: SDMN equipment can be serviced by a wider range of vendors, lowering maintenance costs. SDMN equipment can be manufactured anywhere in the world, lowering production costs. Because SDMN software is based on commodity operating systems and development tools: Support staff can be trained more quickly because they are already familiar with the underlying software systems. Many aspects of the SDMN can be monitored and managed with pre-existing tools, because they are already available in the commodity operating systems. Because SDMN network components run on general purpose computers, the network components can be scaled up in capacity by adding more computing power.
Digital first
Digital first is a communication theory that publishers should release content into new media channels in preference to old media. The premise behind the theory is that after the advent of Internet, most established media organizations continued to give priority to traditional media. Over time, those organizations faced a choice to either publish first in digital media or traditional media. A "digital first" decision occurs when a publisher chooses to distribute information online in preference to or at the expense of traditional media like print publishing. Many employers and employees find it challenging to imagine using digital first practices. Distributing content digital first introduces new practices, including a need to manage the data which tracks readership. Many paper print publishers feel intimidated by the idea of publishing content online before publishing it in paper media. Comedian John Oliver in the show Last Week Tonight criticized digital first practices as a cause of lower standards in journalism. == Digital-First Transformation in Business and Education == The classical perspective of an information system is that it represents and reflects physical reality. However, it is increasingly evident that digital technologies not only represent reality but also actively shape it, as, in many instances, the digital version is created first, and the physical version follows. Gradually, digital infrastructures are integrated in people's work and life, shaping a digital environment through technologies such as 5G, sensors, and blockchain. The Digital First Framework, developed by Professor Youngjin Yoo, is a conceptual approach that helps the physical companies in the integration of digital technologies into the core of product and service design. The shift from traditional cars, where the physical vehicle precedes its digital representation on Google maps, to autonomous vehicles, where the digital representation (the blue dot) is created first, emphasizes the digital-first mindset in the design and operation of systems. In today's business environment, it's critical for organizations to embrace a digital-first strategy. Companies built on digital platforms will significantly diverge from traditional, hierarchical business structures that typically focus on a single product or market. These digitally-centered enterprises will offer products and services that are tailored to individual requirements, utilizing algorithms to assess needs based on specific situations, and relying on external partners to provide these solutions. This highlights the need to transform traditional R&D practices. It's essential for R&D teams to move beyond their laboratories and immerse themselves in the environments of their users. Understanding the context of use is fundamental to creating a relevant platform. As an illustration, the concept of Digital-first, as defined by Rohm et al. (2019), involves the integration of digital projects within educational courses, exemplified by institutions like M-School. The program adopts a programmatic approach, where successive courses progressively build upon one another, adopting an all-encompassing perspective that regards all aspects of marketing as inherently digital. Students actively participate in real-world projects, including campaigns for community improvement, and are tasked with generating content for diverse platforms. Through hands-on collaboration with live clients and the utilization of tools such as Google AdWords and Facebook Advertising, students acquire practical experience in the realms of digital marketing and analytics. == vBook == A vBook is an eBook that is digital first media with embedded video, images, graphs, tables, text, and other media.
Hyperscale computing
In computing, hyperscale is the ability of an architecture to scale appropriately as increased demand is added to the system. This typically involves the ability to seamlessly provide and add computing, memory, networking, and storage resources to a given node or set of nodes that make up a larger computing, distributed computing, or grid computing environment. Hyperscale computing is necessary in order to build a robust and scalable cloud, big data, map reduce, or distributed storage system and is often associated with the infrastructure required to run large distributed sites such as Google, Facebook, Twitter, Amazon, Microsoft, IBM Cloud, Oracle Cloud, or Cloudflare. Companies like Ericsson, AMD, and Intel provide hyperscale infrastructure kits for IT service providers. Companies like Scaleway, Switch, Alibaba, IBM, QTS, Neysa, Digital Realty Trust, Equinix, Oracle, Meta, Amazon Web Services, SAP, Microsoft, Google, and Cloudflare build data centers for hyperscale computing. Such companies are sometimes called "hyperscalers". They are recognized for their massive scale in cloud computing and data management, operating in environments that require extensive infrastructure to accommodate large-scale data processing and storage.
Sample (graphics)
In computer graphics, a sample is an intersection of a channel and a pixel. The diagram below depicts a 24-bit pixel, consisting of 3 samples for Red, Green, and Blue. In this particular diagram, the Red sample occupies 9 bits, the Green sample occupies 7 bits and the Blue sample occupies 8 bits, totaling 24 bits per pixel. Note that the samples do not have to be equal size and not all samples are mandatory in a pixel. Also, a pixel can consist of more than 3 samples (e.g. 4 samples of the RGBA color space). A sample is related to a subpixel on a physical display.
List of operating systems
This is a list of operating systems. Computer operating systems can be categorized by technology, ownership, licensing, working state, usage, and by many other characteristics. In practice, many of these groupings may overlap. Criteria for inclusion is notability, as shown either through an existing Wikipedia article or citation to a reliable source. == Proprietary == === Acorn Computers === Arthur ARX MOS RISC iX RISC OS === Amazon === Fire OS === Amiga Inc. === AmigaOS AmigaOS 1.0-3.9 (Motorola 68000) AmigaOS 4 (PowerPC) Amiga Unix (a.k.a. Amix) === Amstrad === AMSDOS Contiki CP/M 2.2 CP/M Plus SymbOS === Apple === Apple II Apple DOS Apple Pascal ProDOS GS/OS GNO/ME Contiki Apple III Apple SOS Apple Lisa Mac Classic Mac OS A/UX (UNIX System V with BSD extensions) Copland MkLinux Pink Rhapsody macOS (formerly Mac OS X and OS X) macOS Server (formerly Mac OS X Server and OS X Server) Apple Network Server IBM AIX (Apple-customized) Apple MessagePad Newton OS iPhone and iPod Touch iOS (formerly iPhone OS) iPad iPadOS Apple Watch watchOS Apple TV tvOS Embedded operating systems bridgeOS Apple Vision Pro visionOS Embedded operating systems A/ROSE iPod software (unnamed embedded OS for iPod) Unnamed NetBSD variant for Airport Extreme and Time Capsule === Apollo Computer, Hewlett-Packard === Domain/OS – One of the first network-based systems. Run on Apollo/Domain hardware. Later bought by Hewlett-Packard. === Atari === Atari DOS (for 8-bit computers) Atari TOS Atari MultiTOS Contiki (for 8-bit, ST, Portfolio) === BAE Systems === XTS-400 === Be Inc. === BeOS BeIA BeOS r5.1d0 magnussoft ZETA (based on BeOS r5.1d0 source code, developed by yellowTAB) === Bell Labs === Unix ("Ken's new system," for its creator (Ken Thompson), officially Unics and then Unix, the prototypic operating system created in Bell Labs in 1969 that formed the basis for the Unix family of operating systems) UNIX Time-Sharing System v1 UNIX Time-Sharing System v2 UNIX Time-Sharing System v3 UNIX Time-Sharing System v4 UNIX Time-Sharing System v5 UNIX Time-Sharing System v6 MINI-UNIX PWB/UNIX USG CB Unix UNIX Time-Sharing System v7 (It is from Version 7 Unix (and, to an extent, its descendants listed below) that almost all Unix-based and Unix-like operating systems descend.) Unix System III Unix System IV Unix System V Unix System V Releases 2.0, 3.0, 3.2, 4.0, and 4.2 UNIX Time-Sharing System v8 UNIX Time-Sharing System v9 UNIX Time-Sharing System v10 Non-Unix Operating Systems: BESYS Plan 9 from Bell Labs Inferno === Burroughs Corporation, Unisys === Burroughs MCP === CII === Siris 8 === Commodore International === GEOS AmigaOS AROS Research Operating System === Control Data Corporation === ==== Lower 3000 series ==== SCOPE (Supervisory Control Of Program Execution) ==== Upper 3000 series ==== SCOPE (Supervisory Control Of Program Execution) Drum SCOPE ==== 6x00 and related Cyber ==== Chippewa Operating System (COS) MACE (Mansfield and Cahlander Executive) Kronos (Kronographic OS) NOS (Network Operating System) NOS/VE (NOS Virtual Environment) SCOPE (Supervisory Control Of Program Execution) NOS/BE NOS Batch Environment SIPROS (Simultaneous Processing Operating System) ==== Star-100 ==== Multiple Console Time Sharing System (MCTS), from General Motors Research === CloudMosa === Puffin OS === Convergent Technologies === Convergent Technologies Operating System (CTOS) – later acquired by Unisys === Cromemco === Cromemco DOS (CDOS) – a Disk Operating system compatible with CP/M Cromix – a multitasking, multi-user, Unix-like OS for Cromemco microcomputers with Z80A and/or 68000 CPU === Data General === AOS for 16-bit Data General Eclipse computers and AOS/VS for 32-bit (MV series) Eclipses, MP/AOS for microNOVA-based computers DG/UX RDOS Real-time Disk Operating System, with variants: RTOS and DOS (not related to PC DOS, MS-DOS etc.) === Datapoint === CTOS Cassette Tape Operating System for the Datapoint 2200 DOS Disk Operating System for the Datapoint 2200, 5500, and 1100 === DDC-I, Inc. === Deos – Time & Space Partitioned RTOS, Certified to DO-178B, Level A since 1998 HeartOS – POSIX-based Hard Real-Time Operating System === Digital Research, Inc. === CP/M CP/M CP/M for Intel 8080/8085 and Zilog Z80 Personal CP/M, a refinement of CP/M CP/M Plus with BDOS 3.0 CP/M-68K CP/M for Motorola 68000 CP/M-8000 CP/M for Zilog Z8000 CP/M-86 CP/M for Intel 8088/8086 CP/M-86 Plus Personal CP/M-86 MP/M Multi-user version of CP/M-80 MP/M II MP/M-86 Multi-user version of CP/M-86 MP/M 8-16, a dual-processor variant of MP/M for 8086 and 8080 CPUs. Concurrent CP/M, the successor of CP/M-80 and MP/M-80 Concurrent CP/M-86, the successor of CP/M-86 and MP/M-86 Concurrent CP/M 8-16, a dual-processor variant of Concurrent CP/M for 8086 and 8080 CPUs. Concurrent CP/M-68K, a variant for the 68000 DOS Concurrent DOS, the successor of Concurrent CP/M-86 with PC-MODE Concurrent PC DOS, a Concurrent DOS variant for IBM compatible PCs Concurrent DOS 8-16, a dual-processor variant of Concurrent DOS for 8086 and 8080 CPUs Concurrent DOS 286 Concurrent DOS XM, a real-mode variant of Concurrent DOS with EEMS support Concurrent DOS 386 Concurrent DOS 386/MGE, a Concurrent DOS 386 variant with advanced graphics terminal capabilities Concurrent DOS 68K, a port of Concurrent DOS to Motorola 68000 CPUs with DOS source code portability capabilities FlexOS 1.0 – 2.34, a derivative of Concurrent DOS 286 FlexOS 186, a variant of FlexOS for terminals FlexOS 286, a variant of FlexOS for hosts Siemens S5-DOS/MT, an industrial control system based on FlexOS IBM 4680 OS, a POS operating system based on FlexOS IBM 4690 OS, a POS operating system based on FlexOS Toshiba 4690 OS, a POS operating system based on IBM 4690 OS and FlexOS FlexOS 386, a later variant of FlexOS for hosts IBM 4690 OS, a POS operating system based on FlexOS Toshiba 4690 OS, a POS operating system based on IBM 4690 OS and FlexOS FlexOS 68K, a derivative of Concurrent DOS 68K Multiuser DOS, the successor of Concurrent DOS 386 CCI Multiuser DOS Datapac Multiuser DOS Datapac System Manager, a derivative of Datapac Multiuser DOS IMS Multiuser DOS IMS REAL/32, a derivative of Multiuser DOS IMS REAL/NG, the successor of REAL/32 DOS Plus 1.1 – 2.1, a single-user, multi-tasking system derived from Concurrent DOS 4.1 – 5.0 DR-DOS 3.31 – 6.0, a single-user, single-tasking native DOS derived from Concurrent DOS 6.0 Novell PalmDOS 1.0 Novell "Star Trek" Novell DOS 7, a single-user, multi-tasking system derived from DR DOS Caldera OpenDOS 7.01 Caldera DR-DOS 7.02 and higher === Digital Equipment Corporation, Compaq, Hewlett-Packard, Hewlett Packard Enterprise === Batch-11/DOS-11 OS/8 RSTS/E – multi-user time-sharing OS for PDP-11s RSX-11 – multiuser, multitasking OS for PDP-11s RT-11 – single user OS for PDP-11 TOPS-10 – for the PDP-10 TENEX – an ancestor of TOPS-20 from BBN, for the PDP-10 TOPS-20 – for the PDP-10 DEC MICA – for the DEC PRISM Digital UNIX – derived from OSF/1, became HP's Tru64 UNIX Ultrix VMS – originally by DEC (now by VMS Software Inc.) for the VAX mini-computer range; later renamed OpenVMS and ported to Alpha, and subsequently ported to Intel Itanium and then to x86-64 WAITS – for the PDP-6 and PDP-10 === ENEA AB === OSE – Flexible, small footprint, high-performance RTOS for control processors === Fujitsu === Towns OS XSP OS/IV MSP MSP-EX === GEC Computers === COS DOS OS4000 === General Electric, Honeywell, Bull === Real-Time Multiprogramming Operating System GCOS Multics === Google === ChromiumOS is an open source operating system development version of ChromeOS. Both operating systems are based on the Linux kernel. ChromeOS is designed to work exclusively with web applications, though has been updated to run Android apps with full support for Google Play Store. Announced on July 7, 2009, ChromeOS is currently publicly available and was released summer 2011. The ChromeOS source code was released on November 19, 2009, under the BSD license as ChromiumOS. Container-Optimized OS (COS) is an operating system that is optimized for running Docker containers, based on ChromiumOS. Android is an operating system for mobile devices. It consists of Android Runtime (userland) with Linux (kernel), with its Linux kernel modified to add drivers for mobile device hardware and to remove unused Vanilla Linux drivers. gLinux, a Linux distribution that Google uses internally Fuchsia is a capability-based real-time operating system (RTOS) scalable to universal devices, in early development, from the tiniest embedded hardware, wristwatches, tablets to the largest personal computers. Unlike ChromeOS and Android, it is not based on the Linux kernel, but instead began on a new microkernel called "Zircon", derived from "Little Kernel". Wear OS a version of Google's Android operating system designed for smartwatches and other wearables. === Green Hills Software === INTEGRITY – Reliable Operating system INTEGRITY-178B – A DO-178B certified version of INTEGRITY. μ-