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Fluentd : Fluentd is a cross-platform open-source data collection software project originally developed at Treasure Data. It is written primarily in the C programming language with a thin-Ruby wrapper that gives users flexibility.
Fluentd : Fluentd was positioned for "big data," semi- or un-structured data sets. It analyzes event logs, application logs, and clickstreams. According to Suonsyrjä and Mikkonen, the "core idea of Fluentd is to be the unifying layer between different types of log inputs and outputs.", Fluentd is available on Linux, macOS, and Windows.
Fluentd : Fluentd was created by Sadayuki Furuhashi as a project of the Mountain View-based firm Treasure Data. Written primarily in Ruby, its source code was released as open-source software in October 2011. The company announced $5 million of funding in 2013. Treasure Data was then sold to Arm Ltd. in 2018.
Fluentd : Fluentd was one of the data collection tools recommended by Amazon Web Services in 2013, when it was said to be similar to Apache Flume or Scribe. Google Cloud Platform's BigQuery recommends Fluentd as the default real-time data-ingestion tool, and uses Google's customized version of Fluentd, called google-fluent, as a default logging agent.
Fluentd : Fluent Bit is a log processor and log forwarder that is being developed as a CNCF sub-project under the umbrella of Fluent project. Fluentd is written in C and Ruby and consumes at least 60 megabytes of memory. Fluent Bit is written only in C, with no dependencies, and consumes approximately one megabyte of memory, making it easier to run under embedded Linux and in containers.
Fluentd : Goasguen, Sébastien (2014). 60 Recipes for Apache CloudStack: Using the CloudStack Ecosystem, "Chapter 6: Advanced Recipes". O'Reilly Media. ISBN 1491910127 Wilkins, Phil (2022). Logging in Action, With Fluentd, Kubernetes and more. Manning. ISBN 9781617298356
Fluentd : Official website fluentd on GitHub
Folding@home : Folding@home (FAH or F@h) is a distributed computing project aimed to help scientists develop new therapeutics for a variety of diseases by the means of simulating protein dynamics. This includes the process of protein folding and the movements of proteins, and is reliant on simulations run on volunteers' personal computers. Folding@home is currently based at the University of Pennsylvania and led by Greg Bowman, a former student of Vijay Pande. The project utilizes graphics processing units (GPUs), central processing units (CPUs), and ARM processors like those on the Raspberry Pi for distributed computing and scientific research. The project uses statistical simulation methodology that is a paradigm shift from traditional computing methods. As part of the client–server model network architecture, the volunteered machines each receive pieces of a simulation (work units), complete them, and return them to the project's database servers, where the units are compiled into an overall simulation. Volunteers can track their contributions on the Folding@home website, which makes volunteers' participation competitive and encourages long-term involvement. Folding@home is one of the world's fastest computing systems. With heightened interest in the project as a result of the COVID-19 pandemic, the system achieved a speed of approximately 1.22 exaflops by late March 2020 and reached 2.43 exaflops by April 12, 2020, making it the world's first exaflop computing system. This level of performance from its large-scale computing network has allowed researchers to run computationally costly atomic-level simulations of protein folding thousands of times longer than formerly achieved. Since its launch on October 1, 2000, Folding@home has been involved in the production of 226 scientific research papers. Results from the project's simulations agree well with experiments.
Folding@home : Proteins are an essential component to many biological functions and participate in virtually all processes within biological cells. They often act as enzymes, performing biochemical reactions including cell signaling, molecular transportation, and cellular regulation. As structural elements, some proteins act as a type of skeleton for cells, and as antibodies, while other proteins participate in the immune system. Before a protein can take on these roles, it must fold into a functional three-dimensional structure, a process that often occurs spontaneously and is dependent on interactions within its amino acid sequence and interactions of the amino acids with their surroundings. Protein folding is driven by the search to find the most energetically favorable conformation of the protein, i.e., its native state. Thus, understanding protein folding is critical to understanding what a protein does and how it works, and is considered a holy grail of computational biology. Despite folding occurring within a crowded cellular environment, it typically proceeds smoothly. However, due to a protein's chemical properties or other factors, proteins may misfold, that is, fold down the wrong pathway and end up misshapen. Unless cellular mechanisms can destroy or refold misfolded proteins, they can subsequently aggregate and cause a variety of debilitating diseases. Laboratory experiments studying these processes can be limited in scope and atomic detail, leading scientists to use physics-based computing models that, when complementing experiments, seek to provide a more complete picture of protein folding, misfolding, and aggregation. Due to the complexity of proteins' conformation or configuration space (the set of possible shapes a protein can take), and limits in computing power, all-atom molecular dynamics simulations have been severely limited in the timescales that they can study. While most proteins typically fold in the order of milliseconds, before 2010, simulations could only reach nanosecond to microsecond timescales. General-purpose supercomputers have been used to simulate protein folding, but such systems are intrinsically costly and typically shared among many research groups. Further, because the computations in kinetic models occur serially, strong scaling of traditional molecular simulations to these architectures is exceptionally difficult. Moreover, as protein folding is a stochastic process (i.e., random) and can statistically vary over time, it is challenging computationally to use long simulations for comprehensive views of the folding process. Protein folding does not occur in one step. Instead, proteins spend most of their folding time, nearly 96% in some cases, waiting in various intermediate conformational states, each a local thermodynamic free energy minimum in the protein's energy landscape. Through a process known as adaptive sampling, these conformations are used by Folding@home as starting points for a set of simulation trajectories. As the simulations discover more conformations, the trajectories are restarted from them, and a Markov state model (MSM) is gradually created from this cyclic process. MSMs are discrete-time master equation models which describe a biomolecule's conformational and energy landscape as a set of distinct structures and the short transitions between them. The adaptive sampling Markov state model method significantly increases the efficiency of simulation as it avoids computation inside the local energy minimum itself, and is amenable to distributed computing (including on GPUGRID) as it allows for the statistical aggregation of short, independent simulation trajectories. The amount of time it takes to construct a Markov state model is inversely proportional to the number of parallel simulations run, i.e., the number of processors available. In other words, it achieves linear parallelization, leading to an approximately four orders of magnitude reduction in overall serial calculation time. A completed MSM may contain tens of thousands of sample states from the protein's phase space (all the conformations a protein can take on) and the transitions between them. The model illustrates folding events and pathways (i.e., routes) and researchers can later use kinetic clustering to view a coarse-grained representation of the otherwise highly detailed model. They can use these MSMs to reveal how proteins misfold and to quantitatively compare simulations with experiments. Between 2000 and 2010, the length of the proteins Folding@home has studied have increased by a factor of four, while its timescales for protein folding simulations have increased by six orders of magnitude. In 2002, Folding@home used Markov state models to complete approximately a million CPU days of simulations over the span of several months, and in 2011, MSMs parallelized another simulation that required an aggregate 10 million CPU hours of computing. In January 2010, Folding@home used MSMs to simulate the dynamics of the slow-folding 32-residue NTL9 protein out to 1.52 milliseconds, a timescale consistent with experimental folding rate predictions but a thousand times longer than formerly achieved. The model consisted of many individual trajectories, each two orders of magnitude shorter, and provided an unprecedented level of detail into the protein's energy landscape. In 2010, Folding@home researcher Gregory Bowman was awarded the Thomas Kuhn Paradigm Shift Award from the American Chemical Society for the development of the open-source MSMBuilder software and for attaining quantitative agreement between theory and experiment. For his work, Pande was awarded the 2012 Michael and Kate Bárány Award for Young Investigators for "developing field-defining and field-changing computational methods to produce leading theoretical models for protein and RNA folding", and the 2006 Irving Sigal Young Investigator Award for his simulation results which "have stimulated a re-examination of the meaning of both ensemble and single-molecule measurements, making Pande's efforts pioneering contributions to simulation methodology."
Folding@home : Protein misfolding can result in a variety of diseases including Alzheimer's disease, cancer, Creutzfeldt–Jakob disease, cystic fibrosis, Huntington's disease, sickle-cell anemia, and type II diabetes. Cellular infection by viruses such as HIV and influenza also involve folding events on cell membranes. Once protein misfolding is better understood, therapies can be developed that augment cells' natural ability to regulate protein folding. Such therapies include the use of engineered molecules to alter the production of a given protein, help destroy a misfolded protein, or assist in the folding process. The combination of computational molecular modeling and experimental analysis has the possibility to fundamentally shape the future of molecular medicine and the rational design of therapeutics, such as expediting and lowering the costs of drug discovery. The goal of the first five years of Folding@home was to make advances in understanding folding, while the current goal is to understand misfolding and related disease, especially Alzheimer's. The simulations run on Folding@home are used in conjunction with laboratory experiments, but researchers can use them to study how folding in vitro differs from folding in native cellular environments. This is advantageous in studying aspects of folding, misfolding, and their relationships to disease that are difficult to observe experimentally. For example, in 2011, Folding@home simulated protein folding inside a ribosomal exit tunnel, to help scientists better understand how natural confinement and crowding might influence the folding process. Furthermore, scientists typically employ chemical denaturants to unfold proteins from their stable native state. It is not generally known how the denaturant affects the protein's refolding, and it is difficult to experimentally determine if these denatured states contain residual structures which may influence folding behavior. In 2010, Folding@home used GPUs to simulate the unfolded states of Protein L, and predicted its collapse rate in strong agreement with experimental results. The large data sets from the project are freely available for other researchers to use upon request and some can be accessed from the Folding@home website. The Pande lab has collaborated with other molecular dynamics systems such as the Blue Gene supercomputer, and they share Folding@home's key software with other researchers, so that the algorithms which benefited Folding@home may aid other scientific areas. In 2011, they released the open-source Copernicus software, which is based on Folding@home's MSM and other parallelizing methods and aims to improve the efficiency and scaling of molecular simulations on large computer clusters or supercomputers. Summaries of all scientific findings from Folding@home are posted on the Folding@home website after publication.
Folding@home : Like other distributed computing projects, Folding@home is an online citizen science project. In these projects non-specialists contribute computer processing power or help to analyze data produced by professional scientists. Participants receive little or no obvious reward. Research has been carried out into the motivations of citizen scientists and most of these studies have found that participants are motivated to take part because of altruistic reasons; that is, they want to help scientists and make a contribution to the advancement of their research. Many participants in citizen science have an underlying interest in the topic of the research and gravitate towards projects that are in disciplines of interest to them. Folding@home is no different in that respect. Research carried out recently on over 400 active participants revealed that they wanted to help make a contribution to research and that many had friends or relatives affected by the diseases that the Folding@home scientists investigate. Folding@home attracts participants who are computer hardware enthusiasts. These groups bring considerable expertise to the project and are able to build computers with advanced processing power. Other distributed computing projects attract these types of participants and projects are often used to benchmark the performance of modified computers, and this aspect of the hobby is accommodated through the competitive nature of the project. Individuals and teams can compete to see who can process the most computer processing units (CPUs). This latest research on Folding@home involving interview and ethnographic observation of online groups showed that teams of hardware enthusiasts can sometimes work together, sharing best practice with regard to maximizing processing output. Such teams can become communities of practice, with a shared language and online culture. This pattern of participation has been observed in other distributed computing projects. Another key observation of Folding@home participants is that many are male. This has also been observed in other distributed projects. Furthermore, many participants work in computer and technology-based jobs and careers. Not all Folding@home participants are hardware enthusiasts. Many participants run the project software on unmodified machines and do take part competitively. By January 2020, the number of users was down to 30,000. However, it is difficult to ascertain what proportion of participants are hardware enthusiasts. Although, according to the project managers, the contribution of the enthusiast community is substantially larger in terms of processing power.
Folding@home : Folding@home software at the user's end involves three primary components: work units, cores, and a client.
Folding@home : Rosetta@home is a distributed computing project aimed at protein structure prediction and is one of the most accurate tertiary structure predictors. The conformational states from Rosetta's software can be used to initialize a Markov state model as starting points for Folding@home simulations. Conversely, structure prediction algorithms can be improved from thermodynamic and kinetic models and the sampling aspects of protein folding simulations. As Rosetta only tries to predict the final folded state, and not how folding proceeds, Rosetta@home and Folding@home are complementary and address very different molecular questions. Anton is a special-purpose supercomputer built for molecular dynamics simulations. In October 2011, Anton and Folding@home were the two most powerful molecular dynamics systems. Anton is unique in its ability to produce single ultra-long computationally costly molecular trajectories, such as one in 2010 which reached the millisecond range. These long trajectories may be especially helpful for some types of biochemical problems. However, Anton does not use Markov state models (MSM) for analysis. In 2011, the Pande lab constructed a MSM from two 100-μs Anton simulations and found alternative folding pathways that were not visible through Anton's traditional analysis. They concluded that there was little difference between MSMs constructed from a limited number of long trajectories or one assembled from many shorter trajectories. In June 2011 Folding@home added sampling of an Anton simulation in an effort to better determine how its methods compare to Anton's. However, unlike Folding@home's shorter trajectories, which are more amenable to distributed computing and other parallelizing methods, longer trajectories do not require adaptive sampling to sufficiently sample the protein's phase space. Due to this, it is possible that a combination of Anton's and Folding@home's simulation methods would provide a more thorough sampling of this space.
Folding@home : Folding@home (n.d.e), "About", Folding@home, retrieved April 26, 2020 Mims, Christopher (November 8, 2010), "Why China's New Supercomputer Is Only Technically the World's Fastest", Technology Review, MIT, archived from the original on October 21, 2012, retrieved September 20, 2012 News 12 Staff (May 13, 2020), Hofstra University lends resource lab for worldwide COVID-19 research, retrieved May 24, 2020 Pande, Vijay S. (November 10, 2008), "Re: ATI and NVIDIA stats vs. PPD numbers", Folding Forum, the fifth post from below, archived from the original on March 31, 2012, retrieved April 26, 2020
Folding@home : Official website
Apache Giraph : Apache Giraph is an Apache project to perform graph processing on big data. Giraph utilizes Apache Hadoop's MapReduce implementation to process graphs. Facebook used Giraph with some performance improvements to analyze one trillion edges using 200 machines in 4 minutes. Giraph is based on a paper published by Google about its own graph processing system called Pregel. It can be compared to other Big Graph processing libraries such as Cassovary. As of September 2023, it is no longer actively developed.
Apache Giraph : Official website
GNU Octave : GNU Octave is a scientific programming language for scientific computing and numerical computation. Octave helps in solving linear and nonlinear problems numerically, and for performing other numerical experiments using a language that is mostly compatible with MATLAB. It may also be used as a batch-oriented language. As part of the GNU Project, it is free software under the terms of the GNU General Public License.
GNU Octave : The project was conceived around 1988. At first it was intended to be a companion to a chemical reactor design course. Full development was started by John W. Eaton in 1992. The first alpha release dates back to 4 January 1993 and on 17 February 1994 version 1.0 was released. Version 9.2.0 was released on 7 June 2024. The program is named after Octave Levenspiel, a former professor of the principal author. Levenspiel was known for his ability to perform quick back-of-the-envelope calculations.
GNU Octave : In addition to use on desktops for personal scientific computing, Octave is used in academia and industry. For example, Octave was used on a massive parallel computer at Pittsburgh Supercomputing Center to find vulnerabilities related to guessing social security numbers. Acceleration with OpenCL or CUDA is also possible with use of GPUs.
GNU Octave : Octave is written in C++ using the C++ standard library. Octave uses an interpreter to execute the Octave scripting language. Octave is extensible using dynamically loadable modules. Octave interpreter has an OpenGL-based graphics engine to create plots, graphs and charts and to save or print them. Alternatively, gnuplot can be used for the same purpose. Octave includes a graphical user interface (GUI) in addition to the traditional command-line interface (CLI); see #User interfaces for details.
GNU Octave : The Octave language is an interpreted programming language. It is a structured programming language (similar to C) and supports many common C standard library functions, and also certain UNIX system calls and functions. However, it does not support passing arguments by reference although function arguments are copy-on-write to avoid unnecessary duplication. Octave programs consist of a list of function calls or a script. The syntax is matrix-based and provides various functions for matrix operations. It supports various data structures and allows object-oriented programming. Its syntax is very similar to MATLAB, and careful programming of a script will allow it to run on both Octave and MATLAB. Because Octave is made available under the GNU General Public License, it may be freely changed, copied and used. The program runs on Microsoft Windows and most Unix and Unix-like operating systems, including Linux, Android, and macOS.
GNU Octave : Octave has been built with MATLAB compatibility in mind, and shares many features with MATLAB: Matrices as fundamental data type. Built-in support for complex numbers. Powerful built-in math functions and extensive function libraries. Extensibility in the form of user-defined functions. Octave treats incompatibility with MATLAB as a bug; therefore, it could be considered a software clone, which does not infringe software copyright as per Lotus v. Borland court case. MATLAB scripts from the MathWorks' FileExchange repository in principle are compatible with Octave. However, while they are often provided and uploaded by users under an Octave compatible and proper open source BSD license, the FileExchange Terms of use prohibit any usage beside MathWorks' proprietary MATLAB.
GNU Octave : Octave comes with an official graphical user interface (GUI) and an integrated development environment (IDE) based on Qt. It has been available since Octave 3.8, and has become the default interface (over the command-line interface) with the release of Octave 4.0. It was well-received by an EDN contributor, who wrote "[Octave] now has a very workable GUI" in reviewing the then-new GUI in 2014. Several 3rd-party graphical front-ends have also been developed, like ToolboX for coding education.
GNU Octave : With Octave code, the user can create GUI applications. See GUI Development (GNU Octave (version 7.1.0)). Below are some examples: Button, edit control, checkboxTextboxListbox with message boxes.Radiobuttons
GNU Octave : Octave also has many packages available. Those packages are located at Octave-Forge Octave Forge - Packages, or Github Octave Packages. It is also possible for anyone to create and maintain packages.
GNU Octave : Alternatives to GNU Octave under an open source license, other than the aforementioned MATLAB, include Scilab and FreeMat. Octave is more compatible with MATLAB than Scilab is, and FreeMat has not been updated since June 2013. Also the Julia programming language and its plotting capabilities has similarities with GNU Octave.
GNU Octave : List of numerical-analysis software Comparison of numerical-analysis software List of statistical packages List of numerical libraries
GNU Octave : Hansen, Jesper Schmidt (June 2011). GNU Octave. Beginner's Guide. Packt Publishing. ISBN 978-1-849-51332-6.
GNU Octave : Official website
GraphLab : Turi is a graph-based, high performance, distributed computation framework written in C++. The GraphLab project was started by Prof. Carlos Guestrin of Carnegie Mellon University in 2009. It is an open source project that uses the Apache License. While GraphLab was originally developed for machine learning tasks, it has also been developed for other data-mining tasks.
GraphLab : As the amounts of collected data and computing power grow (multicore, GPUs, clusters, clouds), modern datasets no longer fit into one computing node. Efficient distributed parallel algorithms for handling large-scale data are required. The GraphLab framework is a parallel programming abstraction targeted for sparse iterative graph algorithms. GraphLab provides a programming interface, allowing deployment of distributed machine learning algorithms. The main design considerations behind the design of GraphLab are: Sparse data with local dependencies Iterative algorithms Potentially asynchronous execution
GraphLab : On top of GraphLab, several implemented libraries of algorithms: Topic modeling - contains applications like LDA, which can be used to cluster documents and extract topical representations. Graph analytics - contains applications like pagerank and triangle counting, which can be applied to general graphs to estimate community structure. Clustering - contains standard data clustering tools such as Kmeans Collaborative filtering - contains a collection of applications used to make predictions about users interests and factorize large matrices. Graphical models - contains tools for making joint predictions about collections of related random variables. Computer vision - contains a collection of tools for reasoning about images.
GraphLab : Turi (formerly called Dato and before that GraphLab Inc.) is a company that was founded by Prof. Carlos Guestrin from University of Washington in May 2013 to continue development support of the GraphLab open source project. Dato Inc. raised a $6.75M Series A from Madrona Venture Group and New Enterprise Associates (NEA). They raised a $18.5M Series B from Vulcan Capital and Opus Capital, with participation from Madrona and NEA. On August 5, 2016, Turi was acquired by Apple Inc. for $200,000,000.
GraphLab : Turi homepage. SFrame: Extending GraphLab to tables
Gremlin (query language) : Gremlin is a graph traversal language and virtual machine developed by Apache TinkerPop of the Apache Software Foundation. Gremlin works for both OLTP-based graph databases as well as OLAP-based graph processors. Gremlin's automata and functional language foundation enable Gremlin to naturally support: imperative and declarative querying; host language agnosticism; user-defined domain specific languages; an extensible compiler/optimizer, single- and multi-machine execution models; hybrid depth- and breadth-first evaluation with Turing completeness. As an explanatory analogy, Apache TinkerPop and Gremlin are to graph databases what the JDBC and SQL are to relational databases. Likewise, the Gremlin traversal machine is to graph computing as what the Java virtual machine is to general purpose computing.
Gremlin (query language) : 2009-10-30 the project is born, and immediately named "TinkerPop" 2009-12-25 v0.1 is the first release 2011-05-21 v1.0 is released 2012-05-24 v2.0 is released 2015-01-16 TinkerPop becomes an Apache Incubator project 2015-07-09 v3.0.0-incubating is released 2016-05-23 Apache TinkerPop becomes a top-level project 2016-07-18 v3.1.3 and v3.2.1 are first releases as Apache TinkerPop 2017-12-17 v3.3.1 is released 2018-05-08 v3.3.3 is released 2019-08-05 v3.4.3 is released 2020-02-20 v3.4.6 is released
Gremlin (query language) : Gremlin is an Apache2-licensed graph traversal language that can be used by graph system vendors. There are typically two types of graph system vendors: OLTP graph databases and OLAP graph processors. The table below outlines those graph vendors that support Gremlin.
Gremlin (query language) : The following examples of Gremlin queries and responses in a Gremlin-Groovy environment are relative to a graph representation of the MovieLens dataset. The dataset includes users who rate movies. Users each have one occupation, and each movie has one or more categories associated with it. The MovieLens graph schema is detailed below.
Gremlin (query language) : Gremlin is a virtual machine composed of an instruction set as well as an execution engine. An analogy is drawn between Gremlin and Java.
Gremlin (query language) : Cypher Query Language, another query language on graph data SPARQL, another query language on graph data Regular path query, a theoretical query language on graph data Graph query language, a proposed standardization of a query language on graph data
Gremlin (query language) : Apache TinkerPop Homepage sql2gremlin.com (TinkerPop2) Rodriguez, M.A., "The Gremlin Graph Traversal Machine and Language," Proceedings of the ACM Database Programming Languages Conference, October, 2015.
Ilastik : ilastik is a user-friendly free open source software for image classification and segmentation. No previous experience in image processing is required to run the software. Since 2018 ilastik is further developed and maintained by Anna Kreshuk's group at European Molecular Biology Laboratory.
Ilastik : ilastik allows user to annotate an arbitrary number of classes in images with a mouse interface. Using these user annotations and the generic (nonlinear) image features, the user can train a random forest classifier. Trained ilastik classifiers can be applied new data not included in the training set in ilastik via its batch processing functionality, or without using the graphical user interface, in headless mode. Furthermore, ilastik can be integrated into various related tools: Pre-trained workflows can be executed directly from ImageJ/Fiji using the ilastik-ImageJ plugin. Pre-trained ilastik Pixel Classification workflows can be run directly in Python with the ilastik Python package, which is available via conda. ilastik has a CellProfiler module to use ilastik classifiers to process images within a CellProfiler framework.
Ilastik : ilastik was first released in 2011 by scientists at the Heidelberg Collaboratory for Image Processing (HCI), University of Heidelberg.
Ilastik : The Interactive Learning and Segmentation Toolkit Carving Cell classification and neuron classification Synapse detection Cell tracking Neural Network Classification
Ilastik : ilastik project is hosted on GitHub. It is a collaborative project, any contributions such as comments, bug reports, bug fixes or code contributions are welcome. The ilastik team can be contacted for user support on the image.sc forum.
Ilastik : Official website ilastik on GitHub ImageJ plugin on github ilastik on forum.image.sc
Information Harvesting : Information Harvesting (IH) was an early data mining product from the 1990s. It was invented by Ralphe Wiggins and produced by the Ryan Corp, later Information Harvesting Inc., of Cambridge, Massachusetts. Wiggins had a background in genetic algorithms and fuzzy logic. IH sought to infer rules from sets of data. It did this first by classifying various input variables into one of a number of bins, thereby putting some structure on the continuous variables in the input. IH then proceeds to generate rules, trading off generalization against memorization, that will infer the value of the prediction variable, possibly creating many levels of rules in the process. It included strategies for checking if overfitting took place and, if so, correcting for it. Because of its strategies for correcting for overfitting by considering more data, and refining the rules based on that data, IH might also be considered to be a form of machine learning. The advantage of IH, as compared with other data mining products of its time and even later, was that it provided a mechanism for finding multiple rules that would classify the data and determining, according to set criteria, the best rules to use. == References ==
Jubatus : Jubatus is an open-source online machine learning and distributed computing framework developed at Nippon Telegraph and Telephone and Preferred Infrastructure. Its features include classification, recommendation, regression, anomaly detection and graph mining. It supports many client languages, including C++, Java, Ruby and Python. It uses Iterative Parameter Mixture for distributed machine learning.
Jubatus : Jubatus supports: Multi-classification algorithms: Perceptron Passive Aggressive Confidence Weighted Adaptive Regularization of Weight Vectors Normal Herd Recommendation algorithms using: Inverted index Minhash Locality-sensitive hashing Regression algorithms: Passive Aggressive feature extraction method for natural language: n-gram Text segmentation == References ==
Julia (programming language) : Julia is a high-level, general-purpose dynamic programming language, designed to be fast and productive, for e.g. data science, artificial intelligence, machine learning, modeling and simulation, most commonly used for numerical analysis and computational science. Distinctive aspects of Julia's design include a type system with parametric polymorphism and the use of multiple dispatch as a core programming paradigm, a default just-in-time (JIT) compiler (with support for ahead-of-time compilation) and an efficient (multi-threaded) garbage collection implementation. Notably Julia does not support classes with encapsulated methods and instead it relies on structs with generic methods/functions not tied to them. By default, Julia is run similarly to scripting languages, using its runtime, and allows for interactions, but Julia programs/source code can also optionally be sent to users in one ready-to-install/run file, which can be made quickly, not needing anything preinstalled. Julia programs can also be (separately) compiled to binary executables, even allowing no-source-code distribution, and the executables can get much smaller with Julia 1.12. Such compilation is not needed for speed, though it can decrease constant-factor startup cost, since Julia is also compiled when running interactively, but it can help with hiding source code. Features of the language can be separately compiled, so Julia can be used, for example, with its runtime or without it (which allows for smaller executables and libraries but is limited in capabilities). Julia programs can reuse libraries from other languages by calling them, e.g. calling C or Rust libraries, and Julia (libraries) can also be called from other languages, e.g. Python and R, and several Julia packages have been made easily available from those languages, in the form of Python and R libraries for corresponding Julia packages. Calling in either direction has been implemented for many languages, not just those and C++. Julia's Visual Studio Code extension provides a fully-featured integrated development environment with "built-in dynamic autocompletion, inline results, plot pane, integrated REPL, variable view, code navigation, and many other advanced language features" e.g. debugging is possible, linting, and profiling.
Julia (programming language) : Work on Julia began in 2009, when Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and Alan Edelman set out to create a free language that was both high-level and fast. On 14 February 2012, the team launched a website with a blog post explaining the language's mission. In an interview with InfoWorld in April 2012, Karpinski said of the name "Julia": "There's no good reason, really. It just seemed like a pretty name." Bezanson said he chose the name on the recommendation of a friend, then years later wrote: Maybe julia stands for "Jeff's uncommon lisp is automated"? Julia's syntax is now considered stable, since version 1.0 in 2018, and Julia has a backward compatibility guarantee for 1.x and also a stability promise for the documented (stable) API, while in the years before in the early development prior to 0.7 the syntax (and semantics) was changed in new versions. All of the (registered package) ecosystem uses the new and improved syntax, and in most cases relies on new APIs that have been added regularly, and in some cases minor additional syntax added in a forward compatible way e.g. in Julia 1.7. In the 10 years since the 2012 launch of pre-1.0 Julia, the community has grown. The Julia package ecosystem has over 11.8 million lines of code (including docs and tests). The JuliaCon academic conference for Julia users and developers has been held annually since 2014 with JuliaCon2020 welcoming over 28,900 unique viewers, and then JuliaCon2021 breaking all previous records (with more than 300 JuliaCon2021 presentations available for free on YouTube, up from 162 the year before), and 43,000 unique viewers during the conference. Three of the Julia co-creators are the recipients of the 2019 James H. Wilkinson Prize for Numerical Software (awarded every four years) "for the creation of Julia, an innovative environment for the creation of high-performance tools that enable the analysis and solution of computational science problems." Also, Alan Edelman, professor of applied mathematics at MIT, has been selected to receive the 2019 IEEE Computer Society Sidney Fernbach Award "for outstanding breakthroughs in high-performance computing, linear algebra, and computational science and for contributions to the Julia programming language." Both Julia 0.7 and version 1.0 were released on 8 August 2018. Work on Julia 0.7 was a "huge undertaking" (e.g., because of an "entirely new optimizer"), and some changes were made to semantics, e.g. the iteration interface was simplified. Julia 1.6 was the largest release since 1.0, and it was the long-term support (LTS) version for the longest time, faster on many fronts, e.g. introduced parallel precompilation and faster loading of packages, in some cases "50x speedup in load times for large trees of binary artifacts". Since 1.7 Julia development is back to time-based releases. Julia 1.7 was released in November 2021 with many changes, e.g. a new faster random-number generator and Julia 1.7.3 fixed e.g. at least one security issue. Julia 1.8 was released in 2022 and 1.8.5 in January 2023, with 1.8.x improvements for distributing Julia programs without source code, and compiler speedup, in some cases by 25%, and more controllable inlining (i.e. now also allowing applying @inline at the call site, not just on the function itself). Julia 1.9 was released on 7 May 2023. It has many improvements, such as the ability to precompile packages to native machine code (older Julia versions also have precompilation for packages, but only partial, never fully to native code, so those earlier versions had a "first use" penalty, slowing down while waiting to fully compile). Precompiled packages, since version 1.9, can be up to hundreds of times faster on first use (e.g. for CSV.jl and DataFrames.jl), and to improve precompilation of packages a new package PrecompileTools.jl has been introduced. Julia 1.10 was released on 25 December 2023 with many new features, e.g. parallel garbage collection, and improved package load times and a new parser, i.e. it rewritten in Julia, with better error messages and improved stacktrace rendering. Julia 1.11 was released on 7 October 2024 (and 1.11.4 on 10 March 2025), and with it 1.10.5 became the next long-term support (LTS) version (i.e. those are the only two supported versions), since replaced by 1.10.8 released on 10 March, and 1.6 is no longer an LTS version. Julia 1.11 adds e.g. parallel garbage collection and a new public keyword to signal safe public API (Julia users are advised to use such API, not internals, of Julia or packages, and package authors advised to use the keyword, generally indirectly, e.g. prefixed with the @compat macro, from Compat.jl, to also support older Julia versions, at least the LTS version). Julia 1.11.1 has much improved startup (over 1.11.0 that had a regression), and over 1.10, and this can be important for some benchmarks. Much smaller binary executables are possible with juliac which is available in the upcoming Julia 1.12.
Julia (programming language) : Julia is a general-purpose programming language, while also originally designed for numerical/technical computing. It is also useful for low-level systems programming, as a specification language, high-level synthesis (HLS) tool (for hardware, e.g. FPGAs), and for web programming at both server and client side. The main features of the language are: Multiple dispatch: providing ability to define function behavior across combinations of argument types Dynamic type system: types for documentation, optimization, and dispatch Performance approaching that of statically-typed languages like C A built-in package manager Lisp-like macros and other metaprogramming facilities Designed for parallel and distributed computing Coroutines: lightweight green threading Automatic generation of code for different argument types Extensible conversions and promotions for numeric and other types Multiple dispatch (also termed multimethods in Lisp) is a generalization of single dispatch – the polymorphic mechanism used in common object-oriented programming (OOP) languages, such as Python, C++, Java, JavaScript, and Smalltalk – that uses inheritance. In Julia, all concrete types are subtypes of abstract types, directly or indirectly subtypes of the Any type, which is the top of the type hierarchy. Concrete types can not themselves be subtyped the way they can in other languages; composition is used instead (see also inheritance vs subtyping). By default, the Julia runtime must be pre-installed as user-provided source code is run. Alternatively, Julia (GUI) apps can be quickly bundled up into a single file with AppBundler.jl for "building Julia GUI applications in modern desktop application installer formats. It uses Snap for Linux, MSIX for Windows, and DMG for MacOS as targets. It bundles full Julia within the app". PackageCompiler.jl can build standalone executables that need no Julia source code to run. In Julia, everything is an object, much like object-oriented languages; however, unlike most object-oriented languages, all functions use multiple dispatch to select methods, rather than single dispatch. Most programming paradigms can be implemented using Julia's homoiconic macros and packages. Julia's syntactic macros (used for metaprogramming), like Lisp macros, are more powerful than text-substitution macros used in the preprocessor of some other languages such as C, because they work at the level of abstract syntax trees (ASTs). Julia's macro system is hygienic, but also supports deliberate capture when desired (like for anaphoric macros) using the esc construct. Julia draws inspiration from various dialects of Lisp, including Scheme and Common Lisp, and it shares many features with Dylan, also a multiple-dispatch-oriented dynamic language (which features an infix syntax rather than a Lisp-like prefix syntax, while in Julia "everything" is an expression), and with Fortress, another numerical programming language (which features multiple dispatch and a sophisticated parametric type system). While Common Lisp Object System (CLOS) adds multiple dispatch to Common Lisp, not all functions are generic functions. In Julia, Dylan, and Fortress, extensibility is the default, and the system's built-in functions are all generic and extensible. In Dylan, multiple dispatch is as fundamental as it is in Julia: all user-defined functions and even basic built-in operations like + are generic. Dylan's type system, however, does not fully support parametric types, which are more typical of the ML lineage of languages. By default, CLOS does not allow for dispatch on Common Lisp's parametric types; such extended dispatch semantics can only be added as an extension through the CLOS Metaobject Protocol. By convergent design, Fortress also features multiple dispatch on parametric types; unlike Julia, however, Fortress is statically rather than dynamically typed, with separate compiling and executing phases. The language features are summarized in the following table: An example of the extensibility of Julia, the Unitful.jl package adds support for physical units of measurement to the language.
Julia (programming language) : The Julia official distribution includes an interactive command-line read–eval–print loop (REPL), with a searchable history, tab completion, and dedicated help and shell modes, which can be used to experiment and test code quickly. The following fragment represents a sample session example where strings are concatenated automatically by println: The REPL gives user access to the system shell and to help mode, by pressing ; or ? after the prompt (preceding each command), respectively. It also keeps the history of commands, including between sessions. Code can be tested inside Julia's interactive session or saved into a file with a .jl extension and run from the command line by typing: Julia uses UTF-8 and LaTeX codes, allowing it to support common math symbols for many operators, such as ∈ for the in operator, typable with \in then pressing Tab ↹ (i.e. uses LaTeX codes, or also possible by simply copy-pasting, e.g. √ and ∛ possible for sqrt and cbrt functions). Julia has support for Unicode 15.1 (Julia 1.12-DEV supports latest 16.0 release for the languages of the world, even for source code, e.g. variable names (while it's recommended to use English for public code, and e.g. package names). Julia is supported by Jupyter, an online interactive "notebooks" environment, and Pluto.jl, a "reactive notebook" (where notebooks are saved as pure Julia files), a possible replacement for the former kind. In addition Posit's (formerly RStudio Inc's) Quarto publishing system supports Julia, Python, R and Observable JavaScript (those languages have official support by the company, and can even be weaved together in the same notebook document, more languages are unofficially supported). The REPL can be extended with additional modes, and has been with packages, e.g. with an SQL mode, for database access, and RCall.jl adds an R mode, to work with the R language.
Julia (programming language) : Julia has a built-in package manager and includes a default registry system. Packages are most often distributed as source code hosted on GitHub, though alternatives can also be used just as well. Packages can also be installed as binaries, using artifacts. Julia's package manager is used to query and compile packages, as well as managing environments. Federated package registries are supported, allowing registries other than the official to be added locally.
Julia (programming language) : Julia's core is implemented in Julia and C, together with C++ for the LLVM dependency. The code parsing, code-lowering, and bootstrapping were implemented in FemtoLisp, a Scheme dialect, up to version 1.10. Since that version the new pure-Julia stdlib package JuliaSyntax.jl is used for the parsing (while the old one can still be chosen) which improves speed and "greatly improves parser error messages in various cases". The LLVM compiler infrastructure project is used as the back end for generating optimized machine code for all commonly-used platforms. With some exceptions, the standard library is implemented in Julia.
Julia (programming language) : Julia has been adopted at many universities including MIT, Stanford, UC Berkeley, Ferdowsi University of Mashhad and the University of Cape Town. Large private firms across many sectors have adopted the language including Amazon, IBM, JP Morgan AI Research, and ASML. Julia has also been used by government agencies including NASA and the FAA, as well as every US national energy laboratory.
Julia (programming language) : Comparison of numerical-analysis software Comparison of statistical packages Differentiable programming JuMP – an algebraic modeling language for mathematical optimization embedded in Julia Python Nim Ring Mojo
Julia (programming language) : Nagar, Sandeep (2017). Beginning Julia Programming: For Engineers and Scientists. Springer. ISBN 978-1-4842-3171-5. Bezanson, J; Edelman, A; Karpinski, S; Shah, V. B (2017). "Julia: A fresh approach to numerical computing". SIAM Review. 59 (1): 65–98. arXiv:1411.1607. CiteSeerX 10.1.1.760.8894. doi:10.1137/141000671. S2CID 13026838. Joshi, Anshul (2016). Julia for Data Science － Explore the world of data science from scratch with Julia by your side. Packt. ISBN 978-1-78355-386-0. Tobin A Driscoll and Richard J. Braun (Aug. 2022). "Fundamentals of Numerical Computation: Julia Edition". SIAM. ISBN 978-1-611977-00-4. C. T. Kelley (2022). "Solving Nonlinear Equations with Iterative Methods: Solvers and Examples in Julia", SIAM. ISBN 978-1-611977-26-4. Kalicharan, Noel (2021). Julia - Bit by Bit. Undergraduate Topics in Computer Science. Springer. doi:10.1007/978-3-030-73936-2. ISBN 978-3-030-73936-2. S2CID 235917112. Clemens Heitzinger (2022): "Algorithms with Julia", Springer, ISBN 978-3-031-16559-7.
Julia (programming language) : Official website Julia on GitHub
KNIME : KNIME ( ), the Konstanz Information Miner, is a free and open-source data analytics, reporting and integration platform. KNIME integrates various components for machine learning and data mining through its modular data pipelining "Building Blocks of Analytics" concept. A graphical user interface and use of JDBC allows assembly of nodes blending different data sources, including preprocessing (ETL: Extraction, Transformation, Loading), for modeling, data analysis and visualization without, or with minimal, programming. Since 2006, KNIME has been used in pharmaceutical research, and in other areas including CRM customer data analysis, business intelligence, text mining and financial data analysis. Recently, attempts were made to use KNIME as robotic process automation (RPA) tool. KNIME's headquarters are based in Zurich, with additional offices in Konstanz, Berlin, and Austin (USA).
KNIME : Development of KNIME began in January 2004, with a team of software engineers at the University of Konstanz, as an open-source platform. The original team, headed by Michael Berthold, came from a Silicon Valley pharmaceutical industry software company. The initial goal was to create a modular, highly scalable and open data processing platform that allowed for the easy integration of different data loading, processing, transformation, analysis and visual exploration modules, without focus on any particular application area. The platform was intended to be a collaboration and research platform and also serve as an integration platform for various other data analysis projects. In 2006, the first version of KNIME was released. Several pharmaceutical companies started using KNIME, and a number of life science software vendors began integrating their tools into the platform. Later that year, after an article in the German magazine c't, users from a number of other areas joined ship. As of 2012, KNIME is in use by over 15,000 actual users (i.e. not counting downloads, but users regularly retrieving updates) in the life sciences and at banks, publishers, car manufacturer, telcos, consulting firms, and various other industries, and a large number of research groups, worldwide. Latest updates to KNIME Server and KNIME Big Data Extensions, provide support for Apache Spark 2.3, Parquet and HDFS-type storage. For the sixth year in a row, KNIME has been placed as a leader for Data Science and Machine Learning Platforms in Gartner's Magic Quadrant.
KNIME : These are the design principles and features that KNIME software follows: Visual, Interactive Framework: KNIME Software prioritizes a user-friendly and intuitive approach to data analysis. This is achieved through a visual and interactive framework where data flows can be combined using a drag-and-drop interface. Users can develop customized and interactive applications by creating simple to advanced and highly-automated data pipelines. These may include, for example, access to databases, machine learning libraries, logic for workflow control (e.g., loops, switches, etc.), abstraction (e.g., interactive widgets), invocation, dynamic data apps, integrated deployment, or error handling. Modularity: processing units and data containers should remain independent of each other. This design choice enables easy distribution of computation and allows for the independent development of different algorithms. Data types within KNIME are encapsulated, meaning no types are predefined. This design choice facilitates the addition of new data types and their integration with existing ones, bringing along type-specific renderers and comparators. Additionally, this principle enables the inspection of results at the end of each single data operation. Extensibility: KNIME Software is designed to be extensible. Adding new processing nodes or views is made simple through a plugin mechanism. This mechanism ensures that users can distribute their custom functionalities without the need for complicated install or uninstall procedures. Interleaving No-Code with Code: the platform supports the integration of both visual programming (no-code) and script-based programming (e.g., Python, R, Javascript) approaches to data analysis. This design principle is referred to as low-code. Automation and Scalability: for example, the use of parameterization via flow variables, or the encapsulation of workflow segments in components contribute to reduce manual work and errors in analyses. Additionally, the scheduling of workflow execution (available in KNIME Business Hub and KNIME Community Hub for Teams) reduces dependency on human resources. In terms of scalability, a few examples include the ability to handle large datasets (millions of rows), execute multiple processes simultaneously out of the box and reuse workflow segments. Full Usability: due to the open source nature, KNIME Analytics Platform provides free full usability with no limited trial periods.
KNIME : KNIME allows users to visually create data flows (or pipelines), selectively execute some or all analysis steps, and later inspect the results, models, using interactive widgets and views. KNIME is written in Java and based on Eclipse. It makes use of an extension mechanism to add plugins providing additional functionality. The core version includes hundreds of modules for data integration (file I/O, database nodes supporting all common database management systems through JDBC or native connectors: SQLite, MS-Access, SQL Server, MySQL, Oracle, PostgreSQL, Vertica and H2), data transformation (filter, converter, splitter, combiner, joiner), and the commonly used methods of statistics, data mining, analysis and text analytics. Visualization is supported with the Report Designer extension. KNIME workflows can be used as data sets to create report templates that can be exported to document formats such as doc, ppt, xls, pdf and others. Other capabilities of KNIME are: KNIMEs core-architecture allows processing of large data volumes that are only limited by the available hard disk space (not limited to the available RAM). E.g. KNIME allows analysis of 300 million customer addresses, 20 million cell images and 10 million molecular structures. Additional plugins allow the integration of methods for text mining, image mining, as well as time series analysis and network. KNIME integrates various other open-source projects, e.g., machine learning algorithms from Weka, H2O.ai, Keras, Spark, the R project and LIBSVM; as well as plotly, JFreeChart, ImageJ, and the Chemistry Development Kit. KNIME is implemented in Java, allows for wrappers calling other code, in addition to providing nodes that allow it to run Java, Python, R, Ruby and other code fragments.
KNIME : In 2024, KNIME version 5.3 is released under the same GPLv3 license as previous versions. As of version 2.1, KNIME is released under the GPLv3 license, with an exception that allows others to use the well-defined node API to add proprietary extensions. This allows commercial software vendors to add wrappers calling their tools from KNIME.
KNIME : KNIME allows the performance of data analysis without programming skills. Several free, online courses are provided.
KNIME : Weka – machine-learning algorithms that can be integrated in KNIME ELKI – data mining framework with many clustering algorithms Keras - neural network library Orange - an open-source data visualization, machine learning and data mining toolkit with a similar visual programming front-end List of free and open-source software packages
KNIME : KNIME Homepage KNIME Hub - Official community platform to search and find nodes, components, workflows and collaborate on new solutions Nodepit - KNIME node collection supporting versioning and node installation
Kubeflow : Kubeflow is an open-source platform for machine learning and MLOps on Kubernetes introduced by Google. The different stages in a typical machine learning lifecycle are represented with different software components in Kubeflow, including model development (Kubeflow Notebooks), model training (Kubeflow Pipelines, Kubeflow Training Operator), model serving (KServe), and automated machine learning (Katib). Each component of Kubeflow can be deployed separately, and it is not a requirement to deploy every component.
Kubeflow : The Kubeflow project was first announced at KubeCon + CloudNativeCon North America 2017 by Google engineers David Aronchick, Jeremy Lewi, and Vishnu Kannan to address a perceived lack of flexible options for building production-ready machine learning systems. The project has also stated it began as a way for Google to open-source how they ran TensorFlow internally. The first release of Kubeflow (Kubeflow 0.1) was announced at KubeCon + CloudNativeCon Europe 2018. Kubeflow 1.0 was released in March 2020 via a public blog post announcing that many Kubeflow components were graduating to a "stable status", indicating they were now ready for production usage. In October 2022, Google announced that the Kubeflow project had applied to join the Cloud Native Computing Foundation. In July 2023, the foundation voted to accept Kubeflow as an incubating stage project.
Kubeflow : Official website Kubeflow on GitHub
KXEN Inc. : KXEN was an American software company which existed from 1998 to 2013 when it was acquired by SAP AG.
KXEN Inc. : KXEN was founded in June 1998 by Roger Haddad and Michel Bera. It was based in San Francisco, California with offices in Paris and London. On September 10, 2013, SAP AG announced plans to acquire KXEN. On October 1, 2013, a letter to KXEN customers announced the acquisition closed. KXEN primarily marketed predictive analytics software.
KXEN Inc. : InfiniteInsight is a predictive modeling suite developed by KXEN that assists analytic professionals, and business executives to extract information from data. Among other functions, InfiniteInsight is used for variable importance, classification, regression, segmentation, time series, product recommendation, as described and expressed by the Java Data Mining interface, and for social network analysis. InfiniteInsight allows prediction of a behavior or a value, the forecast of a time series or the understanding of a group of individuals with similar behavior. Advanced functions include behavioral modeling, exporting the model code into different target environments or building predictive models on top of SAS or SPSS data files. Competitors are SAS Enterprise Miner, IBM SPSS Modeler, and Statistica. Open source predictive tools like the R package or Weka are also competitors, since they provide similar features free of charge.
KXEN Inc. : Data mining Supervised learning
KXEN Inc. : Official website
L-1 Identity Solutions : L-1 Identity Solutions, Inc. is a large American defense contractor in Connecticut.
L-1 Identity Solutions : L-1 Identity Solutions was formed on August 29, 2006, from a merger of Viisage Technology, Inc. and Identix Incorporated. It specializes in selling face recognition systems, electronic passports, such as Fly Clear, and other biometric technology to governments such as the United States and Saudi Arabia. It also licenses technology to other companies internationally, including China. On July 26, 2011, Safran (NYSE Euronext Paris: SAF) acquired L-1 Identity Solutions, Inc. for a total cash amount of USD 1.09 billion. L-1 was part of Morpho's MorphoTrust department which rebranded to Idemia in 2017. Bioscrypt is a biometrics research, development and manufacturing company purchased by L-1 Identity Solutions. It provides fingerprint IP readers for physical access control systems, Facial recognition system readers for contactless access control authentication and OEM fingerprint modules for embedded applications. According to IMS Research, Bioscrypt has been the world market leader in biometric access control for enterprises (since 2006) with a worldwide market share of over 13%. In 2011, Bioscrypt was sold to Safran Morpho.
L-1 Identity Solutions : Idemia official website
Lattice Miner : Lattice Miner is a formal concept analysis software tool for the construction, visualization and manipulation of concept lattices. It allows the generation of formal concepts and association rules as well as the transformation of formal contexts via apposition, subposition, reduction and object/attribute generalization, and the manipulation of concept lattices via approximation, projection and selection. Lattice Miner allows also the drawing of nested line diagrams.
Lattice Miner : Formal concept analysis (FCA) is a branch of applied mathematics based on the formalization of concept and concept hierarchy and mainly used as a framework for conceptual clustering and rule mining. Over the last two decades, a collection of tools have emerged to help FCA users visualize and analyze concept lattices. They range from the earliest DOS-based implementations (e.g., ConImp and GLAD) to more recent implementations in Java like ToscanaJ, Galicia, ConExp and Coron. A main issue in the development of FCA tools is to visualize large concept lattices and provide efficient mechanisms to highlight patterns (e.g., concepts, associations) that could be relevant to the user. The initial objective of the FCA tool called Lattice Miner was to focus on visualization mechanisms for the representation of concept lattices, including nested line diagrams. Later on, many other interesting features were integrated into the tool.
Lattice Miner : Lattice Miner is a Java-based platform whose functions are articulated around a core. The Lattice Miner core provides all low-level operations and structures for the representation and manipulation of contexts, lattices and association rules. Mainly, the core of Lattice Miner consists of three modules: context, concept and association rule modules. The user interface offers a context editor and concept lattice manipulator to assist the user in a set of tasks. The architecture of Lattice Miner is open and modular enough to allow the integration of new features and facilities in each one of its components.
Lattice Miner : http://www.upriss.org.uk/fca/fca.html http://w3.uqo.ca/icfca10/ http://sourceforge.net/projects/lattice-miner/
LIBSVM : LIBSVM and LIBLINEAR are two popular open source machine learning libraries, both developed at the National Taiwan University and both written in C++ though with a C API. LIBSVM implements the sequential minimal optimization (SMO) algorithm for kernelized support vector machines (SVMs), supporting classification and regression. LIBLINEAR implements linear SVMs and logistic regression models trained using a coordinate descent algorithm. The SVM learning code from both libraries is often reused in other open source machine learning toolkits, including GATE, KNIME, Orange and scikit-learn. Bindings and ports exist for programming languages such as Java, MATLAB, R, Julia, and Python. It is available in e1071 library in R and scikit-learn in Python. Both libraries are free software released under the 3-clause BSD license.
LIBSVM : LIBSVM homepage LIBLINEAR homepage LIBLINEAR in R
Linguamatics : Linguamatics, headquartered in Cambridge, England, with offices in the United States and UK, is a provider of text mining systems through software licensing and services, primarily for pharmaceutical and healthcare applications. Founded in 2001, the company was purchased by IQVIA in January 2019.
Linguamatics : The company develops enterprise search tools for the life sciences sector. The core natural language processing engine (I2E) uses a federated architecture to incorporate data from 3rd party resources. Initially developed to be used interactively through a graphic user interface, the core software also has an application programming interface that can be used to automate searches. LabKey, Penn Medicine, Atrius Health and Mercy all use Linguamatics software to extract electronic health record data into data warehouses. Linguamatics software is used by 17 of the top 20 global pharmaceutical companies, the US Food and Drug Administration, as well as healthcare providers.
Linguamatics : The core software, "I2E", is used by a number of companies to either extend their own software or to publish their data. Copyright Clearance Center uses I2E to produce searchable indexes of material that would otherwise be unsearchable due to copyright. Thomson Reuters produces Cortellis Informatics Clinical Text Analytics, which depends on I2E to make clinical data accessible and searchable. Pipeline Pilot can integrate I2E as part of a workflow. ChemAxon can be used alongside I2E to allow named entity recognition of chemicals within unstructured data. Data sources include MEDLINE, ClinicalTrials.gov, FDA Drug Labels, PubMed Central, and Patent Abstracts.
Linguamatics : List of academic databases and search engines == References ==
Apache Mahout : Apache Mahout is a project of the Apache Software Foundation to produce free implementations of distributed or otherwise scalable machine learning algorithms focused primarily on linear algebra. In the past, many of the implementations use the Apache Hadoop platform, however today it is primarily focused on Apache Spark. Mahout also provides Java/Scala libraries for common math operations (focused on linear algebra and statistics) and primitive Java collections. Mahout is a work in progress; a number of algorithms have been implemented.
Apache Mahout : Official website
Mallet (software project) : MALLET is a Java "Machine Learning for Language Toolkit".
Mallet (software project) : MALLET is an integrated collection of Java code useful for statistical natural language processing, document classification, cluster analysis, information extraction, topic modeling and other machine learning applications to text.
Mallet (software project) : MALLET was developed primarily by Andrew McCallum, of the University of Massachusetts Amherst, with assistance from graduate students and faculty from both UMASS and the University of Pennsylvania.
Mallet (software project) : Official website of the project at the University of Massachusetts Amherst The Topic Modeling Tool is an independently developed GUI that outputs MALLET results in CSV and HTML files
Maple (software) : Maple is a symbolic and numeric computing environment as well as a multi-paradigm programming language. It covers several areas of technical computing, such as symbolic mathematics, numerical analysis, data processing, visualization, and others. A toolbox, MapleSim, adds functionality for multidomain physical modeling and code generation. Maple's capacity for symbolic computing include those of a general-purpose computer algebra system. For instance, it can manipulate mathematical expressions and find symbolic solutions to certain problems, such as those arising from ordinary and partial differential equations. Maple is developed commercially by the Canadian software company Maplesoft. The name 'Maple' is a reference to the software's Canadian heritage.
Maple (software) : The first concept of Maple arose from a meeting in late 1980 at the University of Waterloo. Researchers at the university wished to purchase a computer powerful enough to run the Lisp-based computer algebra system Macsyma. Instead, they opted to develop their own computer algebra system, named Maple, that would run on lower cost computers. Aiming for portability, they began writing Maple in programming languages from the BCPL family (initially using a subset of B and C, and later on only C). A first limited version appeared after three weeks, and fuller versions entered mainstream use beginning in 1982. By the end of 1983, over 50 universities had copies of Maple installed on their machines. In 1984, the research group arranged with Watcom Products Inc to license and distribute the first commercially available version, Maple 3.3. In 1988 Waterloo Maple Inc. (Maplesoft) was founded. The company's original goal was to manage the distribution of the software, but eventually it grew to have its own R&D department, where most of Maple's development takes place today (the remainder being done at various university laboratories). In 1989, the first graphical user interface for Maple was developed and included with version 4.3 for the Macintosh. X11 and Windows versions of the new interface followed in 1990 with Maple V. In 1992, Maple V Release 2 introduced the Maple "worksheet" that combined text, graphics, and input and typeset output. In 1994 a special issue of a newsletter created by Maple developers called MapleTech was published. In 1999, with the release of Maple 6, Maple included some of the NAG Numerical Libraries. In 2003, the current "standard" interface was introduced with Maple 9. This interface is primarily written in Java (although portions, such as the rules for typesetting mathematical formulae, are written in the Maple language). The Java interface was criticized for being slow; improvements have been made in later versions, although the Maple 11 documentation recommends the previous ("classic") interface for users with less than 500 MB of physical memory. Between 1995 and 2005 Maple lost significant market share to competitors due to a weaker user interface. With Maple 10 in 2005, Maple introduced a new "document mode" interface, which has since been further developed across several releases. In September 2009 Maple and Maplesoft were acquired by the Japanese software retailer Cybernet Systems.
Maple (software) : Features of Maple include: Support for symbolic and numeric computation with arbitrary precision Elementary and special mathematical function libraries Complex numbers and interval arithmetic Arithmetic, greatest common divisors and factorization for multivariate polynomials over the rationals, finite fields, algebraic number fields, and algebraic function fields Limits, series and asymptotic expansions Gröbner basis Differential Algebra Matrix manipulation tools including support for sparse arrays Mathematical function graphing and animation tools Solvers for systems of equations, diophantine equations, ODEs, PDEs, DAEs, DDEs and recurrence relations Numeric and symbolic tools for discrete and continuous calculus including definite and indefinite integration, definite and indefinite summation, automatic differentiation and continuous and discrete integral transforms Constrained and unconstrained local and global optimization Statistics including model fitting, hypothesis testing, and probability distributions Tools for data manipulation, visualization and analysis Tools for probability and combinatoric problems Support for time-series and unit based data Connection to online collection of financial and economic data Tools for financial calculations including bonds, annuities, derivatives, options etc. Calculations and simulations on random processes Tools for text mining including regular expressions Tools for signal processing and linear and non-linear control systems Discrete math tools including number theory Tools for visualizing and analysing directed and undirected graphs Group theory including permutation and finitely presented groups Symbolic tensor functions Import and export filters for data, image, sound, CAD, and document formats Technical word processing including formula editing Programming language supporting procedural, functional and object-oriented constructs Tools for adding user interfaces to calculations and applications Tools for connecting to SQL, Java, .NET, C++, Fortran and http Tools for generating code for C, C#, Fortran, Java, JavaScript, Julia, Matlab, Perl, Python, R, and Visual Basic Tools for parallel programming
Maple (software) : The following code, which computes the factorial of a nonnegative integer, is an example of an imperative programming construct within Maple: Simple functions can also be defined using the "maps to" arrow notation:

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