Computing is any goal-oriented activity requiring, benefiting from, or creating computing machinery. It includes the study and experimentation of algorithmic processes, and development of both hardware and software. Computing has scientific, engineering, mathematical, technological, and social aspects. Major computing disciplines include computer engineering, computer science, cybersecurity, data science, information systems, information technology, and software engineering.
Grid computing is the use of widely distributed computer resources to reach a common goal. A computing grid can be thought of as a distributed system with non-interactive workloads that involve many files. Grid computing is distinguished from conventional high-performance computing systems such as cluster computing in that grid computers have each node set to perform a different task/application. Grid computers also tend to be more heterogeneous and geographically dispersed than cluster computers. Although a single grid can be dedicated to a particular application, commonly a grid is used for a variety of purposes. Grids are often constructed with general-purpose grid middleware software libraries. Grid sizes can be quite large.
An application program is a computer program designed to carry out a specific task other than one relating to the operation of the computer itself, typically to be used by end-users. Word processors, media players, and accounting software are examples. The collective noun "application software" refers to all applications collectively. The other principal classifications of software are system software, relating to the operation of the computer, and utility software ("utilities").
The actor model in computer science is a mathematical model of concurrent computation that treats an actor as the basic building block of concurrent computation. In response to a message it receives, an actor can: make local decisions, create more actors, send more messages, and determine how to respond to the next message received. Actors may modify their own private state, but can only affect each other indirectly through messaging.
Business intelligence software is a type of application software designed to retrieve, analyze, transform and report data for business intelligence. The applications generally read data that has been previously stored, often - though not necessarily - in a data warehouse or data mart.
Apache Taverna was an open source software tool for designing and executing workflows, initially created by the myGrid project under the name Taverna Workbench, then a project under the Apache incubator. Taverna allowed users to integrate many different software components, including WSDL SOAP or REST Web services, such as those provided by the National Center for Biotechnology Information, the European Bioinformatics Institute, the DNA Databank of Japan (DDBJ), SoapLab, BioMOBY and EMBOSS. The set of available services was not finite and users could import new service descriptions into the Taverna Workbench.
Kepler is a free software system for designing, executing, reusing, evolving, archiving, and sharing scientific workflows. Kepler's facilities provide process and data monitoring, provenance information, and high-speed data movement. Workflows in general, and scientific workflows in particular, are directed graphs where the nodes represent discrete computational components, and the edges represent paths along which data and results can flow between components. In Kepler, the nodes are called 'Actors' and the edges are called 'channels'. Kepler includes a graphical user interface for composing workflows in a desktop environment, a runtime engine for executing workflows within the GUI and independently from a command-line, and a distributed computing option that allows workflow tasks to be distributed among compute nodes in a computer cluster or computing grid. The Kepler system principally targets the use of a workflow metaphor for organizing computational tasks that are directed towards particular scientific analysis and modeling goals. Thus, Kepler scientific workflows generally model the flow of data from one step to another in a series of computations that achieve some scientific goal.
Cyber–Physical System (CPS) are integrations of computation with physical processes. In cyber–physical systems, physical and software components are deeply intertwined, able to operate on different spatial and temporal scales, exhibit multiple and distinct behavioral modalities, and interact with each other in ways that change with context. CPS involves transdisciplinary approaches, merging theory of cybernetics, mechatronics, design and process science. The process control is often referred to as embedded systems. In embedded systems, the emphasis tends to be more on the computational elements, and less on an intense link between the computational and physical elements. CPS is also similar to the Internet of Things (IoT), sharing the same basic architecture; nevertheless, CPS presents a higher combination and coordination between physical and computational elements.
Cloud computing is the on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. Large clouds often have functions distributed over multiple locations, each of which is a data center. Cloud computing relies on sharing of resources to achieve coherence and typically uses a pay-as-you-go model, which can help in reducing capital expenses but may also lead to unexpected operating expenses for users.
A scientific workflow system is a specialized form of a workflow management system designed specifically to compose and execute a series of computational or data manipulation steps, or workflow, in a scientific application.
OpenStack is a free, open standard cloud computing platform. It is mostly deployed as infrastructure-as-a-service (IaaS) in both public and private clouds where virtual servers and other resources are made available to users. The software platform consists of interrelated components that control diverse, multi-vendor hardware pools of processing, storage, and networking resources throughout a data center. Users manage it either through a web-based dashboard, through command-line tools, or through RESTful web services.
Data-intensive computing is a class of parallel computing applications which use a data parallel approach to process large volumes of data typically terabytes or petabytes in size and typically referred to as big data. Computing applications that devote most of their execution time to computational requirements are deemed compute-intensive, whereas applications are deemed data-intensive require large volumes of data and devote most of their processing time to I/O and manipulation of data.
The Apache Object Oriented Data Technology (OODT) is an open source data management system framework that is managed by the Apache Software Foundation. OODT was originally developed at NASA Jet Propulsion Laboratory to support capturing, processing and sharing of data for NASA's scientific archives.
Apache Storm is a distributed stream processing computation framework written predominantly in the Clojure programming language. Originally created by Nathan Marz and team at BackType, the project was open sourced after being acquired by Twitter. It uses custom created "spouts" and "bolts" to define information sources and manipulations to allow batch, distributed processing of streaming data. The initial release was on 17 September 2011.
Google Cloud Platform (GCP), offered by Google, is a suite of cloud computing services that provides a series of modular cloud services including computing, data storage, data analytics, and machine learning, alongside a set of management tools. It runs on the same infrastructure that Google uses internally for its end-user products, such as Google Search, Gmail, and Google Docs, according to Verma, et.al. Registration requires a credit card or bank account details.
Eclipse Deeplearning4j is a programming library written in Java for the Java virtual machine (JVM). It is a framework with wide support for deep learning algorithms. Deeplearning4j includes implementations of the restricted Boltzmann machine, deep belief net, deep autoencoder, stacked denoising autoencoder and recursive neural tensor network, word2vec, doc2vec, and GloVe. These algorithms all include distributed parallel versions that integrate with Apache Hadoop and Spark.
The cTuning Foundation is a global non-profit organization developing a common methodology and open-source tools to support sustainable, collaborative and reproducible research in Computer science and organize and automate artifact evaluation and reproducibility inititiaves at machine learning and systems conferences and journals.
Science gateways provide access to advanced resources for science and engineering researchers, educators, and students. Through streamlined, online, user-friendly interfaces, gateways combine a variety of cyberinfrastructure (CI) components in support of a community-specific set of tools, applications, and data collections.: In general, these specialized, shared resources are integrated as a Web portal, mobile app, or a suite of applications. Through science gateways, broad communities of researchers can access diverse resources which can save both time and money for themselves and their institutions. As listed below, functions and resources offered by science gateways include shared equipment and instruments, computational services, advanced software applications, collaboration capabilities, data repositories, and networks.
Ilkay Altintas is a Turkish-American data and computer scientist, and researcher in the domain of supercomputing and high-performance computing applications. Since 2015, Altintas has served as chief data science officer of the San Diego Supercomputer Center (SDSC), at the University of California, San Diego (UCSD), where she has also served as founder and director of the Workflows for Data Science Center of Excellence (WorDS) since 2014, as well as founder and director of the WIFIRE lab. Altintas is also the co-initiator of the Kepler scientific workflow system, an open-source platform that endows research scientists with the ability to readily collaborate, share, and design scientific workflows.
