AI App Use In Hindi

AI App Use In Hindi — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Wrike

  Wrike, Inc. is an American project management application service provider based in San Jose, California. Wrike also has offices in India, Dallas, Tallinn, Nicosia, Dublin, Tokyo, Melbourne, and Prague. == History == Wrike was founded in 2006 by Andrew Filev. Currently CEO at Wrike is Thomas Scott. Filev initially self-funded the company before later obtaining investor funding. Wrike released the beta version of its software (also called Wrike) in December 2006. The company then launched a new "Enterprise" platform in December 2013. In June 2015, Wrike announced the opening of an office in Dublin, Ireland and in 2016, Wrike launched a datacenter there to host data in compliance with local privacy regulations. In July 2016, Wrike announced the launch of Wrike for Marketers. That same year, Wrike's headquarters moved from Mountain View to San Jose, California. In January 2021, Citrix Systems announced its intention to acquire Wrike for $2.25 billion. The acquisition closed in March 2021. On January 31, 2022, it was announced that Citrix had been acquired in a $16.5 billion deal by affiliates of Vista Equity Partners and Evergreen Coast Capital. Citrix would merge with TIBCO Software, a Vista portfolio company to form Cloud Software Group (CSG). In September 2022, Wrike separated from Citrix Systems. In July 2023, Vista transferred ownership to Symphony Technology Group. == Investments == Wrike received $1 million in Angel funding in 2012 from TMT Investments. In October, 2013, Wrike secured $10 million in investment funding from Bain Capital. In May 2015, the company secured $15 million in a new round of funding. Investors included Scale Venture Partners, DCM Ventures, and Bain Capital. At that time, Wrike had 8,000 customers, 200 employees, and 30,000 new users each month. On November 29, 2018, Wrike signed a definitive agreement to receive a majority investment by Vista Equity Partners (“Vista”), a firm focused on software, data and technology-enabled businesses. == Software == The Wrike project management software is a Software-as-a-Service (SaaS) product with tools for managing projects, deadlines, schedules, and workflow processes. It includes collaboration features. The application is available in English, French, Spanish, German, Portuguese, Italian, Japanese and Russian. Wrike has triggers for task automation in workflow management. === Features === Wrike features a multi-pane UI and consists of features in two categories: project management, and team collaboration. According to Wrike, project management features are designed to help teams track dates and dependencies associated with projects, manage assignments and resources, and track time. These include an interactive Gantt chart, a workload view, and a sortable table that can be customized to store project data. The software includes a co-editing tool, discussion threads on tasks, and tools for attaching documents, editing them, and tracking their changes. Wrike uses an "inbox" feature and browser notifications to alert users of updates from their colleagues and dashboards for quick overviews of pending tasks. These updates are also available in Wrike's mobile apps on iOS and Android. Wrike has an optional feature set called "Wrike for Marketers" which has several tools for managing marketing workflows. In May 2012, Wrike announced the launch of a freemium version of its software for teams of up to 5 users. That year also saw the integration of a live text coeditor into its workspace to unify collaboration and task management. In late 2013 Wrike released a new feature set called Wrike Enterprise which included advanced analytics and other tools targeted at large business customers. Since then it has released several major updates to Wrike Enterprise, including a customizable spreadsheet called "Dynamic Platform" in late 2014 and custom workflows for teams in 2015. In July 2016, Wrike was updated with a set of add-on features under the name "Wrike for Marketers," which includes integrations with Adobe Photoshop, a tool for submitting requests, and proofing and approval tools for creative assets like videos and images. Wrike is available as native Android and iOS apps. Mobile apps include an interactive Gantt chart that syncs across devices. The apps are available offline, and sync when connection is restored. === Criticism === Critics said new users may have a learning curve with complex features. Wrike has 2,710 customers for an estimated 0.04% market share. Competitors include Google Workspace, Slack (software), and Quip (software).

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  Native cloud application

  A native cloud application (NCA) is a type of computer software that natively utilizes services and infrastructure from cloud computing providers such as Amazon EC2, Force.com, or Microsoft Azure. NCAs exhibit a combined usage of the three fundamental technologies: Computational grid - loosely, e.g. MapReduce Data grids (e.g. distributed in-memory data caches) Auto-scaling on any managed infrastructure

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  Toggl Track

  Toggl Track (formerly Toggl) is a time tracking software developed by Toggl OÜ which is headquartered in Tallinn, Estonia. The company offers online time tracking and reporting services through their website along with mobile and desktop applications. Time can be tracked through a start/stop button, manual entry, or dragging and resizing time blocks in a calendar view. == History == According to Alari Aho, Toggl's CEO and founder, the application has been fully self-funded from the start. The name was created using a random name generator.

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  Carrier cloud

  In cloud computing, a carrier cloud is a class of cloud that integrates wide area networks (WAN) and other attributes of communications service providers’ carrier-grade networks to enable the deployment of highly-complex applications in the cloud. In contrast, classic cloud computing focuses on the data center and does not address the network connecting data centers and cloud users. This may result in unpredictable response times and security issues when business-critical data are transferred over the Internet. == History == The advent of virtualization technology, cost-effective computing hardware, and ubiquitous Internet connectivity have enabled the first wave of cloud services starting in the early years of the 21st century. But many businesses and other organizations hesitated to move to more demanding applications, from on-premises dedicated hardware to private or public clouds. As a response, communications service providers started in the 2010/2011 time frame to develop carrier clouds that address perceived weaknesses in existing cloud services. Cited weaknesses vary but often include possible downtime, security issues, high cost of custom software and data transfer, inflexibility of some cloud apps, poor customer and nonfulfillment of service level agreements (SLAs). == Characteristics == To enable the deployment of time-sensitive and business critical applications in the cloud, the carrier cloud is designed to match or even exceed the characteristics of on-premises deployments. Therefore, the carrier cloud is characterized by some or all of the following items: Configurable, elastic network performance: Typical cloud computing solutions use the best effort of the public Internet to connect cloud users and data centers. This approach provides instant connectivity but does not offer control over network capacities, latencies, and jitter. Carrier clouds address these gaps with content delivery networks and/or dedicated virtual private networks (VPN) at OSI layers 1 (optical wavelengths), 2 (data link layer), and 3 (network layer). These VPNs can be configured to offer the desired performance parameters and exhibit the same type of elasticity for the network that regular clouds provide for servers and storage. To achieve the requested performance parameters, such as low latency, cloud applications can be (automatically) allocated to distributed data centers that are close enough to the cloud users. Automatic resource placement: For a cloud with multiple data centers, information about both the data center and the connecting network is relevant for a decision of where to place cloud images and storage volumes. For this decision, carrier clouds can obtain relevant information about the network, e.g., using the Application-Layer Traffic Optimization (ALTO) protocol. High level of security and governance: Cloud application providers are subject to general and domain specific security, privacy, and governance requirements and regulations, such as the European Data Protection Directive and the U.S. Health Insurance Portability and Accountability Act. For added security, the wide area network of the carrier cloud can provide segregated encrypted or unencrypted network links that are not accessible from the general Internet. At the data center, the carrier cloud provides e.g. virtual private servers, management processes, logs, and documentation to fulfill security and governance rules. Location control: Fundamentally, cloud users should not be concerned with the geographic location of their cloud resources. However, privacy and other regulations may mandate that certain types of data must not be sent outside a national jurisdiction or other geographical region. Open APIs: Carrier clouds provide graphical user interfaces and Web application programming interfaces that allow cloud application providers to set up, manage, and monitor both, the data center and the WAN, of their cloud services. == Architecture == Carrier clouds encompass data centers at different network tiers and wide area networks that connect multiple data centers to each other as well as to the cloud users. Links between data centers are used for failover, overflow, backup, and geographic diversity. Carrier clouds can be set up as public, private, or hybrid clouds. The carrier cloud federates these cloud entities by using a single management system to orchestrate, manage, and monitor data center and network resources as a single system.

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  Morphing

  Morphing is a special effect in motion pictures and animations that changes (or morphs) one image or shape into another through a seamless transition. Traditionally such a depiction would be achieved through dissolving techniques on film. Since the early 1990s, this has been replaced by computer software to create more realistic transitions. A similar method is applied to audio recordings, for example, by changing voices or vocal lines. == Early transformation techniques == Long before digital morphing, several techniques were used for similar image transformations. Some of those techniques are closer to a matched dissolve – a gradual change between two pictures without warping the shapes in the images – while others did change the shapes in between the start and end phases of the transformation. === Tabula scalata === Known since at least the end of the 16th century, Tabula scalata is a type of painting with two images divided over a corrugated surface. Each image is only correctly visible from a certain angle. If the pictures are matched properly, a primitive type of morphing effect occurs when changing from one viewing angle to the other. === Mechanical transformations === Around 1790 French shadow play showman François Dominique Séraphin used a metal shadow figure with jointed parts to have the face of a young woman changing into that of a witch. Some 19th century mechanical magic lantern slides produced changes to the appearance of figures. For instance a nose could grow to enormous size, simply by slowly sliding away a piece of glass with black paint that masked part of another glass plate with the picture. === Matched dissolves === In the first half of the 19th century "dissolving views" were a popular type of magic lantern show, mostly showing landscapes gradually dissolving from a day to night version or from summer to winter. Other uses are known, for instance Henry Langdon Childe showed groves transforming into cathedrals. The 1910 short film Narren-grappen shows a dissolve transformation of the clothing of a female character. Maurice Tourneur's 1915 film Alias Jimmy Valentine featured a subtle dissolve transformation of the main character from respected citizen Lee Randall into his criminal alter ego Jimmy Valentine. The Peter Tchaikovsky Story in a 1959 TV-series episode of Disneyland features a swan automaton transforming into a real ballet dancer. In 1985, Godley & Creme created a "morph" effect using analogue cross-fades on parts of different faces in the video for "Cry". === Animation === In animation, the morphing effect was created long before the introduction of cinema. A phenakistiscope designed by its inventor Joseph Plateau was printed around 1835 and shows the head of a woman changing into a witch and then into a monster. Émile Cohl's 1908 animated film Fantasmagorie featured much morphing of characters and objects drawn in simple outlines. == Digital morphing == In the early 1990s, computer techniques capable of more convincing results saw increasing use. These involved distorting one image at the same time that it faded into another through marking corresponding points and vectors on the "before" and "after" images used in the morph. For example, one would morph one face into another by marking key points on the first face, such as the contour of the nose or location of an eye, and mark where these same points existed on the second face. The computer would then distort the first face to have the shape of the second face at the same time that it faded the two faces. To compute the transformation of image coordinates required for the distortion, the algorithm of Beier and Neely can be used. === Concerns === In 1993 concerns were raised about the authenticity of digitally altered images arising from morphing. Images of fake "tween" people found half way between two morphed people created a skeptical media long before AI. === Early examples === In or before 1986, computer graphics company Omnibus created a digital animation for a Tide commercial with a Tide detergent bottle smoothly morphing into the shape of the United States. The effect was programmed by Bob Hoffman. Omnibus re-used the technique in the movie Flight of the Navigator (1986). It featured scenes with a computer generated spaceship that appeared to change shape. The plaster cast of a model of the spaceship was scanned and digitally modified with techniques that included a reflection mapping technique that was also developed by programmer Bob Hoffman. The 1986 movie The Golden Child implemented early digital morphing effects from animal to human and back. Willow (1988) featured a more detailed digital morphing sequence with a person changing into different animals. A similar process was used a year later in Indiana Jones and the Last Crusade to create Walter Donovan's gruesome demise. Both effects were created by Industrial Light & Magic, using software developed by Tom Brigham and Doug Smythe (AMPAS). In 1991, morphing appeared notably in the Michael Jackson music video "Black or White" and in the movies Terminator 2: Judgment Day and Star Trek VI: The Undiscovered Country. The first application for personal computers to offer morphing was Gryphon Software Morph on the Macintosh. Other early morphing systems included ImageMaster, MorphPlus and CineMorph, all of which premiered for the Amiga in 1992. Other programs became widely available within a year, and for a time the effect became common to the point of cliché. For high-end use, Elastic Reality (based on MorphPlus) saw its first feature film use in In The Line of Fire (1993) and was used in Quantum Leap (work performed by the Post Group). At VisionArt Ted Fay used Elastic Reality to morph Odo for Star Trek: Deep Space Nine. The Snoop Dogg music video "Who Am I? (What's My Name?)", where Snoop Dogg and the others morph into dogs. Elastic Reality was later purchased by Avid, having already become the de facto system of choice, used in many hundreds of films. The technology behind Elastic Reality earned two Academy Awards in 1996 for Scientific and Technical Achievement going to Garth Dickie and Perry Kivolowitz. The effect is technically called a "spatially warped cross-dissolve". The first social network designed for user-generated morph examples to be posted online was Galleries by Morpheus. In late 1991 Yeti Productions employed a young Stephen Regelous to run it's 486 computer graphics system in Wellington New Zealand. After producer Barry Thomas showed him Michael Jackson's "Black or White", Regelous wrote 10,000 lines of C++ code of triangle-based digital morphing software. Together they created morphing based TV commercials for The NZ Cancer Society, Fit food, Salvation Army and others. The Fit food commercial employed morphing with 35mm, pin registered, digitally controlled motion control designed and made by Russell Collins with software by Stephen Regelous. In Taiwan, Aderans, a hair loss solutions provider, did a TV commercial featuring a morphing sequence in which people with lush, thick hair morph into one another, reminiscent of the end sequence of the "Black or White" video. === Present use === Morphing algorithms continue to advance and programs can automatically morph images that correspond closely enough with relatively little instruction from the user. This has led to the use of morphing techniques to create convincing slow-motion effects where none existed in the original film or video footage by morphing between each individual frame using optical flow technology. Morphing has also appeared as a transition technique between one scene and another in television shows, even if the contents of the two images are entirely unrelated. The algorithm in this case attempts to find corresponding points between the images and distort one into the other as they crossfade. While perhaps less obvious than in the past, morphing is used heavily today. Whereas the effect was initially a novelty, today, morphing effects are most often designed to be seamless and invisible to the eye. A particular use for morphing effects is modern digital font design. Using morphing technology, called interpolation or multiple master tech, a designer can create an intermediate between two styles, for example generating a semibold font by compromising between a bold and regular style, or extend a trend to create an ultra-light or ultra-bold. The technique is commonly used by font design studios. == Software == After Effects Animate Elastic Reality FantaMorph Gryphon Software Morph Morph Age Morpheus Nuke SilhouetteFX

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  Ampere Computing

  Ampere Computing LLC is an American fabless semiconductor company that designs ARM-based central processing units (CPUs) with high core counts for use in cloud computing and data center environments. Founded in 2017 by former Intel president Renée James, the company is headquartered in Santa Clara, California, and operates as an independent subsidiary of SoftBank Group since November 2025. == History == Ampere Computing was founded in fall 2017 by Renée James, ex-President of Intel, with funding from The Carlyle Group. James acquired a team from MACOM Technology Solutions (formerly AppliedMicro) in addition to several industry hires to start the company. Ampere Computing is an ARM architecture licensee and develops its own server microprocessors. Ampere fabricates its products at TSMC. In April 2019, Ampere announced its second major investment round, including investment from Arm Holdings and Oracle Corporation. In June 2019, Nvidia announced a partnership with Ampere to bring support for Compute Unified Device Architecture (CUDA). In November 2019, Nvidia announced a reference design platform for graphics processing unit (GPU)-accelerated ARM-based servers including Ampere. In the first half of 2020, Ampere announced Ampere Altra, an 80-core processor, and Ampere Altra Max, a 128-core processor, without the use of simultaneous multithreading. In March 2020, the company announced a partnership with Oracle. In September 2020, Oracle said it would launch bare-metal and virtual machine instances in early 2021 based on Ampere Altra. In November 2020, Ampere was named one of the top 10 hottest semiconductor startups by CRN. In May 2021, the company announced a partnership with Microsoft. In April 2022, Ampere said that it had filed a confidential prospectus with the U.S. Securities and Exchange Commission, signaling its intent to go public. In June 2022, HPE announced their Gen11 ProLiant system would use Ampere Altra and Ampere Altra Max Cloud Native Processors. In July 2022, Google announced T2A instances using Ampere Altra in the Google cloud and in August 2022 Microsoft announced their instances of Ampere running in Azure. On March 19, 2025, investment holding company SoftBank Group announced it will acquire Ampere Computing for $6.5 billion. The deal finalized in November 2025, with Ampere remaining as an independent subsidiary with its headquarters in Santa Clara, California. == Products == Ampere develops ARM-based computer processors and CPU cores under their Altra brands. These are used in databases, media encoding, web services, network acceleration, mobile gaming, AI inference processing, and other applications and programs that need to scale. On February 5, 2018, Ampere announced the eMAG 8180 featuring 32x Skylark cores fabricated on TSMC's 16FF+ process. It supports a turbo of up to 3.3 GHz with a TDP of 125 W, 8ch 64-bit DDR4, up to 1 TB DDR4 per socket, and 42x PCIe 3.0 Lanes. The Skylark cores were based on AppliedMicro's X-Gene 3. Packet offers servers with the eMAG 8180 and 128 GB DRAM, 480 GB SSD, and 2x 10 Gbit/s networking. On September 19, 2018, Ampere announced the availability of a version featuring 16x Skylark cores. === 2020 === On March 3, 2020, Ampere announced the Ampere Altra featuring 80 cores fabricated on TSMC's N7 process for hyperscale computing. It was the first server-grade processor to include 80 cores and the Q80-30 conserves power by running at 161 W in use. The cores are semi-custom Arm Neoverse N1 cores with Ampere modifications. It supports a frequency of up to 3.3 GHz with TDP of 250 W, 8ch 72-bit DDR4, up to 4 TB DDR4-3200 per socket, 128x PCIe 4.0 Lanes, 1 MB L2 per core and 32 MB SLC. Ampere also announced their roadmap with Ampere Altra Max (2021) in development and AmpereOne (2022) defined. === 2021 === The 128-core Altra Max was released in 2021 and targeted hyperscale cloud providers. It uses the same server socket and platforms as Ampere Altra, and both products have one thread per core. The Altra Max CPUs provide 128 Arm v8.2+ cores per chip and run up to 3.0 GHz. They also support eight channels of DDR4-3200 memory and 128 lanes of PCIe Gen4. Also in 2021, Oracle launched its Oracle Cloud Infrastructure (OCI) using Ampere Altra processors. === 2022 === In February 2022, Ampere and Rigetti Computing announced a strategic partnership to create hybrid quantum-classical computers. The companies will combine Ampere's Altra Max CPUs with Rigetti's Quantum Processing Units (QPU) in cloud-based High-Performance Computing (HPC) environments. In April, Microsoft previewed its Azure Virtual Machines running on the Ampere Altra. The VMs run scale-out workloads, web servers, application servers, open source databases, cloud native .NET applications, Java applications, gaming servers, media servers, and other processes. In May, Ampere announced the sampling of AmpereOne CPUs, 5 nanometer chips based on its in-house Ampere-developed core. AmpereOne will add support for DDR5 main memory and PCIe Gen5 peripherals. On June 28, 2022, HPE became first tier-one server provider to offer compute with optimized cloud-native silicon for service providers and enterprises embracing cloud-native development with new line of HPE ProLiant RL Gen11 servers, using Ampere® Altra® and Ampere® Altra® Max processors, delivering high performance and power efficiency. === 2023 === During April 2023, Ampere released the Altra developer's kit, an IoT Prototype Kit based on Ampere Altra, aimed at cloud developers, available in 32-core, 64-core, and 80-core formats. === 2024 === In May 2024, Ampere updated its AmpereOne roadmap to 256 cores and announced a joint effort with Qualcomm on CPUs and accelerators. == Customers == Ampere's customers include Microsoft Azure, Tencent Cloud, Oracle, ByteDance, Hewlett Packard Enterprise (HPE), Cloudflare, Equinix, Kingsoft Cloud, Meituan, Scaleway, UCloud, Foxconn Industrial Internet, Gigabyte, Inspur, Cruise, Hetzner, Project Ronin, Wiwynn and Google Cloud Platform Cruise uses an Ampere Altra variant for its autonomous driving unit. The CPU was selected because of its throughput and low power consumption. In 2021, Oracle, Microsoft, Tencent, and ByteDance committed to using Ampere's customized chips, first announced in May. In April 2022, Microsoft previewed Ampere Altra processors in its new Azure D-and E- series virtual machines. The Dpsv5 series is built for Linux enterprise application types, and the Epsv5 series is for memory-intensive Linux workloads. They provide up to 64 vCPUs, include VM sizes with 2GiB, 4GiB, and 8GiB per vCPU memory configurations, up to 40 Gbit/s networking, and high-performance local SSD storage. In 2022, Microsoft's Ampere Altra-based Azure servers became the first cloud solution provider server to be Arm SystemReady SR certified. The Azure VMs, powered by Altra processors, were also the first to be SystemReady Virtual Environment standard certified. SystemReady defines a set of firmware and hardware standards as a baseline for system development for software developers, original equipment vendors, and chipmakers.

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  Systems development life cycle

  The systems development life cycle (SDLC) describes the typical phases and progression between phases during the development of a computer-based system. These phases progress from inception to retirement. At base, there is just one life cycle, but the taxonomy used to describe it may vary; the cycle may be classified into different numbers of phases and various names may be used for those phases. The SDLC is analogous to the life cycle of a living organism from its birth to its death. In particular, the SDLC varies by system in much the same way that each living organism has a unique path through its life. The SDLC does not prescribe how engineers should go about their work to move the system through its life cycle. Prescriptive techniques are referred to using various terms such as methodology, model, framework, and formal process. Other terms are used for the same concept as SDLC, including software development life cycle (also SDLC), application development life cycle (ADLC), and system design life cycle (also SDLC). These other terms focus on a different scope of development and are associated with different prescriptive techniques, but are about the same essential life cycle. The term "life cycle" is often written without a space, as "lifecycle", with the former more popular in the past and in non-engineering contexts. The acronym SDLC was coined when the longer form was more popular and has remained associated with the expansion, even though the shorter form is popular in engineering. Also, SDLC is relatively unique as opposed to the TLA SDL, which is highly overloaded. == Phases == Depending on the source, the SDLC is described as having different phases and using different terms. Even so, there are common aspects. The following attempts to describe notable phases using notable terminology. The phases are somewhat ordered by the natural sequence of development, although they can be overlapping and iterative. === Conceptualization === During conceptualization (a.k.a. conceptual design, system investigation, feasibility), options and priorities are considered. A feasibility study can determine whether the development effort is worthwhile via activities such as understanding user needs, cost estimation, benefit analysis, and resource analysis. A study should address operational, financial, technical, human factors, and legal/political concerns. === Requirements analysis === Requirements analysis (a.k.a. preliminary design) involves understanding the problem and determining what is needed. Often this involves engaging users to define the requirements and recording them in a document known as a requirements specification. === Design === During the design phase (a.k.a. detail design), a solution is planned. The plan can include relatively high-level information such as describing the major components of the system. The plan can include relatively low-level information such as describing functions, screen layout, business rules, and process flow. The design phase is informed by the requirements of the system. The design must satisfy each requirement. The design may be recorded in textual documents as well as functional hierarchy diagrams, example screen images, business rules, process diagrams, pseudo-code, and data models. === Construction === During construction (a.k.a. implementation, production), the system is realized. Based on the design, hardware and software components are created and integrated. This phase includes testing sub-components, components and the integration of some components, but typically does not include testing at the complete system level. This phase may include the development of training materials, including user manuals and help files. === Acceptance === The acceptance phase (a.k.a. system testing) is about testing the complete system to ensure that it meets customer expectations (requirements). === Deployment === The deployment phase (a.k.a. implementation) involves the logistics of delivery to the customer. Some systems are deployed as a single instance (i.e. in the cloud), and deployment may be ad hoc and manual. Some systems are built in quantity and are associated with manufacturing process and commissioning. This phase may include training users to use the system. It may include transitioning future development to support staff. === Maintenance === During the maintenance phase (a.k.a. operation, utilization, support) development is largely inactive, although this phase does include customer support for resolving user issues and recording suggestions for improvement. Fixes and enhancements are handled by returning to the first phase, conceptualization. For minor changes, the cycle may be significantly abbreviated compared to initial development. === Decommission === Decommission (a.k.a. disposition, retirement, phase-out) is when the system is removed from use, i.e., when it reaches end-of-life. == Practices == === Management and control === SDLC phase objectives are described in this section with key deliverables, a description of recommended tasks, and a summary of related control objectives for effective management. It is critical for the project manager to establish and monitor control objectives while executing projects. Control objectives are clear statements of the desired result or purpose and should be defined and monitored throughout a project. Control objectives can be grouped into major categories (domains), and relate to the SDLC phases as shown in the figure. To manage and control a substantial SDLC initiative, a work breakdown structure (WBS) captures and schedules the work. The WBS and all programmatic material should be kept in the "project description" section of the project notebook. The project manager chooses a WBS format that best describes the project. The diagram shows that coverage spans numerous phases of the SDLC, but the associated MCD (Management Control Domains) shows mappings to SDLC phases. For example, Analysis and Design is primarily performed as part of the Acquisition and Implementation Domain, and System Build and Prototype is primarily performed as part of delivery and support. === Work breakdown structured organization === The upper section of the WBS provides an overview of the project scope and timeline. It should also summarize the major phases and milestones. The middle section is based on the SDLC phases. WBS elements consist of milestones and tasks to be completed rather than activities to be undertaken, and have a deadline. Each task has a measurable output (e.g., an analysis document). A WBS task may rely on one or more activities (e.g., coding). Parts of the project needing support from contractors should have a statement of work (SOW). The development of an SOW does not occur during a specific phase of SDLC but is developed to include the work from the SDLC process that may be conducted by contractors. === Baselines === Baselines are established after four of the five phases of the SDLC, and are critical to the iterative nature of the model. Baselines become milestones. functional baseline: established after the conceptual design phase. allocated baseline: established after the preliminary design phase. product baseline: established after the detailed design and development phase. updated product baseline: established after the production construction phase. In the following diagram, these stages are divided into ten steps, from definition to creation and modification of IT work products:

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  Apache Kudu

  Apache Kudu is a free and open source column-oriented data store of the Apache Hadoop ecosystem. It is compatible with most of the data processing frameworks in the Hadoop environment. It provides completeness to Hadoop's storage layer to enable fast analytics on fast data. The open source project to build Apache Kudu began as internal project at Cloudera. The first version Apache Kudu 1.0 was released 19 September 2016. == Comparison with other storage engines == Kudu was designed and optimized for OLAP workloads. Like HBase, it is a real-time store that supports key-indexed record lookup and mutation. Kudu differs from HBase since Kudu's datamodel is a more traditional relational model, while HBase is schemaless. Kudu's "on-disk representation is truly columnar and follows an entirely different storage design than HBase/Bigtable".

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  Tensor glyph

  In scientific visualization a tensor glyph is an object that can visualize all or most of the nine degrees of freedom, such as acceleration, twist, or shear – of a 3 × 3 {\displaystyle 3\times 3} matrix. It is used for tensor field visualization, where a data-matrix is available at every point in the grid. "Glyphs, or icons, depict multiple data values by mapping them onto the shape, size, orientation, and surface appearance of a base geometric primitive." Tensor glyphs are a particular case of multivariate data glyphs. There are certain types of glyphs that are commonly used: Ellipsoid Cuboid Cylindrical Superquadrics According to Thomas Schultz and Gordon Kindlmann, specific types of tensor fields "play a central role in scientific and biomedical studies as well as in image analysis and feature-extraction methods."

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  Apache Kudu

  Apache Kudu is a free and open source column-oriented data store of the Apache Hadoop ecosystem. It is compatible with most of the data processing frameworks in the Hadoop environment. It provides completeness to Hadoop's storage layer to enable fast analytics on fast data. The open source project to build Apache Kudu began as internal project at Cloudera. The first version Apache Kudu 1.0 was released 19 September 2016. == Comparison with other storage engines == Kudu was designed and optimized for OLAP workloads. Like HBase, it is a real-time store that supports key-indexed record lookup and mutation. Kudu differs from HBase since Kudu's datamodel is a more traditional relational model, while HBase is schemaless. Kudu's "on-disk representation is truly columnar and follows an entirely different storage design than HBase/Bigtable".

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  Product-family engineering

  Product-family engineering (PFE), also known as product-line engineering (PLE), is based on the ideas of "domain engineering" created by the Software Engineering Institute, a term coined by James Neighbors in his 1980 dissertation at University of California, Irvine. Software product lines are quite common in our daily lives, but before a product family can be successfully established, an extensive process has to be followed. This process is known as product-family engineering. Product-family engineering can be defined as a method that creates an underlying architecture of an organization's product platform. It provides an architecture that is based on commonality as well as planned variabilities. The various product variants can be derived from the basic product family, which creates the opportunity to reuse and differentiate on products in the family. Product-family engineering is conceptually similar to the widespread use of vehicle platforms in the automotive industry. Product-family engineering is a relatively new approach to the creation of new products, recently evolving to Model-Based Product Line Engineering (MBPLE), emphasizing the centrality of a model-centric approach in PLE. It focuses on the process of engineering new products in such a way that it is possible to reuse product components and apply variability with decreased costs and time. Product-family engineering is all about reusing components and structures as much as possible, according to the ISO/IEC 26550/2015 and the latest ISO/IEC 26580/2021 that introduced the concept of feature-based Product Line Engineering. Several studies have proven that using a product-family engineering approach for product development can have several benefits. Here is a list of some of them: Higher productivity Higher quality Faster time-to-market Lower labor needs The Nokia case mentioned below also illustrates these benefits. In 2025 the publishing of the book Model-Based Product Line Engineering (MBPLE): The feature-based path to product lines success by Marco Forlingieri, Tim Weilkiens and Hugo Guillermo Chalé-Gongora formalized the foundation of the discipline, including best practices and new industrial cases. == Overall process == The product family engineering process consists of several phases. The three main phases are: Phase 1: Product management Phase 2: Domain engineering Phase 3: Product engineering The process has been modeled on a higher abstraction level. This has the advantage that it can be applied to all kinds of product lines and families, not only software. The model can be applied to any product family. Figure 1 (below) shows a model of the entire process. Below, the process is described in detail. The process description contains elaborations of the activities and the important concepts being used. All concepts printed in italic are explained in Table 1. === Phase 1: product management === The first phase is the starting up of the whole process. In this phase some important aspects are defined especially with regard to economic aspects. This phase is responsible for outlining market strategies and defining a scope, which tells what should and should not be inside the product family. ==== Evaluate business visioning ==== During this first activity all context information relevant for defining the scope of the product line is collected and evaluated. It is important to define a clear market strategy and take external market information into account, such as consumer demands. The activity should deliver a context document that contains guidelines, constraints and the product strategy. ==== Define product line scope ==== Scoping techniques are applied to define which aspects are within the scope. This is based upon the previous step in the process, where external factors have been taken into account. The output is a product portfolio description, which includes a list of current and future products and also a product roadmap. It can be argued whether phase 1, product management, is part of the product-family-engineering process, because it could be seen as an individual business process that is more focused on the management aspects instead of the product aspect. However phase 2 needs some important input from this phase, as a large piece of the scope is defined in this phase. So from this point of view it is important to include the product-management phase (phase 1) into the entire process as a base for the domain-engineering process. === Phase 2: domain engineering === During the domain-engineering phases, the variable and common requirements are gathered for the whole product line. The goal is to establish a reusable platform. The output of this phase is a set of common and variable requirements for all products in the product line. ==== Analyze domain requirements ==== This activity includes all activities for analyzing the domain with regard to concept requirements. The requirements are categorized and split up into two new activities. The output is a document with the domain analysis. As can be seen in Figure 1 the process of defining common requirements is a parallel process with defining variable requirements. Both activities take place at the same time. ==== Define common requirements ==== Includes all activities for eliciting and documenting the common requirements of the product line, resulting in a document with reusable common requirements. ==== Define variable requirements ==== Includes all activities for eliciting and documenting the variable requirements of the product line, resulting in a document with variable requirements. ==== Design domain ==== This process step consists of activities for defining the reference architecture of the product line. This generates an abstract structure for all products in the product line. ==== Implement domain ==== During this step a detailed design of the reusable components and the implementation of these components are created. ==== Test domain ==== Validates and verifies the reusability of components. Components are tested against their specifications. After successful testing of all components in different use cases and scenarios, the domain engineering phase has been completed. === Phase 3: product engineering === In the final phase a product X is being engineered. This product X uses the commonalities and variability from the domain engineering phase, so product X is being derived from the platform established in the domain engineering phase. It basically takes all common requirements and similarities from the preceding phase plus its own variable requirements. Using the base from the domain engineering phase and the individual requirements of the product engineering phase a complete and new product can be built. After the product has been fully tested and approved, the product X can be delivered. ==== Define product requirements ==== Developing the product requirements specification for the individual product and reuse the requirements from the preceding phase. ==== Design product ==== All activities for producing the product architecture. Makes use of the reference architecture from the step "design domain", it selects and configures the required parts of the reference architecture and incorporates product specific adaptations. ==== Build product ==== During this process the product is built, using selections and configurations of the reusable components. ==== Test product ==== During this step the product is verified and validated against its specifications. A test report gives information about all tests that were carried out, this gives an overview of possible errors in the product. If the product in the next step is not accepted, the process will loop back to "build product", in Figure 1 this is indicated as "[unsatisfied]". ==== Deliver and support product ==== The final step is the acceptance of the final product. If it has been successfully tested and approved to be complete, it can be delivered. If the product does not satisfy to the specifications, it has to be rebuilt and tested again. The next figure shows the overall process of product-family engineering as described above. It is a full process overview with all concepts attached to the different steps. == Process data diagram == On the left side the entire process from the top to bottom has been drawn. All activities on the left side are linked to the concepts on the right side through dotted lines. Every concept has a number, which reflects the association with other concepts. == List of concepts == Below the list with concepts will be explained. Most concept definitions are extracted from Pohl, Bockle, & Linden (2005) and also some new definitions have been added. Table 1: List of concepts == Example == There are some good examples of the use of product family engineering, which were quite successful. The abstract model of product family engineering allows different kinds of uses, most of them are related to the consumer electronics m

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  Apache Drill

  Apache Drill is an open-source software framework that supports data-intensive distributed applications for interactive analysis of large-scale datasets. Built chiefly by contributions from developers from MapR, Drill is inspired by Google's Dremel system. Drill is an Apache top-level project. Drill supports a variety of NoSQL databases and file systems, including Alluxio, HBase, MongoDB, MapR-DB, HDFS, MapR-FS, Amazon S3, Azure Blob Storage, Google Cloud Storage, Swift, NAS and local files. A single query can join data from multiple datastores. Drill's datastore-aware optimizer automatically restructures a query plan to leverage the datastore's internal processing capabilities. In addition, Drill supports data locality, if Drill and the datastore are on the same nodes. Tom Shiran is the founder of the Apache Drill Project. It was designated an Apache Software Foundation top-level project in December 2016. == Features == One explicitly stated design goal is that Drill is able to scale to 10,000 servers or more and to be able to process petabytes of data and trillions of records in seconds. Schema-free JSON document model similar to MongoDB and Elasticsearch, without requiring a formal schema to be declared Industry-standard APIs: ANSI SQL, ODBC/JDBC, RESTful APIs Extremely user and developer friendly Pluggable architecture enables connectivity to multiple datastores Version 1.9 added dynamic user-defined functions Version 1.11 added cryptographic-related functions and PCAP file format support == Back-end support == Drill is primarily focused on non-relational datastores, including Apache Hadoop text files, NoSQL, and cloud storage. A notable feature also includes in situ querying of local JSON and Apache Parquet files. Some additional datastores that it supports include: All Hadoop distributions (HDFS API 2.3+), including Apache Hadoop, MapR, CDH and Amazon EMR NoSQL: MongoDB, Apache HBase, Apache Cassandra Online Analytical Processing: Apache Kudu, Apache Druid, OpenTSDB Cloud storage: Amazon S3, Google Cloud Storage, Azure Blob Storage, Swift, IBM Cloud Object Storage Diverse data formats, including Apache Avro, Apache Parquet and JSON RDBMs storage plugins (Using JDBC to connect to MySQL, PostgreSQL, and others) A new datastore can be added by developing a storage plugin. Drill's "schema-free" JSON data model enables it to query non-relational datastores in-situ . == Front-end support == Drill itself can be queried via JDBC, ODBC, or REST through a variety of methods and languages including Python and Java. The default install includes a web interface allowing end-users to execute ANSI SQL directly and export data tables as CSV files without any programming. The dashboard library, Apache Superset, is particularly well suited for visualization of data queried with Drill.

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  Google Vids

  Google Vids (not to be confused with Google Video) is an online timeline-based video editing application included as part of the Google Workspace suite. It is designed to help users create informational videos for work-related purposes. The app uses Google's Gemini technology to enable users to create video storyboards manually or with AI assistance using simple prompts. Features include uploading media, choosing stock videos, images, background music, and a voiceover feature with script generation using AI. The app is currently in testing with select Google Workspace Labs users. Like Kapwing and Capcut, Google Vids is primarily for creating work-related content like sales training, onboarding videos, vendor outreach, and project updates. It offers various styles and templates, collaborative features, and is not limited to videos without the short integration at the moment. Google Vids was announced on April 9, 2024. In September 2025, Google began to roll out a basic version of the application to Google Workspace users.

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  Circular thresholding

  Circular thresholding is an algorithm for automatic image threshold selection in image processing. Most threshold selection algorithms assume that the values (e.g. intensities) lie on a linear scale. However, some quantities such as hue and orientation are a circular quantity, and therefore require circular thresholding algorithms. The example shows that the standard linear version of Otsu's method when applied to the hue channel of an image of blood cells fails to correctly segment the large white blood cells (leukocytes). In contrast the white blood cells are correctly segmented by the circular version of Otsu's method. == Methods == There are a relatively small number of circular image threshold selection algorithms. The following examples are all based on Otsu's method for linear histograms: (Tseng, Li and Tung 1995) smooth the circular histogram, and apply Otsu's method. The histogram is cyclically rotated so that the selected threshold is shifted to zero. Otsu's method and histogram rotation are applied iteratively until several heuristics involving class size, threshold location, and class variance are satisfied. (Wu et al. 2006) smooth the circular histogram until it contains only two peaks. The histogram is cyclically rotated so that the midpoint between the peaks is shifted to zero. Otsu's method and histogram rotation are applied iteratively until convergence of the threshold. (Lai and Rosin 2014) applied Otsu's method to the circular histogram. For the two class circular thresholding task they showed that, for a histogram with an even number of bins, the optimal solution for Otsu's criterion of within-class variance is obtained when the histogram is split into two halves. Therefore the optimal solution can be efficiently obtained in linear rather than quadratic time. == References and further reading == D.-C. Tseng, Y.-F. Li, and C.-T. Tung, Circular histogram thresholding for color image segmentation in Proc. Int. Conf. Document Anal. Recognit., 1995, pp. 673–676. J. Wu, P. Zeng, Y. Zhou, and C. Olivier, A novel color image segmentation method and its application to white blood cell image analysis in Proc. Int. Conf. Signal Process., vol. 2. 2006, pp. 16–20. Y.K. Lai, P.L. Rosin, Efficient Circular Thresholding, IEEE Trans. on Image Processing 23(3), 992–1001 (2014). doi:10.1109/TIP.2013.2297014

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  Inbox by Gmail

  Inbox by Gmail was an email service developed by Google. Announced on a limited invitation-only basis on October 22, 2014, it was officially released to the public on May 28, 2015. Inbox was shut down by Google on April 2, 2019. Available on the web, and through mobile apps for Android and iOS, Inbox by Gmail aimed to improve email productivity and organization through several key features. Bundles gathered emails on the same topic together; highlighted surface key details from messages, reminders and assists; and a "snooze" functionality enabled users to control when specific information would appear. Updates to the service enabled an "undo send" feature; a "Smart Reply" feature that automatically generated short reply examples for certain emails; integration with Google Calendar for event organization, previews of newsletters; and a "Save to Inbox" feature that let users save links for later use. Inbox by Gmail received generally positive reviews. At its launch, it was called "minimalist and lovely, full of layers and easy to navigate", with features deemed helpful in finding the right messages—one reviewer noted that the service felt "a lot like the future of email". However, it also received criticism, particularly for a low density of information, algorithms that needed tweaking, and because the service required users to "give up the control" of organizing their own email, meaning that "Anyone who already has a system for organizing their emails will likely find themselves fighting Google's system". Google noted in March 2016 that 10% of all replies on mobile originated from Inbox's Smart Reply feature. Google announced it would discontinue Inbox by Gmail in March 2019, with many of its features integrated into Gmail proper. == Features == Inbox by Gmail scanned the user's incoming Gmail messages for information. It gathered email messages related to the same overall topic into an organized bundle, with a title describing the bundle's content. For example, flight tickets, car rentals, and hotel reservations were grouped under "Travel", giving the user an easier overview of emails. Users could also group emails together manually, to "teach" the Inbox how the user worked. The service highlighted key details and important information in messages, such as flight itineraries, event information, photos and documents. Inbox could retrieve updated information from the Internet, including the real-time status of flights and package deliveries. Users could set reminders to bring up important messages later. When a user needed particular information, Inbox could assist the user by displaying the necessary details. Where Inbox highlights information was not needed immediately, users could "snooze" a message or reminder, with options to make the information reappear at a later time or specific location. In June 2015, Google added an "Undo Send" feature to Inbox, giving the user 10 seconds to undo sending a message. In November 2015, Google added "Smart Reply" functionality to the mobile apps. With Smart Reply, Inbox determined which emails could be answered with a short reply, generating three example responses from which the user could select one with a single tap. Smart Reply (initially available only on the Android and iOS mobile apps) was added to the Inbox website in March 2016, Google announcing that "10% of all your replies on mobile already use Smart Reply". By May 2017, Google said Smart Reply was driving about 12% of replies in inbox on mobile. In April 2016, Google updated Inbox with three new features; Google Calendar event organization, newsletter previews, and a "Save to Inbox" functionality that let the user save links for later use, rather than having to email links to themselves. In December 2017, Google introduced an "Unsubscribe" card that let users easily unsubscribe from mailing lists. The card appeared for email messages (from specific senders) that the user had not opened for a month. A few popular Inbox by Gmail features were subsequently added to Gmail: "Snoozing" of emails Nudges: Gmail could move old messages back to the top of the inbox when it thought a follow up or reply might be required. Hover actions: Placing the mouse cursor over a certain part of the message could quickly effect an action, such as archiving, without its being opened. Smart reply: This feature employed boilerplate text to suggest appropriate replies. Google reportedly wished, at a time then to be decided, to add the "bundles" feature to Gmail, which at the time was available only in Inbox for Gmail. By March 2020, many Inbox features were still missing from Gmail. == Platforms == Inbox by Gmail was announced on a limited invitation-only basis on October 22, 2014, available on the web, and through the Android and iOS mobile operating systems. It was officially released to the public on May 28, 2015. == Reception == David Pierce of The Verge praised the service, writing that it was "minimalist and lovely, full of layers and easy to navigate. It's remarkably fast and smooth on all platforms, and far better on iOS than the Gmail app". However, he criticized the app's low density of information, with only a few emails visible on the screen at a time, making it "a bit of a challenge" for users who need to go through "hundreds of emails" every day. Although positive that "Inbox feels a lot like the future of email", Pierce wrote that there was "plenty of algorithm tweaking and design condensing to do", with particular attention needed on a "compact view" for denser view of information on the screen. Sarah Mitroff of CNET also praised Inbox, writing, "Not only is it visually appealing, it's also full of features that help you find every message you need, when you need it". She added that users must "give up the control" to organize their email, and that it "won't vibe with everyone", but admitted that "if you're willing ... the app will reward you with a smarter and cleaner inbox." Mitroff noted that, initially, users had to coach the app about which bundle was appropriate for certain emails, writing, "It's a tedious process at first, by [sic] in just a few days Inbox starts to get it right." Regarding any downsides of the service, Mitroff wrote that "Inbox has a built-in strategy for managing your emails that works best on its own. Anyone who already has a system for organizing their emails will likely find themselves fighting Google's system". == Discontinuation and legacy == Google ended the service in March 2019. Google called Inbox "a great place to experiment with new ideas" and noted that many of those ideas had been migrated to Gmail. The company wanted, going forward, to focus its resources on a single email system. Several services, like Shortwave, attempted to resurrect some of the features of Inbox by Gmail to attract its old users. Similarly, Inbox Reborn, an actively maintained browser extension developed by a team of volunteer developers from around the world since 2018, aims to recreate the core features and visual style of Inbox by Gmail within the standard Gmail interface. The project continues to focus on preserving functionalities such as email bundling and streamlined workflows to provide users with a familiar productivity experience. Afterwards, most people moved to Spark, Spike, or Newton. According to a product manager at Google, a "more focused approach" regarding email was the companies goal. This is likely the reason they moved away from Inbox.

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