Apache Pig is a high-level platform for creating programs that run on Apache Hadoop. The language for this platform is called Pig Latin. Pig can execute its Hadoop jobs in MapReduce, Apache Tez, or Apache Spark. Pig Latin abstracts the programming from the Java MapReduce idiom into a notation which makes MapReduce programming high level, similar to that of SQL for relational database management systems. Pig Latin can be extended using user-defined functions (UDFs) which the user can write in Java, Python, JavaScript, Ruby or Groovy and then call directly from the language. == History == Apache Pig was originally developed at Yahoo Research around 2006 for researchers to have an ad hoc way of creating and executing MapReduce jobs on very large data sets. In 2007, it was moved into the Apache Software Foundation. === Naming === Regarding the naming of the Pig programming language, the name was chosen arbitrarily and stuck because it was memorable, easy to spell, and for novelty. The story goes that the researchers working on the project initially referred to it simply as 'the language'. Eventually they needed to call it something. Off the top of his head, one researcher suggested Pig, and the name stuck. It is quirky yet memorable and easy to spell. While some have hinted that the name sounds coy or silly, it has provided us with an entertaining nomenclature, such as Pig Latin for the language, Grunt for the shell, and PiggyBank for the CPAN-like shared repository. == Example == Below is an example of a "Word Count" program in Pig Latin: The above program will generate parallel executable tasks which can be distributed across multiple machines in a Hadoop cluster to count the number of words in a dataset such as all the webpages on the internet. == Pig vs SQL == In comparison to SQL, Pig has a nested relational model, uses lazy evaluation, uses extract, transform, load (ETL), is able to store data at any point during a pipeline, declares execution plans, supports pipeline splits, thus allowing workflows to proceed along DAGs instead of strictly sequential pipelines. On the other hand, it has been argued DBMSs are substantially faster than the MapReduce system once the data is loaded, but that loading the data takes considerably longer in the database systems. It has also been argued RDBMSs offer out of the box support for column-storage, working with compressed data, indexes for efficient random data access, and transaction-level fault tolerance. Pig Latin is procedural and fits very naturally in the pipeline paradigm while SQL is instead declarative. In SQL users can specify that data from two tables must be joined, but not what join implementation to use (You can specify the implementation of JOIN in SQL, thus "... for many SQL applications the query writer may not have enough knowledge of the data or enough expertise to specify an appropriate join algorithm."). Pig Latin allows users to specify an implementation or aspects of an implementation to be used in executing a script in several ways. In effect, Pig Latin programming is similar to specifying a query execution plan, making it easier for programmers to explicitly control the flow of their data processing task. SQL is oriented around queries that produce a single result. SQL handles trees naturally, but has no built in mechanism for splitting a data processing stream and applying different operators to each sub-stream. Pig Latin script describes a directed acyclic graph (DAG) rather than a pipeline. Pig Latin's ability to include user code at any point in the pipeline is useful for pipeline development. If SQL is used, data must first be imported into the database, and then the cleansing and transformation process can begin.
Sentence extraction
Sentence extraction is a technique used for automatic summarization of a text. In this shallow approach, statistical heuristics are used to identify the most salient sentences of a text. Sentence extraction is a low-cost approach compared to more knowledge-intensive deeper approaches which require additional knowledge bases such as ontologies or linguistic knowledge. In short, sentence extraction works as a filter that allows only meaningful sentences to pass. The major downside of applying sentence-extraction techniques to the task of summarization is the loss of coherence in the resulting summary. Nevertheless, sentence extraction summaries can give valuable clues to the main points of a document and are frequently sufficiently intelligible to human readers. == Procedure == Usually, a combination of heuristics is used to determine the most important sentences within the document. Each heuristic assigns a (positive or negative) score to the sentence. After all heuristics have been applied, the highest-scoring sentences are included in the summary. The individual heuristics are weighted according to their importance. === Early approaches and some sample heuristics === Seminal papers which laid the foundations for many techniques used today have been published by Hans Peter Luhn in 1958 and H. P Edmundson in 1969. Luhn proposed to assign more weight to sentences at the beginning of the document or a paragraph. Edmundson stressed the importance of title-words for summarization and was the first to employ stop-lists in order to filter uninformative words of low semantic content (e.g. most grammatical words such as of, the, a). He also distinguished between bonus words and stigma words, i.e. words that probably occur together with important (e.g. the word form significant) or unimportant information. His idea of using key-words, i.e. words which occur significantly frequently in the document, is still one of the core heuristics of today's summarizers. With large linguistic corpora available today, the tf–idf value which originated in information retrieval, can be successfully applied to identify the key words of a text: If for example the word cat occurs significantly more often in the text to be summarized (TF = "term frequency") than in the corpus (IDF means "inverse document frequency"; here the corpus is meant by document), then cat is likely to be an important word of the text; the text may in fact be a text about cats.
LemonStand
LemonStand was a Canadian e-commerce company headquartered in Vancouver, British Columbia, that developed cloud-based computer software for online retailers. LemonStand was shut down on June 5, 2019. == History == LemonStand Version 1 was launched on July 28, 2001. It is written in the PHP programming language. Version 1 was released as an on-premises proprietary licensed software, and the commercial license was not free. However, there was a free trial license available. June 2012, LemonStand raised seed funding from the BDC Venture Capital, and a group of angel investors. December 20, 2013, a cloud-based SaaS version of the LemonStand eCommerce platform was released publicly. May 9, 2014, LemonStand and Payfirma, a payments processing company, partnered to provide integrated services for online retailers. May 3, 2016, LemonStand raised funding from BDC Venture Capital and Silicon Valley–based angel investors. March 5, 2019, LemonStand announced their intention to shut down on June 5, 2019. LemonStand was quietly acquired by Mailchimp at the end of February. == Pricing == LemonStand offered three levels of service plans. LemonStand did not charge any transaction fees.
Springpad
Springpad was a free online application and web service that allowed its registered users to save, organize and share collected ideas and information. As users added content to their Springpad accounts, the application automatically identified and categorized it, then generated additional snippets based on the types of objects added—for example, listing price comparisons for products and showtimes for movies. Springpad was also available as apps on the iPad, iPhone and Android that synchronized with the Web interface. Springpad was bundled on new Toshiba notebook computers through a Web application subscription service. On May 23, 2014, Springpad announced that it would cease operations on June 25, 2014. The company then allowed users to export their data (as JSON and read-only HTML formats), or to automatically migrate it to Evernote accounts before the expiration date. == Features == Springpad users could use the main site interface which uses HTML5 from most browsers or use the smartphone app to capture notes, tasks, or lists which were then added to the user's "My Stuff", the user's personal database or collection. Additionally Springpad let users look up items of interest which were then automatically categorized based on type or manually categorized by the user. Category types included recipes, movies, products, restaurants and wine. Events could also be added to Springpad, and if the user used Google Calendar, they could opt to sync the event to it. In addition to the smartphone app and site, Springpad could be used via browser extension for Google Chrome, or the Springpad Clipper, a bookmarklet to analyze webpages and clip relevant information from them—for example, the ingredients needed for a recipe—or to add the site as a normal bookmark. Another way users could add content to their Springpad "My Stuff" was by emailing entries to an email address specified on Springpad registration. Springpad's smartphone apps could be used to scan barcodes to identify products, save them to the user's "My Stuff", and automatically generate additional product information and links. The mobile app could also save images taken with the phone's camera, and locate nearby businesses. With most of the content added to a user's "My Stuff", relevant news, useful links and other helpful information could be viewed. Users could also attach additional notes and images to content they had already saved, and could add reminders and alerts which could be emailed to the user or texted to their phone. Springpad also added alerts to its own Alerts section for relevant news, deals or coupons for specific products users added. For additional organization, anything added to Springpad could also be tagged. Users could also add entries to "Notebooks" to separate content by projects, or any other way they wished. Each Notebook included a section called a "Board", which acted as a pin board where users could "pin" content they'd added to the Notebook, allowing them to visually lay out items. If the user added a map to the Board and had entries that included an address, Springpad could automatically point out entries on the map. By default, everything added to Springpad was private. However users could change the privacy settings for each of the types of items added, decide to make specific items public and shareable on Facebook and Twitter, add them to their public page, or keep them private but links to them with specific people.
Geo-replication
Geo-replication systems are designed to provide improved availability and disaster tolerance by using geographically distributed data centers. This is intended to improve the response time for applications such as web portals. Geo-replication can be achieved using software, hardware or a combination of the two. == Software == Geo-replication software is a network performance-enhancing technology that is designed to provide improved access to portal or intranet content for users at the most remote parts of large organizations. It is based on the principle of storing complete replicas of portal content on local servers, and then keeping the content on those servers up-to-date using heavily compressed data updates. === Portal acceleration === Geo-replication technologies are used to provide replication of the content of portals, intranets, web applications, content and data between servers, across wide area networks WAN to allow users at remote sites to access central content at LAN speeds. Geo-replication software can improve the performance of data networks that suffer limited bandwidth, latency and periodic disconnection. Terabytes of data can be replicated over a wide area network, giving remote sites faster access to web applications. Geo-replication software uses a combination of data compression and content caching technologies. differencing technologies can also be employed to reduce the volume of data that has to be transmitted to keep portal content accurate across all servers. This update compression can reduce the load that portal traffic places on networks, and improve the response time of a portal. === Portal replication === Remote users of web portals and collaboration environments will frequently experience network bandwidth and latency problems which will slow down their experience of opening and closing files, and otherwise interacting with the portal. Geo-replication technology is deployed to accelerate the remote end user portal performance to be equivalent to that experienced by users locally accessing the portal in the central office. === Differencing engine technologies === To deliver this reduction in the size of the required data updates across a portal, geo-replication systems often use differencing engine technologies. These systems are able to difference the content of each portal server right down to the byte level. This knowledge of the content that is already on each server enables the system to rebuild any changes to the content on one server, across each of the other servers in the deployment from content already hosted on those other servers. This type of differencing system ensures that no content, at the byte level, is ever sent to a server twice. === Offline portal replication on laptops === Geo-replication systems are often extended to deliver local replication beyond the server and down to the laptop used by a single user. Server to laptop replication enables mobile users to have access to a local replica of their business portal on a standard laptop. This technology may be employed to provide in the field access to portal content by, for example, sales forces and combat forces. == Geo-replication systems ==
Secure environment
In computing, a secure environment is any system which implements the controlled storage and use of information. In the event of computing data loss, a secure environment is used to protect personal or confidential data. It may also be known as a trusted execution environment (TEE). Often, secure environments employ cryptography as a means to protect information. This is typically used for processing confidential or restricted information. Some secure environments employ cryptographic hashing, simply to verify that the information has not been altered since it was last modified.
Onshape
Onshape is a computer-aided design (CAD) software system, delivered over the Internet via a software as a service (SaaS) model. It makes extensive use of cloud computing, with compute-intensive processing and rendering performed on Internet-based servers, and users are able to interact with the system via a web browser or the iOS and Android apps. As a SaaS system, Onshape upgrades are released directly to the web interface, and the software does not require maintenance by the user. Onshape allows teams to collaborate on a single shared design, the same way multiple writers can work together editing a shared document via cloud services. It is primarily focused on mechanical CAD (MCAD) and is used for product and machinery design across many industries, including consumer electronics, mechanical machinery, medical devices, 3D printing, machine parts, and industrial equipment. As of 2025, Onshape is popularly used as a CAD suite for the FIRST Robotics Competition (FRC) alongside the MKCad application available in the Onshape App Store. == Company history == Onshape was developed by a company with the same name. Founded in 2012, Onshape was based in Cambridge, Massachusetts (USA), with offices in Singapore and Pune, India. Its leadership team includes several engineers and executives who originated from SolidWorks, a popular 3D CAD program that runs on Microsoft Windows. Onshape’s co-founders include two former SolidWorks CEOs, Jon Hirschtick and John McEleney. In November 2012, former SolidWorks CEOs Jon Hirschtick and John McEleney led six co-founders launching Belmont Technology, a placeholder name that was later changed to Onshape. The company’s first round of funding was $9 million from North Bridge Venture Partners and Commonwealth Capital. In March 2015, Onshape released the public beta version of its cloud CAD software, after pre-production testing with more than a thousand CAD professionals in 52 countries. Included in the beta launch was Onshape for iPhone. In August 2015, the company released its Onshape for Android app. In December 2015, Onshape launched its full commercial release. The company also launched the Onshape App Store, offering CAM, simulation, rendering and other cloud-based engineering tools. The Onshape App Store was launched with 24 developer partners. In April 2016, Onshape introduced its Education Plan, with a free version of Onshape Professional geared for college students and educators. In May 2016, Onshape released FeatureScript, a new open source (MIT licensed) programming language for creating and customizing CAD features. In October 2019, Onshape agreed to be acquired by PTC. The acquisition closed in November 2019 for $470 million. In February 2024, Onshape released iOS support for the Apple Vision Pro, allowing for real world applications of CAD models and prototypes. In January 2025, Onshape released the CAM studio, allowing users to generate G-code for up to 5-axis Simultaneous milling. == Funding == Onshape was a venture-backed company with investments from firms including Andreessen Horowitz, Commonwealth Capital Ventures, New Enterprise Associates (NEA) and North Bridge Venture Partners. Total venture funding amounted to $169 million. == Supported file formats == === Modelling === ==== Importing ==== As of May 2025, Onshape supported importing (opening) the following common CAD file formats: Parasolid X_T (Preferred) STEP (ISO 10303) ISO JT (ISO 14306) ACIS IGES CATIA v4, v5, v6 Autodesk Inventor Part (.IPT) Assembly (.IAM) Presentation (.IPN) Drawing (.IDW) Pro/ENGINEER, Creo Rhinoceros 3D: .3dm .STL .OBJ SolidWorks file formats Siemens NX file formats Drawings (.DXF/.DWG) ==== Exporting ==== Onshape supports exporting to the following formats: STEP (ISO 10303) Parasolid XT ACIS IGES SolidWorks file formats .STL Rhinoceros 3D: .3dm Collada XML-spec based textual file === Drawing === Ordinary engineering or technical drawing can be exported as .PDF file. === Other Formats === In addition to CAD file formats, Onshape supports importing some Non-CAD file formats for viewing and referencing. === Assembly === Assemblies can be imported and exported to: STEP (ISO 10303) Parasolid XT ACIS Pro/ENGINEER, Creo ISO JT Rhinoceros 3D: .3dm Siemens NX file formats SolidWorks Pack and Go zip file File formats that assemblies can be only-exported to, are: IGES .STL Collada XML-spec based textual file