A key-value database, or key-value store, is a data storage paradigm designed for storing, retrieving, and managing associative arrays, a data structure more commonly known today as a dictionary. Dictionaries contain a collection of objects, or records, which in turn have many different fields within them. These records are stored and retrieved using a key that uniquely identifies the record, and is used to find the data within the database. Key-value databases differ from the better known relational databases (RDB). RDBs pre-define the data structure in the database as a series of tables containing fields with well-defined data types. Exposing the data types to the database program allows it to apply various optimizations. In contrast, key-value systems treat the value as opaque to the database itself, and typically support only simple operations such as storing, retrieving, updating, and deleting a value by its key. This offers considerable flexibility and makes such systems well suited to low-latency, high-throughput workloads dominated by direct key lookups, but less suitable for applications that require complex queries or explicit relationships among records. A lack of standardization, limited transaction support, and relatively simple query interfaces long restricted many key-value systems to specialized uses, but the rapid move to cloud computing after 2010 helped drive renewed interest in them as part of the broader NoSQL movement. Some graph databases, such as ArangoDB, are also key–value databases internally, adding the concept of relationships (pointers) between records as a first-class data type. == Types and examples == Key–value systems span a wide consistency spectrum, from eventually consistent designs to strongly consistent or serializable ones, and some allow the consistency level to be configured as part of the trade-off against latency and availability. Renewed interest in key–value and other NoSQL systems was driven in part by the demands of big data, distributed, and cloud applications. Their scalability and availability made them attractive for cloud data management, although limited transaction support, low-level query interfaces, and the lack of standardization remained obstacles to wider adoption. Some maintain data in memory (RAM), while others employ solid-state drives or rotating disks. Some key–value systems add additional structure to their keys. For example, Oracle NoSQL Database organizes records using composite keys with "major" and "minor" components, an arrangement that Oracle compares to a directory-path structure in a file system. More generally, however, key–value stores are defined by their use of unique keys associated with opaque values and by their emphasis on simple key-based operations. Unix included dbm (database manager), a minimal database library written by Ken Thompson for managing associative arrays with a single key and hash-based access. Later implementations and related libraries included sdbm, GNU dbm (gdbm), and Berkeley DB. A more recent example is RocksDB, a persistent key–value storage engine developed at Facebook and designed for large-scale applications. Other examples include in-memory systems such as Memcached and Redis, and persistent systems such as Berkeley DB, Riak, and Voldemort.
Standard test image
A standard test image is a digital image file used across different institutions to test image processing and image compression algorithms. By using the same standard test images, different labs are able to compare results, both visually and quantitatively. The images are in many cases chosen to represent natural or typical images that a class of processing techniques would need to deal with. Other test images are chosen because they present a range of challenges to image reconstruction algorithms, such as the reproduction of fine detail and textures, sharp transitions and edges, and uniform regions. == Historical origins == Test images as transmission system calibration material probably date back to the original Paris to Lyon pantelegraph link. Analogue fax equipment (and photographic equipment for the printing trade) were the largest user groups of the standardized image for calibration technology until the coming of television and digital image transmission systems. == Common test image resolutions == The standard resolution of the images is usually 512×512 or 720×576. Most of these images are available as TIFF files from the University of Southern California's Signal and Image Processing Institute. Kodak has released 768×512 images, available as PNGs, that was originally on Photo CD with higher resolution, that are widely used for comparing image compression techniques.
AirDine
AirDine was a mobile app within the platform economy where individuals acted as both supplier and customer for a supper club. AirDine discontinued their service after 31 October 2017. == Operations == AirDine was an online marketplace for home dining that connected users that liked to cook with users looking for a dining experience. Users were categorized as "Hosts" and "Guests," both of whom needed to register with AirDine. AirDine acted as a two-sided market for home dining that allowed hosts and guests, and did not act as a restaurant or host any dinners itself. AirDine charged a service fee. Security and safety of the host were not vetted by AirDine and were completely left to users based on published reviews. Profiles included user reviews and shared social connections to build trust among users. AirDine also included a private messaging system.
Pocketbook (application)
Pocketbook was a Sydney-based free budget planner and personal finance app launched in 2012. The app helped users setup and manage budgets, track spending and manage bills. As of 2016 Pocketbook claimed to support over 250,000 Australians, in January 2018 that number was 435,000. After being acquired by Zip Co Ltd in 2016, it was announced in 2022 that the app was to be shut down and all user accounts deleted. == History == Pocketbook was founded by Alvin Singh and Bosco Tan in 2012. It was conceived in 2011 in a Wolli Creek apartment as a tool for Alvin and Bosco to take control of their money. In 2013, Pocketbook raised $500,000 from technology fund Tank Stream Ventures, and a group of investors including TV personality David Koch, Geoff Levy, David Shein and Peter Cooper. In September 2016 Digital retail finance and payment industry player zipMoney (now trading as Zip Co Limited) acquired Pocketbook in a $7.5m deal == Features == The app synced with the bank account of users and would organize spending into different categories. Users could also be reminded of bill payments, analyse spending and set spending limits. They can also be alerted of fraudulent transactions and deductions. The app employs security measures like end to end encryption, CloudFlare protection, fraud detection, identity protection etc. Pocketbook was available via web and mobile version. == Awards == Personal Finance Innovator of the Year by Fintech Business Awards 2017 Innovator of the Year by OPTUS MyBusiness Awards 2017 Best Finance App of 2016 by Australian Fintech Best Personal Finance App: Pocketbook won the 2016 Finder Innovation Awards, presented at a gala dinner hosted by media personality and The New Inventors presenter James O'Loghlin. Best Mobile App of the Year Winner: StartCon hosted the first annual Australasian Startup Awards. Over 200 nominations in 14 categories and an overall winner were reviewed, and winners were determined by public voting, with over 63,000 votes in total. Best New Startup 2014 by StartupSmart. Finalist in the SWIFT Innotribe startup competition in Dubai in 2013.
Sprayprinter
SprayPrinter is a device that attaches to aerosol paint cans whereby users can print images via Bluetooth from a smartphone onto a wall or almost any surface. == History == The technology behind SprayPrinter was developed by Mihkel Joala. He explained in a 2016 interview with New Atlas that his idea was inspired by the modern car engine and the Nintendo Wii console. "Engines nowadays use extremely fast valves to spray fuel to [the] combustion chamber," says Joala. "I realized I can use them to shoot paint with pinpoint accuracy." As of December 2021, the company appears to be no longer selling products. == Awards and Recognitions == In 2015, SprayPrinter received €8,000 from the Estonian prototyping contest Prototron for its initial prototype. In 2016, the SprayPrinter team won the grand prize of €30,000 from the televised pitching competition Ajujaht.
Universal psychometrics
Universal psychometrics encompasses psychometrics instruments that could measure the psychological properties of any intelligent agent. Up until the early 21st century, psychometrics relied heavily on psychological tests that require the subject to cooperate and answer questions, the most famous example being an intelligence test. Such methods are only applicable to the measurement of human psychological properties. As a result, some researchers have proposed the idea of universal psychometrics - they suggest developing testing methods that allow for the measurement of non-human entities' psychological properties. For example, it has been suggested that the Turing test is a form of universal psychometrics. This test involves having testers (without any foreknowledge) attempt to distinguish a human from a machine by interacting with both (while not being to see either individuals). It is supposed that if the machine is equally intelligent to a human, the testers will not be able to distinguish between the two, i.e., their guesses will not be better than chance. Thus, Turing test could measure the intelligence (a psychological variable) of an AI. Other instruments proposed for universal psychometrics include reinforcement learning and measuring the ability to predict complexity.
Spanner (database)
Spanner is a distributed SQL database management and storage service developed by Google. It provides features such as global transactions, strongly consistent reads, and automatic multi-site replication and failover. Spanner is used in Google F1, the database for its advertising business Google Ads, as well as Gmail and Google Photos. == Features == Spanner stores large amounts of mutable structured data. Spanner allows users to perform arbitrary queries using SQL with relational data while maintaining strong consistency and high availability for that data with synchronous replication. Key features of Spanner: Transactions can be applied across rows, columns, tables, and databases within a Spanner universe. Clients can control the replication and placement of data using automatic multi-site replication and failover. Replication is synchronous and strongly consistent. Reads are strongly consistent and data is versioned to allow for stale reads: clients can read previous versions of data, subject to garbage collection windows. Supports a native SQL interface for reading and writing data. Support for Graph Query Language == History == Spanner was first described in 2012 for internal Google data centers. Spanner's SQL capability was added in 2017 and documented in a SIGMOD 2017 paper. It became available as part of Google Cloud Platform in 2017, under the name "Cloud Spanner". == Architecture == Spanner uses the Paxos algorithm as part of its operation to shard (partition) data across up to hundreds of servers. It makes heavy use of hardware-assisted clock synchronization using GPS clocks and atomic clocks to ensure global consistency. TrueTime is the brand name for Google's distributed cloud infrastructure, which provides Spanner with the ability to generate monotonically increasing timestamps in data centers around the world. Google's F1 SQL database management system (DBMS) is built on top of Spanner, replacing Google's custom MySQL variant.