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  • Trigram

    Trigram

    Trigrams are a special case of the n-gram, where n is 3. They are often used in natural language processing for performing statistical analysis of texts and in cryptography for control and use of ciphers and codes. See results of analysis of "Letter Frequencies in the English Language". == Frequency == Context is very important, varying analysis rankings and percentages are easily derived by drawing from different sample sizes, different authors; or different document types: poetry, science-fiction, technology documentation; and writing levels: stories for children versus adults, military orders, and recipes. Typical cryptanalytic frequency analysis finds that the 16 most common character-level trigrams in English are: Because encrypted messages sent by telegraph often omit punctuation and spaces, cryptographic frequency analysis of such messages includes trigrams that straddle word boundaries. This causes trigrams such as "edt" to occur frequently, even though it may never occur in any one word of those messages. == Examples == The sentence "the quick red fox jumps over the lazy brown dog" has the following word-level trigrams: the quick red quick red fox red fox jumps fox jumps over jumps over the over the lazy the lazy brown lazy brown dog And the word-level trigram "the quick red" has the following character-level trigrams (where an underscore "_" marks a space): the he_ e_q _qu qui uic ick ck_ k_r _re red

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  • Relational data stream management system

    Relational data stream management system

    A relational data stream management system (RDSMS) is a distributed, in-memory data stream management system (DSMS) that is designed to use standards-compliant SQL queries to process unstructured and structured data streams in real-time. Unlike SQL queries executed in a traditional RDBMS, which return a result and exit, SQL queries executed in a RDSMS do not exit, generating results continuously as new data become available. Continuous SQL queries in a RDSMS use the SQL Window function to analyze, join and aggregate data streams over fixed or sliding windows. Windows can be specified as time-based or row-based. == RDSMS SQL Query Examples == Continuous SQL queries in a RDSMS conform to the ANSI SQL standards. The most common RDSMS SQL query is performed with the declarative SELECT statement. A continuous SQL SELECT operates on data across one or more data streams, with optional keywords and clauses that include FROM with an optional JOIN subclause to specify the rules for joining multiple data streams, the WHERE clause and comparison predicate to restrict the records returned by the query, GROUP BY to project streams with common values into a smaller set, HAVING to filter records resulting from a GROUP BY, and ORDER BY to sort the results. The following is an example of a continuous data stream aggregation using a SELECT query that aggregates a sensor stream from a weather monitoring station. The SELECTquery aggregates the minimum, maximum and average temperature values over a one-second time period, returning a continuous stream of aggregated results at one second intervals. RDSMS SQL queries also operate on data streams over time or row-based windows. The following example shows a second continuous SQL query using the WINDOW clause with a one-second duration. The WINDOW clause changes the behavior of the query, to output a result for each new record as it arrives. Hence the output is a stream of incrementally updated results with zero result latency.

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  • System of record

    System of record

    A system of record (SOR) or source system of record (SSoR) is a data management term for an information storage system (commonly implemented on a computer system running a database management system) that is the authoritative data source for a given data element or piece of information, like for example a row (or record) in a table. In data vault it is referred to as the record source. == Background == The need to identify systems of record can become acute in organizations where management information systems have been built by taking output data from multiple source systems, re-processing this data, and then re-presenting the result for a new business use. In these cases, multiple information systems may disagree about the same piece of information. These disagreements may stem from semantic differences, differences in opinion, use of different sources, differences in the timing of the extract, transform, load processes that create the data they report against, or may simply be the result of bugs. == Use == The integrity and validity of any data set is open to question when there is no traceable connection to a good source, and listing a source system of record is a solution to this. Where the integrity of the data is vital, if there is an agreed system of record, the data element must either be linked to, or extracted directly from it. In other cases, the provenance and estimated data quality should be documented. The "system of record" approach is a good fit for environments where both: there is a single authority over all data consumers, and all consumers have similar needs == Trade-offs == In diverse environments, one instead needs to support the presence of multiple opinions. Consumers may accept different authorities or may differ on what constitutes an authoritative source—researchers may prefer carefully vetted data, while tactical military systems may require the most recent credible report.

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  • ARMA International

    ARMA International

    ARMA International (formerly the Association of Records Managers and Administrators) is an American not-for-profit professional association for information professionals – primarily information management (including records management) and information governance, and related industry practitioners and vendors. The association provides educational opportunities and publications covering aspects of information management broadly. == History == The Association was founded in 1955. In 1975, the Association of Records Executives and Administrators (AREA) and the American Records Management Association merged to form ARMA International. The headquarters for ARMA International is located in Overland Park, Kansas. == Operations == ARMA International services professionals in the United States, Canada, Japan, and the United Kingdom. Its members include records managers, attorneys, information technology professionals, consultants, and archivists involved in various aspects of managing records and information assets. ARMA hosts an annual conference with the goal of bringing together record and information management professionals from around the world – In 2023, ARMA hosted conferences in both the United States and Canada. Topics addressed in the 120+ educational sessions include advanced technology, creating information structure, ediscovery and information law, information management fundamentals, information project management, and reducing organizational information risk. The expo features exhibitors displaying records and information technologies, products, and services.

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  • Radioplayer

    Radioplayer

    Radioplayer is a radio technology platform, owned by UK radio broadcasters and operated under licence in some other countries. It operates an internet radio web tuner, a set of mobile phone apps, an in-car adaptor, and a growing range of integrations with other connected devices and platforms. Radioplayer is operated by UK Radioplayer Ltd which is a not-for-profit organisation owned by UK radio broadcasters. Initial shareholders were the BBC, Global Radio, GMG Radio, Absolute Radio and RadioCentre. After consolidation in the radio market, current shareholders are the BBC, Global Radio, Bauer Media Group and RadioCentre. == History == Launched in the UK on 31 March 2011, Radioplayer set out to offer a simple and accessible way to listen to radio via the internet. It contained 157 stations at launch. Initially working internally at the BBC for Tim Davie, then Director of BBC Audio & Music, Michael Hill led the project since March 2009; he was made Managing Director of UK Radioplayer Ltd on 28 July 2010. At launch, Radioplayer was a simple and straightforward Flash-based radio player, linked-to by radio stations on their own website. The player included searching and bookmarking across all of UK radio station content. On 5 October 2012, Radioplayer launched a mobile app on iOS phones with an Android version following shortly afterwards. The apps are unavailable for download outside the United Kingdom. This was followed by a tablet app on 25 September 2013. The apps also support Android Wear, Android Auto, Smart Device Link, Apple Watch and Apple CarPlay. They are also compatible with Chromecast and Airplay. In September 2016, Radioplayer announced it had been chosen by Amazon to integrate with their new voice-controlled 'Echo' device, ahead of its UK launch. In July 2017, Radioplayer integrated with the Sonos and Bose multi-room speaker platforms. UK Radioplayer currently contains around 500 UK stations, from Ofcom-licensed broadcasters. Online-only 'sister-stations' can also be added, but only by broadcasters with Ofcom licences which have been on the platform for over a year. == Radioplayer Car == Radioplayer Car was announced in September 2014 as a hybrid radio receiver that switches between FM, DAB and streaming to find the strongest signal. Speaking in Oslo in June 2015, Michael Hill said that he hoped to launch the product in the UK and Norway during the summer of 2015. In February 2017, Radioplayer Car was launched. It was marketed as the world’s first voice-controlled hybrid radio adaptor for car stereos. A small box, fitted behind the dashboard, links to the auxiliary input on an existing car radio. It connects wirelessly via Bluetooth to the driver’s smartphone by an app. The adaptor enabled drivers to listen to their own smartphone music collections using Bluetooth, take hands-free calls, listen to inbound text messages and receive instant audio travel news, customised by GPS to their location and direction of travel. The hardware was manufactured under licence by car audio interfaces supplier Connects2, and Hyde Park Corner was promoted as the preferred installer of the audio equipment. There were several spin-off benefits of the Radioplayer Car project, including the creation of the hybrid radio metadata API for cars, known as the 'WRAPI' (Worldwide Radioplayer API). == International == Through a separate company called Radioplayer Worldwide, Radioplayer technology is licensed to a number of different territories.

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  • SIGMOD Edgar F. Codd Innovations Award

    SIGMOD Edgar F. Codd Innovations Award

    The ACM SIGMOD Edgar F. Codd Innovations Award is a lifetime research achievement award given by the ACM Special Interest Group on Management of Data, at its yearly flagship conference (also called SIGMOD). According to its homepage, it is given "for innovative and highly significant contributions of enduring value to the development, understanding, or use of database systems and databases". The award has been given since 1992. Until 2003, this award was known as the “SIGMOD Innovations Award.” In 2004, SIGMOD, with the unanimous approval of ACM Council, decided to rename the award to honor Dr. E.F. (Ted) Codd (1923 – 2003) who invented the relational data model and was responsible for the significant development of the database field as a scientific discipline. == Recipients ==

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  • Read–write conflict

    Read–write conflict

    In computer science, in the field of databases, read–write conflict, also known as unrepeatable reads, is a computational anomaly associated with interleaved execution of transactions. Specifically, a read–write conflict occurs when a "transaction requests to read an entity for which an unclosed transaction has already made a write request." Given a schedule S S = [ T 1 T 2 R ( A ) R ( A ) W ( A ) C o m . R ( A ) W ( A ) C o m . ] {\displaystyle S={\begin{bmatrix}T1&T2\\R(A)&\\&R(A)\\&W(A)\\&Com.\\R(A)&\\W(A)&\\Com.&\end{bmatrix}}} In this example, T1 has read the original value of A, and is waiting for T2 to finish. T2 also reads the original value of A, overwrites A, and commits. However, when T1 reads from A, it discovers two different versions of A, and T1 would be forced to abort, because T1 would not know what to do. This is an unrepeatable read. This could never occur in a serial schedule, in which each transaction executes in its entirety before another begins. Strict two-phase locking (Strict 2PL) or Serializable Snapshot Isolation (SSI) prevent this conflict. == Real-world example == Alice and Bob are using a website to book tickets for a specific show. Only one ticket is left for the specific show. Alice signs on first to see that only one ticket is left, and finds it expensive. Alice takes time to decide. Bob signs on and also finds one ticket left, and orders it instantly. Bob purchases and logs off. Alice decides to buy a ticket, to find there are no tickets. This is a typical read–write conflict situation.

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  • Informedia Digital Library

    Informedia Digital Library

    The Informedia Digital Library is an ongoing research program at Carnegie Mellon University to build search engines and information visualization technology for many types of media. The program has carried out research on spoken document retrieval, video information retrieval, video segmentation, face recognition, and cross-language information retrieval. The Lycos search engine was an early product of the Informedia Digital Library Project. The project is led by Howard Wactlar. Researchers on the project have included: Michael Mauldin, Alex Hauptmann, Michael Christel, Michael Witbrock, Raj Reddy, Takeo Kanade and Scott Stevens.

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  • Alexis Spectral Data

    Alexis Spectral Data

    Alexis Spectral Data is a software developed for colour matching processes that calculates from available spectral data the colour numbers used by computers to display colours on screen. It displays the colour for each spectral reflectance curve and records the calculated trichromatic values and colour numbers along with the spectral curves. This eliminates the need to scan the samples separately with a truecolour Scanner while creating the database. The spectral data can be introduced manually as a series of reflectance values at wavelengths measured in different standard illuminants with an arbitrary but fixed increment that must be kept for each spectral curve throughout the creation of the whole database. Therefore, older UV-VIS Spectrophotometers that can't be interfaced with computers can also be used for creating the database needed for colour matching. Alexis Spectral Data determines the whiteness degree in a less time-consuming method, which permits storage and easier handling of the obtained data. Alexis Spectral Data can export the trichromatic values, calculated from the spectral curves, to Alexis Analyser, software that handles only trichromatic data. The earliest information about the development of this software comes from a paper published by a student at the University Politehnica Bucharest in 1993. The software runs on Windows based computers but not on other operating systems.

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  • Information access

    Information access

    Information access is the freedom or ability to identify, obtain and make use of database or information effectively. There are various research efforts in information access for which the objective is to simplify and make it more effective for human users to access and further process large and unwieldy amounts of data and information. == Technology == Several technologies applicable to the general area are Information Retrieval, Text Mining, Machine Translation, and Text Categorisation. During discussions on free access to information as well as on information policy, information access is understood as concerning the insurance of free and closed access to information. Information access covers many issues including copyright, open source, privacy, and security. == Groups == Groups such as the American Library Association, the American Association of Law Libraries, Ralph Nader's Taxpayers Assets Project have advocated for free access to legal information. The vendor neutral citation movement in the legal field is working to ensure that courts will accept citations from cases on the web which do not have the traditional (copyrighted) page numbers from the West Publishing company. There is a worldwide Free Access to Law Movement which advocates free access to legal information. The Wired article "Who Owns The Law" is an introduction to the access to legal information issue. Postsecondary organizations such as K-12 work to share information. They feel it is a legal and moral obligation to provide access (including to people with disabilities or impairments) to information through the services and programs they offer. Some effects of charging for information access, such as literature searches for physicians, is studied in the article "Fee or Free: The Effect of Charging on Information Demand". In this study, a $5 charge resulted in a 77% decrease in searches.

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  • Object storage

    Object storage

    Object storage (also known as object-based storage or blob storage) is a computer data storage approach that manages data as "blobs" or "objects", as opposed to other storage architectures like file systems, which manage data as a file hierarchy, and block storage, which manages data as blocks within sectors and tracks. Each object is typically associated with a variable amount of metadata, and a globally unique identifier. Object storage can be implemented at multiple levels, including the device level (object-storage device), the system level, and the interface level. In each case, object storage seeks to enable capabilities not addressed by other storage architectures, like interfaces that are directly programmable by the application, a namespace that can span multiple instances of physical hardware, and data-management functions like data replication and data distribution at object-level granularity. Object storage systems allow retention of massive amounts of unstructured data in which data is written once and read once (or many times). Object storage is used for purposes such as storing objects like videos and photos on Facebook, songs on Spotify, or files in online collaboration services, such as Dropbox. One of the limitations with object storage is that it is not intended for transactional data, as object storage was not designed to replace NAS file access and sharing; it does not support the locking and sharing mechanisms needed to maintain a single, accurately updated version of a file. == History == === Origins === Jim Starkey coined the term blob working at Digital Equipment Corporation to refer to opaque data entities. The terminology was adopted for Rdb/VMS. Blob is often humorously explained to be an abbreviation for binary large object. According to Starkey, this backronym arose when Terry McKiever, working in marketing at Apollo Computer felt that the term needed to be an abbreviation. McKiever began using the expansion basic large object. This was later eclipsed by the retroactive explanation of blobs as binary large objects. According to Starkey, "Blob don't stand for nothin'." Rejecting the acronym, he explained his motivation behind the coinage, saying, "A blob is the thing that ate Cincinnatti [sic], Cleveland, or whatever", referring to the 1958 science fiction film The Blob. In 1995, research led by Garth Gibson on Network-Attached Secure Disks first promoted the concept of splitting less common operations, like namespace manipulations, from common operations, like reads and writes, to optimize the performance and scale of both. In the same year, a Belgian company – FilePool – was established to build the basis for archiving functions. Object storage was proposed at Gibson's Carnegie Mellon University lab as a research project in 1996. Another key concept was abstracting the writes and reads of data to more flexible data containers (objects). Fine grained access control through object storage architecture was further described by one of the NASD team, Howard Gobioff, who later was one of the inventors of the Google File System. Other related work includes the Coda filesystem project at Carnegie Mellon, which started in 1987, and spawned the Lustre file system. There is also the OceanStore project at UC Berkeley, which started in 1999 and the Logistical Networking project at the University of Tennessee Knoxville, which started in 1998. In 1999, Gibson founded Panasas to commercialize the concepts developed by the NASD team. === Development === Seagate Technology played a central role in the development of object storage. According to the Storage Networking Industry Association (SNIA), "Object storage originated in the late 1990s: Seagate specifications from 1999 Introduced some of the first commands and how operating system effectively removed from consumption of the storage." A preliminary version of the "OBJECT BASED STORAGE DEVICES Command Set Proposal" dated 10/25/1999 was submitted by Seagate as edited by Seagate's Dave Anderson and was the product of work by the National Storage Industry Consortium (NSIC) including contributions by Carnegie Mellon University, Seagate, IBM, Quantum, and StorageTek. This paper was proposed to INCITS T-10 (International Committee for Information Technology Standards) with a goal to form a committee and design a specification based on the SCSI interface protocol. This defined objects as abstracted data, with unique identifiers and metadata, how objects related to file systems, along with many other innovative concepts. Anderson presented many of these ideas at the SNIA conference in October 1999. The presentation revealed an IP Agreement that had been signed in February 1997 between the original collaborators (with Seagate represented by Anderson and Chris Malakapalli) and covered the benefits of object storage, scalable computing, platform independence, and storage management. == Architecture == === Abstraction of storage === One of the design principles of object storage is to abstract some of the lower layers of storage away from the administrators and applications. Thus, data is exposed and managed as objects instead of blocks or (exclusively) files. Objects contain additional descriptive properties which can be used for better indexing or management. Administrators do not have to perform lower-level storage functions like constructing and managing logical volumes to utilize disk capacity or setting RAID levels to deal with disk failure. Object storage also allows the addressing and identification of individual objects by more than just file name and file path. Object storage adds a unique identifier within a bucket, or across the entire system, to support much larger namespaces and eliminate name collisions. === Inclusion of rich custom metadata within the object === Object storage explicitly separates file metadata from data to support additional capabilities. As opposed to fixed metadata in file systems (filename, creation date, type, etc.), object storage provides for full function, custom, object-level metadata in order to: Capture application-specific or user-specific information for better indexing purposes Support data-management policies (e.g. a policy to drive object movement from one storage tier to another) Centralize management of storage across many individual nodes and clusters Optimize metadata storage (e.g. encapsulated, database or key value storage) and caching/indexing (when authoritative metadata is encapsulated with the metadata inside the object) independently from the data storage (e.g. unstructured binary storage) Additionally, in some object-based file-system implementations: The file system clients only contact metadata servers once when the file is opened and then get content directly via object-storage servers (vs. block-based file systems which would require constant metadata access) Data objects can be configured on a per-file basis to allow adaptive stripe width, even across multiple object-storage servers, supporting optimizations in bandwidth and I/O Object-based storage devices (OSD) as well as some software implementations (e.g., DataCore Swarm) manage metadata and data at the storage device level: Instead of providing a block-oriented interface that reads and writes fixed sized blocks of data, data is organized into flexible-sized data containers, called objects Each object has both data (an uninterpreted sequence of bytes) and metadata (an extensible set of attributes describing the object); physically encapsulating both together benefits recoverability. The command interface includes commands to create and delete objects, write bytes and read bytes to and from individual objects, and to set and get attributes on objects Security mechanisms provide per-object and per-command access control === Programmatic data management === Object storage provides programmatic interfaces to allow applications to manipulate data. At the base level, this includes Create, read, update and delete (CRUD) functions for basic read, write and delete operations. Some object storage implementations go further, supporting additional functionality like object/file versioning, object replication, life-cycle management and movement of objects between different tiers and types of storage. Most API implementations are REST-based, allowing the use of many standard HTTP calls. == Implementation == === Cloud storage === The vast majority of cloud storage available in the market leverages an object-storage architecture. Some notable examples are Amazon S3, which debuted in March 2006, Microsoft Azure Blob Storage, IBM Cloud Object Storage, Rackspace Cloud Files (whose code was donated in 2010 to Openstack project and released as OpenStack Swift), and Google Cloud Storage released in May 2010. === Object-based file systems === Some distributed file systems use an object-based architecture, where file metadata is stored in metadata servers and file data is stored i

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  • Synthetic data

    Synthetic data

    Synthetic data are artificially generated data not produced by real-world events. Typically created using algorithms, synthetic data can be deployed to validate mathematical models and to train machine learning models. Data generated by a computer simulation can be seen as synthetic data. This encompasses most applications of physical modeling, such as music synthesizers or flight simulators. The output of such systems approximates the real thing, but is fully algorithmically generated. Synthetic data is used in a variety of fields as a filter for information that would otherwise compromise the confidentiality of particular aspects of the data. In many sensitive applications, datasets theoretically exist but cannot be released to the general public; synthetic data sidesteps the privacy issues that arise from using real consumer information without permission or compensation. == Usefulness == Synthetic data is generated to meet specific needs or certain conditions that may not be found in the original, real data. One of the hurdles in applying up-to-date machine learning approaches for complex scientific tasks is the scarcity of labeled data, a gap effectively bridged by the use of synthetic data, which closely replicates real experimental data. This can be useful when designing many systems, from simulations based on theoretical value, to database processors, etc. This helps detect and solve unexpected issues such as information processing limitations. Synthetic data are often generated to represent the authentic data and allows a baseline to be set. Another benefit of synthetic data is to protect the privacy and confidentiality of authentic data, while still allowing for use in testing systems. Computer security experts claim generated synthetic data "... enables us to create realistic behavior profiles for users and attackers. The data is used to train the fraud detection system itself, thus creating the necessary adaptation of the system to a specific environment." In defense and military contexts, synthetic data is seen as a potentially valuable tool to develop and improve complex AI systems, particularly in contexts where high-quality real-world data is scarce. At the same time, synthetic data together with the testing approach can give the ability to model real-world scenarios. == History == Scientific modelling of physical systems has a long history that runs concurrent with the history of physics. For example, research into synthesis of audio and voice can be traced back to the 1930s and before, driven forward by the developments of the telephone and audio recording technologies. Digitization gave rise to software synthesizers from the 1970s onwards. In the context of privacy-preserving statistical analysis, in 1993, the idea of original fully synthetic data was created by Donald Rubin. Rubin originally designed this to synthesize the Decennial Census long form responses for the short form households. He then released samples that did not include any actual long form records - in this he preserved anonymity of the household. Later that year, the idea of original partially synthetic data was created by Little. Little used this idea to synthesize the sensitive values on the public use file. A 1993 work fitted a statistical model to 60,000 MNIST digits, then it was used to generate over 1 million examples. Those were used to train a LeNet-4 to reach state of the art performance. In 1994, Stephen Fienberg introduced 'critical refinement', in which a parametric posterior predictive distribution (instead of a Bayes bootstrap) is used to do the sampling. Later, other important contributors to the development of synthetic data generation were Trivellore Raghunathan, Jerry Reiter, Donald Rubin, John M. Abowd, and Jim Woodcock. Collectively they came up with a solution for how to treat partially synthetic data with missing data. Similarly, they developed the technique of Sequential Regression Multivariate Imputation. == Calculations == Researchers test the framework on synthetic data, which is "the only source of ground truth on which they can objectively assess the performance of their algorithms". Synthetic data can be generated through the use of random lines, having different orientations and starting positions. Datasets can get fairly complicated. A more complicated dataset can be generated by using a synthesizer build. To create a synthesizer build, first use the original data to create a model or equation that fits the data the best. This model or equation will be called a synthesizer build. This build can be used to generate more data. Constructing a synthesizer build involves constructing a statistical model. In a linear regression line example, the original data can be plotted, and a best fit linear line can be created from the data. This line is a synthesizer created from the original data. The next step will be generating more synthetic data from the synthesizer build or from this linear line equation. In this way, the new data can be used for studies and research, and it protects the confidentiality of the original data. David Jensen from the Knowledge Discovery Laboratory explains how to generate synthetic data: "Researchers frequently need to explore the effects of certain data characteristics on their data model." To help construct datasets exhibiting specific properties, such as auto-correlation or degree disparity, proximity can generate synthetic data having one of several types of graph structure: random graphs that are generated by some random process; lattice graphs having a ring structure; lattice graphs having a grid structure, etc. In all cases, the data generation process follows the same process: Generate the empty graph structure. Generate attribute values based on user-supplied prior probabilities. Since the attribute values of one object may depend on the attribute values of related objects, the attribute generation process assigns values collectively. == Applications == === Fraud detection and confidentiality systems === Testing and training fraud detection and confidentiality systems are devised using synthetic data. Specific algorithms and generators are designed to create realistic data, which then assists in teaching a system how to react to certain situations or criteria. For example, intrusion detection software is tested using synthetic data. This data is a representation of the authentic data and may include intrusion instances that are not found in the authentic data. The synthetic data allows the software to recognize these situations and react accordingly. If synthetic data was not used, the software would only be trained to react to the situations provided by the authentic data and it may not recognize another type of intrusion. === Scientific research === Researchers doing clinical trials or any other research may generate synthetic data to aid in creating a baseline for future studies and testing. Real data can contain information that researchers may not want released, so synthetic data is sometimes used to protect the privacy and confidentiality of a dataset. Using synthetic data reduces confidentiality and privacy issues since it holds no personal information and cannot be traced back to any individual. Beyond privacy protection, synthetic data is also being explored for methodological innovation in drug development. For instance, synthetic data may be used to construct synthetic control arms as an alternative to conventional external control arms based on real-world data (RWD) or randomized controlled trials (RCTs). Collectively, regulatory agencies such as the FDA and EMA appear to be at various stages of recognizing and integrating AI-generated synthetic data into their methodologies. While there is growing consensus on the potential of such data to support model development and the broader lifecycle of medicinal products, to date no drug or medical device has been approved using solely or predominantly synthetic data—particularly not as a comparator arm generated entirely via data-driven algorithms. The quality and statistical handling of synthetic data are expected to become more prominent in future regulatory discussions, particularly in contexts such as predictive modeling (e.g., digital twins), where innovative approaches have already been referenced. === Machine learning === Synthetic data is increasingly being used for machine learning applications: a model is trained on a synthetically generated dataset with the intention of transfer learning to real data. Efforts have been made to enable more data science experiments via the construction of general-purpose synthetic data generators, such as the Synthetic Data Vault. In general, synthetic data has several natural advantages: once the synthetic environment is ready, it is fast and cheap to produce as much data as needed; synthetic data can have perfectly accurate labels, including labeling that may be very expensive or impo

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  • DreamLab

    DreamLab

    DreamLab was a volunteer computing Android and iOS app launched in 2015 by Imperial College London and the Vodafone Foundation. It was discontinued on 2nd April 2025. == Description == The app helped to research cancer, COVID-19, new drugs and tropical cyclones. To do this, DreamLab accessed part of the device's processing power, with the user's consent, while the owner charged their smartphone, to speed up the calculations of the algorithms from Imperial College London. The aim of the tropical cyclone project was to prepare for climate change risks. Other projects aimed to find existing drugs and food molecules that could help people with COVID-19 and other diseases. The performance of 100,000 smartphones would reach the annual output of all research computers at Imperial College in just three months, with a nightly runtime of six hours. The app was developed in 2015 by the Garvan Institute of Medical Research in Sydney and the Vodafone Foundation. In May 2020, the project had over 490,000 registered users.

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  • Small Data

    Small Data

    Small Data: the Tiny Clues that Uncover Huge Trends is Martin Lindstrom's seventh book. It chronicles his work as a branding expert, working with consumers across the world to better understand their behavior. The theory behind the book is that businesses can better create products and services based on observing consumer behavior in their homes, as opposed to relying solely on big data. == Content == The book is based on a several year period of consumer studies for major corporations across the globe. It features case studies of the author's work interviewing consumers in their homes and using his observations to create hypotheses as to why they use products the way that they do. == Public reception == The book was a New York Times Bestseller upon release and was positively reviewed on several websites, Including Entrepreneur and Forbes. In 2016, it was named a Best Business Book by strategy+business and one of Inc. Magazine's Best Sales and Marketing books.

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  • Systematic review

    Systematic review

    A systematic review is a scholarly synthesis of the evidence on a clearly presented topic using critical methods to identify, define and assess research on the topic. A systematic review extracts and interprets data from published studies on the topic (in the scientific literature), then analyzes, describes, critically appraises and summarizes interpretations into a refined evidence-based conclusion. For example, a systematic review of randomized controlled trials is a way of summarizing and implementing evidence-based medicine. Systematic reviews, sometimes along with meta-analyses, are generally considered the highest level of evidence in medical research. While a systematic review may be applied in the biomedical or health care context, it may also be used where an assessment of a precisely defined subject can advance understanding in a field of research. A systematic review may examine clinical tests, public health interventions, environmental interventions, social interventions, adverse effects, qualitative evidence syntheses, methodological reviews, policy reviews, and economic evaluations. Systematic reviews are closely related to meta-analyses, and often the same instance will combine both (being published with a subtitle of "a systematic review and meta-analysis"). The distinction between the two is that a meta-analysis uses statistical methods to induce a single number from the pooled data set (such as an effect size), whereas the strict definition of a systematic review excludes that step. However, in practice, when one is mentioned, the other may often be involved, as it takes a systematic review to assemble the information that a meta-analysis analyzes, and people sometimes refer to an instance as a systematic review, even if it includes the meta-analytical component. An understanding of systematic reviews and how to implement them in practice is common for professionals in health care, public health, and public policy. Systematic reviews contrast with a type of review often called a narrative review. Systematic reviews and narrative reviews both review the literature (the scientific literature), but the term literature review without further specification refers to a narrative review. == Characteristics == A systematic review can be designed to provide a thorough summary of current literature relevant to a research question. A systematic review uses a rigorous and transparent approach for research synthesis, with the aim of assessing and, where possible, minimizing bias in the findings. While many systematic reviews are based on an explicit quantitative meta-analysis of available data, there are also qualitative reviews and other types of mixed-methods reviews that adhere to standards for gathering, analyzing, and reporting evidence. Systematic reviews of quantitative data or mixed-method reviews sometimes use statistical techniques (meta-analysis) to combine results of eligible studies. Scoring levels are sometimes used to rate the quality of the evidence depending on the methodology used, although this is discouraged by the Cochrane Library. As evidence rating can be subjective, multiple people may be consulted to resolve any scoring differences between how evidence is rated. The EPPI-Centre, Cochrane, and the Joanna Briggs Institute have been influential in developing methods for combining both qualitative and quantitative research in systematic reviews. Several reporting guidelines exist to standardise reporting about how systematic reviews are conducted. Such reporting guidelines are not quality assessment or appraisal tools. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement suggests a standardized way to ensure a transparent and complete reporting of systematic reviews, and is now required for this kind of research by more than 170 medical journals worldwide. The latest version of this commonly used statement corresponds to PRISMA 2020 (the respective article was published in 2021). Several specialized PRISMA guideline extensions have been developed to support particular types of studies or aspects of the review process, including PRISMA-P for review protocols and PRISMA-ScR for scoping reviews. A list of PRISMA guideline extensions is hosted by the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network. However, the PRISMA guidelines have been found to be limited to intervention research and the guidelines have to be changed in order to fit non-intervention research. As a result, Non-Interventional, Reproducible, and Open (NIRO) Systematic Reviews was created to counter this limitation. For qualitative reviews, reporting guidelines include ENTREQ (Enhancing transparency in reporting the synthesis of qualitative research) for qualitative evidence syntheses; RAMESES (Realist And MEta-narrative Evidence Syntheses: Evolving Standards) for meta-narrative and realist reviews; and eMERGe (Improving reporting of Meta-Ethnography) for meta-ethnograph. Developments in systematic reviews during the 21st century included realist reviews and the meta-narrative approach, both of which addressed problems of variation in methods and heterogeneity existing on some subjects. == Types == There are over 30 types of systematic review and Table 1 below non-exhaustingly summarises some of these. There is not always consensus on the boundaries and distinctions between the approaches described below. === Scoping reviews === Scoping reviews are distinct from systematic reviews in several ways. A scoping review is an attempt to search for concepts by mapping the language and data which surrounds those concepts and adjusting the search method iteratively to synthesize evidence and assess the scope of an area of inquiry. This can mean that the concept search and method (including data extraction, organisation and analysis) are refined throughout the process, sometimes requiring deviations from any protocol or original research plan. A scoping review may often be a preliminary stage before a systematic review, which 'scopes' out an area of inquiry and maps the language and key concepts to determine if a systematic review is possible or appropriate, or to lay the groundwork for a full systematic review. The goal can be to assess how much data or evidence is available regarding a certain area of interest. This process is further complicated if it is mapping concepts across multiple languages or cultures. As a scoping review should be systematically conducted and reported (with a transparent and repeatable method), some academic publishers categorize them as a kind of 'systematic review', which may cause confusion. Scoping reviews are helpful when it is not possible to carry out a systematic synthesis of research findings, for example, when there are no published clinical trials in the area of inquiry. Scoping reviews are helpful when determining if it is possible or appropriate to carry out a systematic review, and are a useful method when an area of inquiry is very broad, for example, exploring how the public are involved in all stages systematic reviews. There is still a lack of clarity when defining the exact method of a scoping review as it is both an iterative process and is still relatively new. There have been several attempts to improve the standardisation of the method, for example via a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline extension for scoping reviews (PRISMA-ScR). PROSPERO (the International Prospective Register of Systematic Reviews) does not permit the submission of protocols of scoping reviews, although some journals will publish protocols for scoping reviews. == Stages == While there are multiple kinds of systematic review methods, the main stages of a review can be summarised as follows: === Defining the research question === Some reported that the 'best practices' involve 'defining an answerable question' and publishing the protocol of the review before initiating it to reduce the risk of unplanned research duplication and to enable transparency and consistency between methodology and protocol. Clinical reviews of quantitative data are often structured using the mnemonic PICO, which stands for 'Population or Problem', 'Intervention or Exposure', 'Comparison', and 'Outcome', with other variations existing for other kinds of research. For qualitative reviews, PICo is 'Population or Problem', 'Interest', and 'Context'. === Searching for sources === Relevant criteria can include selecting research that is of good quality and answers the defined question. The search strategy should be designed to retrieve literature that matches the protocol's specified inclusion and exclusion criteria. The methodology section of a systematic review should list all of the databases and citation indices that were searched. The titles and abstracts of identified articles can be checked against predetermined criteria for eligibility and r

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