AI Email Blueprint

AI Email Blueprint — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

• 

  CodeCheck

  CodeCheck is a mobile app that provides consumers with information about the ingredients in cosmetic products, as well as the ingredients and nutritional values of food. Users can access this information by scanning the product’s barcode with a smartphone or by using a text-based search. The app is available for iOS and Android devices in Germany, Austria, Switzerland, the United Kingdom, the United States, and the Netherlands. == History == CodeCheck was founded in 2010 as an association, online database, and app by Roman Bleichenbacher, who was then a student in Zurich. A website of the same name had already been launched in 2002, where users could enter information about ingredients, nutritional values, and manufacturers of products. The first round of financing took place in July 2014 and raised over 1.1 million Swiss francs, which coincided with the founding of CodeCheck AG. Investors included Doodle founders Myke Näf and Paul E. Sevinç. The company subsequently expanded to Austria and Germany. In the same year, Boris Manhart became CEO. CodeCheck GmbH was established in Berlin in 2016. The app became available in the United States in 2017 and in the United Kingdom in November 2019. In 2020, it was also launched in the Netherlands. Following insolvency proceedings, the app has been owned by Producto Check GmbH since 2022. == Functions == The app can be used to scan the barcode of food and cosmetic products. It then displays information about ingredients, nutritional values, manufacturers and certification labels. For many years, users were able to enter and edit product information themselves and indicate advantages and disadvantages of individual products. Since 2020, the app has placed greater emphasis on machine text recognition. The collected data is combined with substance ratings using an algorithm. These ratings are based on scientific studies and expert assessments, including those from the Consumer Advice Centre in Hamburg, Greenpeace, the WWF and the German Association for the Environment and Nature Conservation (BUND e. V.), and cannot be modified by users or manufacturers. The app also provides information on the sugar and fat content of food products. In addition, it indicates whether a product contains hormone-active substances, microplastics, palm oil, animal-derived ingredients, lactose or gluten. Since 2020, the app has displayed a climate score for food products in cooperation with the Eaternity Institute. == Financing == CodeCheck is primarily financed through native advertising and banner ads. Since 2018, the company has also offered analysis services and survey tools directly to fast-moving consumer goods (FMCG) manufacturers. In addition, access to the API is available, enabling other companies to use the product database. With the introduction of a subscription model in 2019, the CodeCheck app can be used ad-free and in offline mode. Since 2021, CodeCheck has also offered its own “Green Label” certification for manufacturers. Products are certified if at least 90 percent of their ingredients are classified as harmless. == Awards == In May 2015, the app topped the download charts for the first time, reaching 2.3 million installations. By September 2019, the app had once again reached the top of the German app charts, surpassing five million downloads.

  Read more →
• 

  Knuth–Plass line-breaking algorithm

  The Knuth–Plass algorithm is a line-breaking algorithm designed for use in Donald Knuth's typesetting program TeX. It integrates the problems of text justification and hyphenation into a single algorithm by using a discrete dynamic programming method to minimize a loss function that attempts to quantify the aesthetic qualities desired in the finished output. The algorithm works by dividing the text into a stream of three kinds of objects: boxes, which are non-resizable chunks of content, glue, which are flexible, resizeable elements, and penalties, which represent places where breaking is undesirable (or, if negative, desirable). The loss function, known as "badness", is defined in terms of the deformation of the glue elements, and any extra penalties incurred through line breaking. Making hyphenation decisions follows naturally from the algorithm, but the choice of possible hyphenation points within words, and optionally their preference weighting, must be performed first, and that information inserted into the text stream in advance. Knuth and Plass' original algorithm does not include page breaking, but may be modified to interface with a pagination algorithm, such as the algorithm designed by Plass in his PhD thesis. Typically, the cost function for this technique should be modified so that it does not count the space left on the final line of a paragraph; this modification allows a paragraph to end in the middle of a line without penalty. The same technique can also be extended to take into account other factors such as the number of lines or costs for hyphenating long words. == Computational complexity == A naive brute-force exhaustive search for the minimum badness by trying every possible combination of breakpoints would take an impractical O ( 2 n ) {\displaystyle O(2^{n})} time. The classic Knuth-Plass dynamic programming approach to solving the minimization problem is a worst-case O ( n 2 ) {\displaystyle O(n^{2})} algorithm but usually runs much faster, in close to linear time. Solving for the Knuth-Plass optimum can be shown to be a special case of the convex least-weight subsequence problem, which can be solved in O ( n ) {\displaystyle O(n)} time. Methods to do this include the SMAWK algorithm. == Simple example of minimum raggedness metric == For the input text AAA BB CC DDDDD with line width 6, a greedy algorithm that puts as many words on a line as possible while preserving order before moving to the next line, would produce: ------ Line width: 6 AAA BB Remaining space: 0 CC Remaining space: 4 DDDDD Remaining space: 1 The sum of squared space left over by this method is 0 2 + 4 2 + 1 2 = 17 {\displaystyle 0^{2}+4^{2}+1^{2}=17} . However, the optimal solution achieves the smaller sum 3 2 + 1 2 + 1 2 = 11 {\displaystyle 3^{2}+1^{2}+1^{2}=11} : ------ Line width: 6 AAA Remaining space: 3 BB CC Remaining space: 1 DDDDD Remaining space: 1 The difference here is that the first line is broken before BB instead of after it, yielding a better right margin and a lower cost 11.

  Read more →
• 

  XOR swap algorithm

  In computer programming, the exclusive or swap (sometimes shortened to XOR swap) is an algorithm that uses the exclusive or bitwise operation to swap the values of two variables without using the temporary variable which is normally required. The algorithm is primarily a novelty and a way of demonstrating properties of the exclusive or operation. It is sometimes discussed as a program optimization, but there are almost no cases where swapping via exclusive or provides benefit over the standard, obvious technique. == The algorithm == Conventional swapping requires the use of a temporary storage variable. Using the XOR swap algorithm, however, no temporary storage is needed. The algorithm is as follows: Since XOR is a commutative operation, either X XOR Y or Y XOR X can be used interchangeably in any of the foregoing three lines. Note that on some architectures the first operand of the XOR instruction specifies the target location at which the result of the operation is stored, preventing this interchangeability. The algorithm typically corresponds to three machine-code instructions, represented by corresponding pseudocode and assembly instructions in the three rows of the following table: In the above System/370 assembly code sample, R1 and R2 are distinct registers, and each XR operation leaves its result in the register named in the first argument. Using x86 assembly, values X and Y are in registers eax and ebx (respectively), and xor places the result of the operation in the first register (Note: x86 supports XCHG instruction so using triple XOR do not make sense on this architecture). In RISC-V assembly, value X and Y are in registers x10 and x11, and xor places the result of the operation in the first operand. However, in the pseudocode or high-level language version or implementation, the algorithm fails if x and y use the same storage location, since the value stored in that location will be zeroed out by the first XOR instruction, and then remain zero; it will not be "swapped with itself". This is not the same as if x and y have the same values. The trouble only comes when x and y use the same storage location, in which case their values must already be equal. That is, if x and y use the same storage location, then the line: sets x to zero (because x = y so X XOR Y is zero) and sets y to zero (since it uses the same storage location), causing x and y to lose their original values. == Proof of correctness == The binary operation XOR over bit strings of length N {\displaystyle N} exhibits the following properties (where ⊕ {\displaystyle \oplus } denotes XOR): L1. Commutativity: A ⊕ B = B ⊕ A {\displaystyle A\oplus B=B\oplus A} L2. Associativity: ( A ⊕ B ) ⊕ C = A ⊕ ( B ⊕ C ) {\displaystyle (A\oplus B)\oplus C=A\oplus (B\oplus C)} L3. Identity exists: there is a bit string, 0, (of length N) such that A ⊕ 0 = A {\displaystyle A\oplus 0=A} for any A {\displaystyle A} L4. Each element is its own inverse: for each A {\displaystyle A} , A ⊕ A = 0 {\displaystyle A\oplus A=0} . Suppose that we have two distinct registers R1 and R2 as in the table below, with initial values A and B respectively. We perform the operations below in sequence, and reduce our results using the properties listed above. === Linear algebra interpretation === As XOR can be interpreted as binary addition and a pair of bits can be interpreted as a vector in a two-dimensional vector space over the field with two elements, the steps in the algorithm can be interpreted as multiplication by 2×2 matrices over the field with two elements. For simplicity, assume initially that x and y are each single bits, not bit vectors. For example, the step: which also has the implicit: corresponds to the matrix ( 1 1 0 1 ) {\displaystyle \left({\begin{smallmatrix}1&1\\0&1\end{smallmatrix}}\right)} as ( 1 1 0 1 ) ( x y ) = ( x + y y ) . {\displaystyle {\begin{pmatrix}1&1\\0&1\end{pmatrix}}{\begin{pmatrix}x\\y\end{pmatrix}}={\begin{pmatrix}x+y\\y\end{pmatrix}}.} The sequence of operations is then expressed as: ( 1 1 0 1 ) ( 1 0 1 1 ) ( 1 1 0 1 ) = ( 0 1 1 0 ) {\displaystyle {\begin{pmatrix}1&1\\0&1\end{pmatrix}}{\begin{pmatrix}1&0\\1&1\end{pmatrix}}{\begin{pmatrix}1&1\\0&1\end{pmatrix}}={\begin{pmatrix}0&1\\1&0\end{pmatrix}}} (working with binary values, so 1 + 1 = 0 {\displaystyle 1+1=0} ), which expresses the elementary matrix of switching two rows (or columns) in terms of the transvections (shears) of adding one element to the other. To generalize to where X and Y are not single bits, but instead bit vectors of length n, these 2×2 matrices are replaced by 2n×2n block matrices such as ( I n I n 0 I n ) . {\displaystyle \left({\begin{smallmatrix}I_{n}&I_{n}\\0&I_{n}\end{smallmatrix}}\right).} These matrices are operating on values, not on variables (with storage locations), hence this interpretation abstracts away from issues of storage location and the problem of both variables sharing the same storage location. == Code example == A C function that implements the XOR swap algorithm: The code first checks if the addresses are distinct and uses a guard clause to exit the function early if they are equal. Without that check, if they were equal, the algorithm would fold to a triple x ^= x resulting in zero. == Reasons for avoidance in practice == On modern CPU architectures, the XOR technique can be slower than using a temporary variable to do swapping. At least on recent x86 CPUs, both by AMD and Intel, moving between registers regularly incurs zero latency. (This is called MOV-elimination.) Even if there is not any architectural register available to use, the XCHG instruction will be at least as fast as the three XORs taken together. Another reason is that modern CPUs strive to execute instructions in parallel via instruction pipelines. In the XOR technique, the inputs to each operation depend on the results of the previous operation, so they must be executed in strictly sequential order, negating any benefits of instruction-level parallelism. === Aliasing === The XOR swap is also complicated in practice by aliasing. If an attempt is made to XOR-swap the contents of some location with itself, the result is that the location is zeroed out and its value lost. Therefore, XOR swapping must not be used blindly in a high-level language if aliasing is possible. This issue does not apply if the technique is used in assembly to swap the contents of two registers. Similar problems occur with call by name, as in Jensen's Device, where swapping i and A[i] via a temporary variable yields incorrect results due to the arguments being related: swapping via temp = i; i = A[i]; A[i] = temp changes the value for i in the second statement, which then results in the incorrect i value for A[i] in the third statement. == Variations == The underlying principle of the XOR swap algorithm can be applied to any operation meeting criteria L1 through L4 above. Replacing XOR by addition and subtraction gives various slightly different, but largely equivalent, formulations. For example: Unlike the XOR swap, this variation requires that the underlying processor or programming language uses a method such as modular arithmetic or bignums to guarantee that the computation of X + Y cannot cause an error due to integer overflow. Therefore, it is seen even more rarely in practice than the XOR swap. However, the implementation of AddSwap above in the C programming language always works even in case of integer overflow, since, according to the C standard, addition and subtraction of unsigned integers follow the rules of modular arithmetic, i. e. are done in the cyclic group Z / 2 s Z {\displaystyle \mathbb {Z} /2^{s}\mathbb {Z} } where s {\displaystyle s} is the number of bits of unsigned int. Indeed, the correctness of the algorithm follows from the fact that the formulas ( x + y ) − y = x {\displaystyle (x+y)-y=x} and ( x + y ) − ( ( x + y ) − y ) = y {\displaystyle (x+y)-((x+y)-y)=y} hold in any abelian group. This generalizes the proof for the XOR swap algorithm: XOR is both the addition and subtraction in the abelian group ( Z / 2 Z ) s {\displaystyle (\mathbb {Z} /2\mathbb {Z} )^{s}} (which is the direct sum of s copies of Z / 2 Z {\displaystyle \mathbb {Z} /2\mathbb {Z} } ). This doesn't hold when dealing with the signed int type (the default for int). Signed integer overflow is an undefined behavior in C and thus modular arithmetic is not guaranteed by the standard, which may lead to incorrect results. The sequence of operations in AddSwap can be expressed via matrix multiplication as: ( 1 − 1 0 1 ) ( 1 0 1 − 1 ) ( 1 1 0 1 ) = ( 0 1 1 0 ) {\displaystyle {\begin{pmatrix}1&-1\\0&1\end{pmatrix}}{\begin{pmatrix}1&0\\1&-1\end{pmatrix}}{\begin{pmatrix}1&1\\0&1\end{pmatrix}}={\begin{pmatrix}0&1\\1&0\end{pmatrix}}} == Application to register allocation == On architectures lacking a dedicated swap instruction, because it avoids the extra temporary register, the XOR swap algorithm is required for optimal register allocatio

  Read more →
• 

  Semantic heterogeneity

  Semantic heterogeneity is when database schema or datasets for the same domain are developed by independent parties, resulting in differences in meaning and interpretation of data values. Beyond structured data, the problem of semantic heterogeneity is compounded due to the flexibility of semi-structured data and various tagging methods applied to documents or unstructured data. Semantic heterogeneity is one of the more important sources of differences in heterogeneous datasets. Yet, for multiple data sources to interoperate with one another, it is essential to reconcile these semantic differences. Decomposing the various sources of semantic heterogeneities provides a basis for understanding how to map and transform data to overcome these differences. == Classification == One of the first known classification schemes applied to data semantics is from William Kent in the late 80s. Kent's approach dealt more with structural mapping issues than differences in meaning, which he pointed to data dictionaries as potentially solving. One of the most comprehensive classifications is from Pluempitiwiriyawej and Hammer, "Classification Scheme for Semantic and Schematic Heterogeneities in XML Data Sources". They classify heterogeneities into three broad classes: Structural conflicts arise when the schema of the sources representing related or overlapping data exhibit discrepancies. Structural conflicts can be detected when comparing the underlying schema. The class of structural conflicts includes generalization conflicts, aggregation conflicts, internal path discrepancy, missing items, element ordering, constraint and type mismatch, and naming conflicts between the element types and attribute names. Domain conflicts arise when the semantics of the data sources that will be integrated exhibit discrepancies. Domain conflicts can be detected by looking at the information contained in the schema and using knowledge about the underlying data domains. The class of domain conflicts includes schematic discrepancy, scale or unit, precision, and data representation conflicts. Data conflicts refer to discrepancies among similar or related data values across multiple sources. Data conflicts can only be detected by comparing the underlying sources. The class of data conflicts includes ID-value, missing data, incorrect spelling, and naming conflicts between the element contents and the attribute values. Moreover, mismatches or conflicts can occur between set elements (a "population" mismatch) or attributes (a "description" mismatch). Michael Bergman expanded upon this schema by adding a fourth major explicit category of language, and also added some examples of each kind of semantic heterogeneity, resulting in about 40 distinct potential categories . This table shows the combined 40 possible sources of semantic heterogeneities across sources: A different approach toward classifying semantics and integration approaches is taken by Sheth et al. Under their concept, they split semantics into three forms: implicit, formal and powerful. Implicit semantics are what is either largely present or can easily be extracted; formal languages, though relatively scarce, occur in the form of ontologies or other description logics; and powerful (soft) semantics are fuzzy and not limited to rigid set-based assignments. Sheth et al.'s main point is that first-order logic (FOL) or description logic is inadequate alone to properly capture the needed semantics. == Relevant applications == Besides data interoperability, relevant areas in information technology that depend on reconciling semantic heterogeneities include data mapping, semantic integration, and enterprise information integration, among many others. From the conceptual to actual data, there are differences in perspective, vocabularies, measures and conventions once any two data sources are brought together. Explicit attention to these semantic heterogeneities is one means to get the information to integrate or interoperate. A mere twenty years ago, information technology systems expressed and stored data in a multitude of formats and systems. The Internet and Web protocols have done much to overcome these sources of differences. While there is a large number of categories of semantic heterogeneity, these categories are also patterned and can be anticipated and corrected. These patterned sources inform what kind of work must be done to overcome semantic differences where they still reside.

  Read more →
• 

  Uniphore

  Uniphore is an American software company that develops artificial intelligence platforms for business use. The company is headquartered in Palo Alto, California, with offices in the United States, United Kingdom, Spain, Israel, United Arab Emirates, and India. Uniphore is known for its "Business AI Cloud," an enterprise AI platform that combines data, knowledge, models, and software agents for use in sales, marketing, and service. The company has also acquired firms in video emotion AI, AI agents, low-code automation, knowledge automation, voice and screen capture, customer data platforms, and data engineering. == History == Uniphore Software Systems was founded by Umesh Sachdev and Ravi Saraogi in 2008 and was incubated at IIT Madras. The company received an initial grant of $100,000 from the National Research Development Corporation. Early work focused on speech technologies for emerging markets. Uniphore partnered with companies that specialized in English and European languages, and adapting the technology for Indian languages and dialects. In 2014, Uniphore released its first flagship products, auMina, along with two other products, Akeira and amVoice. Uniphore raised series A funding, led by Kris Gopalakrishnan (cofounder of Infosys), in April 2015. The next month, Uniphore received additional investment from IDG Ventures. With input from its investors, Uniphore changed its business model from license fee-based income to a software as a service-based subscription fee model in 2015. By June 2016, it had added more than 70 global languages and expanded its services to Southeast Asia, the Middle East, and the United States. The company opened operations in Singapore in October 2016. The company raised Series B funding in October 2017, led by John Chambers and existing investors. Series C funding of $51 million was announced in August 2019 and led by March Capital. Uniphore acquired an exclusive third-party license for robotic process automation technology from NTT DATA in October 2020. In January 2021, Uniphore acquired Emotion Research Lab, a startup based in Spain that uses artificial intelligence and machine learning to analyze video and interpret emotions. The company received $140 million in Series D funding, led by Sorenson Capital Partners, in March 2021, bringing total funding to $210 million. In January 2021, Uniphore acquired Emotion Research Lab. In July 2021, it agreed to acquire Jacada, a provider of low-code/no-code automation; the transaction closed in October 2021. On February 16, 2022, Uniphore announced a $400 million Series E financing led by NEA, which valued the company at $2.5 billion. Hilarie Koplow-McAdams, an NEA venture partner and former Salesforce/New Relic executive, joined Uniphore's board in 2022. Uniphore's board has also included former Cisco CEO John Chambers, former Convergys CEO Andrea J. Ayers, and CrowdStrike CFO Burt Podbere (appointed January 2021). In February 2023, Uniphore acquired UK-based Red Box, a platform for capturing voice and screen recordings used in regulated and large-scale environments. It also acquired France-based Hexagone, a behavioral analytics firm combining computer vision and natural-language techniques. On December 5, 2024, Uniphore announced agreements to acquire ActionIQ, a customer data platform (CDP) vendor, and Infoworks, an enterprise data engineering platform. Uniphore launched the Business AI Cloud on June 9, 2025. The Business AI Cloud consists of a single, unified platform that includes data, knowledge, AI models, and AI agents. Uniphore announced in August 2025 that it had acquired Orby AI and intended to acquire Autonom8 to extend multi-agent and workflow automation capabilities. As of September 2025, Uniphore's customers included the United States Coast Guard, Singapore Police Force, London Underground, DirecTV, JPMorgan Chase, LG, DHL, UPS, Vodafone, Verizon, NTT Data, and as of May 2021, Firstsource. In October 2025, Uniphore raised $260 million in a Series F round at a reported valuation of $2.5 billion. Investors included March Capital, NEA, Nvidia, AMD, Snowflake, and Databricks. In January 2026, KPMG and Uniphore announced a collaboration focused on deploying AI agents powered by specialized small language models. The announcement was made at the World Economic Forum held in Davos. Cognizant and Uniphore announced a partnership in February 2026 to develop industry-specific AI tools for regulated sectors, which would initially focus on life sciences and finance. Uniphore and Rackspace also announced a partnership in March 2026. This partnership was announced in order to create an "Infrastructure-to-Agents" architecture, focusing on Business AI as a private cloud service. == Products == As of 2025, Uniphore's core offering is the Business AI Cloud and Business AI Suite of agentic AI applications. === Business AI Cloud === Uniphore’s Business AI Cloud is a full-stack platform that organizes enterprise data and knowledge for agentic AI applications. The platform enables deployment across clouds and existing data sources. Key layers and capabilities include the following. Agentic layer: Includes prebuilt agents, a natural-language agent builder, and orchestration based on Business Process Model and Notation (BPMN) to run AI workflows across business units. Model layer: Supports an open, interoperable mix of closed and open-source large language models (LLMs). Models can be orchestrated, governed, and replaced as needed. Knowledge layer: Organizes raw data into structured knowledge used for retrieval, explainability, and fine-tuning of small language models (SLMs). Data layer: Connects to data across multiple platforms and clouds through a zero-copy, composable fabric, enabling in-place preparation and supporting data residency and sovereignty requirements. === Business AI Suite === The Uniphore Business AI Suite has various prebuilt AI agents that can be used in customer service, sales, marketing, and human resources. The Uniphore Business AI Suite includes several LOBs (Lines of Business) for business functions with intelligent agents that are prebuilt, but composable. Built on the Uniphore Business AI Cloud, each application combines agentic automation and fine-tuned models. Marketing AI, Customer Service AI, Sales AI, and People AI (for human resources) are included. Competitors include Palantir, Microsoft Azure, Amazon Bedrock, Google's Vertex AI, Databricks, and Snowflake. == Recognition == Deloitte Technology Fast 50 India identified Uniphore as the 17th fastest-growing technology company in India in 2012 and one of the top 500 fastest growing companies in the Asia-Pacific region in 2014. In 2016, Time included Sachdev on its list of "10 millennials who are changing the world" for “building a phone that can understand almost any language”. NASSCOM named Uniphore to its "League of 10" emerging Indian technology companies in 2017. In 2020, the San Francisco Business Times ranked Uniphore as No. 7 among small companies in its list of the best places to work in the San Francisco Bay Area. In 2022, the company was featured on the Forbes AI 50 list. Uniphore was mentioned in the Deloitte Technology Fast 500 list in 2023, 2024, and 2025. In 2025, Inc. included Uniphore in its Best in Business program.

  Read more →
• 

  Computer and information science

  Computer and information science (CIS; also known as information and computer science) is a field that emphasizes both computing and informatics, upholding the strong association between the fields of information sciences and computer sciences and treating computers as a tool rather than a field. Information science is one with a long history, unlike the relatively very young field of computer science, and is primarily concerned with gathering, storing, disseminating, sharing and protecting any and all forms of information. It is a broad field, covering a myriad of different areas but is often referenced alongside computer science because of the incredibly useful nature of computers and computer programs in helping those studying and doing research in the field – particularly in helping to analyse data and in spotting patterns too broad for a human to intuitively perceive. While information science is sometimes confused with information theory, the two have vastly different subject matter. Information theory focuses on one particular mathematical concept of information while information science is focused on all aspects of the processes and techniques of information. Computer science, in contrast, is less focused on information and its different states, but more, in a very broad sense, on the use of computers – both in theory and practice – to design and implement algorithms in order to aid the processing of information during the different states described above. It has strong foundations in the field of mathematics, as the very first recognised practitioners of the field were renowned mathematicians such as Alan Turing. Information science and computing began to converge in the 1950s and 1960s, as information scientists started to realize the many ways computers would improve information storage and retrieval. == Terminology == Due to the distinction between computers and computing, some of the research groups refer to computing or datalogy. The French refer to computer science as the term informatique. The term information and communications technology (ICT), refers to how humans communicate with using machines and computers, making a distinction from information and computer science, which is how computers use and gain information. Informatics is also distinct from computer science, which encompasses the study of logic and low-level computing issues. == Education == Universities may confer degrees with a major in computer and information science, not to be confused with a more specific Bachelor of Computer Science or respective graduate computer science degrees. The QS World University Rankings is one of the most widely recognised and distinguished university comparisons. They ranked the top 10 universities for computer science and information systems in 2015. They are: Massachusetts Institute of Technology (MIT) Stanford University University of Oxford Carnegie Mellon University Harvard University University of California, Berkeley (UCB) University of Cambridge The Hong Kong University of Science and Technology Swiss Federal Institute of Technology (ETH Zurich) Princeton University A Computer Information Science degree gives students both network and computing knowledge which is needed to design, develop, and assist information systems which helps to solve business problems and to support business problems and to support business operations and decision making at a managerial level also. == Areas of information and computer science == Due to the nature of this field, many topics are also shared with computer science and information systems. The discipline of Information and Computer Science spans a vast range of areas from basic computer science theory (algorithms and computational logic) to in depth analysis of data manipulation and use within technology. === Programming theory === The process of taking a given algorithm and encoding it into a language that can be understood and executed by a computer. There are many different types of programming languages and various different types of computers, however, they all have the same goal: to turn algorithms into machine code. Popular programming languages used within the academic study of CIS include, but are not limited to: Java, Python, C#, C++, Perl, Ruby, Pascal, Swift, Visual Basic. === Information and information systems === The academic study of software and hardware systems that process large quantities and data, support large scale data management and how data can be used. This is where the field is unique from the standard study of computer science. The area of information systems focuses on the networks of hardware and software that are required to process, manipulate and distribute such data. === Computer systems and organisations === The process of analysing computer architecture and various logic circuits. This involves looking at low level computer processes at bit level computation. This is an in-depth look into the hardware processing of a computational system, involving looking at the basic structure of a computer and designing such systems. This can also involve evaluating complex circuit diagrams, and being able to construct these to solve a main problem. The main purpose behind this area of study is to achieve an understanding of how computers function on a basic level, often through tracing machine operations. === Machines, languages, and computation === This is the study into fundamental computer algorithms, which are the basis to computer programs. Without algorithms, no computer programs would exist. This also involves the process of looking into various mathematical functions behind computational algorithms, basic theory and functional (low level) programming. In an academic setting, this area would introduce the fundamental mathematical theorems and functions behind theoretical computer science which are the building blocks for other areas in the field. Complex topics such as; proofs, algebraic functions and sets will be introduced during studies of CIS. == Developments == Information and computer science is a field that is rapidly developing with job prospects for students being extremely promising with 75.7% of graduates gaining employment. Also the IT industry employs one in twenty of the workforce with it predicted to increase nearly five times faster than the average of the UK and between 2012 and 2017 more than half a million people will be needed within the industry and the fact that nine out of ten tech firms are suffering from candidate shortages which is having a negative impact on their business as it delays the creation and development of new products, and it's predicted in the US that in the next decade there will be more than one million jobs in the technology sector than computer science graduates to fill them. Because of this programming is now being taught at an earlier age with an aim to interest students from a young age into computer and information science hopefully leading more children to study this at a higher level. For example, children in England will now be exposed to computer programming at the age of 5 due to an updated national curriculum. == Employment == Due to the wide variety of jobs that now involve computer and information science related tasks, it is difficult to provide a comprehensive list of possible jobs in this area, but some of the key areas are artificial intelligence, software engineering and computer networking and communication. Work in this area also tends to require sufficient understanding of mathematics and science. Moreover, jobs that having a CIS degree can lead to, include: systems analyst, network administrator, system architect, information systems developer, web programmer, or software developer. The earning potential for CIS graduates is quite promising. A 2013 survey from the National Association of Colleges and Employers (NACE) found that the average starting salary for graduates who earned a degree in a computer related field was $59,977, up 4.3% from the prior year. This is higher than other popular degrees such as business ($54,234), education ($40,480) and math and sciences ($42,724). Furthermore, Payscale ranked 129 college degrees based on their graduates earning potential with engineering, math, science, and technology fields dominating the ranking. With eight computer related degrees appearing among the top 30. With the lowest starting salary for these jobs being $49,900. A Rasmussen College article describes various jobs CIS graduates may obtain with software applications developers at the top making a median income of $98,260. According to the National Careers Service an Information Scientist can expect to earn £24,000+ per year as a starting salary.

  Read more →
• 

  Geospatial metadata

  Geospatial metadata (also geographic metadata) is a type of metadata applicable to geographic data and information. Such objects may be stored in a geographic information system (GIS) or may simply be documents, data-sets, images or other objects, services, or related items that exist in some other native environment but whose features may be appropriate to describe in a (geographic) metadata catalog (may also be known as a data directory or data inventory). == Definition == ISO 19115:2013 "Geographic Information – Metadata" from ISO/TC 211, the industry standard for geospatial metadata, describes its scope as follows: [This standard] provides information about the identification, the extent, the quality, the spatial and temporal aspects, the content, the spatial reference, the portrayal, distribution, and other properties of digital geographic data and services. ISO 19115:2013 also provides for non-digital mediums: Though this part of ISO 19115 is applicable to digital data and services, its principles can be extended to many other types of resources such as maps, charts, and textual documents as well as non-geographic data. The U.S. Federal Geographic Data Committee (FGDC) describes geospatial metadata as follows: A metadata record is a file of information, usually presented as an XML document, which captures the basic characteristics of a data or information resource. It represents the who, what, when, where, why and how of the resource. Geospatial metadata commonly document geographic digital data such as Geographic Information System (GIS) files, geospatial databases, and earth imagery but can also be used to document geospatial resources including data catalogs, mapping applications, data models and related websites. Metadata records include core library catalog elements such as Title, Abstract, and Publication Data; geographic elements such as Geographic Extent and Projection Information; and database elements such as Attribute Label Definitions and Attribute Domain Values. == History == The growing appreciation of the value of geospatial metadata through the 1980s and 1990s led to the development of a number of initiatives to collect metadata according to a variety of formats either within agencies, communities of practice, or countries/groups of countries. For example, NASA's "DIF" metadata format was developed during an Earth Science and Applications Data Systems Workshop in 1987, and formally approved for adoption in 1988. Similarly, the U.S. FGDC developed its geospatial metadata standard over the period 1992–1994. The Spatial Information Council of Australia and New Zealand (ANZLIC), a combined body representing spatial data interests in Australia and New Zealand, released version 1 of its "metadata guidelines" in 1996. ISO/TC 211 undertook the task of harmonizing the range of formal and de facto standards over the approximate period 1999–2002, resulting in the release of ISO 19115 "Geographic Information – Metadata" in 2003 and a subsequent revision in 2013. As of 2011 individual countries, communities of practice, agencies, etc. have started re-casting their previously used metadata standards as "profiles" or recommended subsets of ISO 19115, occasionally with the inclusion of additional metadata elements as formal extensions to the ISO standard. The growth in popularity of Internet technologies and data formats, such as Extensible Markup Language (XML) during the 1990s led to the development of mechanisms for exchanging geographic metadata on the web. In 2004, the Open Geospatial Consortium released the current version (3.1) of Geography Markup Language (GML), an XML grammar for expressing geospatial features and corresponding metadata. With the growth of the Semantic Web in the 2000s, the geospatial community has begun to develop ontologies for representing semantic geospatial metadata. Some examples include the Hydrology and Administrative ontologies developed by the Ordnance Survey in the United Kingdom. == ISO 19115: Geographic information – Metadata == ISO 19115 is a standard of the International Organization for Standardization (ISO). The standard is part of the ISO geographic information suite of standards (19100 series). ISO 19115 and its parts define how to describe geographical information and associated services, including contents, spatial-temporal purchases, data quality, access and rights to use. The objective of this International Standard is to provide a clear procedure for the description of digital geographic data-sets so that users will be able to determine whether the data in a holding will be of use to them and how to access the data. By establishing a common set of metadata terminology, definitions and extension procedures, this standard promotes the proper use and effective retrieval of geographic data. ISO 19115 was revised in 2013 to accommodate growing use of the internet for metadata management, as well as add many new categories of metadata elements (referred to as codelists) and the ability to limit the extent of metadata use temporally or by user. == ISO 19139 Geographic information Metadata XML schema implementation == ISO 19139:2012 provides the XML implementation schema for ISO 19115 specifying the metadata record format and may be used to describe, validate, and exchange geospatial metadata prepared in XML. The standard is part of the ISO geographic information suite of standards (19100 series), and provides a spatial metadata XML (spatial metadata eXtensible Mark-up Language (smXML)) encoding, an XML schema implementation derived from ISO 19115, Geographic information – Metadata. The metadata includes information about the identification, constraint, extent, quality, spatial and temporal reference, distribution, lineage, and maintenance of the digital geographic data-set. == Metadata directories == Also known as metadata catalogues or data directories. (need discussion of, and subsections on GCMD, FGDC metadata gateway, ASDD, European and Canadian initiatives, etc. etc.) GIS Inventory – National GIS Inventory System which is maintained by the US-based National States Geographic Information Council (NSGIC) as a tool for the entire US GIS Community. Its primary purpose is to track data availability and the status of geographic information system (GIS) implementation in state and local governments to aid the planning and building of statewide spatial data infrastructures (SSDI). The Random Access Metadata for Online Nationwide Assessment (RAMONA) database is a critical component of the GIS Inventory. RAMONA moves its FGDC-compliant metadata (CSDGM Standard) for each data layer to a web folder and a Catalog Service for the Web (CSW) that can be harvested by Federal programs and others. This provides far greater opportunities for discovery of user information. The GIS Inventory website was originally created in 2006 by NSGIC under award NA04NOS4730011 from the Coastal Services Center, National Oceanic and Atmospheric Administration, U.S. Department of Commerce. The Department of Homeland Security has been the principal funding source since 2008 and they supported the development of the Version 5 during 2011/2012 under Order Number HSHQDC-11-P-00177. The Federal Emergency Management Agency and National Oceanic and Atmospheric Administration have provided additional resources to maintain and improve the GIS Inventory. Some US Federal programs require submission of CSDGM-Compliant Metadata for data created under grants and contracts that they issue. The GIS Inventory provides a very simple interface to create the required Metadata. GCMD - Global Change Master Directory's goal is to enable users to locate and obtain access to Earth science data sets and services relevant to global change and Earth science research. The GCMD database holds more than 20,000 descriptions of Earth science data sets and services covering all aspects of Earth and environmental sciences. ECHO - The EOS Clearing House (ECHO) is a spatial and temporal metadata registry, service registry, and order broker. It allows users to more efficiently search and access data and services through the Reverb Client or Application Programmer Interfaces (APIs). ECHO stores metadata from a variety of science disciplines and domains, totalling over 3400 Earth science data sets and over 118 million granule records. GoGeo - GoGeo is a service run by EDINA (University of Edinburgh) and is supported by Jisc. GoGeo allows users to conduct geographically targeted searches to discover geospatial datasets. GoGeo searches many data portals from the HE and FE community and beyond. GoGeo also allows users to create standards compliant metadata through its Geodoc metadata editor. == Geospatial metadata tools == There are many proprietary GIS or geospatial products that support metadata viewing and editing on GIS resources. For example, ESRI's ArcGIS Desktop, SOCET GXP, Autodesk's AutoCAD Map 3D 2008, Arcitecta's Mediaflux and Intergraph's Geo

  Read more →
• 

  Leiden algorithm

  The Leiden algorithm is a community detection algorithm developed by Traag et al at Leiden University. It was developed as a modification of the Louvain method. Like the Louvain method, the Leiden algorithm attempts to optimize modularity in extracting communities from networks; however, it addresses key issues present in the Louvain method, namely poorly connected communities and the resolution limit of modularity. == Improvement over Louvain method == Broadly, the Leiden algorithm uses the same two primary phases as the Louvain algorithm: a local node moving step (though, the method by which nodes are considered in Leiden is more efficient) and a graph aggregation step. However, to address the issues with poorly-connected communities and the merging of smaller communities into larger communities (the resolution limit of modularity), the Leiden algorithm employs an intermediate refinement phase in which communities may be split to guarantee that all communities are well-connected. Consider, for example, the following graph: Three communities are present in this graph (each color represents a community). Additionally, the center "bridge" node (represented with an extra circle) is a member of the community represented by blue nodes. Now consider the result of a node-moving step which merges the communities denoted by red and green nodes into a single community (as the two communities are highly connected): Notably, the center "bridge" node is now a member of the larger red community after node moving occurs (due to the greedy nature of the local node moving algorithm). In the Louvain method, such a merging would be followed immediately by the graph aggregation phase. However, this causes a disconnection between two different sections of the community represented by blue nodes. In the Leiden algorithm, the graph is instead refined: The Leiden algorithm's refinement step ensures that the center "bridge" node is kept in the blue community to ensure that it remains intact and connected, despite the potential improvement in modularity from adding the center "bridge" node to the red community. == Graph components == Before defining the Leiden algorithm, it will be helpful to define some of the components of a graph. === Vertices and edges === A graph is composed of vertices (nodes) and edges. Each edge is connected to two vertices, and each vertex may be connected to zero or more edges. Edges are typically represented by straight lines, while nodes are represented by circles or points. In set notation, let V {\displaystyle V} be the set of vertices, and E {\displaystyle E} be the set of edges: V := { v 1 , v 2 , … , v n } E := { e i j , e i k , … , e k l } {\displaystyle {\begin{aligned}V&:=\{v_{1},v_{2},\dots ,v_{n}\}\\E&:=\{e_{ij},e_{ik},\dots ,e_{kl}\}\end{aligned}}} where e i j {\displaystyle e_{ij}} is the directed edge from vertex v i {\displaystyle v_{i}} to vertex v j {\displaystyle v_{j}} . We can also write this as an ordered pair: e i j := ( v i , v j ) {\displaystyle {\begin{aligned}e_{ij}&:=(v_{i},v_{j})\end{aligned}}} === Community === A community is a unique set of nodes: C i ⊆ V C i ⋂ C j = ∅ ∀ i ≠ j {\displaystyle {\begin{aligned}C_{i}&\subseteq V\\C_{i}&\bigcap C_{j}=\emptyset ~\forall ~i\neq j\end{aligned}}} and the union of all communities must be the total set of vertices: V = ⋃ i = 1 C i {\displaystyle {\begin{aligned}V&=\bigcup _{i=1}C_{i}\end{aligned}}} === Partition === A partition is the set of all communities: P = { C 1 , C 2 , … , C n } {\displaystyle {\begin{aligned}{\mathcal {P}}&=\{C_{1},C_{2},\dots ,C_{n}\}\end{aligned}}} == Partition quality == How communities are partitioned is an integral part on the Leiden algorithm. How partitions are decided can depend on how their quality is measured. Additionally, many of these metrics contain parameters of their own that can change the outcome of their communities. === Modularity === Modularity is a highly used quality metric for assessing how well a set of communities partition a graph. The equation for this metric is defined for an adjacency matrix, A, as: Q = 1 2 m ∑ i j ( A i j − k i k j 2 m ) δ ( c i , c j ) {\displaystyle Q={\frac {1}{2m}}\sum _{ij}(A_{ij}-{\frac {k_{i}k_{j}}{2m}})\delta (c_{i},c_{j})} where: A i j {\displaystyle A_{ij}} represents the edge weight between nodes i {\displaystyle i} and j {\displaystyle j} ; see Adjacency matrix; k i {\displaystyle k_{i}} and k j {\displaystyle k_{j}} are the sum of the weights of the edges attached to nodes i {\displaystyle i} and j {\displaystyle j} , respectively; m {\displaystyle m} is the sum of all of the edge weights in the graph; c i {\displaystyle c_{i}} and c j {\displaystyle c_{j}} are the communities to which the nodes i {\displaystyle i} and j {\displaystyle j} belong; and δ {\displaystyle \delta } is Kronecker delta function: δ ( c i , c j ) = { 1 if c i and c j are the same community 0 otherwise {\displaystyle {\begin{aligned}\delta (c_{i},c_{j})&={\begin{cases}1&{\text{if }}c_{i}{\text{ and }}c_{j}{\text{ are the same community}}\\0&{\text{otherwise}}\end{cases}}\end{aligned}}} === Reichardt Bornholdt Potts Model (RB) === One of the most well used metrics for the Leiden algorithm is the Reichardt Bornholdt Potts Model (RB). This model is used by default in most mainstream Leiden algorithm libraries under the name RBConfigurationVertexPartition. This model introduces a resolution parameter γ {\displaystyle \gamma } and is highly similar to the equation for modularity. This model is defined by the following quality function for an adjacency matrix, A, as: Q = ∑ i j ( A i j − γ k i k j 2 m ) δ ( c i , c j ) {\displaystyle Q=\sum _{ij}(A_{ij}-\gamma {\frac {k_{i}k_{j}}{2m}})\delta (c_{i},c_{j})} where: γ {\displaystyle \gamma } represents a linear resolution parameter === Constant Potts Model (CPM) === Another metric similar to RB is the Constant Potts Model (CPM). This metric also relies on a resolution parameter γ {\displaystyle \gamma } The quality function is defined as: H = − ∑ i j ( A i j w i j − γ ) δ ( c i , c j ) {\displaystyle H=-\sum _{ij}(A_{ij}w_{ij}-\gamma )\delta (c_{i},c_{j})} === Understanding Potts Model resolution parameters/Resolution limit === Typically Potts models such as RB or CPM include a resolution parameter in their calculation. Potts models are introduced as a response to the resolution limit problem that is present in modularity maximization based community detection. The resolution limit problem is that, for some graphs, maximizing modularity may cause substructures of a graph to merge and become a single community and thus smaller structures are lost. These resolution parameters allow modularity adjacent methods to be modified to suit the requirements of the user applying the Leiden algorithm to account for small substructures at a certain granularity. The figure on the right illustrates why resolution can be a helpful parameter when using modularity based quality metrics. In the first graph, modularity only captures the large scale structures of the graph; however, in the second example, a more granular quality metric could potentially detect all substructures in a graph. == Algorithm == The Leiden algorithm starts with a graph of disorganized nodes (a) and sorts it by partitioning them to maximize modularity (the difference in quality between the generated partition and a hypothetical randomized partition of communities). The method it uses is similar to the Louvain algorithm, except that after moving each node it also considers that node's neighbors that are not already in the community it was placed in. This process results in our first partition (b), also referred to as P {\displaystyle {\mathcal {P}}} . Then the algorithm refines this partition by first placing each node into its own individual community and then moving them from one community to another to maximize modularity. It does this iteratively until each node has been visited and moved, and each community has been refined - this creates partition (c), which is the initial partition of P refined {\displaystyle {\mathcal {P}}_{\text{refined}}} . Then an aggregate network (d) is created by turning each community into a node. P refined {\displaystyle {\mathcal {P}}_{\text{refined}}} is used as the basis for the aggregate network while P {\displaystyle {\mathcal {P}}} is used to create its initial partition. Because we use the original partition P {\displaystyle {\mathcal {P}}} in this step, we must retain it so that it can be used in future iterations. These steps together form the first iteration of the algorithm. In subsequent iterations, the nodes of the aggregate network (which each represent a community) are once again placed into their own individual communities and then sorted according to modularity to form a new P refined {\displaystyle {\mathcal {P}}_{\text{refined}}} , forming (e) in the above graphic. In the case depicted by the graph, the nodes were already sorted optimally, so no change too

  Read more →
• 

  Beauty.AI

  Beauty.AI is a mobile beauty pageant for humans and a contest for programmers developing algorithms for evaluating human appearance. The mobile app and website created by Youth Laboratories that uses artificial intelligence technology to evaluate people's external appearance through certain algorithms, such as symmetry, facial blemishes, wrinkles, estimated age and age appearance, and comparisons to actors and models. The Beauty.AI 2.0 contest caused great concern over important ethical issues with deep neural networks such as age, race and gender bias and lead to the creation of the Diversity.AI think tank dedicated to developing new methods for uncovering and managing bias in artificially intelligent systems. Beauty.AI was also an attempt to find approaches on how machines can perceive human face through evaluating particular features, commonly associated with health and beauty. == Concept == The Beauty.AI app was created by Youth Laboratories, a company based out of Russia and Hong Kong that focuses on facial skin analytics. The bioinformation company Insilico Medicine assists in the Beauty.AI app by testing its deep learning techniques to the app. One goal of the app is to reduce the need for human and animal testing as well as improving people's overall health. Its first contest was started in December 2016, and the results were announced in August 2016. More than 60,000 people submitted entries into the contest. The mobile app uses artificial intelligence technology to inspect photographs for certain facial features in order to both determine a person's beauty through artificial means by multiple robots. Part of the Beauty.AI app's purpose is to collect visual and anecdotal data to improve its creator's Youth Laboratories skin analyst skills. == Accusations of racism == There were a total of 44 individuals from different age groups and genders judged as the most attractive, with 37 white entrants, six Asian entrants, and one dark-skinned entrant. The app has received criticism from social justice advocates and computer science professionals. However, Alex Zhavoronkov, PhD, chief science officer of Youth Laboratories and chief technology officer Konstantin Kiselev, both for Youth Laboratories, noted that a lack of data may have contributed to these results. Also, Kiselev added that another issue was that approximately 75% of entrants were white Europeans, whereas only 7% and 1% were from India and Africa, respectively. Kiselev stated that they would work on doing more and better outreach to these areas to improve in this area. Despite this, it was said by Dr. Zhavoronkov that the AI would discard photos of dark-skinned people if the lighting is too poor. Dr. Zhavoronkov vowed to weed out the issues for the next beauty pageant and to try to avoid a similar controversy in the future.

  Read more →
• 

  Hybrid algorithm

  A hybrid algorithm is an algorithm that combines two or more other algorithms that solve the same problem, either choosing one based on some characteristic of the data, or switching between them over the course of the algorithm. This is generally done to combine desired features of each, so that the overall algorithm is better than the individual components. "Hybrid algorithm" does not refer to simply combining multiple algorithms to solve a different problem – many algorithms can be considered as combinations of simpler pieces – but only to combining algorithms that solve the same problem, but differ in other characteristics, notably performance. == Examples == In computer science, hybrid algorithms are very common in optimized real-world implementations of recursive algorithms, particularly implementations of divide-and-conquer or decrease-and-conquer algorithms, where the size of the data decreases as one moves deeper in the recursion. In this case, one algorithm is used for the overall approach (on large data), but deep in the recursion, it switches to a different algorithm, which is more efficient on small data. A common example is in sorting algorithms, where the insertion sort, which is inefficient on large data, but very efficient on small data (say, five to ten elements), is used as the final step, after primarily applying another algorithm, such as merge sort or quicksort. Merge sort and quicksort are asymptotically optimal on large data, but the overhead becomes significant if applying them to small data, hence the use of a different algorithm at the end of the recursion. A highly optimized hybrid sorting algorithm is Timsort, which combines merge sort, insertion sort, together with additional logic (including binary search) in the merging logic. A general procedure for a simple hybrid recursive algorithm is short-circuiting the base case, also known as arm's-length recursion. In this case whether the next step will result in the base case is checked before the function call, avoiding an unnecessary function call. For example, in a tree, rather than recursing to a child node and then checking if it is null, checking null before recursing. This is useful for efficiency when the algorithm usually encounters the base case many times, as in many tree algorithms, but is otherwise considered poor style, particularly in academia, due to the added complexity. Another example of hybrid algorithms for performance reasons are introsort and introselect, which combine one algorithm for fast average performance, falling back on another algorithm to ensure (asymptotically) optimal worst-case performance. Introsort begins with a quicksort, but switches to a heap sort if quicksort is not progressing well; analogously introselect begins with quickselect, but switches to median of medians if quickselect is not progressing well. Centralized distributed algorithms can often be considered as hybrid algorithms, consisting of an individual algorithm (run on each distributed processor), and a combining algorithm (run on a centralized distributor) – these correspond respectively to running the entire algorithm on one processor, or running the entire computation on the distributor, combining trivial results (a one-element data set from each processor). A basic example of these algorithms are distribution sorts, particularly used for external sorting, which divide the data into separate subsets, sort the subsets, and then combine the subsets into totally sorted data; examples include bucket sort and flashsort. However, in general distributed algorithms need not be hybrid algorithms, as individual algorithms or combining or communication algorithms may be solving different problems. For example, in models such as MapReduce, the Map and Reduce step solve different problems, and are combined to solve a different, third problem.

  Read more →
• 

  Algorithmic mechanism design

  Algorithmic mechanism design (AMD) lies at the intersection of economic game theory, optimization, and computer science. The prototypical problem in mechanism design is to design a system for multiple self-interested participants, such that the participants' self-interested actions at equilibrium lead to good system performance. Typical objectives studied include revenue maximization and social welfare maximization. Algorithmic mechanism design differs from classical economic mechanism design in several respects. It typically employs the analytic tools of theoretical computer science, such as worst case analysis and approximation ratios, in contrast to classical mechanism design in economics which often makes distributional assumptions about the agents. It also considers computational constraints to be of central importance: mechanisms that cannot be efficiently implemented in polynomial time are not considered to be viable solutions to a mechanism design problem. This often, for example, rules out the classic economic mechanism, the Vickrey–Clarke–Groves auction. == History == Noam Nisan and Amir Ronen first coined "Algorithmic mechanism design" in a research paper published in 1999.

  Read more →
• 

  Tagsistant

  Tagsistant is a semantic file system for the Linux kernel, written in C and based on FUSE. Unlike traditional file systems that use hierarchies of directories to locate objects, Tagsistant introduces the concept of tags. == Design and differences with hierarchical file systems == In computing, a file system is a type of data store which could be used to store, retrieve and update files. Each file can be uniquely located by its path. The user must know the path in advance to access a file and the path does not necessarily include any information about the content of the file. Tagsistant uses a complementary approach based on tags. The user can create a set of tags and apply those tags to files, directories and other objects (devices, pipes, ...). The user can then search all the objects that match a subset of tags, called a query. This kind of approach is well suited for managing user contents like pictures, audio recordings, movies and text documents but is incompatible with system files (like libraries, commands and configurations) where the univocity of the path is a security requirement to prevent the access to a wrong content. == The tags/ directory == A Tagsistant file system features four main directories: archive/ relations/ stats/ tags/ Tags are created as sub directories of the tags/ directory and can be used in queries complying to this syntax: tags/subquery/[+/subquery/[+/subquery/]]/@/ where a subquery is an unlimited list of tags, concatenated as directories: tag1/tag2/tag3/.../tagN/ The portion of a path delimited by tags/ and @/ is the actual query. The +/ operator joins the results of different sub-queries in one single list. The @/ operator ends the query. To be returned as a result of the following query: tags/t1/t2/+/t1/t4/@/ an object must be tagged as both t1/ and t2/ or as both t1/ and t4/. Any object tagged as t2/ or t4/, but not as t1/ will not be retrieved. The query syntax deliberately violates the POSIX file system semantics by allowing a path token to be a descendant of itself, like in tags/t1/t2/+/t1/t4/@ where t1/ appears twice. As a consequence a recursive scan of a Tagsistant file system will exit with an error or endlessly loop, as done by Unix find: This drawback is balanced by the possibility to list the tags inside a query in any order. The query tags/t1/t2/@/ is completely equivalent to tags/t2/t1/@/ and tags/t1/+/t2/t3/@/ is equivalent to tags/t2/t3/+/t1/@/. The @/ element has the precise purpose of restoring the POSIX semantics: the path tags/t1/@/directory/ refers to a traditional directory and a recursive scan of this path will properly perform. == The reasoner and the relations/ directory == Tagsistant features a simple reasoner which expands the results of a query by including objects tagged with related tags. A relation between two tags can be established inside the relations/ directory following a three level pattern: relations/tag1/rel/tag2/ The rel element can be includes or is_equivalent. To include the rock tag in the music tag, the Unix command mkdir can be used: mkdir -p relations/music/includes/rock The reasoner can recursively resolve relations, allowing the creation of complex structures: mkdir -p relations/music/includes/rock mkdir -p relations/rock/includes/hard_rock mkdir -p relations/rock/includes/grunge mkdir -p relations/rock/includes/heavy_metal mkdir -p relations/heavy_metal/includes/speed_metal The web of relations created inside the relations/ directory constitutes a basic form of ontology. == Autotagging plugins == Tagsistant features an autotagging plugin stack which gets called when a file or a symlink is written. Each plugin is called if its declared MIME type matches The list of working plugins released with Tagsistant 0.6 is limited to: text/html: tags the file with each word in