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The Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text. It supports the most common NLP tasks, such as language detection, tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing and coreference resolution. These tasks are usually required to build more advanced text processing services.
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In computer science, an enumeration algorithm is an algorithm that enumerates the answers to a computational problem. Formally, such an algorithm applies to problems that take an input and produce a list of solutions, similarly to function problems. For each input, the enumeration algorithm must produce the list of all solutions, without duplicates, and then halt. The performance of an enumeration algorithm is measured in terms of the time required to produce the solutions, either in terms of the total time required to produce all solutions, or in terms of the maximal delay between two consecutive solutions and in terms of a preprocessing time, counted as the time before outputting the first solution. This complexity can be expressed in terms of the size of the input, the size of each individual output, or the total size of the set of all outputs, similarly to what is done with output-sensitive algorithms. == Formal definitions == An enumeration problem P {\displaystyle P} is defined as a relation R {\displaystyle R} over strings of an arbitrary alphabet Σ {\displaystyle \Sigma } : R ⊆ Σ ∗ × Σ ∗ {\displaystyle R\subseteq \Sigma ^{}\times \Sigma ^{}} An algorithm solves P {\displaystyle P} if for every input x {\displaystyle x} the algorithm produces the (possibly infinite) sequence y {\displaystyle y} such that y {\displaystyle y} has no duplicate and z ∈ y {\displaystyle z\in y} if and only if ( x , z ) ∈ R {\displaystyle (x,z)\in R} . The algorithm should halt if the sequence y {\displaystyle y} is finite. == Common complexity classes == Enumeration problems have been studied in the context of computational complexity theory, and several complexity classes have been introduced for such problems. A very general such class is EnumP, the class of problems for which the correctness of a possible output can be checked in polynomial time in the input and output. Formally, for such a problem, there must exist an algorithm A which takes as input the problem input x, the candidate output y, and solves the decision problem of whether y is a correct output for the input x, in polynomial time in x and y. For instance, this class contains all problems that amount to enumerating the witnesses of a problem in the class NP. Other classes that have been defined include the following. In the case of problems that are also in EnumP, these problems are ordered from least to most specific: Output polynomial, the class of problems whose complete output can be computed in polynomial time. Incremental polynomial time, the class of problems where, for all i, the i-th output can be produced in polynomial time in the input size and in the number i. Polynomial delay, the class of problems where the delay between two consecutive outputs is polynomial in the input (and independent from the output). Strongly polynomial delay, the class of problems where the delay before each output is polynomial in the size of this specific output (and independent from the input or from the other outputs). The preprocessing is generally assumed to be polynomial. Constant delay, the class of problems where the delay before each output is constant, i.e., independent from the input and output. The preprocessing phase is generally assumed to be polynomial in the input. == Common techniques == Backtracking: The simplest way to enumerate all solutions is by systematically exploring the space of possible results (partitioning it at each successive step). However, performing this may not give good guarantees on the delay, i.e., a backtracking algorithm may spend a long time exploring parts of the space of possible results that do not give rise to a full solution. Flashlight search: This technique improves on backtracking by exploring the space of all possible solutions but solving at each step the problem of whether the current partial solution can be extended to a partial solution. If the answer is no, then the algorithm can immediately backtrack and avoid wasting time, which makes it easier to show guarantees on the delay between any two complete solutions. In particular, this technique applies well to self-reducible problems. Closure under set operations: If we wish to enumerate the disjoint union of two sets, then we can solve the problem by enumerating the first set and then the second set. If the union is non disjoint but the sets can be enumerated in sorted order, then the enumeration can be performed in parallel on both sets while eliminating duplicates on the fly. If the union is not disjoint and both sets are not sorted then duplicates can be eliminated at the expense of a higher memory usage, e.g., using a hash table. Likewise, the cartesian product of two sets can be enumerated efficiently by enumerating one set and joining each result with all results obtained when enumerating the second step. == Examples of enumeration problems == The vertex enumeration problem, where we are given a polytope described as a system of linear inequalities and we must enumerate the vertices of the polytope. Enumerating the minimal transversals of a hypergraph. This problem is related to monotone dualization and is connected to many applications in database theory and graph theory. Enumerating the answers to a database query, for instance a conjunctive query or a query expressed in monadic second-order. There have been characterizations in database theory of which conjunctive queries could be enumerated with linear preprocessing and constant delay. The problem of enumerating maximal cliques in an input graph, e.g., with the Bron–Kerbosch algorithm Listing all elements of structures such as matroids and greedoids Several problems on graphs, e.g., enumerating independent sets, paths, cuts, etc. Enumerating the satisfying assignments of representations of Boolean functions, e.g., a Boolean formula written in conjunctive normal form or disjunctive normal form, a binary decision diagram such as an OBDD, or a Boolean circuit in restricted classes studied in knowledge compilation, e.g., NNF. == Connection to computability theory == The notion of enumeration algorithms is also used in the field of computability theory to define some high complexity classes such as RE, the class of all recursively enumerable problems. This is the class of sets for which there exist an enumeration algorithm that will produce all elements of the set: the algorithm may run forever if the set is infinite, but each solution must be produced by the algorithm after a finite time.
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In mathematics, the sieve of Pritchard is an algorithm for finding all prime numbers up to a specified bound. Like the ancient sieve of Eratosthenes, it has a simple conceptual basis in number theory. It is especially suited to quick hand computation for small bounds. Whereas the sieve of Eratosthenes marks off each non-prime for each of its prime factors, the sieve of Pritchard avoids considering almost all non-prime numbers by building progressively larger wheels, which represent the pattern of numbers not divisible by any of the primes processed thus far. It thereby achieves a better asymptotic complexity, and was the first sieve with a running time sublinear in the specified bound. Its asymptotic running-time has not been improved on, and it deletes fewer composites than any other known sieve. It was created in 1979 by Paul Pritchard. Since Pritchard has created a number of other sieve algorithms for finding prime numbers, the sieve of Pritchard is sometimes singled out by being called the wheel sieve (by Pritchard himself) or the dynamic wheel sieve. == Overview == A prime number is a natural number that has no natural number divisors other than the number 1 and itself. To find all the prime numbers less than or equal to a given integer N, a sieve algorithm examines a set of candidates in the range 2, 3, …, N, and eliminates those that are not prime, leaving the primes at the end. The sieve of Eratosthenes examines all of the range, first removing all multiples of the first prime 2, then of the next prime 3, and so on. The sieve of Pritchard instead examines a subset of the range consisting of numbers that occur on successive wheels, which represent the pattern of numbers left after each successive prime is processed by the sieve of Eratosthenes. For i > 0, the ith wheel Wi represents this pattern. It is the set of numbers between 1 and the product Pi = p1 · p2 ⋯ pi of the first i prime numbers that are not divisible by any of these prime numbers (and is said to have an associated length Pi). This is because adding Pi to a number does not change whether it is divisible by one of the first i prime numbers, since the remainder on division by any one of these primes is unchanged. So W1 = {1} with length P1 = 2 represents the pattern of odd numbers; W2 = {1,5} with length P2 = 6 represents the pattern of numbers not divisible by 2 or 3; etc. Wheels are so-called because Wi can be usefully visualized as a circle of circumference Pi with its members marked at their corresponding distances from an origin. Then rolling the wheel along the number line marks points corresponding to successive numbers not divisible by one of the first i prime numbers. The animation shows W2 being rolled up to 30. It is useful to define Wi → n for n > 0 to be the result of rolling Wi up to n. Then the animation generates W2 → 30 = {1,5,7,11,13,17,19,23,25,29}. Note that up to 52 − 1 = 24, this consists only of 1 and the primes between 5 and 25. The sieve of Pritchard is derived from the observation that this holds generally: for all i > 0, the values in Wi → (p2i+1 − 1) are 1 and the primes between pi+1 and p2i+1. It even holds for i = 0, where the wheel has length 1 and contains just 1 (representing all the natural numbers). So the sieve of Pritchard starts with the trivial wheel W0 and builds successive wheels until the square of the wheel's first member after 1 is at least N. Wheels grow very quickly, but only their values up to N are needed and generated. It remains to find a method for generating the next wheel. Note in the animation that W3 = {1,5,7,11,13,17,19,23,25,29} − {5 · 1 , 5 · 5} can be obtained by rolling W2 up to 30 and then removing 5 times each member of W2.This also holds generally: for all i ≥ 0, Wi+1 = (Wi → Pi+1) − {pi+1 · w | w ∈ Wi}. Rolling Wi past Pi just adds values to Wi, so the current wheel is first extended by getting each successive member starting with w = 1, adding Pi to it, and inserting the result in the set. Then the multiples of pi+1 are deleted. Care must be taken to avoid a number being deleted that itself needs to be multiplied by pi+1. The sieve of Pritchard as originally presented does so by first skipping past successive members until finding the maximum one needed, and then doing the deletions in reverse order by working back through the set. This is the method used in the first animation above. A simpler approach is just to gather the multiples of pi+1 in a list, and then delete them. Another approach is given by Gries and Misra. If the main loop terminates with a wheel whose length is less than N, it is extended up to N to generate the remaining primes. The algorithm, for finding all primes up to N, is therefore as follows: Start with a set W = {1} and length = 1 representing wheel 0, and prime p = 2. As long as p2 ≤ N, do the following: if length < N, then extend W by repeatedly getting successive members w of W starting with 1 and inserting length + w into W as long as it does not exceed p · length or N; increase length to the minimum of p · length and N. repeatedly delete p times each member of W by first finding the largest ≤ length and then working backwards. note the prime p, then set p to the next member of W after 1 (or 3 if p was 2). if length < N, then extend W to N by repeatedly getting successive members w of W starting with 1 and inserting length + w into W as long as it does not exceed N; On termination, the rest of the primes up to N are the members of W after 1. === Example === To find all the prime numbers less than or equal to 150, proceed as follows. Start with wheel 0 with length 1, representing all natural numbers 1, 2, 3...: 1 The first number after 1 for wheel 0 (when rolled) is 2; note it as a prime. Now form wheel 1 with length 2 × 1 = 2 by first extending wheel 0 up to 2 and then deleting 2 times each number in wheel 0, to get: 1 2 The first number after 1 for wheel 1 (when rolled) is 3; note it as a prime. Now form wheel 2 with length 3 × 2 = 6 by first extending wheel 1 up to 6 and then deleting 3 times each number in wheel 1, to get 1 2 3 5 The first number after 1 for wheel 2 is 5; note it as a prime. Now form wheel 3 with length 5 × 6 = 30 by first extending wheel 2 up to 30 and then deleting 5 times each number in wheel 2 (in reverse order), to get 1 2 3 5 7 11 13 17 19 23 25 29 The first number after 1 for wheel 3 is 7; note it as a prime. Now wheel 4 has length 7 × 30 = 210, so we only extend wheel 3 up to our limit 150. (No further extending will be done now that the limit has been reached.) We then delete 7 times each number in wheel 3 until we exceed our limit 150, to get the elements in wheel 4 up to 150: 1 2 3 5 7 11 13 17 19 23 25 29 31 37 41 43 47 49 53 59 61 67 71 73 77 79 83 89 91 97 101 103 107 109 113 119 121 127 131 133 137 139 143 149 The first number after 1 for this partial wheel 4 is 11; note it as a prime. Since we have finished with rolling, we delete 11 times each number in the partial wheel 4 until we exceed our limit 150, to get the elements in wheel 5 up to 150: 1 2 3 5 7 11 13 17 19 23 25 29 31 37 41 43 47 49 53 59 61 67 71 73 77 79 83 89 91 97 101 103 107 109 113 119 121 127 131 133 137 139 143 149 The first number after 1 for this partial wheel 5 is 13. Since 13 squared is at least our limit 150, we stop. The remaining numbers (other than 1) are the rest of the primes up to our limit 150. Just 8 composite numbers are removed, once each. The rest of the numbers considered (other than 1) are prime. In comparison, the natural version of Eratosthenes sieve (stopping at the same point) removes composite numbers 184 times. == Pseudocode == The sieve of Pritchard can be expressed in pseudocode, as follows: algorithm Sieve of Pritchard is input: an integer N >= 2. output: the set of prime numbers in {1,2,...,N}. let W and Pr be sets of integer values, and all other variables integer values. k, W, length, p, Pr := 1, {1}, 2, 3, {2}; {invariant: p = pk+1 and W = Wk ∩ {\displaystyle \cap } {1,2,...,N} and length = minimum of Pk,N and Pr = the primes up to pk} while p2 <= N do if (length < N) then Extend W,length to minimum of plength,N; Delete multiples of p from W; Insert p into Pr; k, p := k+1, next(W, 1) if (length < N) then Extend W,length to N; return Pr ∪ {\displaystyle \cup } W - {1}; where next(W, w) is the next value in the ordered set W after w. procedure Extend W,length to n is {in: W = Wk and length = Pk and n > length} {out: W = Wk → {\displaystyle \rightarrow } n and length = n} integer w, x; w, x := 1, length+1; while x <= n do Insert x into W; w := next(W,w); x := length + w; length := n; procedure Delete multiples of p from W,length is integer w; w := p; while pw <= length do w := next(W,w); while w > 1 do w := prev(W,w); Remove pw from W; where prev(W, w) is the previous value in the ordered set W before w. The algorithm can be initialized with W0 instead of W1 at the minor complication of making next(W, 1) a special case when k = 0. This a
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Transaction data or transaction information is a category of data describing transactions. Transaction data/information gather variables generally referring to reference data or master data – e.g. dates, times, time zones, currencies. Typical transactions are: Financial transactions about orders, invoices, payments; Work transactions about plans, activity records; Logistic transactions about deliveries, storage records, travel records, etc.. == Management == Recording and storing transactions is called records management. The record of the transaction is stored in a place where the retention can be guaranteed and where data is archived or removed following a retention period. Formats of recorded transactions can be digital data in databases and spreadsheets, or handwritten texts in physical documents like former bankbooks. Transaction processing systems are application software that generate transactions and manage transaction data/information, e.g. SAP and Oracle Financials. == Data warehousing == Transaction data can be summarised in a data warehouse, which helps accessibility and analysis of the data.
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The Stevens Award is a software engineering lecture award given by the Reengineering Forum, an industry association. The international Stevens Award was created to recognize outstanding contributions to the literature or practice of methods for software and systems development. The first award was given in 1995. The presentations focus on the current state of software methods and their direction for the future. This award lecture is named in memory of Wayne Stevens (1944-1993), a consultant, author, pioneer, and advocate of the practical application of software methods and tools. The Stevens Award and lecture is managed by the Reengineering Forum. The award was founded by International Workshop on Computer Aided Software Engineering (IWCASE), an international workshop association of users and developers of computer-aided software engineering (CASE) technology, which merged into The Reengineering Forum. Wayne Stevens was a charter member of the IWCASE executive board. == Recipients == 1995: Tony Wasserman 1996: David Harel 1997: Michael Jackson 1998: Thomas McCabe 1999: Tom DeMarco 2000: Gerald Weinberg 2001: Peter Chen 2002: Cordell Green 2003: Manny Lehman 2004: François Bodart 2005: Mary Shaw, Jim Highsmith 2006: Grady Booch 2007: Nicholas Zvegintzov 2008: Harry Sneed 2009: Larry Constantine 2010: Peter Aiken 2011: Jared Spool, Barry Boehm 2012: Philip Newcomb 2013: Jean-Luc Hainaut 2014: François Coallier 2015: Pierre Bourque
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Maritime Informatics is a thematic topic within the broader discipline of informatics. It can be considered as both a field of study and domain of application. As an application domain, it is the outlet of innovations originating from data science and artificial intelligence; as a field of study, it is positioned between computer science and marine engineering. == Beginnings of maritime informatics == As a result of the increasing levels of digitalisation occurring in the maritime sector starting around 2010 and stimulated by the EU-endorsed MonaLisa project for sea traffic management (STM), a number of academics and shipping industry leaders recognised that the maritime transportation sector would benefit from a specific field of study and application to be known as Maritime Informatics - the use of information systems, data sharing and data analytics in the business and operations of maritime transportation. They considered that it would lead to improvements in efficiency, safety, resilience, and ecological sustainability - all of which are currently lacking for many aspects of sea transport. One of the first public airings of the concept of Maritime Informatics was a presentation delivered on 11 September 2014 in Gothenburg, Sweden. A proposal for an inaugural minitrack on Maritime Informatics was accepted for the 2015 Americas Conference on Information Systems in Puerto Rico where three papers were presented. Since then numerous publications has been brought forward captured at www.maritimeinformatics.org and in late 2020 the first reference book on Maritime Informatics was co-written by 81 expert contributors (47 practitioners and 34 researchers) from 20 countries. Most impactful authors and journals in the domain have been documented in a review paper. Dimitrios Zissis, Luca Cazzanti and Leonardo M. Millefiori are the top three authors; top journals and conferences include Ocean Engineering, Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems, Sensors, the international Conference On Engineering, Technology And Innovation, Expert Systems With Applications, IEEE Access, and Journal of Navigation. == Background == The shipping industry has several particular organisational aspects that are recognised and taken into account in maritime informatics: It is predominantly a self-organising ecosystem Many activities are undertaken as part of episodic tight coupling There is a so-called maritime stack There is increasing pressure to balance capital productivity and energy efficiency There is the potential virtuous interplay between different types of systems == Data sharing == Digital data sharing is key to the all-important, arguably fundamental, data analytics aspects of maritime informatics because it opens the way for better access to relevant and reliable data. As in land-based commerce, digital data sharing is a growing phenomenon in maritime operations - though there is a way to go. It is enabling greater transparency for all those involved in the transportation of goods and passengers, not least being the end-customer. This leads to better and more informed decision-making and planning by all those involved. The push for digitalisation and data sharing is being pursued both by governments and the commercial sector. For example, the Member States of the IMO agreed a mandatory requirement for their governments to introduce electronic information exchange between ships and ports as from 8 April 2019. Meanwhile, commercial operators, particularly in the container lines are putting systems in place for sharing data for mutual benefit in their operations. Data sharing is an important aspect of the Port Collaborative Decision Making (PortCDM) and Port Call Optimization initiatives, both of which seek to improve the coordination, synchronization and efficiency of the port call process by enabling a common and shared situational awareness among all those involved. == Standardisation == The availability and sharing of relevant digital data underpins maritime informatics and is key to more effective and efficient coordination and synchronisation in the predominantly self-organising ecosystem that is maritime transportation. For this to occur, a high priority underpinning maritime informatics is the encouragement of standardised digital data exchange and data sharing, leading, in turn, to improvements in shipping analytics. Improved availability of data will support better historical analysis, now-casting and forecasting. The International Maritime Organization (IMO) FAL Committee is taking the lead in ensuring that the common terms used in the various standards being developed or in use in the maritime sector are compatible and therefore interoperable as far as is practicable, by creating and maintaining The IMO Compendium on Facilitation and Electronic Business. The IMO Compendium consists of an IMO Data Set and IMO Reference Data Model agreed by the main organisations involved in the development of standards for the electronic exchange of information related to the FAL Convention: the World Customs Organization (WCO), the United Nations Economic Commission for Europe (UNECE) and the International Organization for Standardization (ISO). There are several other prominent international governmental and non-governmental organisations actively contributing to the ongoing standardisation and harmonisation process including the UN Electronic Data Interchange for Administration, Commerce and Transport (UN EDIFACT), the Digital Container Shipping Association (DCSA), the International Harbour Masters Association (IHMA) and BIMCO - the world's largest direct-membership organisation for shipowners, charterers, shipbrokers and agents.
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Algorism is the technique of performing basic arithmetic by writing numbers in place value form and applying a set of memorized rules and facts to the digits. One who practices algorism is known as an algorist. This positional notation system has largely superseded earlier calculation systems that used a different set of symbols for each numerical magnitude, such as Roman numerals, and in some cases required a device such as an abacus. == Etymology == The word algorism comes from the name Al-Khwārizmī (c. 780–850), a Persian mathematician, astronomer, geographer and scholar in the House of Wisdom in Baghdad, whose name means "the native of Khwarezm", which is now in modern-day Uzbekistan. He wrote a treatise in Arabic language in the 9th century, which was translated into Latin in the 12th century under the title Algoritmi de numero Indorum. This title means "Algoritmi on the numbers of the Indians", where "Algoritmi" was the translator's Latinization of Al-Khwarizmi's name. Al-Khwarizmi was the most widely read mathematician in Europe in the late Middle Ages, primarily through his other book, the Algebra. In late medieval Latin, algorismus, the corruption of his name, simply meant the "decimal number system" that is still the meaning of modern English algorism. During the 17th century, the French form for the word – but not its meaning – was changed to algorithm, following the model of the word logarithm, this form alluding to the ancient Greek arithmos = number. English adopted the French very soon afterwards, but it wasn't until the late 19th century that "algorithm" took on the meaning that it has in modern English. In English, it was first used about 1230 and then by Chaucer in 1391. Another early use of the word is from 1240, in a manual titled Carmen de Algorismo composed by Alexandre de Villedieu. It begins thus: Haec algorismus ars praesens dicitur, in qua / Talibus Indorum fruimur bis quinque figuris. which translates as: This present art, in which we use those twice five Indian figures, is called algorismus. The word algorithm also derives from algorism, a generalization of the meaning to any set of rules specifying a computational procedure. Occasionally algorism is also used in this generalized meaning, especially in older texts. == History == Starting with the integer arithmetic developed in India using base 10 notation, Al-Khwārizmī along with other mathematicians in medieval Islam, documented new arithmetic methods and made many other contributions to decimal arithmetic (see the articles linked below). These included the concept of the decimal fractions as an extension of the notation, which in turn led to the notion of the decimal point. This system was popularized in Europe by Leonardo of Pisa, now known as Fibonacci.
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In computer algorithms, block swap algorithms swap two regions of elements of an array. It is simple to swap two non-overlapping regions of an array of equal size. However, it is not as simple to swap two contiguous regions of an array of unequal sizes (algorithms that perform such swapping are called rotation algorithms). A few well-known algorithms can accomplish this: Bentley's juggling (also known as the dolphin algorithm), Gries-Mills rotation, triple reversal algorithm, conjoined triple reversal algorithm (also known as the trinity rotation) and Successive rotation. == Triple reversal algorithm == The triple reversal algorithm is the simplest to explain, using rotations. A rotation is an in-place reversal of array elements. This method swaps two elements of an array from outside in within a range. The rotation works for an even or odd number of array elements. The reversal algorithm uses three in-place rotations to accomplish an in-place block swap: Rotate region A Rotate region B Rotate region AB Where A and B are adjacent regions of an array that together form the region AB. Gries-Mills and reversal algorithms perform better than Bentley's juggling, because of their cache-friendly memory access pattern behavior. The triple reversal algorithm parallelizes well, because rotations can be split into sub-regions, which can be rotated independently of others.
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vx-underground, also known as VXUG, is an educational website about malware and cybersecurity. It claims to have the largest online repository of malware. The site was launched in May, 2019 and has grown to host over 35 million pieces of malware samples. On their account on Twitter, VXUG reports on and verifies cybersecurity breaches. == Reception == Kim Crawley compared the site to VirusTotal and states that vx-underground is more susceptible to suspicion for law enforcement. == Data breach reports == In May 2024, the International Baccalaureate organizations faced allegations over supposed breaches in their IT infrastructure after an incident of examination leaks. Upon inspecting leaked data, VXUG were the first to report that the breach seemed legitimate on the morning of May 6.
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A query language, also known as data query language or database query language (DQL), is a computer language used to make queries in databases and information systems. In database systems, query languages rely on strict theory to retrieve information. A well known example is the Structured Query Language (SQL). == Types == Broadly, query languages can be classified according to whether they are database query languages or information retrieval query languages. The difference is that a database query language attempts to give factual answers to factual questions, while an information retrieval query language attempts to find documents containing information that is relevant to an area of inquiry. Other types of query languages include: Full-text. The simplest query language is treating all terms as bag of words that are to be matched with the postings in the inverted index and where subsequently ranking models are applied to retrieve the most relevant documents. Only tokens are defined in the CFG. Web search engines often use this approach. Boolean. A query language that also supports the use of the Boolean operators AND, OR, NOT. Structured. A language that supports searching within (a combination of) fields when a document is structured and has been indexed using its document structure. Natural language. A query language that supports natural language by parsing the natural language query to a form that can be best used to retrieve relevant documents, for example with Question answering systems or conversational search. == Examples == Attempto Controlled English is a query language that is also a controlled natural language. AQL is a query language for the ArangoDB native multi-model database system. .QL is a proprietary object-oriented query language for querying relational databases; successor of Datalog. CodeQL is the analysis engine used by developers to automate security checks, and by security researchers to perform variant analysis on GitHub. Contextual Query Language (CQL) a formal language for representing queries to information retrieval systems such as web indexes or bibliographic catalogues. Cypher is a query language for the Neo4j graph database. DMX is a query language for data mining models. Datalog is a query language for deductive databases. F-logic is a declarative object-oriented language for deductive databases and knowledge representation. FQL enables you to use a SQL-style interface to query the data exposed by the Graph API. It provides advanced features not available in the Graph API. Gellish English is a language that can be used for queries in Gellish English Databases, for dialogues (requests and responses) as well as for information modeling and knowledge modeling. Gremlin is an Apache Software Foundation graph traversal language for OLTP and OLAP graph systems. GraphQL is a data query language developed by Facebook as an alternate to REST and ad-hoc webservice architectures. HTSQL is a query language that translates HTTP queries to SQL. ISBL is a query language for PRTV, one of the earliest relational database management systems. Jaql is a functional data processing and query language most commonly used for JSON query processing. JPQL is a query language defined as part of Jakarta Persistence (used in Java applications to make queries to a relational DB using entity objects instead of DB tables). jq is a functional programming language often used for processing queries against one or more JSON documents, including very large ones. JSONiq is a declarative query language designed for collections of JSON documents. KQL (Kusto Query Language), a query language by Microsoft used in Azure Data Explorer LDAP is an application protocol for querying and modifying directory services running over TCP/IP. LogiQL is a variant of Datalog and is the query language for the LogicBlox system. M Formula language, a mashup query language used in Microsoft's Power Query. MQL is a cheminformatics query language for a substructure search allowing beside nominal properties also numerical properties. MDX is a query language for OLAP databases. N1QL is a Couchbase's query language finding data in Couchbase Servers. Object Query Language OCL (Object Constraint Language). Despite its name, OCL is also an object query language and an OMG standard. OPath, intended for use in querying WinFS Stores. Poliqarp Query Language is a special query language designed to analyze annotated text. Used in the Poliqarp search engine. PQL is a special-purpose programming language for managing process models based on information about scenarios that these models describe. PRQL PRQL (Pipelined Relational Query Language) is a modern language for transforming data. Consists of a curated set of orthogonal transformations, which are combined together to form a pipeline. PTQL based on relational queries over program traces, allowing programmers to write expressive, declarative queries about program behavior. QUEL is a relational database access language, similar in most ways to SQL. RDQL is a RDF query language. SMARTS is the cheminformatics standard for a substructure search. SPARQL is a query language for RDF graphs. SQL is a well-known query language and data manipulation language for relational databases. XQuery is a query language for XML data sources. XPath is a declarative language for navigating XML documents. YQL is an SQL-like query language created by Yahoo!. Search engine query languages, e.g., as used by Google. or Bing
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Web data integration (WDI) is the process of aggregating and managing data from different websites into a single, homogeneous workflow. This process includes data access, transformation, mapping, quality assurance and fusion of data. Data that is sourced and structured from websites is referred to as "web data". WDI is an extension and specialization of data integration that views the web as a collection of heterogeneous databases. Data integration techniques in the context of the web, forms the foundation for businesses taking advantage of data available on the ever-increasing number of publicly-accessible websites. Corporate spending on this area amounted to about USD 2.5bn in 2017, and it is expected that by 2020 the market will reach almost USD 7bn.
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Secure Electronic Delivery (SED) is a service created in 2003 and provided by the British Library Document Supply Service (BLDSS). Its purpose is to enable faster delivery of digital materials as encrypted, copyright-compliant PDF Documents, to a personal e-mail address. These documents are supplied from the British Library via its On Demand service. When the British Library supplies articles electronically, it sends them securely in order to ensure its usage is permitted (research purposes) and copyright law is observed. == Methods == As the publishing industry, authors and creators become highly protective of their assets and intellectual property, they impose strict rules on delivery methods to prevent copyright infringement. Nowadays, DRM-enabled secure delivery appears to be the most widely used solution to address issues faced by libraries in supplying ebooks and digital materials to their users. SED, one of these solutions, is using Adobe LiveCycle Digital Rights Management (LCDRM) as an encryption method to deliver documents. == Advantages == SED offers convenience, quality and speed as documents are delivered upon request at any location and on any device. Requested articles are scanned for high quality reproduction, opened anywhere on any machine, including mobile devices. == Restrictions == The following are restrictions hold in a SED service implementation: The digital material is accessible only for 14 days via a link sent to a personal message. Due to copyright reasons, the material can be opened only once, saved for 14 days and does not allow a copy-paste action. Upon display, the material must be printed from the same device and reprinted only once. The On Demand encryption technology works best on the default Safari browser although other browsers may accommodate it.
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Color moments are measures that characterise color distribution in an image in the same way that central moments uniquely describe a probability distribution. Color moments are mainly used for color indexing purposes as features in image retrieval applications in order to compare how similar two images are based on color. Usually one image is compared to a database of digital images with pre-computed features in order to find and retrieve a similar Image. Each comparison between images results in a similarity score, and the lower this score is the more identical the two images are supposed to be. == Overview == Color moments are scaling and rotation invariant. It is usually the case that only the first three color moments are used as features in image retrieval applications as most of the color distribution information is contained in the low-order moments. Since color moments encode both shape and color information they are a good feature to use under changing lighting conditions, but they cannot handle occlusion very successfully. Color moments can be computed for any color model. Three color moments are computed per channel (e.g. 9 moments if the color model is RGB and 12 moments if the color model is CMYK). Computing color moments is done in the same way as computing moments of a probability distribution. === Mean === The first color moment can be interpreted as the average color in the image, and it can be calculated by using the following formula E i = ∑ j = 1 N 1 N p i j {\displaystyle E_{i}=\textstyle \sum _{j=1}^{N}{\frac {1}{N}}p_{ij}} where N is the number of pixels in the image and p i j {\displaystyle p_{ij}} is the value of the j-th pixel of the image at the i-th color channel. === Standard Deviation === The second color moment is the standard deviation, which is obtained by taking the square root of the variance of the color distribution. σ i = ( 1 N ∑ j = 1 N ( p i j − E i ) 2 ) {\displaystyle \sigma _{i}={\sqrt {({\frac {1}{N}}\textstyle \sum _{j=1}^{N}(p_{ij}-E_{i})^{2})}}} where E i {\displaystyle E_{i}} is the mean value, or first color moment, for the i-th color channel of the image. === Skewness === The third color moment is the skewness. It measures how asymmetric the color distribution is, and thus it gives information about the shape of the color distribution. Skewness can be computed with the following formula: s i = ( 1 N ∑ j = 1 N ( p i j − E i ) 3 ) 3 σ i {\displaystyle s_{i}={\frac {\sqrt[{3}]{\left({\frac {1}{N}}\textstyle \sum _{j=1}^{N}(p_{ij}-E_{i})^{3}\right)}}{\sigma _{i}}}} === Kurtosis === Kurtosis is the fourth color moment, and, similarly to skewness, it provides information about the shape of the color distribution. More specifically, kurtosis is a measure of how extreme the tails are in comparison to the normal distribution. === Higher-order color moments === Higher-order color moments are usually not part of the color moments feature set in image retrieval tasks as they require more data in order to obtain a good estimate of their value, and also the lower-order moments generally provide enough information. == Applications == Color moments have significant applications in image retrieval. They can be used in order to compare how similar two images are. This is a relatively new approach to color indexing. The greatest advantage of using color moments comes from the fact that there is no need to store the complete color distribution. This greatly speeds up image retrieval since there are less features to compare. In addition, the first three color moments have the same units, which allows for comparison between them. === Color indexing === Color indexing is the main application of color moments. Images can be indexed, and the index will contain the computed color moments. Then, if someone has a particular image and wants to find similar images in the database, the color moments of the image of interest will also be computed. After that the following function will be used in order to compute a similarity score between the image of interest and all the images in the database: d m o m ( H , I ) = ∑ i = 1 r w i 1 | E i 1 − E i 2 | + w i 2 | σ i 1 − σ i 2 | + w i 3 | s i 1 − s i 2 | {\displaystyle d_{mom}(H,I)=\textstyle \sum _{i=1}^{r}w_{i1}|E_{i}^{1}-E_{i}^{2}|+w_{i2}|\sigma _{i}^{1}-\sigma _{i}^{2}|+w_{i3}|s_{i}^{1}-s_{i}^{2}|} where: H and I are the color distributions of the two images that are being compared i is the channel index and r is the total number of channels E i 1 {\displaystyle E_{i}^{1}} and E i 2 {\displaystyle E_{i}^{2}} are the first order moments computed for the image distributions. σ i 1 {\displaystyle \sigma _{i}^{1}} and σ i 2 {\displaystyle \sigma _{i}^{2}} are the second order moments computed for the image distributions. s_i^1 and s_i^2 are the third order moments computed for the image distributions. w i 1 {\displaystyle w_{i1}} , w i 2 {\displaystyle w_{i2}} , and w i 3 {\displaystyle w_{i3}} are weights, specified by the user, for each of the three color moments used. Finally, the images in the database will be ranked according to the computed similarity score with the image of interest, and the database images with the lowest d m o m ( H , I ) {\displaystyle d_{mom}(H,I)} value should be retrieved. "A retrieval based on d m o m ( H , I ) {\displaystyle d_{mom}(H,I)} may produce false positives because the index contains no information about the correlation between the color channels". == Example == A simple and concise example of the use of color moments for image retrieval tasks is illustrated in. Consider having several test images in a database and a "New Image". The goal is to retrieve images from the database that are similar to the "New Image". The first three color moments are used as features. There are several steps in this computation. Image preprocessing (Optional) - The image preprocessing step of the computation process is optional. For example, in this step all images could be modified to be the same size (in terms of pixels). However, since color moments are invariant to scaling, it is not necessary to make all images the same width and height. Computing the features - Use the color moments formulae in order to compute the first three moments for each of the color channels in the image. For example, if the HSV color space is used, this means that for each of the images, 9 features in total will be computed (the first three order moments for the Hue, Saturation, and Value channels). Calculating the similarity score - After computing the color moments the weights for each of the moments in the d m o m ( H , I ) {\displaystyle d_{mom}(H,I)} function should be determined by the user. The weights have to be adjusted each time in accordance with the application or condition and quality of the images. Following that the d m o m ( H , I ) {\displaystyle d_{mom}(H,I)} function is used to calculate a similarity score for the "New Image" and each of the images in the database. Ranking and image retrieval - From the previous step the d m o m ( H , I ) {\displaystyle d_{mom}(H,I)} values were obtained. Now a comparison of these values can be made in order to decide which of the images in the database are more similar to the "New Image", and thus rank the database images accordingly. The smaller the d m o m ( H , I ) {\displaystyle d_{mom}(H,I)} value is the more similar the two color distributions are supposed to be. Finally, some of the top ranked images (the ones with the smallest d m o m ( H , I ) {\displaystyle d_{mom}(H,I)} value) from the database are retrieved.
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In manufacturing, an operational historian is a time-series database application that is developed for operational process data. Historian software is often embedded or used in conjunction with standard DCS and PLC control systems to provide enhanced data capture, validation, compression, and aggregation capabilities. Historians have been deployed in almost every industry and contribute to functions such as supervisory control, performance monitoring, quality assurance, and, more recently, machine learning applications which can learn from vast quantities of historical data. These systems were originally developed to capture instrumentation and control data, which led many to use the term "tag" for a stream of process data, referring to the physical "tags" which had been placed on instrumentation for manually capturing data. Raw data may be accessed via OPC HDA, SQL, or REST API interfaces. == Operational Support == Operational historians are typically used within the manufacturing facility by engineers and operators for supervisory functions and analysis. An operational historian will typically capture all instrumentation and control data, whereas an enterprise historian that is deployed to support business functions will capture only a subset of the plant data. Typically, these applications offer data access through dedicated APIs (Application Programming Interfaces) and SDKs (Software Development Kits) which offer high-performance read and write operations. These operate through vendor-specific or custom applications. Front-end tools for trending process data over time are the most common interfaces to these databases. Because these applications are typically deployed next to or near the source of their process data, they are often marketed and sold as 'real-time database systems.' This distinction varies among vendors, who often have to make tradeoffs in performance between data capture and presentation, and application and analysis functionality. The following is a list of typical challenges for operational historians: data collection from instrumentation and controls storage and archiving of very large volumes of data organization of data in the form of "tags" or "points" limiting of monitoring (alarms) and validation aggregation and interpolation manual data entry (MDE) == Data access == As opposed to enterprise historians, the data access layer in the operational historian is designed to offer sophisticated data fetching modes without complex information analysis facilities. The following settings are typically available for data access operations: Data scope (single point or tag, history based on time range, history based on sample count) Request modes (raw data, last-known value, aggregation, interpolation) Sampling (single point, all points without sampling, all points with interval sampling) Data omission (based on the sample quality, based on the sample value, based on the count) Even though the operational historians are rarely relational database management systems, they often offer SQL-based interfaces to query the database. In most of such implementations, the dialect does not follow the SQL standard in order to provide syntax for specifying data access operations parameters.
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A query language, also known as data query language or database query language (DQL), is a computer language used to make queries in databases and information systems. In database systems, query languages rely on strict theory to retrieve information. A well known example is the Structured Query Language (SQL). == Types == Broadly, query languages can be classified according to whether they are database query languages or information retrieval query languages. The difference is that a database query language attempts to give factual answers to factual questions, while an information retrieval query language attempts to find documents containing information that is relevant to an area of inquiry. Other types of query languages include: Full-text. The simplest query language is treating all terms as bag of words that are to be matched with the postings in the inverted index and where subsequently ranking models are applied to retrieve the most relevant documents. Only tokens are defined in the CFG. Web search engines often use this approach. Boolean. A query language that also supports the use of the Boolean operators AND, OR, NOT. Structured. A language that supports searching within (a combination of) fields when a document is structured and has been indexed using its document structure. Natural language. A query language that supports natural language by parsing the natural language query to a form that can be best used to retrieve relevant documents, for example with Question answering systems or conversational search. == Examples == Attempto Controlled English is a query language that is also a controlled natural language. AQL is a query language for the ArangoDB native multi-model database system. .QL is a proprietary object-oriented query language for querying relational databases; successor of Datalog. CodeQL is the analysis engine used by developers to automate security checks, and by security researchers to perform variant analysis on GitHub. Contextual Query Language (CQL) a formal language for representing queries to information retrieval systems such as web indexes or bibliographic catalogues. Cypher is a query language for the Neo4j graph database. DMX is a query language for data mining models. Datalog is a query language for deductive databases. F-logic is a declarative object-oriented language for deductive databases and knowledge representation. FQL enables you to use a SQL-style interface to query the data exposed by the Graph API. It provides advanced features not available in the Graph API. Gellish English is a language that can be used for queries in Gellish English Databases, for dialogues (requests and responses) as well as for information modeling and knowledge modeling. Gremlin is an Apache Software Foundation graph traversal language for OLTP and OLAP graph systems. GraphQL is a data query language developed by Facebook as an alternate to REST and ad-hoc webservice architectures. HTSQL is a query language that translates HTTP queries to SQL. ISBL is a query language for PRTV, one of the earliest relational database management systems. Jaql is a functional data processing and query language most commonly used for JSON query processing. JPQL is a query language defined as part of Jakarta Persistence (used in Java applications to make queries to a relational DB using entity objects instead of DB tables). jq is a functional programming language often used for processing queries against one or more JSON documents, including very large ones. JSONiq is a declarative query language designed for collections of JSON documents. KQL (Kusto Query Language), a query language by Microsoft used in Azure Data Explorer LDAP is an application protocol for querying and modifying directory services running over TCP/IP. LogiQL is a variant of Datalog and is the query language for the LogicBlox system. M Formula language, a mashup query language used in Microsoft's Power Query. MQL is a cheminformatics query language for a substructure search allowing beside nominal properties also numerical properties. MDX is a query language for OLAP databases. N1QL is a Couchbase's query language finding data in Couchbase Servers. Object Query Language OCL (Object Constraint Language). Despite its name, OCL is also an object query language and an OMG standard. OPath, intended for use in querying WinFS Stores. Poliqarp Query Language is a special query language designed to analyze annotated text. Used in the Poliqarp search engine. PQL is a special-purpose programming language for managing process models based on information about scenarios that these models describe. PRQL PRQL (Pipelined Relational Query Language) is a modern language for transforming data. Consists of a curated set of orthogonal transformations, which are combined together to form a pipeline. PTQL based on relational queries over program traces, allowing programmers to write expressive, declarative queries about program behavior. QUEL is a relational database access language, similar in most ways to SQL. RDQL is a RDF query language. SMARTS is the cheminformatics standard for a substructure search. SPARQL is a query language for RDF graphs. SQL is a well-known query language and data manipulation language for relational databases. XQuery is a query language for XML data sources. XPath is a declarative language for navigating XML documents. YQL is an SQL-like query language created by Yahoo!. Search engine query languages, e.g., as used by Google. or Bing
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