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21 обрабатывать данные
Русско-английский синонимический словарь > обрабатывать данные
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22 обрабатывать данные
Banks. Exchanges. Accounting. (Russian-English) > обрабатывать данные
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23 обрабатывать данные
Russian-English Dictionary "Microeconomics" > обрабатывать данные
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24 обрабатывать данные
Русско-Английский новый экономический словарь > обрабатывать данные
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25 регистрация данных технологического процесса
Русско-английский большой базовый словарь > регистрация данных технологического процесса
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26 обрабатывать данные
спецификация данных; определение данных — data specification
совокупность данных; данные объединенные в пул — pooled data
Русско-английский словарь по информационным технологиям > обрабатывать данные
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27 данные о ходе технологического процесса
Русско-английский словарь нормативно-технической терминологии > данные о ходе технологического процесса
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28 данные процесса
данные процесса
—
[Л.Г.Суменко. Англо-русский словарь по информационным технологиям. М.: ГП ЦНИИС, 2003.]Тематики
EN
Русско-английский словарь нормативно-технической терминологии > данные процесса
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29 объект данных процесса
объект данных процесса
PDO
Коммуникационный объект, определяемый коммуникационным PDO параметром и параметром PDO отображения. Относится к не подтверждаемым коммуникационным сервисам и не приводит к избыточности протокола.
[ http://can-cia.com/fileadmin/cia/pdfs/CANdictionary-v2_ru.pdf]Тематики
Синонимы
EN
Русско-английский словарь нормативно-технической терминологии > объект данных процесса
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30 устройство регистрации производственных данных
Русско-английский политехнический словарь > устройство регистрации производственных данных
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31 информационный файл процесса
Русско-английский словарь по вычислительной технике и программированию > информационный файл процесса
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32 контрол на технологични параметри
process data monitoringБългарски-Angleščina политехнически речник > контрол на технологични параметри
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33 eisen gesteld aan procesgegevens
• process data requirementsNederlands-Engels Technisch Woordenboek > eisen gesteld aan procesgegevens
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34 Daten
Daten npl 1. COMP data; 2. GEN facts and figures; 3. RW aggregate data; 4. ADMIN, WIWI data • Daten in ein Register eintragen GEN enter information onto a register* * *npl 1. < Comp> data; 2. < Geschäft> facts and figures; 3. < Rechnung> aggregate data; 4. <Verwalt, Vw> data ■ Daten in ein Register eintragen < Geschäft> enter information onto a register* * *einspeisen, Dat(ei)en
(Internet) to upload;
• Daten im Rechner einspeisen to computerize.
herunterladen, Dat(ei)en
(Internet) to download;
• herunterpurzeln (Kurse) to tumble;
• herunterschrauben (fig.) to scale down;
• seine Ansprüche herunterschrauben to moderate one’s claims;
• Ausgaben herunterschrauben to cut down expenses;
• heruntersetzen to reduce, to lower, to mark down, to abate, to depreciate;
• Gehalt stark heruntersetzen to slash a salary (coll.);
• Warenpreise heruntersetzen to lower the price of goods;
• Nachricht herunterspielen to play down (downplay, US) a piece of news;
• herunterwirtschaften to run down, to bring low, to mismanage.
hochladen, Dat(ei)en
(Internet) to upload.
Daten
data, facts, (Computer) information, (Personalangaben) particulars, details;
• im Speicher bis zur Verarbeitung abgelegte Daten on-line available data;
• ausgewählte Daten sample[d]data;
• bankindividuelle Daten individual bank data;
• digitalisierte Daten digitally stored data;
• fragmentarische Daten piecemeal data;
• personenbezogene Daten personal data;
• saisonbereinigte Daten seasonal adjusted data;
• technische Daten specifications;
• zusammengefasste Daten integrated data;
• in Tabellen zusammengefasste Daten tabulated data;
• technische Daten eines Autos specification of a car;
• technische Daten eines Produkts product specification;
• Daten aufbereiten to process data;
• Daten in einer international vergleichbaren Weise aufbereiten to bring data to international comparable standards;
• Daten erfassen to collect data;
• Daten verarbeiten to process data;
• schneller Datenabgleich rapid data exchange;
• elektronische Datenablage electronic filing;
• Datenabruf polling;
• Datenauflistung data listing;
• Datenaufzeichnung data recording;
• elektronische Datenauslieferung electronic data transmission;
• schnurloser Datenaustausch cable-free data exchanges;
• sicherer Datenaustausch reliable data transfer;
• Datenauswertung interpretation of data, data analysis (evaluation);
• Datenautobahn (Computer) data (information) highway;
• hypermoderne Datenautobahn (Computer) hyper-modern data [super]highways. -
35 dato
1. past part vedere dare2. adj ( certo) given, particular( dedito) addicted (a to)in dati casi in certain casesdato che given that3. m piece of datadati pl data* * *dato agg.1 given; ( stabilito) stated, appointed, fixed: data la sua giovane età..., given his youth...; entro un dato periodo, within a given time // dato che, since, as: dato che è tardi, la seduta è aggiornata a domani, as (o since) it's (so) late, the meeting is adjourned till tomorrow // dato e non concesso che..., even supposing...: dato e non concesso che tu riesca a ottenere quel posto..., even supposing you manage to get the job...dato s.m. datum*: i dati di un problema, the data of a problem; dati sperimentali, experimental data; dati statistici esaurienti, exhaustive statistical information; controllare l'accuratezza dei dati, to check the accuracy of data; fare lo spoglio dei dati statistici, to simplify statistical items; è difficile raccogliere dati su questa popolazione nomade, it's difficult to collect data about this nomadic people; riguardo agli indici d'ascolto non abbiamo dati, we don't have (any) data for the listening figures // dato di fatto, fact: l'analfabetismo della popolazione è un dato di fatto di cui dobbiamo tenere conto, the illiteracy of the population is a fact we must reckon with // (inform.): elaborazione elettronica dei dati, electronic data processing; dati campionari, sample data; flusso dei dati, data flow; dati di immissione, input (data); dati di emissione, output (data); immissione dei dati, data entry; raccolta dei dati, data collection // (stat.): dati statistici, statistics; dati relativi al commercio statunitense, US trade figures; dati provvisori, provisional figures (o data).* * *['dato] dato (-a)1. agg1)in quel dato giorno — on that particular day2)entro quel dato giorno — by that particular day3)data la situazione — given o considering o in view of the situationdato che... — given that...
2. smMat, Sci datumdati smpl data pl inv* * *I 1. ['dato]participio passato dare I2.1) (determinato) [quantità, numero] given, certain- e le circostanze — in o under the circumstances
3) (possibile)dato e non concesso che... — even supposing that
4) dato che seeing that, given that, since, asII ['dato]sostantivo maschile1) (elemento noto) fact, element-i demografici, statistici — demographic, statistical data
2) inform. datum, data item•* * *dato1/'dato/→ 1. dareII aggettivo1 (determinato) [quantità, numero] given, certain; a un dato momento at a given moment; un dato giorno a certain day2 (considerato) - e le circostanze in o under the circumstances; - a la natura del fenomeno given the nature of the phenomenon3 (possibile) dato e non concesso che... even supposing that...4 dato che seeing that, given that, since, as.————————dato2/'dato/sostantivo m.1 (elemento noto) fact, element; -i demografici, statistici demographic, statistical datadato di fatto fact; - i anagrafici personal data. -
36 Artificial Intelligence
In my opinion, none of [these programs] does even remote justice to the complexity of human mental processes. Unlike men, "artificially intelligent" programs tend to be single minded, undistractable, and unemotional. (Neisser, 1967, p. 9)Future progress in [artificial intelligence] will depend on the development of both practical and theoretical knowledge.... As regards theoretical knowledge, some have sought a unified theory of artificial intelligence. My view is that artificial intelligence is (or soon will be) an engineering discipline since its primary goal is to build things. (Nilsson, 1971, pp. vii-viii)Most workers in AI [artificial intelligence] research and in related fields confess to a pronounced feeling of disappointment in what has been achieved in the last 25 years. Workers entered the field around 1950, and even around 1960, with high hopes that are very far from being realized in 1972. In no part of the field have the discoveries made so far produced the major impact that was then promised.... In the meantime, claims and predictions regarding the potential results of AI research had been publicized which went even farther than the expectations of the majority of workers in the field, whose embarrassments have been added to by the lamentable failure of such inflated predictions....When able and respected scientists write in letters to the present author that AI, the major goal of computing science, represents "another step in the general process of evolution"; that possibilities in the 1980s include an all-purpose intelligence on a human-scale knowledge base; that awe-inspiring possibilities suggest themselves based on machine intelligence exceeding human intelligence by the year 2000 [one has the right to be skeptical]. (Lighthill, 1972, p. 17)4) Just as Astronomy Succeeded Astrology, the Discovery of Intellectual Processes in Machines Should Lead to a Science, EventuallyJust as astronomy succeeded astrology, following Kepler's discovery of planetary regularities, the discoveries of these many principles in empirical explorations on intellectual processes in machines should lead to a science, eventually. (Minsky & Papert, 1973, p. 11)5) Problems in Machine Intelligence Arise Because Things Obvious to Any Person Are Not Represented in the ProgramMany problems arise in experiments on machine intelligence because things obvious to any person are not represented in any program. One can pull with a string, but one cannot push with one.... Simple facts like these caused serious problems when Charniak attempted to extend Bobrow's "Student" program to more realistic applications, and they have not been faced up to until now. (Minsky & Papert, 1973, p. 77)What do we mean by [a symbolic] "description"? We do not mean to suggest that our descriptions must be made of strings of ordinary language words (although they might be). The simplest kind of description is a structure in which some features of a situation are represented by single ("primitive") symbols, and relations between those features are represented by other symbols-or by other features of the way the description is put together. (Minsky & Papert, 1973, p. 11)[AI is] the use of computer programs and programming techniques to cast light on the principles of intelligence in general and human thought in particular. (Boden, 1977, p. 5)The word you look for and hardly ever see in the early AI literature is the word knowledge. They didn't believe you have to know anything, you could always rework it all.... In fact 1967 is the turning point in my mind when there was enough feeling that the old ideas of general principles had to go.... I came up with an argument for what I called the primacy of expertise, and at the time I called the other guys the generalists. (Moses, quoted in McCorduck, 1979, pp. 228-229)9) Artificial Intelligence Is Psychology in a Particularly Pure and Abstract FormThe basic idea of cognitive science is that intelligent beings are semantic engines-in other words, automatic formal systems with interpretations under which they consistently make sense. We can now see why this includes psychology and artificial intelligence on a more or less equal footing: people and intelligent computers (if and when there are any) turn out to be merely different manifestations of the same underlying phenomenon. Moreover, with universal hardware, any semantic engine can in principle be formally imitated by a computer if only the right program can be found. And that will guarantee semantic imitation as well, since (given the appropriate formal behavior) the semantics is "taking care of itself" anyway. Thus we also see why, from this perspective, artificial intelligence can be regarded as psychology in a particularly pure and abstract form. The same fundamental structures are under investigation, but in AI, all the relevant parameters are under direct experimental control (in the programming), without any messy physiology or ethics to get in the way. (Haugeland, 1981b, p. 31)There are many different kinds of reasoning one might imagine:Formal reasoning involves the syntactic manipulation of data structures to deduce new ones following prespecified rules of inference. Mathematical logic is the archetypical formal representation. Procedural reasoning uses simulation to answer questions and solve problems. When we use a program to answer What is the sum of 3 and 4? it uses, or "runs," a procedural model of arithmetic. Reasoning by analogy seems to be a very natural mode of thought for humans but, so far, difficult to accomplish in AI programs. The idea is that when you ask the question Can robins fly? the system might reason that "robins are like sparrows, and I know that sparrows can fly, so robins probably can fly."Generalization and abstraction are also natural reasoning process for humans that are difficult to pin down well enough to implement in a program. If one knows that Robins have wings, that Sparrows have wings, and that Blue jays have wings, eventually one will believe that All birds have wings. This capability may be at the core of most human learning, but it has not yet become a useful technique in AI.... Meta- level reasoning is demonstrated by the way one answers the question What is Paul Newman's telephone number? You might reason that "if I knew Paul Newman's number, I would know that I knew it, because it is a notable fact." This involves using "knowledge about what you know," in particular, about the extent of your knowledge and about the importance of certain facts. Recent research in psychology and AI indicates that meta-level reasoning may play a central role in human cognitive processing. (Barr & Feigenbaum, 1981, pp. 146-147)Suffice it to say that programs already exist that can do things-or, at the very least, appear to be beginning to do things-which ill-informed critics have asserted a priori to be impossible. Examples include: perceiving in a holistic as opposed to an atomistic way; using language creatively; translating sensibly from one language to another by way of a language-neutral semantic representation; planning acts in a broad and sketchy fashion, the details being decided only in execution; distinguishing between different species of emotional reaction according to the psychological context of the subject. (Boden, 1981, p. 33)Can the synthesis of Man and Machine ever be stable, or will the purely organic component become such a hindrance that it has to be discarded? If this eventually happens-and I have... good reasons for thinking that it must-we have nothing to regret and certainly nothing to fear. (Clarke, 1984, p. 243)The thesis of GOFAI... is not that the processes underlying intelligence can be described symbolically... but that they are symbolic. (Haugeland, 1985, p. 113)14) Artificial Intelligence Provides a Useful Approach to Psychological and Psychiatric Theory FormationIt is all very well formulating psychological and psychiatric theories verbally but, when using natural language (even technical jargon), it is difficult to recognise when a theory is complete; oversights are all too easily made, gaps too readily left. This is a point which is generally recognised to be true and it is for precisely this reason that the behavioural sciences attempt to follow the natural sciences in using "classical" mathematics as a more rigorous descriptive language. However, it is an unfortunate fact that, with a few notable exceptions, there has been a marked lack of success in this application. It is my belief that a different approach-a different mathematics-is needed, and that AI provides just this approach. (Hand, quoted in Hand, 1985, pp. 6-7)We might distinguish among four kinds of AI.Research of this kind involves building and programming computers to perform tasks which, to paraphrase Marvin Minsky, would require intelligence if they were done by us. Researchers in nonpsychological AI make no claims whatsoever about the psychological realism of their programs or the devices they build, that is, about whether or not computers perform tasks as humans do.Research here is guided by the view that the computer is a useful tool in the study of mind. In particular, we can write computer programs or build devices that simulate alleged psychological processes in humans and then test our predictions about how the alleged processes work. We can weave these programs and devices together with other programs and devices that simulate different alleged mental processes and thereby test the degree to which the AI system as a whole simulates human mentality. According to weak psychological AI, working with computer models is a way of refining and testing hypotheses about processes that are allegedly realized in human minds.... According to this view, our minds are computers and therefore can be duplicated by other computers. Sherry Turkle writes that the "real ambition is of mythic proportions, making a general purpose intelligence, a mind." (Turkle, 1984, p. 240) The authors of a major text announce that "the ultimate goal of AI research is to build a person or, more humbly, an animal." (Charniak & McDermott, 1985, p. 7)Research in this field, like strong psychological AI, takes seriously the functionalist view that mentality can be realized in many different types of physical devices. Suprapsychological AI, however, accuses strong psychological AI of being chauvinisticof being only interested in human intelligence! Suprapsychological AI claims to be interested in all the conceivable ways intelligence can be realized. (Flanagan, 1991, pp. 241-242)16) Determination of Relevance of Rules in Particular ContextsEven if the [rules] were stored in a context-free form the computer still couldn't use them. To do that the computer requires rules enabling it to draw on just those [ rules] which are relevant in each particular context. Determination of relevance will have to be based on further facts and rules, but the question will again arise as to which facts and rules are relevant for making each particular determination. One could always invoke further facts and rules to answer this question, but of course these must be only the relevant ones. And so it goes. It seems that AI workers will never be able to get started here unless they can settle the problem of relevance beforehand by cataloguing types of context and listing just those facts which are relevant in each. (Dreyfus & Dreyfus, 1986, p. 80)Perhaps the single most important idea to artificial intelligence is that there is no fundamental difference between form and content, that meaning can be captured in a set of symbols such as a semantic net. (G. Johnson, 1986, p. 250)Artificial intelligence is based on the assumption that the mind can be described as some kind of formal system manipulating symbols that stand for things in the world. Thus it doesn't matter what the brain is made of, or what it uses for tokens in the great game of thinking. Using an equivalent set of tokens and rules, we can do thinking with a digital computer, just as we can play chess using cups, salt and pepper shakers, knives, forks, and spoons. Using the right software, one system (the mind) can be mapped into the other (the computer). (G. Johnson, 1986, p. 250)19) A Statement of the Primary and Secondary Purposes of Artificial IntelligenceThe primary goal of Artificial Intelligence is to make machines smarter.The secondary goals of Artificial Intelligence are to understand what intelligence is (the Nobel laureate purpose) and to make machines more useful (the entrepreneurial purpose). (Winston, 1987, p. 1)The theoretical ideas of older branches of engineering are captured in the language of mathematics. We contend that mathematical logic provides the basis for theory in AI. Although many computer scientists already count logic as fundamental to computer science in general, we put forward an even stronger form of the logic-is-important argument....AI deals mainly with the problem of representing and using declarative (as opposed to procedural) knowledge. Declarative knowledge is the kind that is expressed as sentences, and AI needs a language in which to state these sentences. Because the languages in which this knowledge usually is originally captured (natural languages such as English) are not suitable for computer representations, some other language with the appropriate properties must be used. It turns out, we think, that the appropriate properties include at least those that have been uppermost in the minds of logicians in their development of logical languages such as the predicate calculus. Thus, we think that any language for expressing knowledge in AI systems must be at least as expressive as the first-order predicate calculus. (Genesereth & Nilsson, 1987, p. viii)21) Perceptual Structures Can Be Represented as Lists of Elementary PropositionsIn artificial intelligence studies, perceptual structures are represented as assemblages of description lists, the elementary components of which are propositions asserting that certain relations hold among elements. (Chase & Simon, 1988, p. 490)Artificial intelligence (AI) is sometimes defined as the study of how to build and/or program computers to enable them to do the sorts of things that minds can do. Some of these things are commonly regarded as requiring intelligence: offering a medical diagnosis and/or prescription, giving legal or scientific advice, proving theorems in logic or mathematics. Others are not, because they can be done by all normal adults irrespective of educational background (and sometimes by non-human animals too), and typically involve no conscious control: seeing things in sunlight and shadows, finding a path through cluttered terrain, fitting pegs into holes, speaking one's own native tongue, and using one's common sense. Because it covers AI research dealing with both these classes of mental capacity, this definition is preferable to one describing AI as making computers do "things that would require intelligence if done by people." However, it presupposes that computers could do what minds can do, that they might really diagnose, advise, infer, and understand. One could avoid this problematic assumption (and also side-step questions about whether computers do things in the same way as we do) by defining AI instead as "the development of computers whose observable performance has features which in humans we would attribute to mental processes." This bland characterization would be acceptable to some AI workers, especially amongst those focusing on the production of technological tools for commercial purposes. But many others would favour a more controversial definition, seeing AI as the science of intelligence in general-or, more accurately, as the intellectual core of cognitive science. As such, its goal is to provide a systematic theory that can explain (and perhaps enable us to replicate) both the general categories of intentionality and the diverse psychological capacities grounded in them. (Boden, 1990b, pp. 1-2)Because the ability to store data somewhat corresponds to what we call memory in human beings, and because the ability to follow logical procedures somewhat corresponds to what we call reasoning in human beings, many members of the cult have concluded that what computers do somewhat corresponds to what we call thinking. It is no great difficulty to persuade the general public of that conclusion since computers process data very fast in small spaces well below the level of visibility; they do not look like other machines when they are at work. They seem to be running along as smoothly and silently as the brain does when it remembers and reasons and thinks. On the other hand, those who design and build computers know exactly how the machines are working down in the hidden depths of their semiconductors. Computers can be taken apart, scrutinized, and put back together. Their activities can be tracked, analyzed, measured, and thus clearly understood-which is far from possible with the brain. This gives rise to the tempting assumption on the part of the builders and designers that computers can tell us something about brains, indeed, that the computer can serve as a model of the mind, which then comes to be seen as some manner of information processing machine, and possibly not as good at the job as the machine. (Roszak, 1994, pp. xiv-xv)The inner workings of the human mind are far more intricate than the most complicated systems of modern technology. Researchers in the field of artificial intelligence have been attempting to develop programs that will enable computers to display intelligent behavior. Although this field has been an active one for more than thirty-five years and has had many notable successes, AI researchers still do not know how to create a program that matches human intelligence. No existing program can recall facts, solve problems, reason, learn, and process language with human facility. This lack of success has occurred not because computers are inferior to human brains but rather because we do not yet know in sufficient detail how intelligence is organized in the brain. (Anderson, 1995, p. 2)Historical dictionary of quotations in cognitive science > Artificial Intelligence
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37 технологические данные
1) Engineering: machining information2) Economy: production data3) Metallurgy: process data4) Information technology: tooling data5) Sakhalin energy glossary: functional data (OPL Tender Update)6) Automation: engineering information, manufacturing data, planning information, process information, production engineering information, technological information, technology data7) Chemical weapons: engineering dataУниверсальный русско-английский словарь > технологические данные
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38 análisis del rendimiento
(n.) = performance analysisEx. We distinguish three basic steps in the performance analysis process: data collection, data transformation, and data visualization.* * *(n.) = performance analysisEx: We distinguish three basic steps in the performance analysis process: data collection, data transformation, and data visualization.
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39 aufbereiten
aufbereiten v 1. COMP edit; 2. GEN recycle; 3. IND process; 4. WIWI organize* * ** * *aufbereiten
(Erz) to dress, to wash, (techn.) to enrich, (wieder verwerten) to recycle;
• Daten aufbereiten to process data;
• statistisches Material aufbereiten to develop statistical information, to process data;
• Tabellen aufbereiten to prepare tables. -
40 Verarbeitungsparameter
m < prod> ■ set-up data; process data; process parameterGerman-english technical dictionary > Verarbeitungsparameter
Страницы
См. также в других словарях:
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Process mining — techniques allow for the analysis of business processes based on event logs. They are often used when no formal description of the process can be obtained by other means, or when the quality of an existing documentation is questionable. For… … Wikipedia
