Μετάφραση και ανάλυση λέξεων από την τεχνητή νοημοσύνη ChatGPT
Σε αυτήν τη σελίδα μπορείτε να λάβετε μια λεπτομερή ανάλυση μιας λέξης ή μιας φράσης, η οποία δημιουργήθηκε χρησιμοποιώντας το ChatGPT, την καλύτερη τεχνολογία τεχνητής νοημοσύνης μέχρι σήμερα:
- πώς χρησιμοποιείται η λέξη
- συχνότητα χρήσης
- χρησιμοποιείται πιο συχνά στον προφορικό ή γραπτό λόγο
- επιλογές μετάφρασης λέξεων
- παραδείγματα χρήσης (πολλές φράσεις με μετάφραση)
- ετυμολογία
YARN - translation to αραβικά
ألاسم
تَحَاوُر ; تَحَدُّث ; حَدِيث ; حِكَايَة ; حِوَار ; رِوَايَة ; سَرْد ; قَصّ ; قصص ; قِصَّة ; قَوْل ; كَلَام ; مُحَادَثَة ; مُحَاوَرَة ; مُخَاطَبَة ; مُكَالَمة ; مُنَاظَرَة ; مُنَاقَشَة
الفعل
أَفَاضَ بكَلمَة ; اِقْتَصَّ ; اِنْتَطَقَ ; تَحَاجَّ ; تَحَاوَرَ ; تَحَدَّثَ ; تَخَاطَبَ ; تَفَوَّهَ ; تَكَالَمَ ; تَكَلَّمَ ; تَلَفَّظَ ; حَكَى ; رَوَى ; سَرَدَ ; سَلْسَلَ ; لَغَا ; لَفَظَ ( كَلِمَةً ونَحْوَهَا ) ; نَطَقَ
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فِعْل : يروي حكايةً . يتحدَّث
غزل = غزل معدنى او زجاجى او لدائنى الخ = خيط حكاية ، قصة = حديث ، محادثة
VI
روى حكاية تحدث
Ορισμός
Βικιπαίδεια
Apache Hadoop ( ) is a collection of open-source software utilities that facilitates using a network of many computers to solve problems involving massive amounts of data and computation. It provides a software framework for distributed storage and processing of big data using the MapReduce programming model. Hadoop was originally designed for computer clusters built from commodity hardware, which is still the common use. It has since also found use on clusters of higher-end hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common occurrences and should be automatically handled by the framework.
The core of Apache Hadoop consists of a storage part, known as Hadoop Distributed File System (HDFS), and a processing part which is a MapReduce programming model. Hadoop splits files into large blocks and distributes them across nodes in a cluster. It then transfers packaged code into nodes to process the data in parallel. This approach takes advantage of data locality, where nodes manipulate the data they have access to. This allows the dataset to be processed faster and more efficiently than it would be in a more conventional supercomputer architecture that relies on a parallel file system where computation and data are distributed via high-speed networking.
The base Apache Hadoop framework is composed of the following modules:
- Hadoop Common – contains libraries and utilities needed by other Hadoop modules;
- Hadoop Distributed File System (HDFS) – a distributed file-system that stores data on commodity machines, providing very high aggregate bandwidth across the cluster;
- Hadoop YARN – (introduced in 2012) a platform responsible for managing computing resources in clusters and using them for scheduling users' applications;
- Hadoop MapReduce – an implementation of the MapReduce programming model for large-scale data processing.
- Hadoop Ozone – (introduced in 2020) An object store for Hadoop
The term Hadoop is often used for both base modules and sub-modules and also the ecosystem, or collection of additional software packages that can be installed on top of or alongside Hadoop, such as Apache Pig, Apache Hive, Apache HBase, Apache Phoenix, Apache Spark, Apache ZooKeeper, Apache Impala, Apache Flume, Apache Sqoop, Apache Oozie, and Apache Storm.
Apache Hadoop's MapReduce and HDFS components were inspired by Google papers on MapReduce and Google File System.
The Hadoop framework itself is mostly written in the Java programming language, with some native code in C and command line utilities written as shell scripts. Though MapReduce Java code is common, any programming language can be used with Hadoop Streaming to implement the map and reduce parts of the user's program. Other projects in the Hadoop ecosystem expose richer user interfaces.
