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Wyświetlanie 1-3 z 3
Tytuł:
High Frequency Rule Synthesis in a Large Scale Multiple Database with MapReduce
Autorzy:
Bisoyi, Sudhanshu Shekhar
Mishra, Pragnyaban
Mishra, Saroja Nanda
Tematy:
multiple database
frequent itemset
association rule
rule synthesis
MapReduce
HDFS
Pokaż więcej
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:
https://bibliotekanauki.pl/articles/2055260.pdf  Link otwiera się w nowym oknie
Opis:
Increasing development in information and communication technology leads to the generation of large amount of data from various sources. These collected data from multiple sources grows exponentially and may not be structurally uniform. In general, these are heterogeneous and distributed in multiple databases. Because of large volume, high velocity and variety of data mining knowledge in this environment becomes a big data challenge. Distributed Association Rule Mining(DARM) in these circumstances becomes a tedious task for an effective global Decision Support System(DSS). The DARM algorithms generate a large number of association rules and frequent itemset in the big data environment. In this situation synthesizing highfrequency rules from the big database becomes more challenging. Many algorithms for synthesizing association rule have been proposed in multiple database mining environments. These are facing enormous challenges in terms of high availability, scalability, efficiency, high cost for the storage and processing of large intermediate results and multiple redundant rules. In this paper, we have proposed a model to collect data from multiple sources into a big data storage framework based on HDFS. Secondly, a weighted multi-partitioned method for synthesizing high-frequency rules using MapReduce programming paradigm has been proposed. Experiments have been conducted in a parallel and distributed environment by using commodity hardware. We ensure the efficiency, scalability, high availability and costeffectiveness of our proposed method.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Data locality in Hadoop
Autorzy:
Kałużka, J.
Napieralska, M.
Romero, O.
Jovanovic, P.
Tematy:
distributed file system
big data
Apache Hadoop
HDFS
rozproszony system plików
Pokaż więcej
Wydawca:
Politechnika Łódzka. Wydział Mikroelektroniki i Informatyki
Powiązania:
https://bibliotekanauki.pl/articles/397706.pdf  Link otwiera się w nowym oknie
Opis:
The Apache Hadoop framework is an answer to the market tendencies regarding the need for storing and processing rapidly growing amounts of data, providing a fault-tolerant distributed storage and data processing. Dealing with large volumes of data, Hadoop, and its storage system HDFS (Hadoop Distributed File System), face challenges to keep the high efficiency with computing in a reasonable time. The typical Hadoop implementation transfers computation to the data. However, in the isolated configuration, namenode (playing the role of a master in the cluster) still favours the closer nodes. Basically it means that before the whole task has run, significant delays can be caused by moving single blocks of data closer to the starting datanode. Currently, a Hadoop user does not have influence how the data is distributed across the cluster. This paper presents an innovative functionality to the Hadoop Distributed File System (HDFS) that enables moving data blocks on request within the cluster. Data can be shifted either by a user running the proper HDFS shell command or programmatically by other modules, like an appropriate scheduler.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Recommendation system in Big Data environment based on Apache Hadoop and Spark software
System rekomendacji oparty o platformę Apache Hadoop oraz Spark
Autorzy:
Ujma, Krystian
Opis:
Stworzenie skalowalnego systemu rekomendacji filmów, który będzie w stanie polecać filmy dla użytkowników na podstawie zebranych danych.Praca zawiera:- opis systemów Big Data oraz rekomendacji,- opis projektu własnego systemu rekomendacji,- testy systemu,- podsumowanie.
Creation of a scalable movie recommendation system that will be able to recommend movies to users based on the collected data.The work includes:- description of Big Data systems and recommendations,- description of the design of own recommendation system,- system tests,- summary.
Dostawca treści:
Repozytorium Uniwersytetu Jagiellońskiego
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