This document proposes a model to parallelize the frequent itemset mining process using GPUs instead of multi-core processors. It aims to speed up the mining process and allow it to handle large datasets more efficiently. The model parallelizes the FP-growth algorithm at different levels without generating the FP-tree. It first sorts the transaction database in parallel using GPUs for preprocessing. It then groups the transactions based on the first item and mines for frequent itemsets within each group in parallel on the GPU. Preliminary results show the sorting step is significantly faster when parallelized on the GPU compared to serial processing. The overall goal is to efficiently mine large datasets using the low-cost and high-performance capabilities of GPUs.
Parallel Key Value Pattern Matching Modelijsrd.com
Mining frequent itemsets from the huge transactional database is an important task in data mining. To find frequent itemsets in databases involves big decision in data mining for the purpose of extracting association rules. Association rule mining is used to find relationships among large datasets. Many algorithms were developed to find those frequent itemsets. This work presents a summarization and new model of parallel key value pattern matching model which shards a large-scale mining task into independent, parallel tasks. It produces a frequent pattern showing their capabilities and efficiency in terms of time consumption. It also avoids the high computational cost. It discovers the frequent item set from the database.
AN ENHANCED FREQUENT PATTERN GROWTH BASED ON MAPREDUCE FOR MINING ASSOCIATION...IJDKP
In mining frequent itemsets, one of most important algorithm is FP-growth. FP-growth proposes an
algorithm to compress information needed for mining frequent itemsets in FP-tree and recursively
constructs FP-trees to find all frequent itemsets. In this paper, we propose the EFP-growth (enhanced FPgrowth)
algorithm to achieve the quality of FP-growth. Our proposed method implemented the EFPGrowth
based on MapReduce framework using Hadoop approach. New method has high achieving
performance compared with the basic FP-Growth. The EFP-growth it can work with the large datasets to
discovery frequent patterns in a transaction database. Based on our method, the execution time under
different minimum supports is decreased..
Distributed Algorithm for Frequent Pattern Mining using HadoopMap Reduce Fram...idescitation
With the rapid growth of information technology and in many business
applications, mining frequent patterns and finding associations among them requires
handling large and distributed databases. As FP-tree considered being the best compact data
structure to hold the data patterns in memory there has been efforts to make it parallel and
distributed to handle large databases. However, it incurs lot of communication over head
during the mining. In this paper parallel and distributed frequent pattern mining algorithm
using Hadoop Map Reduce framework is proposed, which shows best performance results
for large databases. Proposed algorithm partitions the database in such a way that, it works
independently at each local node and locally generates the frequent patterns by sharing the
global frequent pattern header table. These local frequent patterns are merged at final stage.
This reduces the complete communication overhead during structure construction as well as
during pattern mining. The item set count is also taken into consideration reducing
processor idle time. Hadoop Map Reduce framework is used effectively in all the steps of the
algorithm. Experiments are carried out on a PC cluster with 5 computing nodes which
shows execution time efficiency as compared to other algorithms. The experimental result
shows that proposed algorithm efficiently handles the scalability for very large datab ases.
Index Terms—
Improving performance of apriori algorithm using hadoopeSAT Journals
Abstract Spatial data is a data having a geological information. This paper explores the use of Hadoop framework to improve the performance of Apriori algorithm for spatial data mining. FP growth algorithm is better than Apriori but it fails in certain situations. By applying the Apriori algorithm parallely using Hadoop framework to spatial data, we can perform well as compare to FP growth. This paper includes clustering based on geological location, classification based on mineral resource type and spatial coherence between mineral resources. Spatial data mining find out the different association rules by observing the spatial data by using Apriori algorithm. The result of the paper will indicate the accurate prediction of occurrence of commodity with respect to other commodity of mineral resources. Keywords: Hadoop, data mining, association rules, clustering, spatial coherence
Review: Data Driven Traffic Flow Forecasting using MapReduce in Distributed M...AM Publications
from last decade, the use of communication and transportation technology increases in urban traffic
management system. To predict the correct result forecasting technique is used. Furthermore, as more data are
collected, increase in traffic data. In short, traffic flow forecasting system find out collection of historical observations
for records similar to the current conditions and uses these to estimate the future state of the system. In this paper we
focus on data driven traffic flow forecasting system which is based on MapReduce framework for distributed system
with Bayesian network approach. For probability distribution of data between two adjacent node i.e. data used for
forecasting(Input node) and data which is forecasted (output node) used a Gaussian mixture model (GMM) whose
parameters are updated using Expectation Maximization algorithm. Finally focus on model fusion, main problem in
distributed modelling for data storage and processing in traffic flow forecasting system.
Efficient discovery of frequent itemsets in large datasets is a crucial task of data mining. In recent
years, several approaches have been proposed for generating high utility patterns; they arise the problems of
producing a large number of candidate itemsets for high utility itemsets and probably degrade mining
performance in terms of speed and space. Mining high utility itemsets from a transactional database refers to the
discovery of itemsets with high utility like profits. Although a number of relevant approaches have been
proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high
utility itemsets. Such a large number of candidate itemsets degrades the mining performance in terms of
execution time and space requirement
Parallel Key Value Pattern Matching Modelijsrd.com
Mining frequent itemsets from the huge transactional database is an important task in data mining. To find frequent itemsets in databases involves big decision in data mining for the purpose of extracting association rules. Association rule mining is used to find relationships among large datasets. Many algorithms were developed to find those frequent itemsets. This work presents a summarization and new model of parallel key value pattern matching model which shards a large-scale mining task into independent, parallel tasks. It produces a frequent pattern showing their capabilities and efficiency in terms of time consumption. It also avoids the high computational cost. It discovers the frequent item set from the database.
AN ENHANCED FREQUENT PATTERN GROWTH BASED ON MAPREDUCE FOR MINING ASSOCIATION...IJDKP
In mining frequent itemsets, one of most important algorithm is FP-growth. FP-growth proposes an
algorithm to compress information needed for mining frequent itemsets in FP-tree and recursively
constructs FP-trees to find all frequent itemsets. In this paper, we propose the EFP-growth (enhanced FPgrowth)
algorithm to achieve the quality of FP-growth. Our proposed method implemented the EFPGrowth
based on MapReduce framework using Hadoop approach. New method has high achieving
performance compared with the basic FP-Growth. The EFP-growth it can work with the large datasets to
discovery frequent patterns in a transaction database. Based on our method, the execution time under
different minimum supports is decreased..
Distributed Algorithm for Frequent Pattern Mining using HadoopMap Reduce Fram...idescitation
With the rapid growth of information technology and in many business
applications, mining frequent patterns and finding associations among them requires
handling large and distributed databases. As FP-tree considered being the best compact data
structure to hold the data patterns in memory there has been efforts to make it parallel and
distributed to handle large databases. However, it incurs lot of communication over head
during the mining. In this paper parallel and distributed frequent pattern mining algorithm
using Hadoop Map Reduce framework is proposed, which shows best performance results
for large databases. Proposed algorithm partitions the database in such a way that, it works
independently at each local node and locally generates the frequent patterns by sharing the
global frequent pattern header table. These local frequent patterns are merged at final stage.
This reduces the complete communication overhead during structure construction as well as
during pattern mining. The item set count is also taken into consideration reducing
processor idle time. Hadoop Map Reduce framework is used effectively in all the steps of the
algorithm. Experiments are carried out on a PC cluster with 5 computing nodes which
shows execution time efficiency as compared to other algorithms. The experimental result
shows that proposed algorithm efficiently handles the scalability for very large datab ases.
Index Terms—
Improving performance of apriori algorithm using hadoopeSAT Journals
Abstract Spatial data is a data having a geological information. This paper explores the use of Hadoop framework to improve the performance of Apriori algorithm for spatial data mining. FP growth algorithm is better than Apriori but it fails in certain situations. By applying the Apriori algorithm parallely using Hadoop framework to spatial data, we can perform well as compare to FP growth. This paper includes clustering based on geological location, classification based on mineral resource type and spatial coherence between mineral resources. Spatial data mining find out the different association rules by observing the spatial data by using Apriori algorithm. The result of the paper will indicate the accurate prediction of occurrence of commodity with respect to other commodity of mineral resources. Keywords: Hadoop, data mining, association rules, clustering, spatial coherence
Review: Data Driven Traffic Flow Forecasting using MapReduce in Distributed M...AM Publications
from last decade, the use of communication and transportation technology increases in urban traffic
management system. To predict the correct result forecasting technique is used. Furthermore, as more data are
collected, increase in traffic data. In short, traffic flow forecasting system find out collection of historical observations
for records similar to the current conditions and uses these to estimate the future state of the system. In this paper we
focus on data driven traffic flow forecasting system which is based on MapReduce framework for distributed system
with Bayesian network approach. For probability distribution of data between two adjacent node i.e. data used for
forecasting(Input node) and data which is forecasted (output node) used a Gaussian mixture model (GMM) whose
parameters are updated using Expectation Maximization algorithm. Finally focus on model fusion, main problem in
distributed modelling for data storage and processing in traffic flow forecasting system.
Efficient discovery of frequent itemsets in large datasets is a crucial task of data mining. In recent
years, several approaches have been proposed for generating high utility patterns; they arise the problems of
producing a large number of candidate itemsets for high utility itemsets and probably degrade mining
performance in terms of speed and space. Mining high utility itemsets from a transactional database refers to the
discovery of itemsets with high utility like profits. Although a number of relevant approaches have been
proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high
utility itemsets. Such a large number of candidate itemsets degrades the mining performance in terms of
execution time and space requirement
TASK-DECOMPOSITION BASED ANOMALY DETECTION OF MASSIVE AND HIGH-VOLATILITY SES...ijdpsjournal
The Science Information Network (SINET) is a Japanese academic backbone network for more than 800 universities and research institutions. The characteristic of SINET traffic is that it is enormous and highly variable. In this paper, we present a task-decomposition based anomaly detection of massive and highvolatility session data of SINET. Three main features are discussed: Tash scheduling, Traffic discrimination, and Histogramming. We adopt a task-decomposition based dynamic scheduling method to handle the massive session data stream of SINET. In the experiment, we have analysed SINET traffic from 2/27 to 3/8 and detect some anomalies by LSTM based time-series data processing.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Survey of Machine Learning Techniques for Self-tuning Hadoop Performance IJECEIAES
The Apache Hadoop framework is an open source implementation of MapReduce for processing and storing big data. However, to get the best performance from this is a big challenge because of its large number configuration parameters. In this paper, the concept of critical issues of Hadoop system, big data and machine learning have been highlighted and an analysis of some machine learning techniques applied so far, for improving the Hadoop performance is presented. Then, a promising machine learning technique using deep learning algorithm is proposed for Hadoop system performance improvement.
Implementation of p pic algorithm in map reduce to handle big dataeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Cloud computing is the one of the emerging techniques to process the big data. Large collection of set or large
volume of data is known as big data. Processing of big data (MRI images and DICOM images) normally takes
more time compare with other data. The main tasks such as handling big data can be solved by using the concepts
of hadoop. Enhancing the hadoop concept it will help the user to process the large set of images or data. The
Advanced Hadoop Distributed File System (AHDF) and MapReduce are the two default main functions which
are used to enhance hadoop. HDF method is a hadoop file storing system, which is used for storing and retrieving
the data. MapReduce is the combinations of two functions namely maps and reduce. Map is the process of
splitting the inputs and reduce is the process of integrating the output of map’s input. Recently, in medical fields
the experienced problems like machine failure and fault tolerance while processing the result for the scanned
data. A unique optimized time scheduling algorithm, called Advanced Dynamic Handover Reduce Function
(ADHRF) algorithm is introduced in the reduce function. Enhancement of hadoop and cloud introduction of
ADHRF helps to overcome the processing risks, to get optimized result with less waiting time and reduction in
error percentage of the output image
STORAGE GROWING FORECAST WITH BACULA BACKUP SOFTWARE CATALOG DATA MININGcsandit
Backup software information is a potential source for data mining: not only the unstructured
stored data from all other backed-up servers, but also backup jobs metadata, which is stored in
a formerly known catalog database. Data mining this database, in special, could be used in
order to improve backup quality, automation, reliability, predict bottlenecks, identify risks,
failure trends, and provide specific needed report information that could not be fetched from
closed format property stock property backup software database. Ignoring this data mining
project might be costly, with lots of unnecessary human intervention, uncoordinated work and
pitfalls, such as having backup service disruption, because of insufficient planning. The specific
goal of this practical paper is using Knowledge Discovery in Database Time Series, Stochastic
Models and R scripts in order to predict backup storage data growth. This project could not be
done with traditional closed format proprietary solutions, since it is generally impossible to
read their database data from third party software because of vendor lock-in deliberate
overshadow. Nevertheless, it is very feasible with Bacula: the current third most popular backup
software worldwide, and open source. This paper is focused on the backup storage demand
prediction problem, using the most popular prediction algorithms. Among them, Holt-Winters
Model had the highest success rate for the tested data sets.
MAP/REDUCE DESIGN AND IMPLEMENTATION OF APRIORIALGORITHM FOR HANDLING VOLUMIN...acijjournal
Apriori is one of the key algorithms to generate frequent itemsets. Analysing frequent itemset is a crucial
step in analysing structured data and in finding association relationship between items. This stands as an
elementary foundation to supervised learning, which encompasses classifier and feature extraction
methods. Applying this algorithm is crucial to understand the behaviour of structured data. Most of the
structured data in scientific domain are voluminous. Processing such kind of data requires state of the art
computing machines. Setting up such an infrastructure is expensive. Hence a distributed environment
such as a clustered setup is employed for tackling such scenarios. Apache Hadoop distribution is one of
the cluster frameworks in distributed environment that helps by distributing voluminous data across a
number of nodes in the framework. This paper focuses on map/reduce design and implementation of
Apriori algorithm for structured data analysis.
CLOHUI: AN EFFICIENT ALGORITHM FOR MINING CLOSED + HIGH UTILITY ITEMSETS FROM...ijcsit
High-utility itemset mining (HUIM) is an important research topic in data mining field and extensive algorithms have been proposed. However, existing methods for HUIM present too many high-utility
itemsets (HUIs), which reduces not only efficiency but also effectiveness of mining since users have to sift
through a large number of HUIs to find useful ones. Recently a new representation, closed+ high-utility itemset (CHUI), has been proposed. With this concept, the number of HUIs is reduced massively. Existing
methods adopt two phases to discover CHUIs from a transaction database. In phase I, an itemset is first
checked whether it is closed. If the itemset is closed, an overestimation technique is adopted to set an upper
bound of the utility of this itemset in the database. The itemsets whose overestimated utilities are no less
than a given threshold are selected as candidate CHUIs. In phase II, the candidate CHUIs generated from
phase 1 are verified through computing their utilities in the database. However, there are two problems in
these methods. 1) The number of candidate CHUIs is usually very huge and extensive memory is required.
2) The method computing closed itemsets is time consuming. Thus in this paper we propose an efficient
algorithm CloHUI for mining CHUIs from a transaction database. CloHUI does not generate any
candidate CHUIs during the mining process, and verifies closed itemsets from a tree structure. We propose
a strategy to make the verifying process faster. Extensive experiments have been performed on sparse and
dense datasets to compare CloHUI with the state-of-the-art algorithm CHUD, the experiment results show
that for dense datasets our proposed algorithm CloHUI significantly outperforms CHUD: it is more than an order of magnitude faster, and consumes less memory.
In recent years the data mining applications become musty and outmoded over time. Energy wastage is the major
problem more in big data analytics and applications. More workload and more computational time will increase high energy
cost and decrease efficiency. The Incremental computational time processing is a promising approach to refreshing mining
results. It utilizes previously saved states to avoid the expense of re-computation from scratch. In this paper, we propose
Energy efficiency Map Reduce Scheduling Algorithm, a novel incremental processing extension to reduce the Map, the most
widely used framework for mining big data. Map reduce is a programming model for processing and generating large amount of data in parallel processing time. In this paper, Energy Efficiency reduce Map (EEMP) is algorithm provide more energy
and less maps in big data. Priority based scheduling is a task will allocate the schedules based on necessary and utilization of
the Jobs. For reducing the maps, it will reduce the system computational time so easily energy has improved in terms of big data applications.. Final results show the experimental comparison of the different algorithms involved in the paper.
Enhancement of Map Function Image Processing System Using DHRF Algorithm on B...AM Publications
Cloud computing is the concept of distributing a work and also processing the same work over the internet. Cloud
computing is called as service on demand. It is always available on the internet in Pay and Use mode. Processing of the Big
Data takes more time to compute MRI and DICOM data. The processing of hard tasks like this can be solved by using the
concept of MapReduce. MapReduce function is a concept of Map and Reduce functions. Map is the process of splitting or
dividing data. Reduce function is the process of integrating the output of the Map’s input to produce the result. The Map
function does two various image processing techniques to process the input data. Java Advanced Imaging (JAI) is introduced
in the map function in this proposed work. The processed intermediate data of the Map function is sent to the Reduce function
for the further process. The Dynamic Handover Reduce Function (DHRF) algorithm is introduced in the reduce function in
this work. This algorithm is implemented in the Reduce function to reduce the waiting time while processing the intermediate
data. The DHRF algorithm gives the final output by processing the Reduce function. The enhanced MapReduce concept and
proposed optimized algorithm is made to work on Euca2ool (a Cloud tool) to produce an effective and better output when
compared with the previous work in the field of Cloud Computing and Big Data.
Enhancing Big Data Analysis by using Map-reduce TechniquejournalBEEI
Database is defined as a set of data that is organized and distributed in a manner that permits the user to access the data being stored in an easy and more convenient manner. However, in the era of big-data the traditional methods of data analytics may not be able to manage and process the large amount of data. In order to develop an efficient way of handling big-data, this work enhances the use of Map-Reduce technique to handle big-data distributed on the cloud. This approach was evaluated using Hadoop server and applied on Electroencephalogram (EEG) Big-data as a case study. The proposed approach showed clear enhancement on managing and processing the EEG Big-data with average of 50% reduction on response time. The obtained results provide EEG researchers and specialist with an easy and fast method of handling the EEG big data.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
DEVELOPING A NOVEL MULTIDIMENSIONAL MULTIGRANULARITY DATA MINING APPROACH FOR...cscpconf
Data Mining is one of the most significant tools for discovering association patterns that are useful for many knowledge domains. Yet, there are some drawbacks in existing mining techniques. Three main weaknesses of current data-mining techniques are: 1) re-scanning of the entire database must be done whenever new attributes are added. 2) An association rule may be true on a certain granularity but fail on a smaller ones and vise verse. 3) Current methods can only be used to find either frequent rules or infrequent rules, but not both at the same time. This research proposes a novel data schema and an algorithm that solves the above weaknesses while improving on the efficiency and effectiveness of data mining strategies. Crucial mechanisms in each step will be clarified in this paper. Finally, this paper presents experimental results regarding efficiency, scalability, information loss, etc. of the proposed approach to prove its advantages.
A Novel Approach for Clustering Big Data based on MapReduce IJECEIAES
Clustering is one of the most important applications of data mining. It has attracted attention of researchers in statistics and machine learning. It is used in many applications like information retrieval, image processing and social network analytics etc. It helps the user to understand the similarity and dissimilarity between objects. Cluster analysis makes the users understand complex and large data sets more clearly. There are different types of clustering algorithms analyzed by various researchers. Kmeans is the most popular partitioning based algorithm as it provides good results because of accurate calculation on numerical data. But Kmeans give good results for numerical data only. Big data is combination of numerical and categorical data. Kprototype algorithm is used to deal with numerical as well as categorical data. Kprototype combines the distance calculated from numeric and categorical data. With the growth of data due to social networking websites, business transactions, scientific calculation etc., there is vast collection of structured, semi-structured and unstructured data. So, there is need of optimization of Kprototype so that these varieties of data can be analyzed efficiently.In this work, Kprototype algorithm is implemented on MapReduce in this paper. Experiments have proved that Kprototype implemented on Mapreduce gives better performance gain on multiple nodes as compared to single node. CPU execution time and speedup are used as evaluation metrics for comparison.Intellegent splitter is proposed in this paper which splits mixed big data into numerical and categorical data. Comparison with traditional algorithms proves that proposed algorithm works better for large scale of data.
TASK-DECOMPOSITION BASED ANOMALY DETECTION OF MASSIVE AND HIGH-VOLATILITY SES...ijdpsjournal
The Science Information Network (SINET) is a Japanese academic backbone network for more than 800 universities and research institutions. The characteristic of SINET traffic is that it is enormous and highly variable. In this paper, we present a task-decomposition based anomaly detection of massive and highvolatility session data of SINET. Three main features are discussed: Tash scheduling, Traffic discrimination, and Histogramming. We adopt a task-decomposition based dynamic scheduling method to handle the massive session data stream of SINET. In the experiment, we have analysed SINET traffic from 2/27 to 3/8 and detect some anomalies by LSTM based time-series data processing.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Survey of Machine Learning Techniques for Self-tuning Hadoop Performance IJECEIAES
The Apache Hadoop framework is an open source implementation of MapReduce for processing and storing big data. However, to get the best performance from this is a big challenge because of its large number configuration parameters. In this paper, the concept of critical issues of Hadoop system, big data and machine learning have been highlighted and an analysis of some machine learning techniques applied so far, for improving the Hadoop performance is presented. Then, a promising machine learning technique using deep learning algorithm is proposed for Hadoop system performance improvement.
Implementation of p pic algorithm in map reduce to handle big dataeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Cloud computing is the one of the emerging techniques to process the big data. Large collection of set or large
volume of data is known as big data. Processing of big data (MRI images and DICOM images) normally takes
more time compare with other data. The main tasks such as handling big data can be solved by using the concepts
of hadoop. Enhancing the hadoop concept it will help the user to process the large set of images or data. The
Advanced Hadoop Distributed File System (AHDF) and MapReduce are the two default main functions which
are used to enhance hadoop. HDF method is a hadoop file storing system, which is used for storing and retrieving
the data. MapReduce is the combinations of two functions namely maps and reduce. Map is the process of
splitting the inputs and reduce is the process of integrating the output of map’s input. Recently, in medical fields
the experienced problems like machine failure and fault tolerance while processing the result for the scanned
data. A unique optimized time scheduling algorithm, called Advanced Dynamic Handover Reduce Function
(ADHRF) algorithm is introduced in the reduce function. Enhancement of hadoop and cloud introduction of
ADHRF helps to overcome the processing risks, to get optimized result with less waiting time and reduction in
error percentage of the output image
STORAGE GROWING FORECAST WITH BACULA BACKUP SOFTWARE CATALOG DATA MININGcsandit
Backup software information is a potential source for data mining: not only the unstructured
stored data from all other backed-up servers, but also backup jobs metadata, which is stored in
a formerly known catalog database. Data mining this database, in special, could be used in
order to improve backup quality, automation, reliability, predict bottlenecks, identify risks,
failure trends, and provide specific needed report information that could not be fetched from
closed format property stock property backup software database. Ignoring this data mining
project might be costly, with lots of unnecessary human intervention, uncoordinated work and
pitfalls, such as having backup service disruption, because of insufficient planning. The specific
goal of this practical paper is using Knowledge Discovery in Database Time Series, Stochastic
Models and R scripts in order to predict backup storage data growth. This project could not be
done with traditional closed format proprietary solutions, since it is generally impossible to
read their database data from third party software because of vendor lock-in deliberate
overshadow. Nevertheless, it is very feasible with Bacula: the current third most popular backup
software worldwide, and open source. This paper is focused on the backup storage demand
prediction problem, using the most popular prediction algorithms. Among them, Holt-Winters
Model had the highest success rate for the tested data sets.
MAP/REDUCE DESIGN AND IMPLEMENTATION OF APRIORIALGORITHM FOR HANDLING VOLUMIN...acijjournal
Apriori is one of the key algorithms to generate frequent itemsets. Analysing frequent itemset is a crucial
step in analysing structured data and in finding association relationship between items. This stands as an
elementary foundation to supervised learning, which encompasses classifier and feature extraction
methods. Applying this algorithm is crucial to understand the behaviour of structured data. Most of the
structured data in scientific domain are voluminous. Processing such kind of data requires state of the art
computing machines. Setting up such an infrastructure is expensive. Hence a distributed environment
such as a clustered setup is employed for tackling such scenarios. Apache Hadoop distribution is one of
the cluster frameworks in distributed environment that helps by distributing voluminous data across a
number of nodes in the framework. This paper focuses on map/reduce design and implementation of
Apriori algorithm for structured data analysis.
CLOHUI: AN EFFICIENT ALGORITHM FOR MINING CLOSED + HIGH UTILITY ITEMSETS FROM...ijcsit
High-utility itemset mining (HUIM) is an important research topic in data mining field and extensive algorithms have been proposed. However, existing methods for HUIM present too many high-utility
itemsets (HUIs), which reduces not only efficiency but also effectiveness of mining since users have to sift
through a large number of HUIs to find useful ones. Recently a new representation, closed+ high-utility itemset (CHUI), has been proposed. With this concept, the number of HUIs is reduced massively. Existing
methods adopt two phases to discover CHUIs from a transaction database. In phase I, an itemset is first
checked whether it is closed. If the itemset is closed, an overestimation technique is adopted to set an upper
bound of the utility of this itemset in the database. The itemsets whose overestimated utilities are no less
than a given threshold are selected as candidate CHUIs. In phase II, the candidate CHUIs generated from
phase 1 are verified through computing their utilities in the database. However, there are two problems in
these methods. 1) The number of candidate CHUIs is usually very huge and extensive memory is required.
2) The method computing closed itemsets is time consuming. Thus in this paper we propose an efficient
algorithm CloHUI for mining CHUIs from a transaction database. CloHUI does not generate any
candidate CHUIs during the mining process, and verifies closed itemsets from a tree structure. We propose
a strategy to make the verifying process faster. Extensive experiments have been performed on sparse and
dense datasets to compare CloHUI with the state-of-the-art algorithm CHUD, the experiment results show
that for dense datasets our proposed algorithm CloHUI significantly outperforms CHUD: it is more than an order of magnitude faster, and consumes less memory.
In recent years the data mining applications become musty and outmoded over time. Energy wastage is the major
problem more in big data analytics and applications. More workload and more computational time will increase high energy
cost and decrease efficiency. The Incremental computational time processing is a promising approach to refreshing mining
results. It utilizes previously saved states to avoid the expense of re-computation from scratch. In this paper, we propose
Energy efficiency Map Reduce Scheduling Algorithm, a novel incremental processing extension to reduce the Map, the most
widely used framework for mining big data. Map reduce is a programming model for processing and generating large amount of data in parallel processing time. In this paper, Energy Efficiency reduce Map (EEMP) is algorithm provide more energy
and less maps in big data. Priority based scheduling is a task will allocate the schedules based on necessary and utilization of
the Jobs. For reducing the maps, it will reduce the system computational time so easily energy has improved in terms of big data applications.. Final results show the experimental comparison of the different algorithms involved in the paper.
Enhancement of Map Function Image Processing System Using DHRF Algorithm on B...AM Publications
Cloud computing is the concept of distributing a work and also processing the same work over the internet. Cloud
computing is called as service on demand. It is always available on the internet in Pay and Use mode. Processing of the Big
Data takes more time to compute MRI and DICOM data. The processing of hard tasks like this can be solved by using the
concept of MapReduce. MapReduce function is a concept of Map and Reduce functions. Map is the process of splitting or
dividing data. Reduce function is the process of integrating the output of the Map’s input to produce the result. The Map
function does two various image processing techniques to process the input data. Java Advanced Imaging (JAI) is introduced
in the map function in this proposed work. The processed intermediate data of the Map function is sent to the Reduce function
for the further process. The Dynamic Handover Reduce Function (DHRF) algorithm is introduced in the reduce function in
this work. This algorithm is implemented in the Reduce function to reduce the waiting time while processing the intermediate
data. The DHRF algorithm gives the final output by processing the Reduce function. The enhanced MapReduce concept and
proposed optimized algorithm is made to work on Euca2ool (a Cloud tool) to produce an effective and better output when
compared with the previous work in the field of Cloud Computing and Big Data.
Enhancing Big Data Analysis by using Map-reduce TechniquejournalBEEI
Database is defined as a set of data that is organized and distributed in a manner that permits the user to access the data being stored in an easy and more convenient manner. However, in the era of big-data the traditional methods of data analytics may not be able to manage and process the large amount of data. In order to develop an efficient way of handling big-data, this work enhances the use of Map-Reduce technique to handle big-data distributed on the cloud. This approach was evaluated using Hadoop server and applied on Electroencephalogram (EEG) Big-data as a case study. The proposed approach showed clear enhancement on managing and processing the EEG Big-data with average of 50% reduction on response time. The obtained results provide EEG researchers and specialist with an easy and fast method of handling the EEG big data.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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DEVELOPING A NOVEL MULTIDIMENSIONAL MULTIGRANULARITY DATA MINING APPROACH FOR...cscpconf
Data Mining is one of the most significant tools for discovering association patterns that are useful for many knowledge domains. Yet, there are some drawbacks in existing mining techniques. Three main weaknesses of current data-mining techniques are: 1) re-scanning of the entire database must be done whenever new attributes are added. 2) An association rule may be true on a certain granularity but fail on a smaller ones and vise verse. 3) Current methods can only be used to find either frequent rules or infrequent rules, but not both at the same time. This research proposes a novel data schema and an algorithm that solves the above weaknesses while improving on the efficiency and effectiveness of data mining strategies. Crucial mechanisms in each step will be clarified in this paper. Finally, this paper presents experimental results regarding efficiency, scalability, information loss, etc. of the proposed approach to prove its advantages.
A Novel Approach for Clustering Big Data based on MapReduce IJECEIAES
Clustering is one of the most important applications of data mining. It has attracted attention of researchers in statistics and machine learning. It is used in many applications like information retrieval, image processing and social network analytics etc. It helps the user to understand the similarity and dissimilarity between objects. Cluster analysis makes the users understand complex and large data sets more clearly. There are different types of clustering algorithms analyzed by various researchers. Kmeans is the most popular partitioning based algorithm as it provides good results because of accurate calculation on numerical data. But Kmeans give good results for numerical data only. Big data is combination of numerical and categorical data. Kprototype algorithm is used to deal with numerical as well as categorical data. Kprototype combines the distance calculated from numeric and categorical data. With the growth of data due to social networking websites, business transactions, scientific calculation etc., there is vast collection of structured, semi-structured and unstructured data. So, there is need of optimization of Kprototype so that these varieties of data can be analyzed efficiently.In this work, Kprototype algorithm is implemented on MapReduce in this paper. Experiments have proved that Kprototype implemented on Mapreduce gives better performance gain on multiple nodes as compared to single node. CPU execution time and speedup are used as evaluation metrics for comparison.Intellegent splitter is proposed in this paper which splits mixed big data into numerical and categorical data. Comparison with traditional algorithms proves that proposed algorithm works better for large scale of data.
Web Oriented FIM for large scale dataset using Hadoopdbpublications
In large scale datasets, mining frequent itemsets using existing parallel mining algorithm is to balance the load by distributing such enormous data between collections of computers. But we identify high performance issue in existing mining algorithms [1]. To handle this problem, we introduce a new approach called data partitioning using Map Reduce programming model.In our proposed system, we have introduced new technique called frequent itemset ultrametric tree rather than conservative FP-trees. An investigational outcome tells us that, eradicating redundant transaction results in improving the performance by reducing computing loads.
FP-Tree is also a huge hierarchical data structure and cannot fit into the main memory also it is not suitable for “Incremental-mining” nor used in “Interactive-mining” system
In the recent years the scope of data mining has evolved into an active area of research because of the previously unknown and interesting knowledge from very large database collection. The data mining is applied on a variety of applications in multiple domains like in business, IT and many more sectors. In Data Mining the major problem which receives great attention by the community is the classification of the data. The classification of data should be such that it could be they can be easily verified and should be easily interpreted by the humans. In this paper we would be studying various data mining techniques so that we can find few combinations for enhancing the hybrid technique which would be having multiple techniques involved so enhance the usability of the application. We would be studying CHARM Algorithm, CM-SPAM Algorithm, Apriori Algorithm, MOPNAR Algorithm and the Top K Rules.
Frequent pattern mining techniques helpful to find interesting trends or patterns in
massive data. Prior domain knowledge leads to decide appropriate minimum support threshold. This
review article show different frequent pattern mining techniques based on apriori or FP-tree or user
define techniques under different computing environments like parallel, distributed or available data
mining tools, those helpful to determine interesting frequent patterns/itemsets with or without prior
domain knowledge. Proposed review article helps to develop efficient and scalable frequent pattern
mining techniques.
Multi-threaded approach in generating frequent itemset of Apriori algorithm b...TELKOMNIKA JOURNAL
This research is about the application of multi-threaded and trie data structures to the support calculation problem in the Apriori algorithm.
The support calculation results can search the association rule for market basket analysis problems. The support calculation process is a bottleneck process and can cause delays in the following process. This work observed five multi-threaded models based on Flynn’s taxonomy, which are single process, multiple data (SPMD), multiple process, single data (MPSD), multiple process, multiple data (MPMD), double SPMD first variant, and double SPMD second variant to shorten the processing time of the support calculation. In addition to the processing time, this works also consider the time difference between each multi-threaded model when the number of item variants increases. The time obtained from the experiment shows that the multi-threaded model that applies a double SPMD variant structure can perform almost three times faster than the multi-threaded model that applies the SPMD structure, MPMD structure, and combination of MPMD and SPMD based on the time difference of 5-itemsets and 10-itemsets experimental result.
A Survey on Improve Efficiency And Scability vertical mining using Agriculter...Editor IJMTER
Basic idea is that the search tree could be divided into sub process of equivalence
classes. And since generating item sets in sub process of equivalence classes is independent from
each other, we could do frequent item set mining in sub trees of equivalence classes in parallel. So
the straightforward approach to parallelize Éclat is to consider each equivalence class as a data
(agriculture). We can distribute data to different nodes and nodes could work on data without any
synchronization. Even though the sorting helps to produce different sets in smaller sizes, there is a
cost for sorting. Our Research to analysis is that the size of equivalence class is relatively small
(always less than the size of the item base) and this size also reduces quickly as the search goes
deeper in the recursion process. Base on time using more than using agriculture data we can handle
large amount of data so first we develop éclat algorithm then develop parallel éclat algorithm then
compare with using same data with respect time .with the help of support and confidence.
A Survey Report on High Utility Itemset Mining for Frequent Pattern MiningIJSRD
Data Mining is the Process to extract knowledge from the data or data stream, Mining high utility itemsets focus on the itemset with high profit only, as data or itemset may be static or in a stream mine this type if data has become a significant research topic. In this research paper we have presented different methods available for mining high utility dataset , to develop algorithm for this requires logic to mine the high utility itemsets from data streams, We have compared following algorithms, One pass Algorithm, Two Phase Algorithm, Mining top k-Utility Frequent itemset, and Sliding window based algorithms like MHUI-BIT(Mining High Utility Itemset Based on BITVector), MHUI-TID(Mining HighUtility Itemset based on TID-List) for lexicographical tree based summary data structure. And the FHM algorithm which we will used for our proposed work “Fast High Utility Miner) Using estimated utility Co-occurrence Pruning.
COMPARATIVE STUDY OF DISTRIBUTED FREQUENT PATTERN MINING ALGORITHMS FOR BIG S...IAEME Publication
Association rule mining plays an important role in decision support system. Nowadays in the era of internet, various online marketing sites and social networking sites are generating enormous amount of structural/semi structural data in the form of sales data, tweets, emails, web pages and so on. This online generated data is too large that it becomes very complex to process and analyze it using traditional systems which consumes more time. This paper overcomes the main memory bottleneck in single computing system. There are two major goals of this paper. In this paper, big sales dataset of AMUL dairy is preprocessed using Hadoop Map Reduce that convert it into the transactional dataset. Then, after removing the null transactions; distributed frequent pattern mining algorithm MR-DARM (Map Reduce based Distributed Association Rule Mining) is used to find most frequent item set. Finally, strong association rules are generated from frequent item sets. The paper also compares the time efficiency of MR-DARM algorithm with existing Count Distributed Algorithm (CDA) and Fast Distributed Mining (FDM) distributed frequent pattern mining algorithms. The compared algorithms are presented together with experimental results that lead to the final conclusions.
Existing parallel digging calculations for visit itemsets do not have a component that empowers programmed parallelization, stack adjusting, information conveyance, and adaptation to non-critical failure on substantial bunches. As an answer for this issue, we outline a parallel incessant itemsets mining calculation called FiDoop utilizing the MapReduce programming model. To accomplish compacted capacity and abstain from building contingent example bases, FiDoop joins the incessant things Ultrametric tree, as opposed to ordinary FP trees. In FiDoop, three MapReduce occupations are actualized to finish the mining undertaking. In the essential third MapReduce work, the mappers autonomously disintegrate itemsets, the reducers perform mix activities by building little Ultrametric trees, and the genuine mining of these trees independently. We actualize FiDoop on our in-house Hadoop group. We demonstrate that FiDoop on the group is touchy to information dissemination and measurements, in light of the fact that itemsets with various lengths have diverse decay and development costs. To enhance FiDoop's execution, we build up a workload adjust metric to quantify stack adjust over the group's registering hubs. We create FiDoop-HD, an augmentation of FiDoop, to accelerate the digging execution for high-dimensional information investigation. Broad tests utilizing genuine heavenly phantom information exhibit that our proposed arrangement is productive and versatile.
Scalable frequent itemset mining using heterogeneous computing par apriori a...ijdpsjournal
Association Rule mining is one of the dominant tasks of data mining, which concerns in finding frequent
itemsets in large volumes of data in order to produce summarized models of mined rules. These models are
extended to generate association rules in various applications such as e-commerce, bio-informatics,
associations between image contents and non image features, analysis of effectiveness of sales and retail
industry, etc. In the vast increasing databases, the major challenge is the frequent itemsets mining in a
very short period of time. In the case of increasing data, the time taken to process the data should be
almost constant. Since high performance computing has many processors, and many cores, consistent runtime
performance for such very large databases on association rules mining is achieved. We, therefore,
must rely on high performance parallel and/or distributed computing. In literature survey, we have studied
the sequential Apriori algorithms and identified the fundamental problems in sequential environment and
parallel environment. In our proposed ParApriori, we have proposed parallel algorithm for GPGPU, and
we have also done the results analysis of our GPU parallel algorithm. We find that proposed algorithm
improved the computing time, consistency in performance over the increasing load. The empirical analysis
of the algorithm also shows that efficiency and scalability is verified over the series of datasets
experimented on many core GPU platform.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Delivering Micro-Credentials in Technical and Vocational Education and TrainingAG2 Design
Explore how micro-credentials are transforming Technical and Vocational Education and Training (TVET) with this comprehensive slide deck. Discover what micro-credentials are, their importance in TVET, the advantages they offer, and the insights from industry experts. Additionally, learn about the top software applications available for creating and managing micro-credentials. This presentation also includes valuable resources and a discussion on the future of these specialised certifications.
For more detailed information on delivering micro-credentials in TVET, visit this https://tvettrainer.com/delivering-micro-credentials-in-tvet/
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.