This document discusses data mining of big data using Hadoop and MongoDB. It provides an overview of Hadoop and MongoDB and their uses in big data analysis. Specifically, it proposes using Hadoop for distributed processing and MongoDB for data storage and input. The document reviews several related works that discuss big data analysis using these tools, as well as their capabilities for scalable data storage and mining. It aims to improve computational time and fault tolerance for big data analysis by mining data stored in Hadoop using MongoDB and MapReduce.
This document discusses scheduling algorithms for processing big data using Hadoop. It provides background on big data and Hadoop, including that big data is characterized by volume, velocity, and variety. Hadoop uses MapReduce and HDFS to process and store large datasets across clusters. The default scheduling algorithm in Hadoop is FIFO, but performance can be improved using alternative scheduling algorithms. The objective is to study and analyze various scheduling algorithms that could increase performance for big data processing in Hadoop.
This document provides a survey of distributed heterogeneous big data mining adaptation in the cloud. It discusses how big data is large, heterogeneous, and distributed, making it difficult to analyze with traditional tools. The cloud helps overcome these issues by providing scalable infrastructure on demand. However, directly applying Hadoop MapReduce in the cloud is inefficient due to its assumption of homogeneous nodes. The document surveys different approaches for improving MapReduce performance in heterogeneous cloud environments through techniques like optimized task scheduling and resource allocation.
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
EVALUATING CASSANDRA, MONGO DB LIKE NOSQL DATASETS USING HADOOP STREAMINGijiert bestjournal
This document summarizes a research paper that evaluates Cassandra and MongoDB NoSQL databases for processing unstructured data using Hadoop streaming. It proposes a system with three stages: data preparation where data is downloaded from Cassandra servers to file systems; data transformation where JSON data is converted to other formats using MapReduce; and data processing where non-Java executables run on the transformed data. The document reviews related work on Cassandra and Hadoop performance and discusses the data models of key-value, document, column-oriented, and graph databases. It concludes that comparing Cassandra and MongoDB can help process unstructured data and outline new approaches.
This document presents a framework that migrates data from MySQL to NoSQL databases like MongoDB and HBase, and maps MySQL queries to queries in the NoSQL databases. The framework consists of a front-end GUI and modules for migrating data between the databases and mapping queries. It migrates data from MySQL tables to collections in MongoDB and HBase. When a user enters a MySQL query, a decision maker selects the target database and the query is mapped to that database's format to retrieve the data. The mapping time for various query types is measured to be very small, making query execution on NoSQL databases efficient using this framework.
Iaetsd mapreduce streaming over cassandra datasetsIaetsd Iaetsd
This document discusses processing large datasets from Denmark's traffic using Apache Cassandra and MapReduce. It begins with an introduction to big data and how the volume, velocity, and variety of data requires alternative processing methods. Apache Cassandra is introduced as a distributed and scalable NoSQL database for storing large amounts of structured and unstructured data across servers. The document then discusses Cassandra's data model and system architecture. It describes how MapReduce can be used for distributed processing of datasets stored in Cassandra. The paper aims to process traffic datasets from Denmark using Cassandra and MapReduce to help the transportation department monitor traffic.
MongoDB NoSQL database a deep dive -MyWhitePaperRajesh Kumar
This document provides an overview of MongoDB, a popular NoSQL database. It discusses why NoSQL databases were created, the different types of NoSQL databases, and focuses on MongoDB. MongoDB is a document-oriented database that stores data in JSON-like documents with dynamic schemas. It provides horizontal scaling, high performance, and flexible data models. The presentation covers MongoDB concepts like databases, collections, documents, CRUD operations, indexing, sharding, replication, and use cases. It provides examples of modeling data in MongoDB and considerations for data and schema design.
Analysis and evaluation of riak kv cluster environment using basho benchStevenChike
This document analyzes and evaluates the performance of the Riak KV NoSQL database cluster using the Basho-bench benchmark tool. Experiments were conducted on a 5-node Riak KV cluster to test throughput and latency under different workloads, data sizes, and operations (read, write, update). The results found that Riak KV can handle large volumes of data and various workloads effectively with good throughput, though latency increased with larger data sizes. Overall, Riak KV is suitable for distributed big data environments where high availability, scalability and fault tolerance are important.
This document discusses scheduling algorithms for processing big data using Hadoop. It provides background on big data and Hadoop, including that big data is characterized by volume, velocity, and variety. Hadoop uses MapReduce and HDFS to process and store large datasets across clusters. The default scheduling algorithm in Hadoop is FIFO, but performance can be improved using alternative scheduling algorithms. The objective is to study and analyze various scheduling algorithms that could increase performance for big data processing in Hadoop.
This document provides a survey of distributed heterogeneous big data mining adaptation in the cloud. It discusses how big data is large, heterogeneous, and distributed, making it difficult to analyze with traditional tools. The cloud helps overcome these issues by providing scalable infrastructure on demand. However, directly applying Hadoop MapReduce in the cloud is inefficient due to its assumption of homogeneous nodes. The document surveys different approaches for improving MapReduce performance in heterogeneous cloud environments through techniques like optimized task scheduling and resource allocation.
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
EVALUATING CASSANDRA, MONGO DB LIKE NOSQL DATASETS USING HADOOP STREAMINGijiert bestjournal
This document summarizes a research paper that evaluates Cassandra and MongoDB NoSQL databases for processing unstructured data using Hadoop streaming. It proposes a system with three stages: data preparation where data is downloaded from Cassandra servers to file systems; data transformation where JSON data is converted to other formats using MapReduce; and data processing where non-Java executables run on the transformed data. The document reviews related work on Cassandra and Hadoop performance and discusses the data models of key-value, document, column-oriented, and graph databases. It concludes that comparing Cassandra and MongoDB can help process unstructured data and outline new approaches.
This document presents a framework that migrates data from MySQL to NoSQL databases like MongoDB and HBase, and maps MySQL queries to queries in the NoSQL databases. The framework consists of a front-end GUI and modules for migrating data between the databases and mapping queries. It migrates data from MySQL tables to collections in MongoDB and HBase. When a user enters a MySQL query, a decision maker selects the target database and the query is mapped to that database's format to retrieve the data. The mapping time for various query types is measured to be very small, making query execution on NoSQL databases efficient using this framework.
Iaetsd mapreduce streaming over cassandra datasetsIaetsd Iaetsd
This document discusses processing large datasets from Denmark's traffic using Apache Cassandra and MapReduce. It begins with an introduction to big data and how the volume, velocity, and variety of data requires alternative processing methods. Apache Cassandra is introduced as a distributed and scalable NoSQL database for storing large amounts of structured and unstructured data across servers. The document then discusses Cassandra's data model and system architecture. It describes how MapReduce can be used for distributed processing of datasets stored in Cassandra. The paper aims to process traffic datasets from Denmark using Cassandra and MapReduce to help the transportation department monitor traffic.
MongoDB NoSQL database a deep dive -MyWhitePaperRajesh Kumar
This document provides an overview of MongoDB, a popular NoSQL database. It discusses why NoSQL databases were created, the different types of NoSQL databases, and focuses on MongoDB. MongoDB is a document-oriented database that stores data in JSON-like documents with dynamic schemas. It provides horizontal scaling, high performance, and flexible data models. The presentation covers MongoDB concepts like databases, collections, documents, CRUD operations, indexing, sharding, replication, and use cases. It provides examples of modeling data in MongoDB and considerations for data and schema design.
Analysis and evaluation of riak kv cluster environment using basho benchStevenChike
This document analyzes and evaluates the performance of the Riak KV NoSQL database cluster using the Basho-bench benchmark tool. Experiments were conducted on a 5-node Riak KV cluster to test throughput and latency under different workloads, data sizes, and operations (read, write, update). The results found that Riak KV can handle large volumes of data and various workloads effectively with good throughput, though latency increased with larger data sizes. Overall, Riak KV is suitable for distributed big data environments where high availability, scalability and fault tolerance are important.
Big Data Processing with Hadoop : A ReviewIRJET Journal
1. This document provides an overview of big data processing with Hadoop. It defines big data and describes the challenges of volume, velocity, variety and variability.
2. Traditional data processing approaches are inadequate for big data due to its scale. Hadoop provides a distributed file system called HDFS and a MapReduce framework to address this.
3. HDFS uses a master-slave architecture with a NameNode and DataNodes to store and retrieve file blocks. MapReduce allows distributed processing of large datasets across clusters through mapping and reducing functions.
This document discusses using Apache Hadoop and SQL Server to analyze large datasets. It finds that SQL Server struggles to efficiently query and analyze datasets with over 100 million rows, with query times increasing substantially with larger datasets. Apache Hadoop provides a more scalable solution by distributing data processing across a cluster. The document evaluates Hadoop and MongoDB for big data analysis, and chooses Hadoop for its ability to process large amounts of data for analytical purposes. It then discusses implementing Hortonworks Data Platform with Apache Ambari to analyze a 97GB population dataset using Hadoop.
Big Data Mining, Techniques, Handling Technologies and Some Related Issues: A...IJSRD
The Size of the data is increasing day by day with the using of social site. Big Data is a concept to manage and mine the large set of data. Today the concept of Big Data is widely used to mine the insight data of organization as well outside data. There are many techniques and technologies used in Big Data mining to extract the useful information from the distributed system. It is more powerful to extract the information compare with traditional data mining techniques. One of the most known technologies is Hadoop, used in Big Data mining. It takes many advantages over the traditional data mining technique but it has some issues like visualization technique, privacy etc.
BIG DATA SUMMARIZATION: FRAMEWORK, CHALLENGES AND POSSIBLE SOLUTIONSaciijournal
This document discusses big data summarization, including the challenges and potential solutions. It presents a framework for big data summarization with four main stages: 1) data clustering to group similar documents, 2) data generalization to abstract data to a higher conceptual level, 3) semantic term identification to identify metadata for more efficient data representation, and 4) evaluation of the summaries. Key challenges addressed include initializing clustering methods, selecting attributes to control generalization, and ensuring semantic associations in representations. Solutions proposed are detailed assessments of clustering initialization methods and statistical approaches for clustering, generalization and term identification.
This document provides a review of Hadoop storage and clustering algorithms. It begins with an introduction to big data and the challenges of storing and processing large, diverse datasets. It then discusses related technologies like cloud computing and Hadoop, including the Hadoop Distributed File System (HDFS) and MapReduce processing model. The document analyzes and compares various clustering techniques like K-means, fuzzy C-means, hierarchical clustering, and Self-Organizing Maps based on parameters such as number of clusters, size of clusters, dataset type, and noise.
The growth of data and its effi cient handling is becoming more popular trend in recent years bringing
new challenges to explore new avenues. Data analytics can be done more effi ciently with the availability of
distributed architecture of “Not Only SQL” NoSQL databases.
1. The document discusses Big Data analytics using Hadoop. It defines Big Data and explains the characteristics of volume, velocity, and variety.
2. Hadoop is introduced as a framework for distributed storage and processing of large data sets across clusters of commodity hardware. It uses HDFS for reliable storage and streaming of large data sets.
3. Key Hadoop components are the NameNode, which manages file system metadata, and DataNodes, which store and retrieve data blocks. Hadoop provides scalability, fault tolerance, and high performance on large data sets.
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.
Key aspects of big data storage and its architectureRahul Chaturvedi
This paper helps understand the tools and technologies related to a classic BigData setting. Someone who reads this paper, especially Enterprise Architects, will find it helpful in choosing several BigData database technologies in a Hadoop architecture.
Big Data is an evolution of Business Intelligence (BI).
Whereas traditional BI relies on data warehouses limited in size
(some terabytes) and it hardly manages unstructured data and
real-time analysis, the era of Big Data opens up a new technological
period offering advanced architectures and infrastructures
allowing sophisticated analyzes taking into account these new
data integrated into the ecosystem of the business . In this article,
we will present the results of an experimental study on the performance
of the best framework of Big Analytics (Spark) with the
most popular databases of NoSQL MongoDB and Hadoop. The
objective of this study is to determine the software combination
that allows sophisticated analysis in real time.
A Quantified Approach for large Dataset Compression in Association MiningIOSR Journals
Abstract: With the rapid development of computer and information technology in the last several decades, an
enormous amount of data in science and engineering will continuously be generated in massive scale; data
compression is needed to reduce the cost and storage space. Compression and discovering association rules by
identifying relationships among sets of items in a transaction database is an important problem in Data Mining.
Finding frequent itemsets is computationally the most expensive step in association rule discovery and therefore
it has attracted significant research attention. However, existing compression algorithms are not appropriate in
data mining for large data sets. In this research a new approach is describe in which the original dataset is
sorted in lexicographical order and desired number of groups are formed to generate the quantification tables.
These quantification tables are used to generate the compressed dataset, which is more efficient algorithm for
mining complete frequent itemsets from compressed dataset. The experimental results show that the proposed
algorithm performs better when comparing it with the mining merge algorithm with different supports and
execution time.
Keywords: Apriori Algorithm, mining merge Algorithm, quantification table
Implementation of Multi-node Clusters in Column Oriented Database using HDFSIJEACS
Generally HBASE is NoSQL database which runs in the Hadoop environment, so it can be called as Hadoop Database. By using Hadoop distributed file system and map reduce with the implementation of key/value store as real time data access combines the deep capabilities and efficiency of map reduce. Basically testing is done by using single node clustering which improved the performance of query when compared to SQL, even though performance is enhanced, the data retrieval becomes complicated as there is no multi node clusters and totally based on SQL queries. In this paper, we use the concepts of HBase, which is a column oriented database and it is on the top of HDFS (Hadoop distributed file system) along with multi node clustering which increases the performance. HBase is key/value store which is Consistent, Distributed, Multidimensional and Sorted map. Data storage in HBase in the form of cells, and here those cells are grouped by a row key. Hence our proposal yields better results regarding query performance and data retrieval compared to existing approaches.
A Comprehensive Study on Big Data Applications and Challengesijcisjournal
Big Data has gained much interest from the academia and the IT industry. In the digital and computing
world, information is generated and collected at a rate that quickly exceeds the boundary range. As
information is transferred and shared at light speed on optic fiber and wireless networks, the volume of
data and the speed of market growth increase. Conversely, the fast growth rate of such large data
generates copious challenges, such as the rapid growth of data, transfer speed, diverse data, and security.
Even so, Big Data is still in its early stage, and the domain has not been reviewed in general. Hence, this
study expansively surveys and classifies an assortment of attributes of Big Data, including its nature,
definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a
data life cycle that uses the technologies and terminologies of Big Data. Map/Reduce is a programming
model for efficient distributed computing. It works well with semi-structured and unstructured data. A
simple model but good for a lot of applications like Log processing and Web index building.
This document provides an introduction to NoSQL databases. It discusses that NoSQL databases are non-relational, do not require a fixed table schema, and do not require SQL for data manipulation. It also covers characteristics of NoSQL such as not using SQL for queries, partitioning data across machines so JOINs cannot be used, and following the CAP theorem. Common classifications of NoSQL databases are also summarized such as key-value stores, document stores, and graph databases. Popular NoSQL products including Dynamo, BigTable, MongoDB, and Cassandra are also briefly mentioned.
Social Media World News Impact on Stock Index Values - Investment Fund Analyt...Bernardo Najlis
Presentation for project on Social Media World News Impact on Stock Index Values (DJIA) for Investment Fund Analytics. Group project done in course DS8004 - Data Mining at Ryerson University for Masters in Data Science and Analytics.
The document discusses big data analysis and provides an introduction to key concepts. It is divided into three parts: Part 1 introduces big data and Hadoop, the open-source software framework for storing and processing large datasets. Part 2 provides a very quick introduction to understanding data and analyzing data, intended for those new to the topic. Part 3 discusses concepts and references to use cases for big data analysis in the airline industry, intended for more advanced readers. The document aims to familiarize business and management users with big data analysis terms and thinking processes for formulating analytical questions to address business problems.
Mankind has stored more than 295 billion gigabytes (or 295 Exabyte) of data since 1986, as per a report by the University of Southern California. Storing and monitoring this data in widely distributed environments for 24/7 is a huge task for global service organizations. These datasets require high processing power which can’t be offered by traditional databases as they are stored in an unstructured format. Although one can use Map Reduce paradigm to solve this problem using java based Hadoop, it cannot provide us with maximum functionality. Drawbacks can be overcome using Hadoop-streaming techniques that allow users to define non-java executable for processing this datasets. This paper proposes a THESAURUS model which allows a faster and easier version of business analysis.
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
A Study on Graph Storage Database of NOSQLIJSCAI Journal
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
A Study on Graph Storage Database of NOSQLIJSCAI Journal
This document summarizes a research paper on graph storage databases in NoSQL. It discusses big data and the need for alternative databases to handle large, diverse datasets. It defines the key aspects of big data including volume, velocity, variety and complexity. It also describes different types of NoSQL databases, focusing on the basic structure of graph databases. Graph databases use nodes and relationships to model connected data. The document compares several graph database systems and discusses advantages like performance and flexibility as well as disadvantages like complexity. It outlines several applications of graph databases in areas like social networks and logistics.
Bridging the gap between the semantic web and big data: answering SPARQL que...IJECEIAES
Nowadays, the database field has gotten much more diverse, and as a result, a variety of non-relational (NoSQL) databases have been created, including JSON-document databases and key-value stores, as well as extensible markup language (XML) and graph databases. Due to the emergence of a new generation of data services, some of the problems associated with big data have been resolved. In addition, in the haste to address the challenges of big data, NoSQL abandoned several core databases features that make them extremely efficient and functional, for instance the global view, which enables users to access data regardless of how it is logically structured or physically stored in its sources. In this article, we propose a method that allows us to query non-relational databases based on the ontology-based access data (OBDA) framework by delegating SPARQL protocol and resource description framework (RDF) query language (SPARQL) queries from ontology to the NoSQL database. We applied the method on a popular database called Couchbase and we discussed the result obtained.
MONGODB VS MYSQL: A COMPARATIVE STUDY OF PERFORMANCE IN SUPER MARKET MANAGEME...ijcsity
A database is information collection that is organized in tables so that it can easily be accessed, managed, and updated. It is the collection of tables, schemas, queries, reports, views and other objects. The data are typically organized to model in a way that supports processes requiring information, such as modelling to find a hotel with availability of rooms, thus the people can easily locate the hotels with vacancies. There are many databases commonly, relational and non relational databases. Relational databases usually work with structured data and non relational databases are work with semi structured data. In this paper, the performance evaluation of MySQL and MongoDB is performed where MySQL is an example of relational database and MongoDB is an example of non relational databases. A relational database is a data structure that allows you to connect information from different 'tables', or different types of data buckets. Non-relational database stores data without explicit and structured mechanisms to link data from different buckets to one another. This paper discuss about the performance of MongoDB and MySQL in the field of Super Market Management System. A supermarket is a large form of the traditional grocery store also a self-service shop offering a wide variety of food and household products, organized in systematic manner. It is larger and has a open selection than a traditional grocery store.
Big Data Processing with Hadoop : A ReviewIRJET Journal
1. This document provides an overview of big data processing with Hadoop. It defines big data and describes the challenges of volume, velocity, variety and variability.
2. Traditional data processing approaches are inadequate for big data due to its scale. Hadoop provides a distributed file system called HDFS and a MapReduce framework to address this.
3. HDFS uses a master-slave architecture with a NameNode and DataNodes to store and retrieve file blocks. MapReduce allows distributed processing of large datasets across clusters through mapping and reducing functions.
This document discusses using Apache Hadoop and SQL Server to analyze large datasets. It finds that SQL Server struggles to efficiently query and analyze datasets with over 100 million rows, with query times increasing substantially with larger datasets. Apache Hadoop provides a more scalable solution by distributing data processing across a cluster. The document evaluates Hadoop and MongoDB for big data analysis, and chooses Hadoop for its ability to process large amounts of data for analytical purposes. It then discusses implementing Hortonworks Data Platform with Apache Ambari to analyze a 97GB population dataset using Hadoop.
Big Data Mining, Techniques, Handling Technologies and Some Related Issues: A...IJSRD
The Size of the data is increasing day by day with the using of social site. Big Data is a concept to manage and mine the large set of data. Today the concept of Big Data is widely used to mine the insight data of organization as well outside data. There are many techniques and technologies used in Big Data mining to extract the useful information from the distributed system. It is more powerful to extract the information compare with traditional data mining techniques. One of the most known technologies is Hadoop, used in Big Data mining. It takes many advantages over the traditional data mining technique but it has some issues like visualization technique, privacy etc.
BIG DATA SUMMARIZATION: FRAMEWORK, CHALLENGES AND POSSIBLE SOLUTIONSaciijournal
This document discusses big data summarization, including the challenges and potential solutions. It presents a framework for big data summarization with four main stages: 1) data clustering to group similar documents, 2) data generalization to abstract data to a higher conceptual level, 3) semantic term identification to identify metadata for more efficient data representation, and 4) evaluation of the summaries. Key challenges addressed include initializing clustering methods, selecting attributes to control generalization, and ensuring semantic associations in representations. Solutions proposed are detailed assessments of clustering initialization methods and statistical approaches for clustering, generalization and term identification.
This document provides a review of Hadoop storage and clustering algorithms. It begins with an introduction to big data and the challenges of storing and processing large, diverse datasets. It then discusses related technologies like cloud computing and Hadoop, including the Hadoop Distributed File System (HDFS) and MapReduce processing model. The document analyzes and compares various clustering techniques like K-means, fuzzy C-means, hierarchical clustering, and Self-Organizing Maps based on parameters such as number of clusters, size of clusters, dataset type, and noise.
The growth of data and its effi cient handling is becoming more popular trend in recent years bringing
new challenges to explore new avenues. Data analytics can be done more effi ciently with the availability of
distributed architecture of “Not Only SQL” NoSQL databases.
1. The document discusses Big Data analytics using Hadoop. It defines Big Data and explains the characteristics of volume, velocity, and variety.
2. Hadoop is introduced as a framework for distributed storage and processing of large data sets across clusters of commodity hardware. It uses HDFS for reliable storage and streaming of large data sets.
3. Key Hadoop components are the NameNode, which manages file system metadata, and DataNodes, which store and retrieve data blocks. Hadoop provides scalability, fault tolerance, and high performance on large data sets.
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.
Key aspects of big data storage and its architectureRahul Chaturvedi
This paper helps understand the tools and technologies related to a classic BigData setting. Someone who reads this paper, especially Enterprise Architects, will find it helpful in choosing several BigData database technologies in a Hadoop architecture.
Big Data is an evolution of Business Intelligence (BI).
Whereas traditional BI relies on data warehouses limited in size
(some terabytes) and it hardly manages unstructured data and
real-time analysis, the era of Big Data opens up a new technological
period offering advanced architectures and infrastructures
allowing sophisticated analyzes taking into account these new
data integrated into the ecosystem of the business . In this article,
we will present the results of an experimental study on the performance
of the best framework of Big Analytics (Spark) with the
most popular databases of NoSQL MongoDB and Hadoop. The
objective of this study is to determine the software combination
that allows sophisticated analysis in real time.
A Quantified Approach for large Dataset Compression in Association MiningIOSR Journals
Abstract: With the rapid development of computer and information technology in the last several decades, an
enormous amount of data in science and engineering will continuously be generated in massive scale; data
compression is needed to reduce the cost and storage space. Compression and discovering association rules by
identifying relationships among sets of items in a transaction database is an important problem in Data Mining.
Finding frequent itemsets is computationally the most expensive step in association rule discovery and therefore
it has attracted significant research attention. However, existing compression algorithms are not appropriate in
data mining for large data sets. In this research a new approach is describe in which the original dataset is
sorted in lexicographical order and desired number of groups are formed to generate the quantification tables.
These quantification tables are used to generate the compressed dataset, which is more efficient algorithm for
mining complete frequent itemsets from compressed dataset. The experimental results show that the proposed
algorithm performs better when comparing it with the mining merge algorithm with different supports and
execution time.
Keywords: Apriori Algorithm, mining merge Algorithm, quantification table
Implementation of Multi-node Clusters in Column Oriented Database using HDFSIJEACS
Generally HBASE is NoSQL database which runs in the Hadoop environment, so it can be called as Hadoop Database. By using Hadoop distributed file system and map reduce with the implementation of key/value store as real time data access combines the deep capabilities and efficiency of map reduce. Basically testing is done by using single node clustering which improved the performance of query when compared to SQL, even though performance is enhanced, the data retrieval becomes complicated as there is no multi node clusters and totally based on SQL queries. In this paper, we use the concepts of HBase, which is a column oriented database and it is on the top of HDFS (Hadoop distributed file system) along with multi node clustering which increases the performance. HBase is key/value store which is Consistent, Distributed, Multidimensional and Sorted map. Data storage in HBase in the form of cells, and here those cells are grouped by a row key. Hence our proposal yields better results regarding query performance and data retrieval compared to existing approaches.
A Comprehensive Study on Big Data Applications and Challengesijcisjournal
Big Data has gained much interest from the academia and the IT industry. In the digital and computing
world, information is generated and collected at a rate that quickly exceeds the boundary range. As
information is transferred and shared at light speed on optic fiber and wireless networks, the volume of
data and the speed of market growth increase. Conversely, the fast growth rate of such large data
generates copious challenges, such as the rapid growth of data, transfer speed, diverse data, and security.
Even so, Big Data is still in its early stage, and the domain has not been reviewed in general. Hence, this
study expansively surveys and classifies an assortment of attributes of Big Data, including its nature,
definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a
data life cycle that uses the technologies and terminologies of Big Data. Map/Reduce is a programming
model for efficient distributed computing. It works well with semi-structured and unstructured data. A
simple model but good for a lot of applications like Log processing and Web index building.
This document provides an introduction to NoSQL databases. It discusses that NoSQL databases are non-relational, do not require a fixed table schema, and do not require SQL for data manipulation. It also covers characteristics of NoSQL such as not using SQL for queries, partitioning data across machines so JOINs cannot be used, and following the CAP theorem. Common classifications of NoSQL databases are also summarized such as key-value stores, document stores, and graph databases. Popular NoSQL products including Dynamo, BigTable, MongoDB, and Cassandra are also briefly mentioned.
Social Media World News Impact on Stock Index Values - Investment Fund Analyt...Bernardo Najlis
Presentation for project on Social Media World News Impact on Stock Index Values (DJIA) for Investment Fund Analytics. Group project done in course DS8004 - Data Mining at Ryerson University for Masters in Data Science and Analytics.
The document discusses big data analysis and provides an introduction to key concepts. It is divided into three parts: Part 1 introduces big data and Hadoop, the open-source software framework for storing and processing large datasets. Part 2 provides a very quick introduction to understanding data and analyzing data, intended for those new to the topic. Part 3 discusses concepts and references to use cases for big data analysis in the airline industry, intended for more advanced readers. The document aims to familiarize business and management users with big data analysis terms and thinking processes for formulating analytical questions to address business problems.
Mankind has stored more than 295 billion gigabytes (or 295 Exabyte) of data since 1986, as per a report by the University of Southern California. Storing and monitoring this data in widely distributed environments for 24/7 is a huge task for global service organizations. These datasets require high processing power which can’t be offered by traditional databases as they are stored in an unstructured format. Although one can use Map Reduce paradigm to solve this problem using java based Hadoop, it cannot provide us with maximum functionality. Drawbacks can be overcome using Hadoop-streaming techniques that allow users to define non-java executable for processing this datasets. This paper proposes a THESAURUS model which allows a faster and easier version of business analysis.
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
A Study on Graph Storage Database of NOSQLIJSCAI Journal
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
A Study on Graph Storage Database of NOSQLIJSCAI Journal
This document summarizes a research paper on graph storage databases in NoSQL. It discusses big data and the need for alternative databases to handle large, diverse datasets. It defines the key aspects of big data including volume, velocity, variety and complexity. It also describes different types of NoSQL databases, focusing on the basic structure of graph databases. Graph databases use nodes and relationships to model connected data. The document compares several graph database systems and discusses advantages like performance and flexibility as well as disadvantages like complexity. It outlines several applications of graph databases in areas like social networks and logistics.
Bridging the gap between the semantic web and big data: answering SPARQL que...IJECEIAES
Nowadays, the database field has gotten much more diverse, and as a result, a variety of non-relational (NoSQL) databases have been created, including JSON-document databases and key-value stores, as well as extensible markup language (XML) and graph databases. Due to the emergence of a new generation of data services, some of the problems associated with big data have been resolved. In addition, in the haste to address the challenges of big data, NoSQL abandoned several core databases features that make them extremely efficient and functional, for instance the global view, which enables users to access data regardless of how it is logically structured or physically stored in its sources. In this article, we propose a method that allows us to query non-relational databases based on the ontology-based access data (OBDA) framework by delegating SPARQL protocol and resource description framework (RDF) query language (SPARQL) queries from ontology to the NoSQL database. We applied the method on a popular database called Couchbase and we discussed the result obtained.
MONGODB VS MYSQL: A COMPARATIVE STUDY OF PERFORMANCE IN SUPER MARKET MANAGEME...ijcsity
A database is information collection that is organized in tables so that it can easily be accessed, managed, and updated. It is the collection of tables, schemas, queries, reports, views and other objects. The data are typically organized to model in a way that supports processes requiring information, such as modelling to find a hotel with availability of rooms, thus the people can easily locate the hotels with vacancies. There are many databases commonly, relational and non relational databases. Relational databases usually work with structured data and non relational databases are work with semi structured data. In this paper, the performance evaluation of MySQL and MongoDB is performed where MySQL is an example of relational database and MongoDB is an example of non relational databases. A relational database is a data structure that allows you to connect information from different 'tables', or different types of data buckets. Non-relational database stores data without explicit and structured mechanisms to link data from different buckets to one another. This paper discuss about the performance of MongoDB and MySQL in the field of Super Market Management System. A supermarket is a large form of the traditional grocery store also a self-service shop offering a wide variety of food and household products, organized in systematic manner. It is larger and has a open selection than a traditional grocery store.
MONGODB VS MYSQL: A COMPARATIVE STUDY OF PERFORMANCE IN SUPER MARKET MANAGEME...ijcsity
A database is information collection that is organized in tables so that it can easily be accessed, managed,
and updated. It is the collection of tables, schemas, queries, reports, views and other objects. The data are
typically organized to model in a way that supports processes requiring information, such as modelling to
find a hotel with availability of rooms, thus the people can easily locate the hotels with vacancies. There
are many databases commonly, relational and non relational databases. Relational databases usually work
with structured data and non relational databases are work with semi structured data. In this paper, the
performance evaluation of MySQL and MongoDB is performed where MySQL is an example of relational
database and MongoDB is an example of non relational databases. A relational database is a data
structure that allows you to connect information from different 'tables', or different types of data buckets.
Non-relational database stores data without explicit and structured mechanisms to link data from different
buckets to one another. This paper discuss about the performance of MongoDB and MySQL in the field of
Super Market Management System. A supermarket is a large form of the traditional grocery store also a
self-service shop offering a wide variety of food and household products, organized in systematic manner.
It is larger and has a open selection than a traditional grocery store.
MONGODB VS MYSQL: A COMPARATIVE STUDY OF PERFORMANCE IN SUPER MARKET MANAGEME...ijcsity
A database is information collection that is organized in tables so that it can easily be accessed, managed,
and updated. It is the collection of tables, schemas, queries, reports, views and other objects. The data are
typically organized to model in a way that supports processes requiring information, such as modelling to
find a hotel with availability of rooms, thus the people can easily locate the hotels with vacancies. There
are many databases commonly, relational and non relational databases. Relational databases usually work
with structured data and non relational databases are work with semi structured data. In this paper, the
performance evaluation of MySQL and MongoDB is performed where MySQL is an example of relational
database and MongoDB is an example of non relational databases. A relational database is a data
structure that allows you to connect information from different 'tables', or different types of data buckets.
Non-relational database stores data without explicit and structured mechanisms to link data from different
buckets to one another. This paper discuss about the performance of MongoDB and MySQL in the field of
Super Market Management System. A supermarket is a large form of the traditional grocery store also a
self-service shop offering a wide variety of food and household products, organized in systematic manner.
It is larger and has a open selection than a traditional grocery store.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable and distributed processing of large datasets. Hadoop consists of Hadoop Distributed File System (HDFS) for storage and Hadoop MapReduce for processing vast amounts of data in parallel on large clusters of commodity hardware in a reliable, fault-tolerant manner. HDFS stores data reliably across machines in a Hadoop cluster and MapReduce processes data in parallel by breaking the job into smaller fragments of work executed across cluster nodes.
The document provides an overview of Hadoop and its core components. It discusses:
- Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers.
- The two core components of Hadoop are HDFS for distributed storage, and MapReduce for distributed processing. HDFS stores data reliably across machines, while MapReduce processes large amounts of data in parallel.
- Hadoop can operate in three modes - standalone, pseudo-distributed and fully distributed. The document focuses on setting up Hadoop in standalone mode for development and testing purposes on a single machine.
This document provides a literature review of NoSQL databases. It discusses how the rise of big data from sources like social media, sensors, and surveillance footage has led organizations to adopt NoSQL databases that can handle large volumes of unstructured data more efficiently than traditional relational databases. The document evaluates several popular NoSQL databases like MongoDB, Cassandra, and HBase, categorizing them as either document stores, column family databases, or key-value stores. It also provides examples of major companies that use NoSQL and discusses factors like flexibility and scalability that have driven adoption.
DATABASE SYSTEMS PERFORMANCE EVALUATION FOR IOT APPLICATIONSijdms
ABSTRACT
The amount of data stored in IoT databases increases as the IoT applications extend throughout smart city appliances, industry and agriculture. Contemporary database systems must process huge amounts of sensory and actuator data in real-time or interactively. Facing this first wave of IoT revolution, database vendors struggle day-by-day in order to gain more market share, develop new capabilities and attempt to overcome the disadvantages of previous releases, while providing features for the IoT.
There are two popular database types: The Relational Database Management Systems and NoSQL databases, with NoSQL gaining ground on IoT data storage. In the context of this paper these two types are examined. Focusing on open source databases, the authors experiment on IoT data sets and pose an answer to the question which one performs better than the other. It is a comparative study on the performance of the commonly market used open source databases, presenting results for the NoSQL MongoDB database and SQL databases of MySQL and PostgreSQL
Introduction to Big Data and Hadoop using Local Standalone Modeinventionjournals
Big Data is a term defined for data sets that are extreme and complex where traditional data processing applications are inadequate to deal with them. The term Big Data often refers simply to the use of predictive investigation on analytic methods that extract value from data. Big data is generalized as a large data which is a collection of big datasets that cannot be processed using traditional computing techniques. Big data is not purely a data, rather than it is a complete subject involves various tools, techniques and frameworks. Big data can be any structured collection which results incapability of conventional data management methods. Hadoop is a distributed example used to change the large amount of data. This manipulation contains not only storage as well as processing on the data. Hadoop is an open- source software framework for dispersed storage and processing of big data sets on computer clusters built from commodity hardware. HDFS was built to support high throughput, streaming reads and writes of extremely large files. Hadoop Map Reduce is a software framework for easily writing applications which process vast amounts of data. Wordcount example reads text files and counts how often words occur. The input is text files and the result is wordcount file, each line of which contains a word and the count of how often it occurred separated by a tab.
Building a Big Data platform with the Hadoop ecosystemGregg Barrett
This presentation provides a brief insight into a Big Data platform using the Hadoop ecosystem.
To this end the presentation will touch on:
-views of the Big Data ecosystem and it’s components
-an example of a Hadoop cluster
-considerations when selecting a Hadoop distribution
-some of the Hadoop distributions available
-a recommended Hadoop distribution
Very basic Introduction to Big Data. Touches on what it is, characteristics, some examples of Big Data frameworks. Hadoop 2.0 example - Yarn, HDFS and Map-Reduce with Zookeeper.
Performance Improvement of Heterogeneous Hadoop Cluster using Ranking AlgorithmIRJET Journal
This document proposes using a ranking algorithm and sampling algorithm to improve the performance of a heterogeneous Hadoop cluster. The ranking algorithm prioritizes data distribution based on node frequency, so that higher frequency nodes are processed first. The sampling algorithm randomly selects nodes for data distribution instead of evenly distributing across all nodes. The proposed approach reduces computation time and improves overall cluster performance compared to the existing approach of evenly distributing data across nodes of varying sizes. Results show the proposed approach reduces execution time for various file sizes compared to the existing approach.
This document discusses data migration in schemaless NoSQL databases. It begins by defining NoSQL databases and comparing them to traditional relational databases. It then covers aggregate data models and the concepts of schemalessness and implicit schemas in NoSQL databases. The main focus is on data migration when an implicit schema changes, including principles, strategies, and test options for ensuring data matches the new implicit schema in applications.
Big data refers to large datasets that cannot be processed using traditional computing techniques. Hadoop is an open-source framework that allows processing of big data across clustered, commodity hardware. It uses MapReduce as a programming model to parallelize processing and HDFS for reliable, distributed file storage. Hadoop distributes data across clusters, parallelizes processing, and can dynamically add or remove nodes, providing scalability, fault tolerance and high availability for large-scale data processing.
IRJET- Deduplication Detection for Similarity in Document Analysis Via Vector...IRJET Journal
The document discusses techniques for detecting similarity and deduplication in document analysis using vector analysis. It proposes analyzing documents by extracting abstract content, separating words and combining them in a word cloud to determine frequency. This approach aims to identify whether documents are duplicates by analyzing word vectors at the word, sentence and paragraph level while also applying techniques like stemming, stopping words and semantic similarity.
IRJET- Deduplication Detection for Similarity in Document Analysis Via Vector...IRJET Journal
The document discusses techniques for detecting similarity and deduplication in document analysis using vector analysis. It proposes analyzing documents by extracting abstract content, separating words and combining them in a word cloud to determine frequency. This approach aims to identify whether documents are duplicates by analyzing word vectors at the word, sentence and paragraph level while also applying techniques like stemming, stopping words and semantic similarity.
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A survey on data mining and analysis in hadoop and mongo db
1. Computer Engineering and Intelligent Systems www.iiste.org
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A Survey on Data Mining and Analysis in Hadoop and MongoDb
Manmitsinh C.Zala
Department of CS&E, Governmernt Engineering Collage,Modasa, Aravalli,Gujarat,India
E-mail manmit.zala@gmail.com
Prof. Jitendra S.Dhobi
Department of CS&E, Governmernt Engineering Collage,Modasa, Aravalli,Gujarat,India
E-mail: jsdhobi@gmail.com
Abstract:
Data Mining is a process to generate pattern and rules from various types of data marts and data warehouses ,in
this process there are several steps which contains data cleaning data anomaly detection then clean data is mined
with various approaches .In this research we have discussed data mining on large datasets ( Big Data) with this
large data set major issues are scalability and security ,Hadoop is the tool to mine the data and Mongo db
provides input for it, which is a key-value paradigm for parsing the data ,Other approaches are discussed with
this report and their capability for data storage ,Map reduce is method which can be used to reduce the data set
to reduce query processing time and improve system throughput, In the Proposed system we are going to mine
the big data this Hadoop and Mongo db and we will try to mine the data with sorted or double sorted key value
pair ,for and analyze the outcome of system.
Keywords- DataMIning , Hadoop, MapReduce, HDFS, MongoDb.
1. Introduction
“Big Data” is data whose scale, diversity, and complexity require new architecture, Techniques, algorithms, and
analytics to manage it and extract value and hidden knowledge from it.
Amount of data generated every day is expanding in drastic manner. Big data is a popular term used to
describe the data which is in zeta byte.[1] . Big Data is large amount of data. This vast amount of data is
generated by social media and networks, scientific instruments, mobile devices, sensor technology and
networks. Ability to manage, analyze, summarize, visualize, and discover knowledge from the collected
unstructured data in a timely manner and in a scalable fashion is very difficult task using traditional data mining
tools. To analyze the data Apache introduce a new technology called Hadoop. We can describe the
characteristics of big data using three Vs Volume, Variety and Velocity.[ 13][14]
Hadoop is the part of Apache projects, Hadoop software library is a framework that supports
distributed processing of large data across clusters of computers using simple programming models. Hadoop is
combination of Map Reduce and Hadoop distributed file system (HDFS). Work of Map Reduce is to process the
data and work of HDFS is to store the data into file system.
NOSQL [3][7] is the term related to “ Not Only Sql “ Sql is a relational database language but for big
data analysis these techniques are not enough so alternative solutions are NoSql databases like
Mongodb,Cassandra,Voldmort etc ..
2. Literature Survey
2.1 Applications
This proposed will provide a new approach analysis Big datamining with hadoop and mongodb which is based
on MapReduce Paradigm. This new approach will try to improve the computational time, more fault tolerance of
system and will handle or deal with Bigdata analysis.
2.2 Related Work
This chapter will provide information about the work done in big data mining and various approaches use and
method proposed
In [1] author has discussed the meaning and importance of big data analysis programming tool use for
big data mining and important of big data , with the example of facebook we can understood that today it is
required to process large number of data sets ,our traditional data sets are not enough for that ,for example
instead of taking large MySql tables we can use caching approach from memcached for n tier elements as Mysql
has very good performance in read but they are lagging in write ,which leads us to very high reliability but low
partition tolerance in our CAP model ,another example author has given is Yelp which uses AWS and Hadoop
for data analysis which uses Amazon S3 server to store large datasets which is RAID service.
The author proposed such data analysis using Apache Hadoop and JSON and data stored From
Amazon web services using their web services and analyze the data, the analysis showed that this method can
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analyze the large data from different sources with minimum utilization of resources
In [2] In this paper author has utilized Nosql database Mongo db to implement the big data analysis as
it is advantageous over rigid sql tables which is not useful in today’s large scale data for web logs generated
every day .more over author has compared performance between Mongo db and HDFS frame work using inbuilt
map reduce method with mongo db , author has not defined the modern data store technology and integration
available with hadoop like Hbase , and HIVE for that experiments and results are shown for large amount of data
sets , this is the motive why we choose mongo db data store for
Large data sets .
Figure 2.1 [ HDFS –Mongo Db comparison]
With this framework proposed by author the output comparison shows that Figure shows the effect of
the split size on performance using mongo-hadoop. The number of input records is _ 9:3 million, or 4GB of
input data. With the default split size of 8MB, Hadoop schedules over 500 mappers; by increasing the split size,
we are able to reduce this number to around 40 and achieve a considerable performance improvement. The curve
levels o_ between 128MB and 256MB, so we decided to use 128MB as the split size for the rest of our tests both
for native Hadoop-HDFS and mongo-hadoop.
In , MS At el.[3] has discussed various security issues and threats available with Big data as data is in
zeta byte size it also contains some sensitive and confidential information it is necessary to prevent unauthorized
use of data so apart from storage retrieve and processing security is also an important concern for data mining ,
data application from social web ,consumer oriented work has large impact on big data security according to
author vast use of smart phones has increased photo uploading and other sensitive information on web it is an
issue for that author has proposed metadata analysis in big data which creates an index of each images uploaded
on social web and we can identify from link which gives confidentiality over social media, so each images can
be scanned from big data bases of social media and can be apply for future security policies .
In [4] After considering security in analysis we again come with our problem of analysis the big data
with this paper integration of NOSQL with big data analysis author proposed model of unity architecture for
analysis of data as shown in figure 3.2
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Figure 2.2 Unity Architecture
The objectives of this architecture is as follow
• SQL is a declarative language that allows descriptive queries while hiding implementation and query execution
details.
• SQL is a standardized language allowing portability between systems and leveraging a massive existing
knowledge base of database developers.
• Supporting SQL allows a NoSQL system to seamlessly interact with other enterprise systems that use SQL and
JDBC/ODBC without requiring changes.
This system provides combination of both Relational data base system and Nosql system for this
interaction we can translate one schema to another schema by JDBC API and Mongo db connector .
In [6] Mongo db and Oracle databases are compared by their storage method ,syntax and their retrieval
methods also various experiments conducted with different query processing time and number of processing the
results here we are discussing few results achieved with this research .
Figure 2.3. Insert query with Mongo db and Oracle[6]
As we can see for small records inserting oracle databases are faster then mongo db but as the size increases for
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records the mongo db is impressively ahead then Oracle database
Same results are achieved with update query comparison
Figure 2.4 Update query on records and time comparison Mongo db vs Oracle
From this we can conclude that mongodb is flexible and scalable for large data sets which provides
batter integration for data storage and retrieval .
In [5] In this paper author has discussed about some very important parameters of mongo db focusing
on CAP model and compared various types of data store available with no Nosql and tested them among various
business intelligent system provided , and concluded that Nosql data stores provides huge opportunity where Sql
data bases are not useful basic advantages are their scalability and cross node operation .the intersection
algorithm for mongo db states the effectiveness of mongo db data store for key value approach to modelize the
data.
In [7] [10] and [11] some practical approaches are shown to interact no sql data stores with various
systems such as distributed architecture[7] ,Hashdoop[11] and evolution in hadoop [10] are proposed in
distributed system data bases are handled by structure system but it fails when data items increase so
unstructured data stores are useful for such problems some major industries are capable to develop their own
unstructured data stores for ex. Google’s Big table, Yahoo’s PNUTS ,Hadoop’s Hbase and many more but what
about small industries ,author stated that there are many open source products are available to handle such data
the comparison between them is shown in below figure
Figure 2.5 Comparison of Difference Data stores [7]
Among all this data stores mongo db is better replacement for MySql as it is semi structured, and
provides batter joins contains laser time for searching and in performing other queries.
In paper [10] multiple Nosql data stores are compared and we can see that mongo db provides
consistency , partition tolerance and crash handling over any other data stores
But in this paper author has limited computation power this system can be improve by adding some
more computation power over large datasets by cloud computing or distributor approach .[11] is an example of
hadoop hash function for anomaly detection using map reduce programming model .the hashdoop framework
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splits the traffic using has functions and the detector detects the anomaly from carious hadoop clusters then the
traffic has been divided in less traffic lines how ever author has not applied to store the data back to original data
sets which will be lost vice versa.
3. Background Study
3.1 Big Data
3.1.1 Architecture
Figure 3.1.1. Bigdata [ 9]
Big data is a distributed architecture for storing large amount of data ,According to a research recently the online
data has increased in size CERN research says that data without operating online. For example, “will produce
roughly 15 peta bytes (15 million gigabytes) of data annually – enough to fill more than 1.7 million dual-layer
DVDs a year!” [11 ]
Bigdata architecture consists following three segments
• Storage System
• Processing
• Analysis
Figure 3.1.1 BigData system [9]
3.2 What is NOSQL?
No SQL means an alternative of traditional database system the term was generated ny a scientist named Eric Evans
in Sanfransisco. The nosql databases have variety of different database systems and they provides the data
manipulation as well as low time in reading and writing
Many large organizations have their own NoSql Databases as BIG Table in Google which has much effect
on the no sql The whole point is that they provides alternatives to the traditional databases product. For Example the
many nosql products are available in the market and they are widely used by many companies .
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3.2.1 Mongo DB
MongoDb is the nosql type database which is document oriented , the organization which developed it is
10gen ,mongo word means large size it is very fast and reliable it is written in C++ it is also used to store large
files over a distribute location
Also we can store binary data like images , videos , mp3 files.
3.2.1.1: Query Model
Queries for MongoDB are bidding in a JSON like syntax and are forward to MongoDB as BSON altar by the
database driver. The concern archetypal of MongoDB allows queries over all abstracts central a collection,
including the anchored altar and arrays. Through the acceptance of predefined indexes queries can dynamically
be formulated during runtime.
Not all aspects of a concern are formulated aural a concern accent in MongoDB, depending on the MongoDB
disciplinarian for a programming language, some things may be bidding through the abracadabra of a adjustment
of the driver.
{ “employees":[{ "firstName":"Manmit","lastName":"Zala" },
{"firstName":"Pradip" , "lastName":"Chavda" },{"firstName":"Nilay", "lastName":"Parekh" }]} The query
model supports the following features:
1. Queries over documents and embedded subdocuments
2. Comparators (<;_;_;>)
3. Conditional operators (equals, not equals, exists, in, not in ...)
4. Logical perators: AND
5. Sorting by multiple Attributes
6. Group by
3.2.1.2: Sharding
Mongodb harding means the components of mongodb it is also uses following components :
1) Configuration servers
2 ) Shard nodes
3) Services for Routing
These are known as mongos .
3.3Apache Hadoop
Apache Hadoop is java based programming framework which is used for processing large data sets in distributed
computer environment. Hadoop is used in system where multiple nodes are present which can process terabytes
of data hadoop uses its own file system HDFS which facilitates fast transfer of data which can sustain node
failure and avoid system failure as whole.[1]
3.3.1 Architecutre
Figure 3.3.1 HDFS ARCHITECTURE [14]
3.4 Map Reduce Algorithm and Approaches
Map/Reduce is a programming paradigm that was made popular by Google where a task is divided into small
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portions and distributed to a large number of nodes for processing (map), and the results are then summarized
into the final answer (reduce). Hadoop also uses Map/Reduce for data processing. Hence different functions for
the processing are written in the form of Hadoop job. A Hadoop job consists of mapper and reducer functions
framework i.e. STS is used as the integrated development environment (IDE) [1].
3.4.1Map reduce with Hadoop
A .Many company uses Hadoop for big data analysis ,For example facebook use HIVE with Hadoop [1]
B. Yelp :uses AWS and Hadoop Yelp uses Amazon S3 to store daily logs and photos, generating around 100GB
of logs per day. The company also uses Amazon Elastic Map Reduce to power approximately 20 separate batch
scripts, most of those processing the logs.
Features powered by Amazon ElasticMapReduce include:[1]
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3.4.2 Map Reduce using MongoDb
Mongo db uses key value pair type of storage The Map primitive consists in processing a data list in order to
create key/value pairs. Then, the Reduce primitive will process each pair in order to create new aggregated
key/value pairs.[5]
Example[5]: map(k1, v1) = list(k2, v2) ………...(1)
reduce(k2, list(v2)) = list(v3) …………………...(2)
List : (a; 2)(a; 4)(b; 4)(c; 5)(b; 2)(a; 1)………… (3)
After mapping : (a; [2, 4, 1]), (b; [4, 2]), (c[5]) …(4)
After reducing : (a; 7), (b; 6), (c; 5) …………….(5)
Equations (1) , (2) show both map and reduce primitives.
Figure 3.5.2 Map reduce example[5]
4. Conclusion
Now a day data increases day by day the storage, retrieval and analysis of bigdata in structured databases like
Oracle and Mysql is not possible so we have presented many Nosql system among them Mongo db is preferable
for as an alternate for Mysql , still it is an Active search are for data mining to mine knowledge from bigdata.In
future we are interested in batter method and system for efficient mining of bigdata.
5. References
[1] Jyoti Nandimath , Ankur Patil , Ekata Banerjee , Pratima Kakade :”Big Data Analysis using Apache
Hadoop “ In SKNCOE Pune India,2013
[2] E. Dede, M. Govindaraju ,D. Gunter, R. Canon, L. Ramakrishnan “Performance Evaluation of a MongoDB
and HadoopPlatform for Scientific Data Analysis” In Lawrence Berekely National Lab Berkeley, CA 94720
[3] Matthew Smith, Christian Szongott,Benjamin Henne, Gabriele von Voigt” Big Data Privacy Issues in Public
Social Media”,2013
[4] Ramon Lawrence :“Integration and Virtualization of Relational SQL and NoSQL Systems including MySQL
and MongoDB”At 2014 International Conference on Computational Science and Computational
Intelligence,2014
[5] Laurent Bonne, Anne Laurent, Michel Sala,Benedicte Laurent,Nicolas Sicard:” REDUCE, YOU SAY: What
NoSQL can do for Data Aggregation and BI in Large Repositories “In 2011 22nd International Workshop on
Database and Expert Systems Applications,2011
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