This document discusses NoSQL databases as a new type of database designed for big data, big users, and cloud computing. It describes how the growth of data volumes, increased numbers of global users, and cloud architectures are driving organizations to adopt NoSQL databases over traditional relational databases. The document provides an overview of the characteristics of NoSQL databases, including how they are classified based on the CAP theorem and how their scale-out architecture provides improved performance and scalability over relational databases. Security challenges of NoSQL databases are also mentioned.
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 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.
A COMPARATIVE STUDY OF NOSQL SYSTEM VULNERABILITIES WITH BIG DATAIJMIT JOURNAL
With the emerging third wave in the development of the Internet, the past year has witnessed huge data exposure resulting in cyber-attacks that have increased four times as that of the previous year’s record. In this digital era, businesses are making use of NoSQL technologies for managing such Big Data. However, the NoSQL database systems come with inherent security issues, which pose a major challenge to many
organisations worldwide. There is a paucity of research studies for exposing the security threats and vulnerabilities of NoSQL technologies comprehensively. This paper presents an in-depth study of NoSQL security issues by performing a detailed comparative study of the security vulnerabilities identified in
NoSQL database systems. A set of key security features offered by the four commonly used NoSQL database systems, namely Redis, Cassandra, MongoDB and Neo4j are analysed with an aim to identify their strengths and weaknesses. The vulnerabilities associated with built-in security, encryption,
authentication/authorization and auditing that impact Big Data management are compared among these popular NoSQL database systems and the risk levels are identified. In addition, illustrations of possible injection attacks experimented with these NoSQL systems are provided. Finally, a high-level framework is proposed for NoSQL databases with considerations for security measures in Big Data deployments. The discussion forms a significant technical contribution for learners, application developers and Big Data deployers paving way for a better awareness and management of the NoSQL systems in an organization.
A COMPARATIVE STUDY OF NOSQL SYSTEM VULNERABILITIES WITH BIG DATAIJMIT JOURNAL
With the emerging third wave in the development of the Internet, the past year has witnessed huge data exposure resulting in cyber-attacks that have increased four times as that of the previous year’s record. In this digital era, businesses are making use of NoSQL technologies for managing such Big Data. However, the NoSQL database systems come with inherent security issues, which pose a major challenge to many organisations worldwide. There is a paucity of research studies for exposing the security threats and vulnerabilities of NoSQL technologies comprehensively. This paper presents an in-depth study of NoSQL security issues by performing a detailed comparative study of the security vulnerabilities identified in NoSQL database systems. A set of key security features offered by the four commonly used NoSQL database systems, namely Redis, Cassandra, MongoDB and Neo4j are analysed with an aim to identify their strengths and weaknesses. The vulnerabilities associated with built-in security, encryption, authentication/authorization and auditing that impact Big Data management are compared among these popular NoSQL database systems and the risk levels are identified. In addition, illustrations of possible injection attacks experimented with these NoSQL systems are provided. Finally, a high-level framework is proposed for NoSQL databases with considerations for security measures in Big Data deployments. The discussion forms a significant technical contribution for learners, application developers and Big Data deployers paving way for a better awareness and management of the NoSQL systems in an organization.
BIG DATA NETWORKING: REQUIREMENTS, ARCHITECTURE AND ISSUESijwmn
A flexible, efficient and secure networking architecture is required in order to process big data. However, existing network architectures are mostly unable to handle big data. As big data pushes network resources
to the limits it results in network congestion, poor performance, and detrimental user experiences. This paper presents the current state-of-the-art research challenges and possible solutions on big data networking theory. More specifically, we present the state of networking issues of big data related to
capacity, management and data processing. We also present the architectures of MapReduce and Hadoop paradigm with research challenges, fabric networks and software defined networks (SDN) that are used to handle today’s idly growing digital world and compare and contrast them to identify relevant problems and solutions.
NOSQL Database Engines for Big Data Managementijtsrd
The document discusses NoSQL database engines and their use for managing large amounts of data. It describes that NoSQL databases were developed to address the challenges of scaling faced by traditional relational databases. The three main types of NoSQL databases discussed are key-value stores, document databases, and extensible record stores. MongoDB is provided as an example of a popular open source document database that is designed to store, retrieve, and manage semi-structured data through flexible document schemas.
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 NETWORKING: REQUIREMENTS, ARCHITECTURE AND ISSUESijwmn
A flexible, efficient and secure networking architecture is required in order to process big data. However,
existing network architectures are mostly unable to handle big data. As big data pushes network resources
to the limits it results in network congestion, poor performance, and detrimental user experiences. This
paper presents the current state-of-the-art research challenges and possible solutions on big data
networking theory. More specifically, we present the state of networking issues of big data related to
capacity, management and data processing. We also present the architectures of MapReduce and Hadoop
paradigm with research challenges, fabric networks and software defined networks (SDN) that are used to
handle today’s idly growing digital world and compare and contrast them to identify relevant problems and
solutions.
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 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.
A COMPARATIVE STUDY OF NOSQL SYSTEM VULNERABILITIES WITH BIG DATAIJMIT JOURNAL
With the emerging third wave in the development of the Internet, the past year has witnessed huge data exposure resulting in cyber-attacks that have increased four times as that of the previous year’s record. In this digital era, businesses are making use of NoSQL technologies for managing such Big Data. However, the NoSQL database systems come with inherent security issues, which pose a major challenge to many
organisations worldwide. There is a paucity of research studies for exposing the security threats and vulnerabilities of NoSQL technologies comprehensively. This paper presents an in-depth study of NoSQL security issues by performing a detailed comparative study of the security vulnerabilities identified in
NoSQL database systems. A set of key security features offered by the four commonly used NoSQL database systems, namely Redis, Cassandra, MongoDB and Neo4j are analysed with an aim to identify their strengths and weaknesses. The vulnerabilities associated with built-in security, encryption,
authentication/authorization and auditing that impact Big Data management are compared among these popular NoSQL database systems and the risk levels are identified. In addition, illustrations of possible injection attacks experimented with these NoSQL systems are provided. Finally, a high-level framework is proposed for NoSQL databases with considerations for security measures in Big Data deployments. The discussion forms a significant technical contribution for learners, application developers and Big Data deployers paving way for a better awareness and management of the NoSQL systems in an organization.
A COMPARATIVE STUDY OF NOSQL SYSTEM VULNERABILITIES WITH BIG DATAIJMIT JOURNAL
With the emerging third wave in the development of the Internet, the past year has witnessed huge data exposure resulting in cyber-attacks that have increased four times as that of the previous year’s record. In this digital era, businesses are making use of NoSQL technologies for managing such Big Data. However, the NoSQL database systems come with inherent security issues, which pose a major challenge to many organisations worldwide. There is a paucity of research studies for exposing the security threats and vulnerabilities of NoSQL technologies comprehensively. This paper presents an in-depth study of NoSQL security issues by performing a detailed comparative study of the security vulnerabilities identified in NoSQL database systems. A set of key security features offered by the four commonly used NoSQL database systems, namely Redis, Cassandra, MongoDB and Neo4j are analysed with an aim to identify their strengths and weaknesses. The vulnerabilities associated with built-in security, encryption, authentication/authorization and auditing that impact Big Data management are compared among these popular NoSQL database systems and the risk levels are identified. In addition, illustrations of possible injection attacks experimented with these NoSQL systems are provided. Finally, a high-level framework is proposed for NoSQL databases with considerations for security measures in Big Data deployments. The discussion forms a significant technical contribution for learners, application developers and Big Data deployers paving way for a better awareness and management of the NoSQL systems in an organization.
BIG DATA NETWORKING: REQUIREMENTS, ARCHITECTURE AND ISSUESijwmn
A flexible, efficient and secure networking architecture is required in order to process big data. However, existing network architectures are mostly unable to handle big data. As big data pushes network resources
to the limits it results in network congestion, poor performance, and detrimental user experiences. This paper presents the current state-of-the-art research challenges and possible solutions on big data networking theory. More specifically, we present the state of networking issues of big data related to
capacity, management and data processing. We also present the architectures of MapReduce and Hadoop paradigm with research challenges, fabric networks and software defined networks (SDN) that are used to handle today’s idly growing digital world and compare and contrast them to identify relevant problems and solutions.
NOSQL Database Engines for Big Data Managementijtsrd
The document discusses NoSQL database engines and their use for managing large amounts of data. It describes that NoSQL databases were developed to address the challenges of scaling faced by traditional relational databases. The three main types of NoSQL databases discussed are key-value stores, document databases, and extensible record stores. MongoDB is provided as an example of a popular open source document database that is designed to store, retrieve, and manage semi-structured data through flexible document schemas.
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 NETWORKING: REQUIREMENTS, ARCHITECTURE AND ISSUESijwmn
A flexible, efficient and secure networking architecture is required in order to process big data. However,
existing network architectures are mostly unable to handle big data. As big data pushes network resources
to the limits it results in network congestion, poor performance, and detrimental user experiences. This
paper presents the current state-of-the-art research challenges and possible solutions on big data
networking theory. More specifically, we present the state of networking issues of big data related to
capacity, management and data processing. We also present the architectures of MapReduce and Hadoop
paradigm with research challenges, fabric networks and software defined networks (SDN) that are used to
handle today’s idly growing digital world and compare and contrast them to identify relevant problems and
solutions.
This document discusses big data and its applications. It begins with an abstract that introduces big data and how extracting valuable information from large amounts of structured and unstructured data can help governments and organizations develop policies. It then discusses key aspects of big data including volume, velocity, and variety. Current big data technologies are outlined such as Hadoop, HBase, and Hive. Some big data problems and applications are also mentioned like using big data in commerce, business, and scientific research to improve forecasting, policies, productivity, and research.
An elastic , effective, activety or intelligent ,graceful networking architecture layout be desired to make processing massive data. next to that ,existent network architectures be considerably incapable for
cleatting the huge data. massive data thrusts network exchequers into border it consequence with in network overcrowding ,needy achievement, then permicious employer exprtises. this offered the current state-of-the-art research affronts ,potential solutions into huge data networking notion. More specifically, present the state of networking problems into massive data connected intrequirements,capacity,running ,
data manipulating also will introduce the architectures of MapReduce , Hadoop paradigm within research
requirements, fabric networks and software defined networks which utilizized into making today’s idly growing digital world and compare and contrast into identify relevant drawbacks and solutions.
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.
Big Data Mining - Classification, Techniques and IssuesKaran Deep Singh
The document discusses big data mining and provides an overview of related concepts and techniques. It describes how big data is characterized by large volume, variety, and velocity of data that is difficult to manage with traditional methods. Common techniques for big data mining discussed include NoSQL databases, MapReduce, and Hadoop. Some challenges of big data mining are also mentioned, such as dealing with high volumes of unstructured data and limitations of traditional databases in handling diverse and continuously growing data sources.
Big data is a prominent term which characterizes the improvement and availability of data in all three
formats like structure, unstructured and semi formats. Structure data is located in a fixed field of a record
or file and it is present in the relational data bases and spreadsheets whereas an unstructured data file
includes text and multimedia contents. The primary objective of this big data concept is to describe the
extreme volume of data sets i.e. both structured and unstructured. It is further defined with three “V”
dimensions namely Volume, Velocity and Variety, and two more “V” also added i.e. Value and Veracity.
Volume denotes the size of data, Velocity depends upon the speed of the data processing, Variety is
described with the types of the data, Value which derives the business value and Veracity describes about
the quality of the data and data understandability. Nowadays, big data has become unique and preferred
research areas in the field of computer science. Many open research problems are available in big data
and good solutions also been proposed by the researchers even though there is a need for development of
many new techniques and algorithms for big data analysis in order to get optimal solutions. In this paper,
a detailed study about big data, its basic concepts, history, applications, technique, research issues and
tools are discussed.
IRJET- A Comparative Study on Big Data Analytics Approaches and ToolsIRJET Journal
This document provides an overview of big data analytics approaches and tools. It begins with an abstract discussing the need to evaluate different methodologies and technologies based on organizational needs to identify the optimal solution. The document then reviews literature on big data analytics tools and techniques, and evaluates challenges faced by small vs large organizations. Several big data application examples across industries are presented. The document also introduces concepts of big data including the 3Vs (volume, velocity, variety), describes tools like Hadoop, Cloudera and Cassandra, and discusses scaling big data technologies based on an organization's requirements.
Paper Final Taube Bienert GridInterop 2012Bert Taube
This document discusses using NoSQL data management and advanced analytics for automated demand response (ADR) programs. It notes that ADR will require good data management and analytics to support program execution, accounting, and fault detection. NoSQL databases are proposed as they can better handle the large volumes of diverse data from multiple sources in ADR, including telemetry, usage, events and metadata. Object-oriented data models in NoSQL allow fast, reliable access to different data types and relationships needed for ADR strategies, management and compliance with standards like CIM.
Big data security and privacy issues in theIJNSA Journal
Many organizations demand efficient solutions to store and analyze huge amount of information. Cloud computing as an enabler provides scalable resources and significant economic benefits in the form of reduced operational costs. This paradigm raises a broad range of security and privacy issues that must be taken into consideration. Multi-tenancy, loss of control, and trust are key challenges in cloud computing environments. This paper reviews the existing technologies and a wide array of both earlier and state-ofthe-art projects on cloud security and privacy. We categorize the existing research according to the cloud reference architecture orchestration, resource control, physical resource, and cloud service management layers, in addition to reviewing the recent developments for enhancing the Apache Hadoop security as one of the most deployed big data infrastructures. We also outline the frontier research on privacy-preserving data-intensive applications in cloud computing such as privacy threat modeling and privacy enhancing solutions.
This document discusses big data characteristics, issues, challenges, and technologies. It describes the key characteristics of big data as volume, velocity, variety, value, and complexity. It outlines issues related to these characteristics like data volume and velocity. Challenges of big data include privacy and security, data access and sharing, analytical challenges, human resources, and technical challenges around fault tolerance, scalability, data quality, and heterogeneous data. The document also discusses technologies used for big data like Hadoop, HDFS, and cloud computing and provides examples of big data projects.
This document discusses big data and related technologies. It begins with an overview of big data, describing its characteristics and sources. It then discusses different data types including structured and unstructured data. Next, it covers big data technologies for storage, processing, and transfer of large datasets. It compares SQL and NoSQL databases. The document also discusses big data security and trends. It concludes with references and a demonstration of MongoDB.
28 15141Secure Data Sharing with Data Partitioning in Big Data33289 24 12-2017rahulmonikasharma
Hadoop is a framework for the transformation analysis of very huge data. This paper presents an distributed approach for data storage with the help of Hadoop distributed file system (HDFS). This scheme overcomes the drawbacks of other data storage scheme, as it stores the data in distributed format. So, there is no chance of any loss of data. HDFS stores the data in the form of replica’s, it is advantageous in case of failure of any node; user is able to easily recover from data loss unlike other storage system, if you loss then you cannot. We have implemented ID-Based Ring Signature Scheme to provide secure data sharing among the network, so that only authorized person have access to the data. System is became more attack prone with the help of Advanced Encryption Standard (AES). Even if attacker is successful in getting source data but it’s unable to decode it.
Advanced Analytics and Machine Learning with Data VirtualizationDenodo
This document summarizes a webinar on data virtualization presented by Denodo. It discusses common data challenges faced by data scientists, including spending significant time locating, transforming, and preparing data from various sources. It then introduces data virtualization as a solution, which provides a centralized catalog and logical view of data that reduces the data science workflow from months to days. Examples are given of customers like a healthcare company and industrial real estate company using Denodo's data virtualization platform to more easily discover, access, and analyze their diverse data sources. Key benefits highlighted include increased data and analytics agility, reduced data preparation time, and enabling self-service analytics.
This document discusses perspectives on big data applications for database engineers and IT students. It summarizes key concepts of big data and MongoDB, a popular NoSQL database for managing big data. It then demonstrates practical learning activities using MongoDB, such as installation, terminology, and basic syntax. The document concludes by emphasizing the importance of skills in big data and cloud computing for IT professionals and recommends further research on MongoDB security.
Class lecture by Prof. Raj Jain on Big Data. The talk covers Why Big Data Now?, Big Data Applications, ACID Requirements, Terminology, Google File System, BigTable, MapReduce, MapReduce Optimization, Story of Hadoop, Hadoop, Apache Hadoop Tools, Apache Other Big Data Tools, Other Big Data Tools, Analytics, Types of Databases, Relational Databases and SQL, Non-relational Databases, NewSQL Databases, Columnar Databases. Video recording available in YouTube.
Myth Busters: I’m Building a Data Lake, So I Don’t Need Data Virtualization (...Denodo
Watch full webinar here: https://bit.ly/3kr0oq4
So you’re building a data lake to solve your big data challenges. A data lake will allow you to keep all of your raw, detailed data in a single, consolidated repository; therefore, your problem is solved. Or is it? Is it really that easy?
Data lakes have their use and purpose, and we’re not here to argue that. However, data lakes on their own are constrained by factors such as duplication of data and therefore higher costs, governance limitations, and the risk of becoming another data silo.
With the addition of data virtualization, a physical data lake, can turn into a virtual or logical data like through an abstraction layer. Data virtualization can facilitate and expedite accessing and exploring critical data in a cost-effective manner and assist in deriving a greater return on the data lake investment.
You might still not be convinced. Give us an opportunity and join us as we try to bust this myth!
Watch this webinar as we explore the promises of a data lake as well as its downfalls to draw a final conclusion.
IRJET-Unraveling the Data Structures of Big data, the HDFS Architecture and I...IRJET Journal
This document discusses big data, its data structures, the Hadoop Distributed File System (HDFS) architecture, and data replication in HDFS. It begins by defining big data and describing its four main data structures: structured, semi-structured, quasi-structured, and unstructured. It then provides details on the master/slave architecture of HDFS and the roles of the NameNode and DataNodes. It explains how data is replicated across multiple DataNodes for reliability and how the replicas are placed on different racks to prevent total data loss if a rack fails. The importance of data replication in HDFS for availability, reliability, and fault tolerance is also highlighted.
Identifying and analyzing the transient and permanent barriers for big datasarfraznawaz
The document discusses identifying and analyzing the transient and permanent barriers for adopting big data. It begins by providing background on big data and its opportunities. It then identifies five transient barriers: data storage and transfer, scalability, data quality, data complexity, and timeliness. The barriers are analyzed in depth. Four permanent barriers are also identified: security, privacy, trust, data ownership, and transparency. The barriers are discussed and the challenges of overcoming the permanent barriers through technology alone are noted.
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 an overview of big data, including its definition, size and growth, characteristics, analytics uses and challenges. It discusses operational vs analytical big data systems and technologies like NoSQL databases, Hadoop and MapReduce. Considerations for selecting big data technologies include whether they support online vs offline use cases, licensing models, community support, developer appeal, and enabling agility.
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.
Study and comparative analysis of resonat frequency for microsrtip fractal an...eSAT 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.
Testing and ergonomically evaluation of tractor mounted and self mounted coco...eSAT Publishing House
This summary provides an overview of the key points from the document:
1. The document discusses the development and testing of a Tractor Mounted and Self Propelled Coconut Climber (TMSPCC) to improve the efficiency of coconut harvesting over traditional manual climbing methods.
2. Ergonomic evaluations were conducted to assess the physiological cost, safety, and ease of operation of the TMSPCC compared to manual climbing. Heart rate, oxygen consumption, and discomfort levels were measured for operators of both methods.
3. Testing results found the TMSPCC to be safer and less physically demanding for operators compared to manual climbing. It was able to harvest 100-120 coconut trees per day
This document discusses big data and its applications. It begins with an abstract that introduces big data and how extracting valuable information from large amounts of structured and unstructured data can help governments and organizations develop policies. It then discusses key aspects of big data including volume, velocity, and variety. Current big data technologies are outlined such as Hadoop, HBase, and Hive. Some big data problems and applications are also mentioned like using big data in commerce, business, and scientific research to improve forecasting, policies, productivity, and research.
An elastic , effective, activety or intelligent ,graceful networking architecture layout be desired to make processing massive data. next to that ,existent network architectures be considerably incapable for
cleatting the huge data. massive data thrusts network exchequers into border it consequence with in network overcrowding ,needy achievement, then permicious employer exprtises. this offered the current state-of-the-art research affronts ,potential solutions into huge data networking notion. More specifically, present the state of networking problems into massive data connected intrequirements,capacity,running ,
data manipulating also will introduce the architectures of MapReduce , Hadoop paradigm within research
requirements, fabric networks and software defined networks which utilizized into making today’s idly growing digital world and compare and contrast into identify relevant drawbacks and solutions.
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.
Big Data Mining - Classification, Techniques and IssuesKaran Deep Singh
The document discusses big data mining and provides an overview of related concepts and techniques. It describes how big data is characterized by large volume, variety, and velocity of data that is difficult to manage with traditional methods. Common techniques for big data mining discussed include NoSQL databases, MapReduce, and Hadoop. Some challenges of big data mining are also mentioned, such as dealing with high volumes of unstructured data and limitations of traditional databases in handling diverse and continuously growing data sources.
Big data is a prominent term which characterizes the improvement and availability of data in all three
formats like structure, unstructured and semi formats. Structure data is located in a fixed field of a record
or file and it is present in the relational data bases and spreadsheets whereas an unstructured data file
includes text and multimedia contents. The primary objective of this big data concept is to describe the
extreme volume of data sets i.e. both structured and unstructured. It is further defined with three “V”
dimensions namely Volume, Velocity and Variety, and two more “V” also added i.e. Value and Veracity.
Volume denotes the size of data, Velocity depends upon the speed of the data processing, Variety is
described with the types of the data, Value which derives the business value and Veracity describes about
the quality of the data and data understandability. Nowadays, big data has become unique and preferred
research areas in the field of computer science. Many open research problems are available in big data
and good solutions also been proposed by the researchers even though there is a need for development of
many new techniques and algorithms for big data analysis in order to get optimal solutions. In this paper,
a detailed study about big data, its basic concepts, history, applications, technique, research issues and
tools are discussed.
IRJET- A Comparative Study on Big Data Analytics Approaches and ToolsIRJET Journal
This document provides an overview of big data analytics approaches and tools. It begins with an abstract discussing the need to evaluate different methodologies and technologies based on organizational needs to identify the optimal solution. The document then reviews literature on big data analytics tools and techniques, and evaluates challenges faced by small vs large organizations. Several big data application examples across industries are presented. The document also introduces concepts of big data including the 3Vs (volume, velocity, variety), describes tools like Hadoop, Cloudera and Cassandra, and discusses scaling big data technologies based on an organization's requirements.
Paper Final Taube Bienert GridInterop 2012Bert Taube
This document discusses using NoSQL data management and advanced analytics for automated demand response (ADR) programs. It notes that ADR will require good data management and analytics to support program execution, accounting, and fault detection. NoSQL databases are proposed as they can better handle the large volumes of diverse data from multiple sources in ADR, including telemetry, usage, events and metadata. Object-oriented data models in NoSQL allow fast, reliable access to different data types and relationships needed for ADR strategies, management and compliance with standards like CIM.
Big data security and privacy issues in theIJNSA Journal
Many organizations demand efficient solutions to store and analyze huge amount of information. Cloud computing as an enabler provides scalable resources and significant economic benefits in the form of reduced operational costs. This paradigm raises a broad range of security and privacy issues that must be taken into consideration. Multi-tenancy, loss of control, and trust are key challenges in cloud computing environments. This paper reviews the existing technologies and a wide array of both earlier and state-ofthe-art projects on cloud security and privacy. We categorize the existing research according to the cloud reference architecture orchestration, resource control, physical resource, and cloud service management layers, in addition to reviewing the recent developments for enhancing the Apache Hadoop security as one of the most deployed big data infrastructures. We also outline the frontier research on privacy-preserving data-intensive applications in cloud computing such as privacy threat modeling and privacy enhancing solutions.
This document discusses big data characteristics, issues, challenges, and technologies. It describes the key characteristics of big data as volume, velocity, variety, value, and complexity. It outlines issues related to these characteristics like data volume and velocity. Challenges of big data include privacy and security, data access and sharing, analytical challenges, human resources, and technical challenges around fault tolerance, scalability, data quality, and heterogeneous data. The document also discusses technologies used for big data like Hadoop, HDFS, and cloud computing and provides examples of big data projects.
This document discusses big data and related technologies. It begins with an overview of big data, describing its characteristics and sources. It then discusses different data types including structured and unstructured data. Next, it covers big data technologies for storage, processing, and transfer of large datasets. It compares SQL and NoSQL databases. The document also discusses big data security and trends. It concludes with references and a demonstration of MongoDB.
28 15141Secure Data Sharing with Data Partitioning in Big Data33289 24 12-2017rahulmonikasharma
Hadoop is a framework for the transformation analysis of very huge data. This paper presents an distributed approach for data storage with the help of Hadoop distributed file system (HDFS). This scheme overcomes the drawbacks of other data storage scheme, as it stores the data in distributed format. So, there is no chance of any loss of data. HDFS stores the data in the form of replica’s, it is advantageous in case of failure of any node; user is able to easily recover from data loss unlike other storage system, if you loss then you cannot. We have implemented ID-Based Ring Signature Scheme to provide secure data sharing among the network, so that only authorized person have access to the data. System is became more attack prone with the help of Advanced Encryption Standard (AES). Even if attacker is successful in getting source data but it’s unable to decode it.
Advanced Analytics and Machine Learning with Data VirtualizationDenodo
This document summarizes a webinar on data virtualization presented by Denodo. It discusses common data challenges faced by data scientists, including spending significant time locating, transforming, and preparing data from various sources. It then introduces data virtualization as a solution, which provides a centralized catalog and logical view of data that reduces the data science workflow from months to days. Examples are given of customers like a healthcare company and industrial real estate company using Denodo's data virtualization platform to more easily discover, access, and analyze their diverse data sources. Key benefits highlighted include increased data and analytics agility, reduced data preparation time, and enabling self-service analytics.
This document discusses perspectives on big data applications for database engineers and IT students. It summarizes key concepts of big data and MongoDB, a popular NoSQL database for managing big data. It then demonstrates practical learning activities using MongoDB, such as installation, terminology, and basic syntax. The document concludes by emphasizing the importance of skills in big data and cloud computing for IT professionals and recommends further research on MongoDB security.
Class lecture by Prof. Raj Jain on Big Data. The talk covers Why Big Data Now?, Big Data Applications, ACID Requirements, Terminology, Google File System, BigTable, MapReduce, MapReduce Optimization, Story of Hadoop, Hadoop, Apache Hadoop Tools, Apache Other Big Data Tools, Other Big Data Tools, Analytics, Types of Databases, Relational Databases and SQL, Non-relational Databases, NewSQL Databases, Columnar Databases. Video recording available in YouTube.
Myth Busters: I’m Building a Data Lake, So I Don’t Need Data Virtualization (...Denodo
Watch full webinar here: https://bit.ly/3kr0oq4
So you’re building a data lake to solve your big data challenges. A data lake will allow you to keep all of your raw, detailed data in a single, consolidated repository; therefore, your problem is solved. Or is it? Is it really that easy?
Data lakes have their use and purpose, and we’re not here to argue that. However, data lakes on their own are constrained by factors such as duplication of data and therefore higher costs, governance limitations, and the risk of becoming another data silo.
With the addition of data virtualization, a physical data lake, can turn into a virtual or logical data like through an abstraction layer. Data virtualization can facilitate and expedite accessing and exploring critical data in a cost-effective manner and assist in deriving a greater return on the data lake investment.
You might still not be convinced. Give us an opportunity and join us as we try to bust this myth!
Watch this webinar as we explore the promises of a data lake as well as its downfalls to draw a final conclusion.
IRJET-Unraveling the Data Structures of Big data, the HDFS Architecture and I...IRJET Journal
This document discusses big data, its data structures, the Hadoop Distributed File System (HDFS) architecture, and data replication in HDFS. It begins by defining big data and describing its four main data structures: structured, semi-structured, quasi-structured, and unstructured. It then provides details on the master/slave architecture of HDFS and the roles of the NameNode and DataNodes. It explains how data is replicated across multiple DataNodes for reliability and how the replicas are placed on different racks to prevent total data loss if a rack fails. The importance of data replication in HDFS for availability, reliability, and fault tolerance is also highlighted.
Identifying and analyzing the transient and permanent barriers for big datasarfraznawaz
The document discusses identifying and analyzing the transient and permanent barriers for adopting big data. It begins by providing background on big data and its opportunities. It then identifies five transient barriers: data storage and transfer, scalability, data quality, data complexity, and timeliness. The barriers are analyzed in depth. Four permanent barriers are also identified: security, privacy, trust, data ownership, and transparency. The barriers are discussed and the challenges of overcoming the permanent barriers through technology alone are noted.
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 an overview of big data, including its definition, size and growth, characteristics, analytics uses and challenges. It discusses operational vs analytical big data systems and technologies like NoSQL databases, Hadoop and MapReduce. Considerations for selecting big data technologies include whether they support online vs offline use cases, licensing models, community support, developer appeal, and enabling agility.
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.
Study and comparative analysis of resonat frequency for microsrtip fractal an...eSAT 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.
Testing and ergonomically evaluation of tractor mounted and self mounted coco...eSAT Publishing House
This summary provides an overview of the key points from the document:
1. The document discusses the development and testing of a Tractor Mounted and Self Propelled Coconut Climber (TMSPCC) to improve the efficiency of coconut harvesting over traditional manual climbing methods.
2. Ergonomic evaluations were conducted to assess the physiological cost, safety, and ease of operation of the TMSPCC compared to manual climbing. Heart rate, oxygen consumption, and discomfort levels were measured for operators of both methods.
3. Testing results found the TMSPCC to be safer and less physically demanding for operators compared to manual climbing. It was able to harvest 100-120 coconut trees per day
Fusion method used to tolerate the faults occurred in disrtibuted systemeSAT Publishing House
This document discusses a fusion method for tolerating faults in distributed systems. The fusion method uses a combination of error correcting codes and selective replication to tolerate crash faults. It maintains "K" additional fused backups instead of "K+1" replicas required by replication. This provides an O(n) space savings over replication and results in minimal overhead during normal operation. The method encodes primary data structures into the fused backups. It can insert, delete, and update data in both the primary structures and backups efficiently. The proposed fusion technique saves space for fault tolerance compared to replication.
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.
Design and implementation of an ancrchitecture of embedded web server for wir...eSAT 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.
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.
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.
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
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.
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.
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.
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
Parametric study of response of an asymmetric building for various earthquake...eSAT 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
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
An experiental investigation of effect of cutting parameters and tool materia...eSAT 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
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
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
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.
This document summarizes a study on minimum shear reinforcement for reinforced concrete beams. The study investigated factors that influence the minimum shear reinforcement required, including concrete strength, beam size, shear span-to-depth ratio, and longitudinal reinforcement ratio. An expression was proposed for minimum shear reinforcement that incorporates these parameters. The proposed expression was compared to code provisions. Results showed that minimum shear reinforcement increases with increasing concrete strength, shear span-to-depth ratio, and decreasing longitudinal reinforcement ratio. Shear reinforcement was also found to improve beam ductility.
AUTOMATIC TRANSFER OF DATA USING SERVICE-ORIENTED ARCHITECTURE TO NoSQL DATAB...IRJET Journal
This document summarizes an academic paper that proposes a model for automatically migrating data from relational databases to NoSQL databases using service-oriented architecture. The model encapsulates popular NoSQL databases like MongoDB, Cassandra, and Neo4j as web services. This allows data to be efficiently migrated from a relational database like Apache Derby to a NoSQL database with minimal knowledge of how each database works. The document provides details of the proposed migration model and discusses its implementation and testing migrating data from Derby to the NoSQL databases successfully.
Analysis and evaluation of riak kv cluster environment using basho benchStevenChike
Many institutions and companies with technological development have been producing large size of structured and unstructured data. Therefore, we need special databases to deal with these data and thus emerged NoSQL databases. They are widely used in the cloud databases and the distributed systems. In the era of big data, those databases provide a scalable high availability solution. So we need new architectures to try to meet the need to store more and more different kinds of different data. In order to arrive at a good structure of large and diverse data, this structure must be tested and analyzed in depth with the use of different benchmark tools. In this paper, we experiment the Riak key-value database to measure their performance in terms of throughput and latency, where huge amounts of data are stored and retrieved in different sizes in a distributed database environment. Throughput and latency of the NoSQL database over different types of experiments and different sizes of data are compared and then results were discussed.
BIG DATA NETWORKING: REQUIREMENTS, ARCHITECTURE AND ISSUESijwmn
The document discusses requirements, architectures, and issues related to networking for big data. It begins by outlining the network requirements for big data, including resiliency, congestion mitigation, performance consistency, scalability, partitioning, and application awareness. It then describes the MapReduce and Hadoop architectures commonly used for big data processing and some of the research challenges they present for networks. Finally, it discusses fabric network infrastructures and software defined networks that can help address networking needs for big data.
Secure Transaction Model for NoSQL Database Systems: Reviewrahulmonikasharma
NoSQL cloud database frameworks would consist new sorts of databases that would construct over many cloud hubs and would be skilled about storing and transforming enormous information. NoSQL frameworks need to be progressively utilized within substantial scale provisions that require helter skelter accessibility. What’s more effectiveness for weaker consistency? Consequently, such frameworks need help for standard transactions which give acceptable and stronger consistency. This task proposes another multi-key transactional model which gives NoSQL frameworks standard for transaction backing and stronger level from claiming information consistency. Those methodology is to supplement present NoSQL structural engineering with an additional layer that manages transactions. The recommended model may be configurable the place consistency, accessibility Furthermore effectiveness might make balanced In view of requisition prerequisites. The recommended model may be approved through a model framework utilizing MongoDB. Preliminary examinations show that it ensures stronger consistency Furthermore supports great execution.
The Big Data Importance – Tools and their UsageIRJET Journal
This document discusses big data, tools for analyzing big data, and opportunities that big data analytics provides. It begins by defining big data and its key characteristics of volume, variety and velocity. It then discusses tools for storing, managing and processing big data like Hadoop, MapReduce and HDFS. Finally, it outlines how big data analytics can be applied across different domains to enable new insights and informed decision making through analyzing large datasets.
Web usage Mining Based on Request Dependency GraphIRJET Journal
This document discusses using request dependency graphs (RDGs) to model the dependency relationships between HTTP requests for web usage mining. RDGs can improve data quality and enhance network and web server performance. The authors evaluated their approach using a large real-world web access log and found that RDGs are a useful tool for web usage mining by extracting patterns from user access behaviors and decomposing websites.
IRJET- Recommendation System based on Graph Database TechniquesIRJET Journal
This document proposes a recommendation system based on graph database techniques. It uses Neo4j to develop a recommendation approach using content-based filtering, collaborative filtering, and hybrid filtering. The system recommends restaurants and meals to customers based on reviews and friend recommendations. It stores data about restaurants, meals, customers and their reviews in a graph database to allow for complex queries and recommendations. The implementation and results of the proposed recommendation system are also discussed.
The document summarizes key aspects of NOSQL databases for interactive applications. It discusses how NOSQL databases provide better scalability, performance, and availability compared to traditional databases, which is important for applications that need to handle large amounts of data and users. The document also outlines some important criteria for choosing a database for interactive applications, including scalability, performance, availability, and architecture. It concludes that NOSQL databases are well-suited for these criteria and are becoming more popular for enterprises due to their ability to address issues with traditional databases.
This document provides an overview of big data storage technologies and their role in the big data value chain. It identifies key insights about data storage, including that scalable storage technologies have enabled virtually unbounded data storage and advanced analytics across sectors. However, lack of standards and challenges in distributing graph-based data limit interoperability and scalability. The document also notes the social and economic impacts of big data storage in enabling a data-driven society and transforming sectors like health and media through consolidated data analysis.
This document discusses the suitability of NoSQL databases for interactive applications. It notes that traditional databases do not provide the necessary scalability and performance for interactive applications that require reading and writing huge amounts of data swiftly. NoSQL databases are better suited for these tasks as they are scalable, fast, and robust. The document provides an overview of NoSQL databases and how their flexible schema, high performance, availability, and ability to scale horizontally make them a better fit than traditional databases for modern, large-scale, interactive applications.
This document provides an overview of how MySQL can be used to deliver high scalability and availability while maintaining the benefits of relational data and rich queries. It discusses using Memcached APIs for NoSQL access to MySQL data, scaling MySQL with replication and sharding, and the capabilities of MySQL Cluster for auto-sharding, high availability, and online schema maintenance.
- Data lakes emerged as a concept during the Big Data era and offer a highly flexible way to store both structured and unstructured data using a schema-on-read approach. However, they lack adequate security and authentication mechanisms.
- The document discusses the key concepts of data lakes including how they ingest and store raw data without transforming it initially. It also covers the typical architectural layers of a data lake and some challenges in ensuring proper governance and management of data in the lake.
- Improving data quality, metadata management, and security/access controls are identified as important areas to address some of the current limitations of data lakes.
Managing Big data using Hadoop Map Reduce in Telecom DomainAM Publications
Map reduce is a programming model for analysing and processing large massive data sets. Apache Hadoop is an efficient frame work and the most popular implementation of the map reduce model. Hadoop’s success has motivated research interest and has led to different modifications as well as extensions to framework. In this paper, the challenges faced in different domains like data storage, analytics, online processing and privacy/ security issues while handling big data are explored. Also, the various possible solutions with respect to Telecom domain with Hadoop Map reduce implementation is discussed in this paper.
Making Sense of NoSQL and Big Data Amidst High ExpectationsRackspace
1) There is a lot of hype around NoSQL and Big Data technologies but they provide value for specific problems involving large, varied datasets with high rates of change.
2) NoSQL databases are useful for problems that don't require a relational data model and involve huge datasets, while SQL databases remain critical for transaction processing and maintaining relationships between structured data.
3) Organizations should choose technologies based on their specific business requirements and understand each technology's strengths rather than favoring "cool" technologies.
Cloud Analytics Ability to Design, Build, Secure, and Maintain Analytics Solu...YogeshIJTSRD
Cloud Analytics is another area in the IT field where different services like Software, Infrastructure, storage etc. are offered as services online. Users of cloud services are under constant fear of data loss, security threats, and availability issues. However, the major challenge in these methods is obtaining real time and unbiased datasets. Many datasets are internal and cannot be shared due to privacy issues or may lack certain statistical characteristics. As a result of this, researchers prefer to generate datasets for training and testing purposes in simulated or closed experimental environments which may lack comprehensiveness. Advances in sensor technology, the Internet of things IoT , social networking, wireless communications, and huge collection of data from years have all contributed to a new field of study Big Data is discussed in this paper. Through this analysis and investigation, we provide recommendations for the research public on future directions on providing data based decisions for cloud supported Big Data computing and analytic solutions. This paper concentrates upon the recent trends in Big Data storage and analysing, in the clouds, and also points out the security limitations. Rajan Ramvilas Saroj "Cloud Analytics: Ability to Design, Build, Secure, and Maintain Analytics Solutions on the Cloud" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd43728.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/43728/cloud-analytics-ability-to-design-build-secure-and-maintain-analytics-solutions-on-the-cloud/rajan-ramvilas-saroj
Abstract In early days information contain in increasingly corporate area, now IT organization help to right module to store, manage ,retrieve and transfer information in the more reliable and powerful manner. As part of an Information Lifecycle Management (ILM) best-practices strategy, organizations require solutions for migrating data between in heterogeneous environments and system storage. In early days information contain in increasingly corporate area, today IT organization help to right module to store, manage ,retrieve and transfer information in the more reliable and powerful manner. This paper helps to planned to design powerful modules that high-performances data migration of storage area with less time complexity. This project contain unique information of data migration in dynamic IT nature and business advantage that design to provide new tool used for data migration. Keywords— Heterogeneous Environment, data migration, data mapping
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
The document discusses big data and NoSQL technologies. It defines big data, discusses its key characteristics of volume, velocity, and variety. It then discusses NoSQL databases as an alternative to traditional SQL databases for handling big data workloads. Specific NoSQL technologies and how they provide more scalability and flexibility for big data are covered. The document also addresses whether NoSQL is replacing SQL databases and argues it depends on the specific use case.
CASE STUDY ON METHODS AND TOOLS FOR THE BIG DATA ANALYSISIRJET Journal
This document discusses big data analysis tools and methods. It begins by defining big data as large volumes of structured, semi-structured, and unstructured data from various sources that cannot be processed with traditional computing approaches due to its size and complexity. It then discusses some of the major challenges in big data such as capturing, storing, searching, sharing, and analyzing large amounts of diverse data. The document provides an overview of different big data tools and methods for processing large datasets and addresses their limitations. It focuses on using cloud technologies and improving data management to better handle big data challenges.
Similar to No sql databases new millennium database for big data, big users, cloud computing and its security challenges (20)
Hudhud cyclone caused extensive damage in Visakhapatnam, India in October 2014, especially to tree cover. This will likely impact the local environment in several ways: increased air pollution as trees absorb less; higher temperatures without tree canopy; increased erosion and landslides. It also created large amounts of waste from destroyed trees. Proper management of solid waste is needed to prevent disease spread. Suggested measures include restoring damaged plants, building fountains to reduce heat, mandating light-colored buildings, improving waste management, and educating public on health risks. Overall, changes are needed to water, land, and waste practices to rebuild the environment after the cyclone removed green cover.
Impact of flood disaster in a drought prone area – case study of alampur vill...eSAT Publishing House
1) In September-October 2009, unprecedented heavy rainfall and dam releases caused widespread flooding in Alampur village in Mahabub Nagar district, a historically drought-prone area.
2) The flood damaged or destroyed homes, buildings, infrastructure, crops, and documents. It displaced many residents and cut off the village.
3) The socioeconomic conditions and mud-based construction of homes in the village exacerbated the flood's impacts, making damage more severe and recovery more difficult.
The document summarizes the Hudhud cyclone that struck Visakhapatnam, India in October 2014. It describes the cyclone's formation, rapid intensification to winds of 175 km/h, and landfall near Visakhapatnam. The cyclone caused extensive damage estimated at over $1 billion and at least 109 deaths in India and Nepal. Infrastructure like buildings, bridges, and power lines were destroyed. Crops and fishing boats were also damaged. The document then discusses coping strategies and improvements needed to disaster management plans to better prepare for future cyclones.
Groundwater investigation using geophysical methods a case study of pydibhim...eSAT Publishing House
This document summarizes the results of a geophysical investigation using vertical electrical sounding (VES) methods at 13 locations around an industrial area in India. The VES data was interpreted to generate geo-electric sections and pseudo-sections showing subsurface resistivity variations. Three main layers were typically identified - a high resistivity topsoil, a weathered middle layer, and a basement rock. Pseudo-sections revealed relatively more weathered areas in the northwest and southwest. Resistivity sections helped identify zones of possible high groundwater potential based on low resistivity anomalies sandwiched between more resistive layers. The study concluded the electrical resistivity method was useful for understanding subsurface geology and identifying areas prospective for groundwater exploration.
Flood related disasters concerned to urban flooding in bangalore, indiaeSAT Publishing House
1. The document discusses urban flooding in Bangalore, India. It describes how factors like heavy rainfall, population growth, and improper land use have contributed to increased flooding in the city.
2. Flooding events in 2013 are analyzed in detail. A November rainfall caused runoff six times higher than the drainage capacity, inundating low-lying residential areas.
3. Impacts of urban flooding include disrupted daily life, damaged infrastructure, and decreased economic activity in affected areas. The document calls for improved flood management strategies to better mitigate urban flooding risks in Bangalore.
Enhancing post disaster recovery by optimal infrastructure capacity buildingeSAT Publishing House
This document discusses enhancing post-disaster recovery through optimal infrastructure capacity building. It presents a model to minimize the cost of meeting demand using auxiliary capacities when disaster damages infrastructure. The model uses genetic algorithms to select optimal capacity combinations. The document reviews how infrastructure provides vital services supporting recovery activities and discusses classifying infrastructure into six types. When disaster reduces infrastructure services, a gap forms between community demands and available support, hindering recovery. The proposed research aims to identify this gap and optimize capacity selection to fill it cost-effectively.
Effect of lintel and lintel band on the global performance of reinforced conc...eSAT Publishing House
This document analyzes the effect of lintels and lintel bands on the seismic performance of reinforced concrete masonry infilled frames through non-linear static pushover analysis. Four frame models are considered: a frame with a full masonry infill wall; a frame with a central opening but no lintel/band; a frame with a lintel above the opening; and a frame with a lintel band above the opening. The results show that the full infill wall model has 27% higher stiffness and 32% higher strength than the model with just an opening. Models with lintels or lintel bands have slightly higher strength and stiffness than the model with just an opening. The document concludes lintels and lintel
Wind damage to trees in the gitam university campus at visakhapatnam by cyclo...eSAT Publishing House
1) A cyclone with wind speeds of 175-200 kph caused massive damage to the green cover of Gitam University campus in Visakhapatnam, India. Thousands of trees were uprooted or damaged.
2) A study assessed different types of damage to trees from the cyclone, including defoliation, salt spray damage, damage to stems/branches, and uprooting. Certain tree species were more vulnerable than others.
3) The results of the study can help in selecting more wind-resistant tree species for future planting and reducing damage from future storms.
Wind damage to buildings, infrastrucuture and landscape elements along the be...eSAT Publishing House
1) A visual study was conducted to assess wind damage from Cyclone Hudhud along the 27km Visakha-Bheemli Beach road in Visakhapatnam, India.
2) Residential and commercial buildings suffered extensive roof damage, while glass facades on hotels and restaurants were shattered. Infrastructure like electricity poles and bus shelters were destroyed.
3) Landscape elements faced damage, including collapsed trees that damaged pavements, and debris in parks. The cyclone wiped out over half the city's green cover and caused beach erosion around protected areas.
1) The document reviews factors that influence the shear strength of reinforced concrete deep beams, including compressive strength of concrete, percentage of tension reinforcement, vertical and horizontal web reinforcement, aggregate interlock, shear span-to-depth ratio, loading distribution, side cover, and beam depth.
2) It finds that compressive strength of concrete, tension reinforcement percentage, and web reinforcement all increase shear strength, while shear strength decreases as shear span-to-depth ratio increases.
3) The distribution and amount of vertical and horizontal web reinforcement also affects shear strength, but closely spaced stirrups do not necessarily enhance capacity or performance.
Role of voluntary teams of professional engineers in dissater management – ex...eSAT Publishing House
1) A team of 17 professional engineers from various disciplines called the "Griha Seva" team volunteered after the 2001 Gujarat earthquake to provide technical assistance.
2) The team conducted site visits, assessments, testing and recommended retrofitting strategies for damaged structures in Bhuj and Ahmedabad. They were able to fully assess and retrofit 20 buildings in Ahmedabad.
3) Factors observed that exacerbated the earthquake's impacts included unplanned construction, non-engineered buildings, improper prior retrofitting, and defective materials and workmanship. The professional engineers' technical expertise was crucial for effective post-disaster management.
This document discusses risk analysis and environmental hazard management. It begins by defining risk, hazard, and toxicity. It then outlines the steps involved in hazard identification, including HAZID, HAZOP, and HAZAN. The document presents a case study of a hypothetical gas collecting station, identifying potential accidents and hazards. It discusses quantitative and qualitative approaches to risk analysis, including calculating a fire and explosion index. The document concludes by discussing hazard management strategies like preventative measures, control measures, fire protection, relief operations, and the importance of training personnel on safety.
Review study on performance of seismically tested repaired shear wallseSAT Publishing House
This document summarizes research on the performance of reinforced concrete shear walls that have been repaired after damage. It begins with an introduction to shear walls and their failure modes. The literature review then discusses the behavior of original shear walls as well as different repair techniques tested by other researchers, including conventional repair with new concrete, jacketing with steel plates or concrete, and use of fiber reinforced polymers. The document focuses on evaluating the strength retention of shear walls after being repaired with various methods.
Monitoring and assessment of air quality with reference to dust particles (pm...eSAT Publishing House
This document summarizes a study on monitoring and assessing air quality with respect to dust particles (PM10 and PM2.5) in the urban environment of Visakhapatnam, India. Sampling was conducted in residential, commercial, and industrial areas from October 2013 to August 2014. The average PM2.5 and PM10 concentrations were within limits in residential areas but moderate to high in commercial and industrial areas. Exceedance factor levels indicated moderate pollution for residential areas and moderate to high pollution for commercial and industrial areas. There is a need for management measures like improved public transport and green spaces to combat particulate air pollution in the study areas.
Low cost wireless sensor networks and smartphone applications for disaster ma...eSAT Publishing House
This document describes a low-cost wireless sensor network and smartphone application system for disaster management. The system uses an Arduino-based wireless sensor network comprising nodes with various sensors to monitor the environment. The sensor data is transmitted to a central gateway and then to the cloud for analysis. A smartphone app connected to the cloud can detect disasters from the sensor data and send real-time alerts to users to help with early evacuation. The system aims to provide low-cost localized disaster detection and warnings to improve safety.
Coastal zones – seismic vulnerability an analysis from east coast of indiaeSAT Publishing House
This document summarizes an analysis of seismic vulnerability along the east coast of India. It discusses the geotectonic setting of the region as a passive continental margin and reports some moderate seismic activity from offshore in recent decades. While seismic stability cannot be assumed given events like the 2004 tsunami, no major earthquakes have been recorded along this coast historically. The document calls for further study of active faults, neotectonics, and implementation of improved seismic building codes to mitigate vulnerability.
Can fracture mechanics predict damage due disaster of structureseSAT Publishing House
This document discusses how fracture mechanics can be used to better predict damage and failure of structures. It notes that current design codes are based on small-scale laboratory tests and do not account for size effects, which can lead to more brittle failures in larger structures. The document outlines how fracture mechanics considers factors like size effect, ductility, and minimum reinforcement that influence the strength and failure behavior of structures. It provides examples of how fracture mechanics has been applied to problems like evaluating shear strength in deep beams and investigating a failure of an oil platform structure. The document argues that fracture mechanics provides a more scientific basis for structural design compared to existing empirical code provisions.
This document discusses the assessment of seismic susceptibility of reinforced concrete (RC) buildings. It begins with an introduction to earthquakes and the importance of vulnerability assessment in mitigating earthquake risks and losses. It then describes modeling the nonlinear behavior of RC building elements and performing pushover analysis to evaluate building performance. The document outlines modeling RC frames and developing moment-curvature relationships. It also summarizes the results of pushover analyses on sample 2D and 3D RC frames with and without shear walls. The conclusions emphasize that pushover analysis effectively assesses building properties but has limitations, and that capacity spectrum method provides appropriate results for evaluating building response and retrofitting impact.
A geophysical insight of earthquake occurred on 21 st may 2014 off paradip, b...eSAT Publishing House
1) A 6.0 magnitude earthquake occurred off the coast of Paradip, Odisha in the Bay of Bengal on May 21, 2014 at a depth of around 40 km.
2) Analysis of magnetic and bathymetric data from the area revealed the presence of major lineaments in NW-SE and NE-SW directions that may be responsible for seismic activity through stress release.
3) Movements along growth faults at the margins of large Bengal channels, due to large sediment loads, could also contribute to seismic events by triggering movements along the faults.
Effect of hudhud cyclone on the development of visakhapatnam as smart and gre...eSAT Publishing House
This document discusses the effects of Cyclone Hudhud on the development of Visakhapatnam as a smart and green city through a case study and preliminary surveys. The surveys found that 31% of participants had experienced cyclones, 9% floods, and 59% landslides previously in Visakhapatnam. Awareness of disaster alarming systems increased from 14% before the 2004 tsunami to 85% during Cyclone Hudhud, while awareness of disaster management systems increased from 50% before the tsunami to 94% during Hudhud. The surveys indicate that initiatives after the tsunami improved awareness and preparedness. Developing Visakhapatnam as a smart, green city should consider governance
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
No sql databases new millennium database for big data, big users, cloud computing and its security challenges
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NoSQL DATABASES: NEW MILLENNIUM DATABASE FOR BIG DATA, BIG USERS, CLOUD COMPUTING AND ITS SECURITY CHALLENGES Asadulla Khan Zaki Student, Department of Computer Science and Engineering, BMS College of Engineering, Bangalore, India Abstract The field of databases has emerged in last decades of years. New architectures try to meet the need to store more and more various kinds of diverse data. The current trend of Big Data(too diverse data, unstructured data, semi-structured data, fast changing data), Big Users(global users 24 hours a day, 365 days a year) and Cloud Computing(new applications use a three-tier internet architecture, run in a public or private cloud) are the driving force for the organizations to migrate towards Non-Relational databases(referring as NoSQL popularly called as “NOT ONLY SQL”)from Relational Databases. The main market of Relational Databases is business data processing and these databases are architected to run a single machine and uses a rigid and scheme- based approach to modeling the data and dealing with Big Data and global users on a cloud environment becomes more and more difficult with relational databases. Non-relational databases (NoSQL databases) are considering as new Era database, it provides dynamic schemas, flexible data model, scale-out architecture, efficient big data storage and access requirement. Today the use of NoSQL is mainly due to its Scalability and Performance characteristics. Only a few years ago the Scalability and Performance were not such a big problem but the huge amount of data that is collected today is infinitely much more than ten years ago and also the growth of cloud computing results in large data store even more. This paper includes the introduction, causes of migrating towards NoSQL databases, characteristics, classification of NoSQL databases. Finally the security issues in NoSQL Databases are described and the security enforcement mechanism is proposed. Keywords: NoSQL, Big Data, Big Users, Key-value store, RDBMS, Security
-----------------------------------------------------------------------***----------------------------------------------------------------------- 1. INTRODUCTION Today, there exist many different types of databases, not only the traditional relational databases but several other architectures designed to handle different types of data. Since the 70s the relational model was the dominant, with the implementations like Oracle database, MySQL and Microsoft SQL Servers and almost all databases followed the same basic architecture. At the beginning of the new millennium, developers started to realize that their data did not fit for the relational model and some of them started to develop other architectures for storing data in databases. When choosing a database today the problem is much more complex to decide the best architecture for data storage and retrieval of data. [1] Building of applications is now continuously changing.In decade of 90’, web companies come up with the scaling features in various dimensions of applications due to the following factors [2]:
The increase in number of concurrent global users access the applications via web and mobile devices, these users are popularly known as big users.
The huge volume of data is getting collected and processed today, and it becomes mandatory to collect various kinds of structured and unstructured data and
its use became an integral part and it adds richness to applications, popularly known as big data.
Today, with the emergence of cloud, applications use a three-tier internet architecture that run in a public or private cloud that support big users and big data.
Dealing with big users and big data using relational database technology becomes more and more difficult. The main reason is that relational databases depend on static schemas, and a rigid approach toward modeling the data. Google, Amazon, Facebook, and Linkedln are among the first companies to discover the serious limitations of relational database technology for supporting big data and big user’s requirements. To overcome these limitations, these companies brought up with new data management techniques, their initiatives results in producing a large interest among several developing companies that werefacing the related problems.
As a result, a new database is designed with novel data management model called as NoSQL (populary called as ―Not Only SQL‖). Today, the NoSQL databases are rapidly growing and deploying in many internet companies and other enterprises. It’s gradually considered as a feasiblechoice when compared to relational databases, especially, more organizations identify that, the performance and scalability
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requirements of big users and big data on a cloud environment can besuccessfully achieved by using NoSQL databases. This paper begins with the causes of migrating towards NoSQL databases by introducing big data, big users and cloud. Meanwhile, this paper takes a deeper look on scalability and performance characteristics of NoSQL databases by explaining CAP theorem, and at the end it address the different security issues related to NoSQL databases. 2. MIGRATING TOWARDS NoSQL DATABASES Out of the many different data-model architectures, the relational data model architecture has been dominating since the 80s, with the implementations like Oracle database[3], MySQL[4] and Microsoft SQL Servers[5]. Laterly, however, the relational databases leads to the problems in many cases because of its data modeling techniques. The exponential growth of complexity of data generated by social networks, sensors, real time systems, and global users etc, and the storage of this huge amount of data on big distributed system, demands evolution of new data management model [6]. Organizations that collect large amount of unstructured and ever changing data are increasingly turning to non-relational or NoSQL databases [7].
Fig -1: Organizations migrating towards NoSQL database
NoSQL databases focus on analytical processing of large scale datasets in warehouses, offering increased scalability over commodity hardware and servers[8]. Computational and storage requirements of applications such as for Big Data Analytics [9], Business Intelligence [10] and social networking over peta-byte datasets have published SQL-like Centralized database to their limits [11]. This led to the development of non-relational data stores called NoSQL databases which are distributed and horizontally scalable, such as Google’s Bigtable[12] and its open source implementation HBase[13] and facebook Cassandra[14]. The emergence of distributed key-value stores, such as Cassandra and Voldemort [15], proves the efficiency and cost effectiveness of their approaches[16]. The limitations with non-relational databases are it is hard to scale with Data warehousing, Grid, Web 2.0 and cloud applications[17]. The strict relational schema of relational databases can be a burden for web applications like blogs, which consists of much different kind of attributes. Text, audios, pictures, videos, real time data and other fast changing information have to be stored within multiple tables. Since such web applications are very agile, underlying database have to be flexible and dynamic as well in order to support easy schema evaluation process [18]. NoSQL systems exhibit the ability to store and index arbitrarily Big Data sets while enabling a large amount of concurrent user requests [8]. Main advantages of NoSQL are the following aspects [20]:
1) Reading and writing data quickly; 2) Support mass storage; 3) Easy to expand; 4) low cost.
2.1Big Data Capturing and collecting the data becomes easier and can be access via third parties such as D&B, Facebook, and Twitter etc.User related personal information, location dependent data, graph oriented data, user generateddata, system logging data, and real time generated data are just a few examples of the ever-changing and expanding blocks of data being collected. It’s not amazing that developers feels the increasing value in leveraging this data to improve existing applications and develops new ones made possible by it. The application of the data is continuously changing the nature of web life that includes web communication, online shopping, web advertisement, entertainment hobbies, and relationship management. The Applications that doesn’t meet the current big data market trends will quickly fall behind.
Fig -2: Big Data: The amount of data is growing
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rapidly, and the nature of data is changing as well [36]
The various kinds of data is collected and it demands for a
very different type of database which should be very flexible
and easily incorporate any new type of data. So the database
must have a capability of efficiently storing and very fast
access to the new types of data that includes semi-structured
and unstructured data.
Fig -3:Big Data Transactions with Interactions and
Observations [37]
Unfortunately, the relational databases have very poor features
to quickly adopt new types of data because of its rigid and
static schema based approach, and is not suitable for semi-structured
and unstructured data.
Finally, the NoSQL meets the growing trends of storage,
processing and retrieval of data by providing a flexible,
schema-less data model that maps the organization’s
requirement and simplifies the communication between the
application and database, that results in less writing code,
debugging and maintenance becomes more easier.
2.2 Big Users
Not then long ago, one thousand users of an application
treated as a lot of users and ten thousand users treated as an
extreme case. But today with the emerging field of cloud,
many applications are hosted on it and it is made available
over the internet 24 hours a day and 365 days a year so that it
supports many users globally [2]. A survey shows that more
than two billion peoples are connected to worldwide and
amount of time they spent online per day is gradually
increasing and results in increase number of concurrent users.
And now many applications have millions of different daily
users.
Fig -4: BigUsers:With the growth in global Internet use,the
number of hours spent online,and increase in
Smartphoneusers,its not uncommon for apps to have millions
of users per day [36]
Because of huge number of concurrent users, it is very
difficult to predict at application usage requirement. It is very
much important that an application dynamically support
rapidly growing huge number of concurrent users.
To achieve this goal, an application must possess following
features:
An application can have features that supports zero
to millions of users.
Application must support frequent active global
users while considering those users which access
application for some time.
New applications can be dramatically scalable and
provide higher fast access process.
The huge number of global users along with dynamic, flexible
usage pattern is the driving force for easily scalable new
database technology.Many application developers find very
much complication to get scalability and faster access rate
with relational database technologies. To overcome this
limitation of relational database technology many application
developers are turning toward NoSQL for help.
2.3 Cloud Computing
Cloud Computing [21] was initially proposed by Google,
Amazon and IBM. There are many definitions, and each
described cloud computing from a different point of view.
A comprehensive definition [22] is ―Cloud computing is a
platform (system) or a type of application. In a cloud
computing environment, the server can be physical server or
virtual server. Cloud computing describes a scalable
application which can access through the internet.‖
Not long ago, most applications are used by single user that
runs on a single system and these applications uses a two-tier
client server architecture supported by a limited number of
users [2].
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Today, with the emergence of cloud, applications uses a three-tier
internet architecture that run in a public or private cloud
that support a huge number of global concurrent users. With
this shift in software architecture, cloud provides many data
intensive business services like platform-as-a-service,
software-as-a-service and infrastructure-as-a-service and these
service models have become more prevalent.
Fig -5: Applications today are increasingly developed using
three-tier internet architecture, requiring a horizontally
scalable database tier that easily scales with the number of
users and amount of data that an application has [36]
In the three-tier architecture, users interact with the
applications through a web browser or by using mobile apps
that is connected to the internet. In the cloud, scale-out
approach is used. If the number of global concurrent user
increasesthen another commodity server is added to the
web/application tier to manage the incoming traffic and this
work will be done by a load balancer very beautifully.
When we compared relational databases and NoSQL
databases, relational databases are problematic because they
are centralized, share-everything technology and scale-up
rather than scale-out.
NoSQL databases are emerged with scale-out approach and
better fit for the three-tier internet architecture and cloud
services.
4. CHARACTERISTICS OF NOSQL
Eric Brewer [23] introduces the CAP theorem for the shared-data
systems. It states that there are three properties of shared-data
systems namely data consistency, system availability and
tolerance to network partitions. The NoSQL databases
primarily classified based on CAP theorem [24] as follows
[25]:
Availability and Partition tolerance (AP)
Such systems ensure availability and partition
tolerance primarily by achieving consistency.
Systems concern AP are Voldemart(Key-value),
CouchDB(Document), Riak(Document) and so on…
Fig -6: Characteristics of NoSQL database [38]
Consistency and Availability(CA)
Here the database mainly uses replication approach to
ensure data consistency and availability. And the Part
of the database is not concerned about the partition
tolerance.
System concerns the CA are Vertica(column-oriented),
GreenPlum(Relational) and so on..
Consistency and Partition tolerance (CP)
The database ensures the consistency and the data is
stored in distributed nodes but database support for
availability is not good.
System concerns the CP are BigTable(Column
Oriented), MongoDB(Document), Berkeley DB(Key-value)
and so on.
4.1 NoSQL’s Performance andScalability
Applications and their underlying databases need to choose
either scale-up approach or scale-out approach to deal with the
concurrent global users, commonly referred as Big Users.[2].
Scaling-up approachrefers to a centralized architecture in
which functionalities are added to existing serversbased on the
increase number of global concurrent users and these servers
becomes bigger and bigger.
Scaling-out refers to a distributed architecture, instead of
adding functionalities to the existing servers the commodity
servers are added to meet the requirement of global users.
NoSQL uses scale-out approach on the three-tier internet
architecture and worked very well. If more global users use
the application, more commodity servers are added to the
application/web tier, and performance is achieved by
distributing the load on increased number of commodity
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servers, and cost depends on number of users. As users
increases, cost increases linearly.
Fig-7(a) Fig-7(b)
Fig -7: a. With relational databases,to support more users or
store more data,need bigger servers with more CPU’s, more
memory and more disk storage, b. NoSQL databases provide a
more linear,scalable approach than relational database [36]
The scale-out approach of NoSQL databases is very much
easier. If huge number of users start using application then
another commodity server is added very simply. There is no
need to modify the application since the application always
sees a single (distributed) database.
Along with performance, cost and scalability of NoSQL
databases, the flexibility is also equally attractive. As users
come and go, commodity servers/virtual machines can be
quickly added or removed from the server pool by keeping
track of the user population and thus operating cost is also
reduced. And, the NoSQL databases are highy fault tolerent
databases because the load is distributed across many
commodity servers and thus support incontinuous operations.
The advantage of scale-out approach is cheaper than the scale-up
approach. in scale-out approach, it is very much expensive
to build, design and support the large big server and such
server is less fault tolerent when compared to commodity
servers. The relational databases are commercially available
and these are expensive,need to purchase liscence, whereas
NoSQL databases are generally open source, priced based on
addition of servers and relatively inexpensive.
4.2 Classification of NoSQL Databases
Based on the data model, NoSQL databases can be classified,
some important are listed as follows [26][20]:
4.3 Key Value StoreDatabases
These are the simplest NoSQL databases. It helps developers
to build applications with schema-less, unformatted data
storage approach, resulting in elimination of fixed data model.
Here the data is stored as a key-value pair. The key is
associated with every single item in the database and it
represents an attribute name together with its value.This type
of database support high concurrency, faster execution of
queries compared to non relational databases.
Ex:Redis [28], TC and TT.
4.4 Column Oriented Databases
These database stores their data in the form of columns,
making it faster read a particular column to memory and
making calculations on all values in a column.These are
optimized for queries over large datasets, and stores column of
data together.
Example: Cassandara [29], Hypertable [30] etc…
4.5 Document Oriented Databases
These databases make use of JSON or XML format to store
the values which is then called as document.These databases
support complex data structures and helps in easy debugging,
conceptualizing data.
Fig -8: Unlike relational databases which stores and retrieve
data from interrelated tables,Document database can store an
entire object in a single JSON document, making it faster to
retrieve [36]
In comparison with relational databases, document databases
support in freely addition of fields to JSON documents, no
need to define changes initially. And also these databases
support dynamic data that can be changed at any time.
5. SECURITY CHALLENGES IN NoSQL
DATABASES
The NoSQL databases emerge with different security issues
[33]. The main focus of NoSQL databases is handling the new
data sets, with less priority on security [35]. The NoSQL
databases are built to meet the requirements of analytical
world of big data, and less emphasis on security is given
during design stage. NoSQL databases donot provide any
feature of embedding security in the database itself.
Developers need to impose security in the middleware.
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In comparison with the relational databases, NoSQL databases provide a very thin layer of security. Generally, an external security enforcement mechanism is essential for NoSQL databases.The major security threats of NoSQL databases are listed below [31[32[34]: 5.1 Transactional Integrity NoSQL databases are failed to ensure transactional integitybecause of its soft nature. Complex integrity constraints cannot be added in NoSQL database architecture because it results in failure to meet the NoSQL’s main objective of attaining better performance and scalability. 5.2 Authentication Mechanisms NoSQL databases are exposing to replay attacks, password brute force attacks, cross-site request forgery, injection attack and man-in-the middle attack results in information leakage. The main reason is NoSQL databases incorporate the weak authentication mechanism and weak password storage techniques. Some NoSQL databases enforce authentication mechanism at local node level, but fail to enforce authentication across all commodity servers. 5.3 Susceptibility to Injection Attacks: Injection attacks add its own choice of data to the noSQL database results in unavailability and corrupted data. Since NoSQL employs very light weight protocols and loosely coupled mechanism in its architecture that allows an attacker to backdoor access of a file system for malicious activities. 5.4 Lack of Consistency NoSQL databases does not satisfies simultaneously all the three properties (consistency, availability, and partition fault tolerance) stated by CAP theorem. NoSQL databases make use of many distributed commodity servers, it doesnot assure consistent results at all time, as all participating commodity servers may not entirely synchronized with other servers holding latest information. If a single commodity server gets fail, results in load imbalance among other commodity servers. 5.5 Insider Attacks: NoSQL databases has poor logging and log analysis methods, due to this an insider attack can gain access to critical data of other users. As NoSQL databases has very thin security layer, it becomes very much difficult for users to maintain control over their data.
6. CONCLUSIONS
Big Users, Big Data, and cloud computing are changing the way that many applications are being developed. The relational databases have dominated industries for many years, but NoSQL databases are now getting attention of application developers due to the following reasons:
NoSQL databases provides schema-less dyanamic flexible data model, that is most suitable for the big users and big data.
NoSQL databases have an ability to scale dramatically to support global users and big data.
NoSQL databases provide an improved performance to satisfy big users expectation without compromising scalability.
To overcome the security issues of NoSQL databases, developers must embed the security mechanism at the middleware along with strengthening the database itself in comparison with the relational databases without compromising the scalability and performance features. REFERENCES [1]. Anna Bjorklund, ―NoSQL databases for Software Project data‖. January 18, 2011.
[2]. Find Couchbase from http://www.couchbase.com
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[4]. MySQL Databases from web: http://www.mysql.com/
[5]. Microsoft SQL Server Databases from web: http://www.microsoft.com/en-us/sqlserver/default.aspx [6]. A B M Moniruzzaman and syed Akhtar Hossain, ―NoSQL database: New Era of databases for Big data Analytics- Classification, Characteristics and camparision.‖ [7]. Levih,N(2010). ―Will NoSQL databases live up to their promise?‖ computer43(2), 12-14. [8].Konstantinou,I.,Angelou, E. Boumpouka,C., Tsoumakos,D., andKoziris,N(2011) October. ―On the elasticity of NoSQL databases over cloud management platforms.‖ [9]. Russom, P. (2011). big data analytics. TDWI Best Practices Report, 4 th Quarter 2011. [10]. Luhn, H. P. (1958). A business intelligence system. IBM Journal of Research and Development, 2(4), 314-319. [11]. Abadi, D. J. (2009). Data management in the cloud: Limitations and opportunities. IEEE Data Eng. Bull, 32(1), 3- 12. [12]. Chang, Fay, et al. "Bigtable: A distributed storage system for structured data."ACM Transactions on Computer Systems (TOCS) 26.2 (2008): 4.
[13]. HBase Databases from web: http://hbase.apache.org/ [14]. Lakshman, A., & Malik, P. (2010). Cassandra—A decentralized structured storage.
[15]. http://www.slideshare.net/adorepump/voldemort-nosql