Abstract: Big data refers to the organizational data asset that exceeds the volume, velocity, and variety of data typically stored using traditional structured database technologies. This type of data has become the important resource from which organizations can get valuable insightand make business decision by applying predictive analysis. This paper provides a comprehensive view of current status of big data development,starting from the definition and the description of Hadoop and MapReduce – the framework that standardizes the use of cluster of commodity machines to analyze big data. For the organizations that are ready to embrace big data technology, significant adjustments on infrastructure andthe roles played byIT professionals and BI practitioners must be anticipated which is discussed in the challenges of big data section. The landscape of big data development change rapidly which is directly related to the trend of big data. Clearly, a major part of the trend is the result ofthe attempt to deal with the challenges discussed earlier. Lastly the paper includes the most recent job prospective related to big data. The description of several job titles that comprise the workforce in the area of big data are also included.
Hadoop and Big Data Readiness in Africa: A Case of Tanzaniaijsrd.com
Big data has been referred to as a forefront pillar of any modern analytics application. Together with Hadoop which is open source software, they have emerged to be a solution to the processing of massive generated both structured and unstructured data. With different strategies and initiatives taken by governments and private institutions in the world towards deployment and support of big data analytics and hadoop, Africa cannot be left isolated. In this paper, we assessed the readiness of Africa with a case study of Tanzania in harnessing the power of big data analytics and hadoop as a tool for drawing insights that might help them make crucial decisions. We used a survey in collecting the data using questionnaires. Results reveal that majority of the companies are either not aware of the technologies or still in their infancy stages in using big data analytics and hadoop. We identified that most companies are in either awakening or advancing stages of the big data continuum. This is attributed by challenges such as lack of IT skills to manage big data projects, cost of technology infrastructure, making decision on which data are relevant, lack of skills to analyze the data, lack of business support and deciding on what technology is best compared to others. It has also been found out that most of the companies' IT officers are not aware with the concepts and techniques of big data analytics and hadoop.
In this paper, we discuss about the Big Data. We
analyze and reveals the benefits of Big Data. We analyze the
big data challenges and how Hadoop gives solution to it. This
research paper gives the comparison between relational
databases and Hadoop. This research paper also gives reason
of why Big Data and Hadoop.
General Terms
Data Explosion, Big Data, Big Data Analytics, Hadoop, Hadoop
Distributed File System, MapReduce
Big Data Analytics: Recent Achievements and New ChallengesEditor IJCATR
The era of Big data is being generated by everything around us at all times. Every digital process and social media
exchange produces it. Systems, sensors and mobile devices transmit it. Big data is arriving from multiple sources at an alarming
velocity, volume and variety. To extract meaningful value from big data, you need optimal processing power, analytics
capabilities and skills. Big data has become an important issue for a large number of research areas such as data mining,
machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The combination of
big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas
as social media and social networks. These new challenges are focused mainly on problems such as data processing, data
storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and
tracking data, among others. In this paper, discussion about the new concept big data and data analytic their concept, tools
and methodologies that is designed to allow for efficient data mining and information sharing fusion from social media and of
the new applications and frameworks that are currently appearing under the “umbrella” of the social networks, social media
and big data paradigms.
Hadoop and Big Data Readiness in Africa: A Case of Tanzaniaijsrd.com
Big data has been referred to as a forefront pillar of any modern analytics application. Together with Hadoop which is open source software, they have emerged to be a solution to the processing of massive generated both structured and unstructured data. With different strategies and initiatives taken by governments and private institutions in the world towards deployment and support of big data analytics and hadoop, Africa cannot be left isolated. In this paper, we assessed the readiness of Africa with a case study of Tanzania in harnessing the power of big data analytics and hadoop as a tool for drawing insights that might help them make crucial decisions. We used a survey in collecting the data using questionnaires. Results reveal that majority of the companies are either not aware of the technologies or still in their infancy stages in using big data analytics and hadoop. We identified that most companies are in either awakening or advancing stages of the big data continuum. This is attributed by challenges such as lack of IT skills to manage big data projects, cost of technology infrastructure, making decision on which data are relevant, lack of skills to analyze the data, lack of business support and deciding on what technology is best compared to others. It has also been found out that most of the companies' IT officers are not aware with the concepts and techniques of big data analytics and hadoop.
In this paper, we discuss about the Big Data. We
analyze and reveals the benefits of Big Data. We analyze the
big data challenges and how Hadoop gives solution to it. This
research paper gives the comparison between relational
databases and Hadoop. This research paper also gives reason
of why Big Data and Hadoop.
General Terms
Data Explosion, Big Data, Big Data Analytics, Hadoop, Hadoop
Distributed File System, MapReduce
Big Data Analytics: Recent Achievements and New ChallengesEditor IJCATR
The era of Big data is being generated by everything around us at all times. Every digital process and social media
exchange produces it. Systems, sensors and mobile devices transmit it. Big data is arriving from multiple sources at an alarming
velocity, volume and variety. To extract meaningful value from big data, you need optimal processing power, analytics
capabilities and skills. Big data has become an important issue for a large number of research areas such as data mining,
machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The combination of
big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas
as social media and social networks. These new challenges are focused mainly on problems such as data processing, data
storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and
tracking data, among others. In this paper, discussion about the new concept big data and data analytic their concept, tools
and methodologies that is designed to allow for efficient data mining and information sharing fusion from social media and of
the new applications and frameworks that are currently appearing under the “umbrella” of the social networks, social media
and big data paradigms.
Memory Management in BigData: A Perpective Viewijtsrd
The requirement to perform complicated statistic analysis of big data by institutions of engineering, scientific research, health care, commerce, banking and computer research is immense. However, the limitations of the widely used current desktop software like R, excel, minitab and spss gives a researcher limitation to deal with big data and big data analytic tools like IBM BigInsight, HP Vertica, SAP HANA & Pentaho come at an overpriced license. Apache Hadoop is an open source distributed computing framework that uses commodity hardware. With this project, I intend to collaborate Apache Hadoop and R software to develop an analytic platform that stores big data (using open source Apache Hadoop) and perform statistical analysis (using open source R software).Due to the limitations of vertical scaling of computer unit, data storage is handled by several machines and so analysis becomes distributed over all these machines. Apache Hadoop is what comes handy in this environment. To store massive quantities of data as required by researchers, we could use commodity hardware and perform analysis in distributed environment. Bhavna Bharti | Prof. Avinash Sharma"Memory Management in BigData: A Perpective View" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14436.pdf http://www.ijtsrd.com/engineering/computer-engineering/14436/memory-management-in-bigdata-a-perpective-view/bhavna-bharti
Big data is a term that describes a large or complex
data volume. That data volume can be processes using traditional
data processing software or techniques that are insufficient to deal
with them. But big data is often noisy, heterogeneous, irrelevant
and untrustworthy. As the speed of information growth exceeds
Moore’s Law at the beginning of this new century, excessive data
is making great troubles to human beings. However this data with
special attributes can’t be managed and processed by the current
traditional software system, which become a real problem. In this
paper was discussed some big data challenges and problems that
are faced by organizations. These challenges may relate
heterogeneity, scale, timelines, privacy and human collaboration.
Survey method was used as a theoretical solution framework.
Survey method consists of a questionnaires report. Questionnaires
report consists of all challenges and problems faced by
organizations. After knowing the problem and challenges of
organizations, a solution was given to organization to solve big
data challenges.
Are you having doubts and questions about how to use Big Data in your organizations? The presentation here would clear some of your doubts.
Feel free to comment if you have more queries or write to us at: bigdata@xoriant.com
Influence of Hadoop in Big Data Analysis and Its Aspects IJMER
This paper is an effort to present the basic understanding of BIG DATA and
HADOOP and its usefulness to an organization from the performance perspective. Along-with the
introduction of BIG DATA, the important parameters and attributes that make this emerging concept
attractive to organizations has been highlighted. The paper also evaluates the difference in the
challenges faced by a small organization as compared to a medium or large scale operation and
therefore the differences in their approach and treatment of BIG DATA. As Hadoop is a Substantial
scale, open source programming system committed to adaptable, disseminated, information
concentrated processing. A number of application examples of implementation of BIG DATA across
industries varying in strategy, product and processes have been presented. This paper also deals
with the technology aspects of BIG DATA for its implementation in organizations. Since HADOOP has
emerged as a popular tool for BIG DATA implementation. Map reduce is a programming structure for
effectively composing requisitions which prepare boundless measures of information (multi-terabyte
information sets) in- parallel on extensive bunches of merchandise fittings in a dependable,
shortcoming tolerant way. A Map reduce skeleton comprises of two parts. They are “mapper" and
"reducer" which have been examined in this paper. The paper deals with the overall architecture of
HADOOP along with the details of its various components in Big Data.
Identifying and analyzing the transient and permanent barriers for big datasarfraznawaz
Auspiciously, big data analytics had made it possible to generate value from immense amounts of raw data. Organizations are able to seek incredible insights which assist them in effective decision making and providing quality of service by establishing innovative strategies to recognize, examine and address the customers’ preferences. However, organizations are reluctant to adopt big data solutions due to several barriers such as data storage and transfer, scalability, data quality, data complexity, timeliness, security, privacy, trust, data ownership, and transparency. Despite the discussion on big data opportunities, in this paper, we present the findings of our in-depth review process that was focused on identifying as well as analyzing the transient and permanent barriers for adopting big data. Although, the transient barriers for big data can be eliminated in the near future with the advent of innovative technical contributions, however, it is challenging to eliminate the permanent barriers enduringly, though their impact could be recurrently reduced with the efficient and effective use of technology, standards, policies, and procedures.
Big Data Tutorial | What Is Big Data | Big Data Hadoop Tutorial For Beginners...Simplilearn
This presentation about Big Data will help you understand how Big Data evolved over the years, what is Big Data, applications of Big Data, a case study on Big Data, 3 important challenges of Big Data and how Hadoop solved those challenges. The case study talks about Google File System (GFS), where you’ll learn how Google solved its problem of storing increasing user data in early 2000. We’ll also look at the history of Hadoop, its ecosystem and a brief introduction to HDFS which is a distributed file system designed to store large volumes of data and MapReduce which allows parallel processing of data. In the end, we’ll run through some basic HDFS commands and see how to perform wordcount using MapReduce. Now, let us get started and understand Big Data in detail.
Below topics are explained in this Big Data presentation for beginners:
1. Evolution of Big Data
2. Why Big Data?
3. What is Big Data?
4. Challenges of Big Data
5. Hadoop as a solution
6. MapReduce algorithm
7. Demo on HDFS and MapReduce
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Big Data Summarization : Framework, Challenges and Possible Solutionsaciijournal
In this paper, we first briefly review the concept of big data, including its definition, features, and value. We then present background technology for big data summarization brings to us. The objective of this paper is to discuss the big data summarization framework, challenges and possible solutions as well as methods of evaluation for big data summarization. Finally, we conclude the paper with a discussion of open problems and future directions..
Big Data Summarization : Framework, Challenges and Possible Solutionsaciijournal
In this paper, we first briefly review the concept of big data, including its definition, features, and value.
We then present background technology for big data summarization brings to us. The objective of this
paper is to discuss the big data summarization framework, challenges and possible solutions as well as
methods of evaluation for big data summarization. Finally, we conclude the paper with a discussion of open problems and future directions..
Memory Management in BigData: A Perpective Viewijtsrd
The requirement to perform complicated statistic analysis of big data by institutions of engineering, scientific research, health care, commerce, banking and computer research is immense. However, the limitations of the widely used current desktop software like R, excel, minitab and spss gives a researcher limitation to deal with big data and big data analytic tools like IBM BigInsight, HP Vertica, SAP HANA & Pentaho come at an overpriced license. Apache Hadoop is an open source distributed computing framework that uses commodity hardware. With this project, I intend to collaborate Apache Hadoop and R software to develop an analytic platform that stores big data (using open source Apache Hadoop) and perform statistical analysis (using open source R software).Due to the limitations of vertical scaling of computer unit, data storage is handled by several machines and so analysis becomes distributed over all these machines. Apache Hadoop is what comes handy in this environment. To store massive quantities of data as required by researchers, we could use commodity hardware and perform analysis in distributed environment. Bhavna Bharti | Prof. Avinash Sharma"Memory Management in BigData: A Perpective View" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14436.pdf http://www.ijtsrd.com/engineering/computer-engineering/14436/memory-management-in-bigdata-a-perpective-view/bhavna-bharti
Big data is a term that describes a large or complex
data volume. That data volume can be processes using traditional
data processing software or techniques that are insufficient to deal
with them. But big data is often noisy, heterogeneous, irrelevant
and untrustworthy. As the speed of information growth exceeds
Moore’s Law at the beginning of this new century, excessive data
is making great troubles to human beings. However this data with
special attributes can’t be managed and processed by the current
traditional software system, which become a real problem. In this
paper was discussed some big data challenges and problems that
are faced by organizations. These challenges may relate
heterogeneity, scale, timelines, privacy and human collaboration.
Survey method was used as a theoretical solution framework.
Survey method consists of a questionnaires report. Questionnaires
report consists of all challenges and problems faced by
organizations. After knowing the problem and challenges of
organizations, a solution was given to organization to solve big
data challenges.
Are you having doubts and questions about how to use Big Data in your organizations? The presentation here would clear some of your doubts.
Feel free to comment if you have more queries or write to us at: bigdata@xoriant.com
Influence of Hadoop in Big Data Analysis and Its Aspects IJMER
This paper is an effort to present the basic understanding of BIG DATA and
HADOOP and its usefulness to an organization from the performance perspective. Along-with the
introduction of BIG DATA, the important parameters and attributes that make this emerging concept
attractive to organizations has been highlighted. The paper also evaluates the difference in the
challenges faced by a small organization as compared to a medium or large scale operation and
therefore the differences in their approach and treatment of BIG DATA. As Hadoop is a Substantial
scale, open source programming system committed to adaptable, disseminated, information
concentrated processing. A number of application examples of implementation of BIG DATA across
industries varying in strategy, product and processes have been presented. This paper also deals
with the technology aspects of BIG DATA for its implementation in organizations. Since HADOOP has
emerged as a popular tool for BIG DATA implementation. Map reduce is a programming structure for
effectively composing requisitions which prepare boundless measures of information (multi-terabyte
information sets) in- parallel on extensive bunches of merchandise fittings in a dependable,
shortcoming tolerant way. A Map reduce skeleton comprises of two parts. They are “mapper" and
"reducer" which have been examined in this paper. The paper deals with the overall architecture of
HADOOP along with the details of its various components in Big Data.
Identifying and analyzing the transient and permanent barriers for big datasarfraznawaz
Auspiciously, big data analytics had made it possible to generate value from immense amounts of raw data. Organizations are able to seek incredible insights which assist them in effective decision making and providing quality of service by establishing innovative strategies to recognize, examine and address the customers’ preferences. However, organizations are reluctant to adopt big data solutions due to several barriers such as data storage and transfer, scalability, data quality, data complexity, timeliness, security, privacy, trust, data ownership, and transparency. Despite the discussion on big data opportunities, in this paper, we present the findings of our in-depth review process that was focused on identifying as well as analyzing the transient and permanent barriers for adopting big data. Although, the transient barriers for big data can be eliminated in the near future with the advent of innovative technical contributions, however, it is challenging to eliminate the permanent barriers enduringly, though their impact could be recurrently reduced with the efficient and effective use of technology, standards, policies, and procedures.
Big Data Tutorial | What Is Big Data | Big Data Hadoop Tutorial For Beginners...Simplilearn
This presentation about Big Data will help you understand how Big Data evolved over the years, what is Big Data, applications of Big Data, a case study on Big Data, 3 important challenges of Big Data and how Hadoop solved those challenges. The case study talks about Google File System (GFS), where you’ll learn how Google solved its problem of storing increasing user data in early 2000. We’ll also look at the history of Hadoop, its ecosystem and a brief introduction to HDFS which is a distributed file system designed to store large volumes of data and MapReduce which allows parallel processing of data. In the end, we’ll run through some basic HDFS commands and see how to perform wordcount using MapReduce. Now, let us get started and understand Big Data in detail.
Below topics are explained in this Big Data presentation for beginners:
1. Evolution of Big Data
2. Why Big Data?
3. What is Big Data?
4. Challenges of Big Data
5. Hadoop as a solution
6. MapReduce algorithm
7. Demo on HDFS and MapReduce
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Big Data Summarization : Framework, Challenges and Possible Solutionsaciijournal
In this paper, we first briefly review the concept of big data, including its definition, features, and value. We then present background technology for big data summarization brings to us. The objective of this paper is to discuss the big data summarization framework, challenges and possible solutions as well as methods of evaluation for big data summarization. Finally, we conclude the paper with a discussion of open problems and future directions..
Big Data Summarization : Framework, Challenges and Possible Solutionsaciijournal
In this paper, we first briefly review the concept of big data, including its definition, features, and value.
We then present background technology for big data summarization brings to us. The objective of this
paper is to discuss the big data summarization framework, challenges and possible solutions as well as
methods of evaluation for big data summarization. Finally, we conclude the paper with a discussion of open problems and future directions..
BIG DATA SUMMARIZATION: FRAMEWORK, CHALLENGES AND POSSIBLE SOLUTIONSaciijournal
In this paper, we first briefly review the concept of big data, including its definition, features, and value.We then present background technology for big data summarization brings to us. The objective of this paper is to discuss the big data summarization framework, challenges and possible solutions as well as methods of evaluation for big data summarization. Finally, we conclude the paper with a discussion of open problems and future directions..
Big Data Summarization : Framework, Challenges and Possible Solutionsaciijournal
In this paper, we first briefly review the concept of big data, including its definition, features, and value.
We then present background technology for big data summarization brings to us. The objective of this
paper is to discuss the big data summarization framework, challenges and possible solutions as well as
methods of evaluation for big data summarization. Finally, we conclude the paper with a discussion of
open problems and future directions..
Big data is the term for any gathering of information sets, so expensive and complex, that it gets to be hard to process for utilizing customary information handling applications. The difficulties incorporate investigation, catch, duration, inquiry, sharing, stockpiling, Exchange, perception, and protection infringement. To reduce spot business patterns, anticipate diseases, conflict etc., we require bigger data sets when compared with the smaller data sets. Enormous information is hard to work with utilizing most social database administration frameworks and desktop measurements and perception bundles, needing rather enormously parallel programming running on tens, hundreds, or even a large number of servers. In this paper there was an observation on Hadoop architecture, different tools used for big data and its security issues.
In today’s context, the big data market is rapidly undergoing contortions that define market maturity, such as consolidation. Big data refers to large volumes of data. This can be both structured and unstructured data. Big data is data that is huge in size and grows exponentially with time. As the data is too large and complex, traditional data management tools are not sufficient for storing or processing it efficiently. But analyzing big data is crucial to know the patterns and trends to be adopted to improve your business.
With many organisations considering getting on the Hadoop bandwagon, this document provides an overview of the planned use cases for Hadoop, an illustration of some of the common technology components, suggestions on when Hadoop is worth considering, some the challenges organisations are experiencing, cost considerations and finally, how an organisation should position for a Big Data initiative. Any organisation considering a Big Data initiative with Hadoop should thoroughly consider each of these areas before embarking on a course of action.
An Comprehensive Study of Big Data Environment and its Challenges.ijceronline
Big Data is a data analysis methodology enabled by recent advances in technologies and Architecture. Big data is a massive volume of both structured and unstructured data, which is so large that it's difficult to process with traditional database and software techniques. This paper provides insight to Big data and discusses its nature, definition that include such features as Volume, Velocity, and Variety .This paper also provides insight to source of big data generation, tools available for processing large volume of variety of data, applications of big data and challenges involved in handling big data
A Comprehensive Study on Big Data Applications and Challengesijcisjournal
Big Data has gained much interest from the academia and the IT industry. In the digital and computing
world, information is generated and collected at a rate that quickly exceeds the boundary range. As
information is transferred and shared at light speed on optic fiber and wireless networks, the volume of
data and the speed of market growth increase. Conversely, the fast growth rate of such large data
generates copious challenges, such as the rapid growth of data, transfer speed, diverse data, and security.
Even so, Big Data is still in its early stage, and the domain has not been reviewed in general. Hence, this
study expansively surveys and classifies an assortment of attributes of Big Data, including its nature,
definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a
data life cycle that uses the technologies and terminologies of Big Data. Map/Reduce is a programming
model for efficient distributed computing. It works well with semi-structured and unstructured data. A
simple model but good for a lot of applications like Log processing and Web index building.
Injection Analysis of Hera And Betano New Power Plants At the Interconnection...IJRESJOURNAL
ABSTRACT: Electricity system in Timor Leste is supplied from small scale Diesel Power Plants (DPP) which are distributed at each district that not interconnected, as the consequences, electric continuity at some districts are disturbed so caused power outage. To overcome the matters, the steps taken by Timor Leste government by establishing 2 units of centered DPP with total capacity of 250 MW at Dili District of 120 MW and at BetanoDistrict of 130 MW. The new power plant will be injected at the electricity system of Timor Leste through transmission line of 150 kV. The new power plant injection will cause the power flow and system stability of Timor Leste entirely. Steady state analysis done including the power flow analysis before and after injection of DPP of Hera and Betano so can be seen the voltage profile change and the decrease of electric power losses. Beside the steady state analysis, also done the power system stability analysis to know whether the system can operate normally after short circuit disturbance of three phases before and after new DPP injection. The steady state analysis showed that the system voltage condition before injection of DPP of Hera and Betano experience decrease of -13% from the sending voltage, and the active power losses of 6.8%. After DPP of Hera and Betano injection the decrease only -6% and active power losses can be minimized become 5.3%. The results of power system stability showed the rotor angle stability, frequency and voltage stability during disturbance occurrence become more stable after injection with recovery time faster if compared with before injection of new power plant.
Harmonic AnalysisofDistribution System Due to Embedded Generation InjectionIJRESJOURNAL
ABSTRACT:The increased demand for electricity and the depletion of fossil energy sources are a challenge to exploit new and renewable energy sources. Relatively cheap renewable energy sources are Wind Power Plant (WPP) and Photovoltaic System (PV). Currently, many small scale generating plants are being evolved into conventional systems known as Embedded Generation (EG). EG as a source of electricity in the distribution system will affect the flow of system power, system reliability, voltage profile and others. Besides, with the placement of converter technology in WPP and PV system will give contribution of harmonic enhancement to the system. This paper presents the harmonic analysis of WPP and PV designs that are injected into conventional distribution system in one bus at Pujon feeder station Malang, Indonesia. The bus is chosen because in this area the need for electric power in the area is very high and the existing system has a harmonic value of 11%. Hybrid Active Filter (HAF) is designed to lower the voltage harmonics due to EG injection into existing systems without affecting harmonics in other buses. To analyze the harmonics in this study there are 4 scenarios offered: Scenario 1 starts with analysis on existing system, scenario 2 existing in WPP 2 MVA injection, scenario 3, injection1.3 MVA PV, scenario 4 injected EG (WPP and PV). The simulation result using PSCAD 4.5 shows in scenario 4 to generate harmonic voltage of 18.6% and after added with HAF, the harmonic value of the voltage becomes 2.434%
Decision Support System Implementation for Candidate Selection of the Head of...IJRESJOURNAL
ABSTRACT: One of agency under the subdivision of The Indonesian National Army is Bintaldam V Brawijaya, acts as the mental founding agency. The head of affairs position replacement is often occurred in this agency, but the positions currently have a large number of incompetence person in charge. Subjection inthe election process leads to the inaccurate placement, resulting in poor leadership. The process of head of affairs assignment starts from candidates dispatching from each head of administrative section. Those candidates must then meet the three elements of assessment, i.e. the personality element, qualification element, and potential element. The candidates will be selected by head of agency as the top leader in the agency. The head of agency, however, poses difficulties to determine which candidate to put into position, frequently because of no proportional system exists to provide assistance in decision making process. A method is needed tomake more accurate placement for better leadership result.This research utilizegroup technology as the assessment elements hierarchical data structure and decision table as the rule evaluation engine to form a decision support system for making the replacement process of the heads of affairs easier and more accurate.
Yield Forecasting to Sustain the Agricultural Transportation UnderStochastic ...IJRESJOURNAL
ABSTRACT: Agricultural transportation is a major part of the United States’ transportation systems. This system follows a complex multimodal network consisting of highway, railway, and waterways which are mostly based on the yield of the agricultural commodities and their market values. The yield of agricultural commodities is dependent on stochastic environment such as weather conditions, rainfall, soil type and natural disasters. Different techniques such as leaf growth index, Normalized Difference Vegetation Index (NDVI), and regression analysis are used to forecast the yield for the end of harvest season. The yield forecasting techniques are used to predict the agricultural transportation needs and improve the cost minimization. This study provides a model for yield forecasting using NDVI data, Geographical Information System (GIS), and statistical analysis. A case study is presented to demonstrate this model with a novel tool for collecting NDVI data.
Injection Analysis of 5 Mwp Photovoltaic Power Plant on 20 Kv Distribution Ne...IJRESJOURNAL
ABSTRACT : A new 5 MWp PLTS or PV has been built in Oelpuah Village, Kupang Tengah. According to PT. PLN NTT,the amount of injected power to the distribution network cannot reach its maximum capability. At the moment, the average value of injected active power is 2 MWp. In this paper, a solution to achieve maximum injected power will be discussed. As a result of the study, the main problem is absence of energy storage. It cause the output power of the PV strongly affected by the weather. Hence a fluctuation of generated power cannot be avoided. The frequency in the distribution network system can be easily increased or decreased. Sudden drop in frequency due to 5 MWp lost, will cause the frequency to decrease beyond the standard value. The under frequency relay trips because it takes a long time to return to the originalvalue. A 5% of droop setting value is not suitable with those conditions. The droop characteristic setting should be reduced to 1% as a proposed solution. So that the response to frequency changes is more sensitive
Developing A Prediction Model for Tensile Elastic Modulus of Steel Fiber – Ce...IJRESJOURNAL
ABSTRACT: This paper attempts to develop a prediction model that can be used in line with prescribed laboratory experiments for indirect tensile test such that tensile elastic modulus can be predicted for cement stabilized lateritic soil reinforced with steel fiber using measured properties of the material. The results of the tensile elastic modulus obtained from the Derived Prediction Model almost nearly replicates that obtained from calculations from laboratory experimentation. Results obtained revealed that both the predicted values and calculated values have a linear correlation with an R2 of 96.4%. On this basis the Derived Prediction Model can be said to be valid within the limits of the study.
Effect of Liquid Viscosity on Sloshing in A Rectangular TankIJRESJOURNAL
ABSTRACT : Liquid sloshing was investigated for a moving partially filled rectangular tank with/without vertical baffles. A set of experiments was conducted using two types of liquids: water and sunflower oil. For these liquids, the effects of varying the external excitation amplitude and the number of vertical baffles on sloshing are discussed. It was found that the mechanical dissipation due to the liquid viscosity has a remarkable influence on the sloshing characteristics
Design and Implementation Decision Support System using MADM Methode for Bank...IJRESJOURNAL
ABSTRACT: The function of banking process can be broadly defined as an institution functioning as a capital receiver and lender, as well as support for trading and payment transactions. In order to maintain the stability of the economy through lending, Bank Indonesia issued a form letter on March 15, 2012 on the application of risk management for the bank conducting credit. In an effort to minimize these problems, Bank Indonesia recommends the precautionary principle in arranging the loan terms and choose the prospective customer in the credit granting institutions, both banks and cooperatives to take into account the risk on lending. A method is needed to select bank for credit applications to the public, i.e. the customer. This research uses the comparison of MADM (Multiple Attribute Decision Making) between TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method and ELECTRE (ELimination Et Choix TRaduisant la realitE) method for the loan provisions to the customers. With the hope of getting the quickest and the most accurate solutions, the hesitancy in determining customers for lending can then be minimized.
Investigations on The Effects of Different Heat Transfer Coefficients in The ...IJRESJOURNAL
ABSTRACT: In the metal machining, the cutting fluid has become a tough problem in term of the health of works and environmental protection. The heat transfer coefficients of the water-based fluid, mineral oil and plant oil are distinct. An investigation focused on the effects of different heat transfer coefficients (h) on the cutting thickness compression ratio, chip formation, stress distribution and specific cutting energy is presented and discussed. In this study, three heat transfer coefficients have been simulated by Third WaveAdvantedge in machining AISI 1045 steel during different cutting speeds.It has been shown that the Mises stress and temperature are both affected by the heat transfer coefficient. When the h reaches higher, the Mises stress increases and the temperature shows the opposite trend. Also, the results can be found that the chip compression ratio decreases and shear angle increases when hrises. The relationship between specific cutting energy and heat transfer coefficients can be found in this paper.
Strategy of Adaptation of Traditional House Architecture Bali AgaIJRESJOURNAL
ABSTRACT : Adaptation is defined as a change to adapt to the environment or change the environment to fit the need to achieve balance. The Bali Aga community in Pengotan village has a tradition of building a house that refers to the concept of Tri Angga based on the Tri Hita Karana philosophy which is an expression of harmony with God, with human beings, and with the natural surroundings. Each element and layout of the building represents the alignment. In line with the development of the era, the pattern of community life changed resulting in traditional buildings are very regular and uniforms individually undergo a process of change without leaving the concept of Tri Angga and the philosophy of Tri Hita Karana.This paper is the result of field research that examines building changes by taking the example of traditional homes that undergo many changes to be able to conclude how traditional value changes occur in the original house.The results indicate that the change occurred partly due to the personal tastes of its inhabitants without the loss of fundamental changes in their philosophical value.Changes are found in non-structural building components and are strongly influenced by the ease of implementation of construction aspects. The building facade is striking with the appearance of ornaments on traditional buildings previously unknown in Pengotan
Design of Universal Measuring Equipment for Bogie FrameIJRESJOURNAL
ABSTRACT: The performance parameters of the bogie which directly affect the quality and safety of train operation should be detected during maintenance, especially the structure parameters of bogie frame after many operation years. Based on the related research of the bogie frame measuring requirement, a universal measuring equipment is designed for metro overhaul. The function, basic principle, structure and software design of the universal measuring equipment for bogie frame parameters are described, and the summary meanwhile prospect are given.
A Design Of Omni-Directional Mobile Robot Based On Mecanum WheelsIJRESJOURNAL
ABSTRACT:As one of the important branch of mobile robotics, wheel mobile robot has long been paid atten tion to by the research people at home and abroad for its high load ability, positioning accuracy, high efficiency, simple control, etc. Mobile robot has close relation to many technologies suc-h as control theory, computer tech nology, sensor technology, etc. Therefore, research on the mobile robot has important significance
An Investigation Into The Prevalence of Code Switching in the Teaching of Nat...IJRESJOURNAL
ABSTRACT: This study examines the functions of code-switching in primary schools by science teachers. In Namibia,English is the official language of instruction for science at primary school. At lower primary, Silozi is the language of instruction. Classroom interaction data was obtained from two science lessons. Analysis of the teachers' code-switching shows that code-switching in the two lessons was vastly different, with little codeswitching in the teacher-facilitated lesson.Evident in other lessons, in which science was taught as a content subjectbut with abstract names that had no corresponding local names in Silozi, there was frequent use of codeswitching for reiteration and message qualification. The direction of the language switch from Silozi to English as well as the proportion of teachersspeaking in English suggests that the official language for teaching is English at upper primary, grade 4 to 7. The science lesson and code-switching is a necessary tool for teachers to achieve teaching goals in content-based lessons involving students who lack proficiency in the instructional language. The study was conducted in five government primary schools in Katima Mulilo, the capital of the Zambezi region in Namibia.The national language is English language, with no exception inscience, mathematics, and language subjects.All Schools are located in a Katima Mulilo-urban. The students are from mixed classes, lower, middle and upper class families with their parents typically working as unemployed single mothers, domestic workers, clerks, nurses, teachers, and accountants. Some of the students could understand English because of their parents‟ educational background or in instances where English is spoken at home.
The Filling Up of Yogyakarta Governor Position in Social Harmony PerspectiveIJRESJOURNAL
ABSTRACT: This paper aims to provide an overview in filling up the position of Governor of Yogyakarta, Distinctive Region, Indonesia, that considering social harmony perspective. Discussion conducts using social harmony perspectives, because the Sultan as the King was suspected to devise making her daughter the Queen who will be automatically the Governor succeeding him. This paper based on sociology legal research, this study approached the legal issues in accordance with the fact in social life, to provide an overview existing condition rules and public perseptions in filling position of Governor of Yogyakarta, especially about gender (female) position. Dataas are collected by interviewed and distributed questionnaires to various groups. Considering the result of research, that the Sultan’s way generated a sufficiently deep polemics in the Palace environment, government and society, so that this even kept away from the customary values of Javanese custom and Islam religion emphasizing on social harmony.
Quality Function Deployment And Proactive Quality Techniques Applied to Unive...IJRESJOURNAL
ABSTRACT: Lecturing and instruction to students at university has traditionally been based on qualifications, experience and position of academics within ones department or college. The higher the level and more advanced the subject then the most experienced lecturers are traditionally selected for that task. Visiting lecturers are never asked to teach basic mathematics or science, they are to share their experience and enlighten the students from a vast knowledge and history. This paper reviews and discusses Kano’s model with Quality Function Deployment related to customer satisfaction and compares if the traditional approach is in keeping with university practice. Furthermore, it argues that industry has concepts and ideas that can be more proactive if applied to an educational environment where students’ demands are ever increasing and their expectations are becoming higher. If universities are to improve student-learning experiences then novel and successful techniques are needed. One such approach is discussed in this research paper to find better ways to improve student satisfaction.
The Analysis And Perspective on Development of Chinese Automotive Heavy-Duty ...IJRESJOURNAL
ABSTRACT: In recent years, under the influence of both China's domestic market demand and emissions standard improvement, Chinese manufacturers put great effort on the research and design of automotive heavy-duty diesel engine. This paper analyzes the technical parameters of heavy duty diesel engine in 11 / 13L displacement section and introduces its performance. At the same time, combined with the development of foreign heavy-duty diesel engine, the future development direction of Chinese heavy-duty diesel engine is forecasted.
Research on the Bottom Software of Electronic Control System in Automobile El...IJRESJOURNAL
ABSTRACT: With the development of science and technology, car replacement faster and faster. The development of the automotive industry has a contradiction, on the one hand, the speed of upgrading the car technology can not keep up with the speed of the performance requirements of the car, on the other hand, the country's automobile exhaust emission standards become more stringent. In addition, the depletion of oil resources led to the rise in gasoline prices, the traditional car is facing a crisis. Considering the situation of gas fuel resource structure and supply situation in China, it is feasible to promote gas fuel engine[1].However, the pollution caused by the car has become one of the major pollution sources in the urban environment and the atmospheric environment, and this trend continues to deteriorate[2].Therefore, alternative energy vehicles and hybrid cars is the main direction of development, and any improvement in the car will be car electronics and software replacement for the premise. On the one hand, natural gas as an alternative to gasoline, with its low prices, excellent combustion emissions, the relative sustainable development and other characteristics of more and more car manufacturers favor;On the other hand, the mainstream of the automotive electronic control unit ECU software development to AUTOSAR structure, low power consumption, functional safety for the development direction. Based on the actual development of natural gas engine control unit, the structure and function of ECU software are studied with reference to AUTOSAR software design standard. This paper studies the structure of the application of the software layer of the electronic control system and the main control strategy under the various conditions of the structure, and puts forward the underlying software resources needed by the application layer software. This paper analyzes the internal and peripheral resources of Infineon XC2785x microcontroller and designs hardware abstraction layer software and ECU abstraction layer software. The current characteristics of the jet valve driven by the natural gas multi-point injection engine were investigated. Automotive electronics technology has been widely used in modern vehicles which, and gradually become the development of new models, improve the performance of the key technical factors[3] .
Risk Based Underwater Inspection (RBUI) For Existing Fixed Platforms In Indon...IJRESJOURNAL
Abstract For existing fixed platforms in Indonesian waters, a method to determine underwater inspection basedon the risk level is needed as an alternative for the conventional time-based underwater inspection. This paper discusses the development of Risk Based Underwater Inspection (RBUI) for Indonesian fixed offshore platforms by adopting the inspection scope from API RP 2A-WSD and API RP 2SIM. The risk will be determined based on the calculated Consequence of Failure (CoF) and Probability of Failure (PoF), and then it will be converted into a relevant inspection interval according to the references. In addition, it had also been discovered that the minimum fatigue of a platform that is shorter than the intended design life appeared to be the major problem of the Indonesian existing platforms. Therefore, this condition would be taken into consideration as a factor to override the preliminary inspection interval plan. Sample of 10 platform data located in Indonesian waters were used for RBUI analysis in this paper.
ABSTRACT: In order to achieve the precise control of the four rotorcraft, we must first obtain the accurate mathematical model of the four rotorcraft.This study analyzes the mechanical structure of the four rotorcraft and ignores the effects of the corresponding air resistance and the associated external secondary factors.The actual environment of the four rotorcraft is simplified, and the main factors of the four rotorcraft in motion are seized to establish a more accurate mathematical model.In the modeling process four-rotor aircraft, using the most current hot research field research methods.Mode using the angular velocity and the linear velocity of the separated solver and attitude by the coordinate transformation, and motion Newton Euler's formula to solve.Thereby establishing a nonlinear dynamic model four-rotor aircraft.Finally, a simplified mathematical model of four rotorcraft is obtained by comprehensive analysis of the relevant constraints of mathematical model of four rotorcraft.This makes the accuracy of the model aircraft system higher, more convenient control four rotorcraft.Which has certain reference value and guiding significance for the study of future stability of aircraft system.
A New Approach on Proportional Fuzzy Likelihood Ratio orderings of Triangular...IJRESJOURNAL
ABSTRACT: In this chapter, we introduce a new approach on the concept of proportional fuzzy likelihood ratio orderings, increasing and decreasing proportional fuzzy likelihood ratio orderings of triangular fuzzy random variables are presented. Based on these orderings, some theorems are also established.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
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Moving Toward Big Data: Challenges, Trends and Perspectives
1. International Journal of Research in Engineering and Science (IJRES)
ISSN (Online): 2320-9364, ISSN (Print): 2320-9356
www.ijres.org Volume 5 Issue 1 ǁ January. 2017 ǁ PP.16-21
www.ijres.org 16 | Page
Moving Toward Big Data: Challenges, Trends and Perspectives
Ming-Hsing Chiu1
, Joshua Mcadams2
1
Department of Computer Science, Dillard University, New Orleans, LA, USA
2
Google-in-Residence Scholar, Dillard University, New Orleans, LA, USA
Abstract: Big data refers to the organizational data asset that exceeds the volume, velocity, and variety of data
typically stored using traditional structured database technologies. This type of data has become the important
resource from which organizations can get valuable insightand make business decision by applying predictive
analysis. This paper provides a comprehensive view of current status of big data development,starting from the
definition and the description of Hadoop and MapReduce – the framework that standardizes the use of cluster of
commodity machines to analyze big data. For the organizations that are ready to embrace big data technology,
significant adjustments on infrastructure andthe roles played byIT professionals and BI practitioners must be
anticipated which is discussed in the challenges of big data section. The landscape of big data development
change rapidly which is directly related to the trend of big data. Clearly, a major part of the trend is the result
ofthe attempt to deal with the challenges discussed earlier. Lastly the paper includes the most recent job
prospective related to big data. The description of several job titles that comprise the workforce in the area of
big data are also included.
Keywords: Big Data, MapReduce and Hadoop, Data Scientist,
I. INTRODUCTION
Big data technology has revolutionize the way organizations collect, manage and utilize the
explosive amount of data that has been generated in recent years. According to the report by International
Data Corporation (IDC), from the beginning of time through 2011, the world contained nearly 1 zettabyte
(1021
bytes) of data. Then the floodgate opened. 2011 saw the generation of twice (1.8 zettabytes) the
amount of all-through-history number in just one year.It was estimated by IDC that by 2020, just 4 years
from now, we’ll be generating 35 zettabytes per year. This amount of data is clearly far exceeding that
traditional structured database technologies can handle. Owing to the development of new data processing
technology such as Hadoop and MapReduce, organizations are now able to utilize predictive analytics on
the vast amount of data to obtain insightful information. Many organization have been using the data asset
to gain the valuable insight that are not considered possible before.
Famous examples include: 1.IBM were using electronic health record data to predict heart
disease [4], and using data from the World Health Organization to predictthe location of future malaria
outbreaks [5]. 2. Target uses a wealth of customer data to predict future purchasing habits. Specifically,
pregnancy kicks off a chain of purchases that are fairly distinctive. Target’s data collection is spookily
prescient, sending one teen customer nappy vouchers before her own father knew she was pregnant [3]. 3.
WeatherSignal App works by repurposing the sensors in Android devices to map atmospheric readings
[6]. Handsets such as the Samsun S4, contain a barometer, hygrometer (humidity), ambient thermometer
and lightmeter. Obviously, the prospect of millions of personal weather stations feeding into one machine
that will average out readings is exciting, and one that has the potential to improve forecasting. 4. One of
the most exciting examples is using big data to predict the ongoing 2016 presidential election. OpenText’s
U.S. Election Tracker reads, analyzes and visualizes key U.S. Election big data, every day, from hundreds
of news sources, helping the U.S. voters make more informed decisions. The analysis includes natural
language processing, semantic processing, sentiment analysis and opinion algorithms [7].
In the following section, 5Vs of big data isdescribed. The frameworkof Hadoop and MapReduce
that enables the big data analytics is introduced in section 3. Followed by the discussion of challenges of
big data. Section 5 lists the trend of big data development. Section 6 covers the job perspectives related to
big data. Finally, section 7 contains some concluding remarks.
II. 5Vs OF BIG DATA
Big data is high-volume, high-velocity and high-variety information assets that demand cost-
effective, innovative forms of information processing for enhanced insight and decision making. Volume
describes the amount of data generated by various sources. Big data is usually associated with this
characteristic. Velocity describes the frequency at which data is generated, captured and shared. Variety
describes the diversity of data, which include structured and unstructured data. Structured data refers to
2. Moving Toward Big Data: Challenges, Trends And Perspectives
www.ijres.org 17 | Page
the type of data found in spreadsheet or in the relational database, which comprise only about 5% of all
active data. All other data are considered unstructured data,including but not limiting to audio, video, log
files, social media streams, and sensor data from the “internet of things”.To illustrate how fast data can be
generated, Twitter produces over 90 million tweets per day. And Walmart handles more than 1 million
customer transactions every hour, which are imported into databases estimated to contain more than 2.5
petabytes (2560 terabytes) of data, the equivalent of 167 times the information contained in all the books
in the US Library of Congress.
Veracity is the 4th
V coined by IBM, which symbolizes the untrustworthy nature in some sources of
data.Due to the integration of disparate data systems, the correctness and accuracy of information is often
questionable. Value, the 5th
V, was introduced by Oracle as a crucial characteristic of Big Data. The ability to
understand and manage the vast amount of data, and then integrate them into the larger Business Intelligence
ecosystem can provide previously unknown insights associated with the organizations.
III. MAPREDUCE AND HADOOP
The underliningengine that allows the quick processing of big data is the framework of Hadoop and
MapReduce. The concept of MapReduce was first proposed by Dean and Ghemawat from Google in 2004
[1],which became the primary programming framework for processing big data. It first splits the input data into
a number of chunks that is manageable by a single processor, then goes through the mapping, shuffling, and
reducing stages to get the final result.Apache Hadoop was implemented in 2006 by a group of software
developers headed by Cutting [2]. Hadoop is an architecture that enables MapReduce to be run on clusters of
commodity machines in parallel that are dynamically scalable. In other word, the number of machines can be
added or reduced on demand, depending on the load of data. The Distributed file system among the machines in
the clusters is called Hadoop Distributed File System (HDFS).
Table 1 depicts the size of data that would require the use of Hadoop. In 2009, Yahoo demonstrated the
power of Hadoop that ran 17 cluster of 24,000 machine, sorting one Terabyte of data in 62 second. In 2011,
Yahoo announced its new humongous Hadoop cluster, reaching 42K Hadoop nodes and hundreds of petabytes
of storage.
Table 1: Size of Data and Corresponding Content
IV. THE CHALLENGES OF BIG DATA
Conventionally, the relationship between IT (information technology) practitioners and BI (business
intelligence) professionals in an organization is relatively well defined: IT processes organizational data and
generatesstructured data such as relational database or spreadsheet, which then used by BI to make decisions.As
big data technology matures to a point in which more organizations are prepared to pilot and adopt it as a core
component of the information management and integrated analytics, IT and BI alike will bump up against a
number of challenges that must be addressed before any big data program can be successfully implemented.
Below is the discussion of the five most common faced challenges[8, 9].
4.1 Talent Gap Challenge
There is a growing community of application developers who are relying on leveraging Hadoop
MapReduce framework. The promotion of these technologies creates a group of experts that are experienced in
tool implementation and its use as a programming model, rather than the data management aspects. This
3. Moving Toward Big Data: Challenges, Trends And Perspectives
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suggests that many big data tool experts remain somewhat immature on the practical aspects of data modeling,
data architecture, and data integration.
There is no doubt that as more data practitioners become engaged, the talent gap will gradually narrow
down, but when developers are not skilled at addressing these fundamental data architecture and data
management issues, the ability to achieve and maintain a competitive edge through technology adoption will be
severely impaired. Section 6 includes a description of all job titles that comprise the talents needed for the
complete big data operation.
4.2 Infrastructure/Platform Challenge
Big Data is a much talked about technology across businesses today. The majority of organizations are
convinced of its usefulness, however the implementations primarily focus on applications rather than
infrastructure. Most organizations have their own legacy computer systems. Some may have storage system
based on NetApp, EMC or other major players. Moving toward Hadoop environment may prove to be daunting
and challenging.
The organizations opt to take advantage of Big Data technology must answer a number of questions.
How do you apply big data to business applications? What kind of storage system and hardware architecture do
you attempt to build? Are you going to build it all by yourself? Unpredictability of business world must also be
taken into consideration. The underlying platform chosen must be robust and scalable in a cost effective manner.
Cloud service can be a viable solution for organizations choose to avoid installing their own infrastructures.
4.3 Data Sources Integration Challenge
The volume, velocity, and variety of big data has direct impact on data integration and management.
Organizations must first deal with the variety of data which may be generated by IT, Internet of things, or users.
It takes a lot of skills to put data in the right platform so that you can use it properly. For example, if the data
comes from social media content, you need to know who the user is in a general sense, such as a customer using
a particular set of products, and figure out what it’s you are trying to “visualize” out of the data.
Another aspect is due to the velocity of the data which make data synchronization necessary across the
data sources under certain circumstances. There are two types of synchronizations. From a data currency
perspective, it implies that the data coming from one source is not out of date with data coming from another
source. From a semantics perspective, synchronization implies the similarity of data concepts, definitions,
metadata, and the like. One of the promise of big data is that it’s possible to reduce the costly upfront data
preparation and data engineering which typically constitute 80% of the time and cost of data management.
4.4 The Governance Challenge
Data governance is the process of ensuring the compliance of organizational data policy, which can be
a difficult task considering the variety and reliability of the unstructured data. The objective of data governance
is to institute the right levels of control to achieve one of three outcomes: identify data issues that might have
negative business impact; prioritize those issues in relation to their corresponding business value drivers; and
have data stewards take the proper actions when alerted to the existence of those issues [10].
It is naive to assume that when it comes to big data governance the approach to data quality is the same
as traditional approaches. When we examine the key characteristics of big data analytics, the analogy with the
conventional approaches to data quality and data governance starts to break down.Big data applications look at
many input streams originating within and outside the organization, some taken from a variety of social
networking streams, syndicated data streams, news feeds, preconfigured search filters, public or open-sourced
datasets, sensor networks, or other unstructured data streams. Such diverse datasets resist singular approaches to
governance.
4.5 Big Data Syndication Challenge
Most of the practical cases for big data involve data availability, ranging from augmenting existing data
storage to providing access to end-users employing business intelligence tools for the purpose of value
discovery. These BI tools not only must be able to connect to one or more big data platforms, they must provide
transparency to the data consumers to reduce or eliminate the need for custom coding. At the same time, as the
number of data consumers grows, it can be anticipated a need to support a rapidly expanding collection of many
simultaneous user accesses. That demand may spike at different times or the day or in reaction to different
aspects of business process cycles. Ensuring right-time data availability and reusability to the community of data
consumers becomes a critical success factor.
Big data syndication provides a supply chain to the different types of downstream applications in a way
that is seamless and transparent to the consuming applications while elastically supports demands. Finally, it’s
important to realize that funding is a challenge that always plays a dominant role at every stage of big data
integration because these days most budget is very tight. As a result, every penny must be spent wisely. Clearly,
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in establishing infrastructure, what we are looking for is predictability in cost. One of the reason developers of
big data move to Hadoop is because it’s complying with industrial standard hardware, and scalable. In addition,
the long term cost of managing the storage facilities, analytic platforms, and all the related infrastructure must
be taken into consideration.
V. THE TREND OF BIG DATA
The year 2015 was an important one in the world of big data. What used to be hype became the norm
as more businesses realized that data, in all forms and sizes, is critical to making the best possible decisions. In
2016 and beyond, we’ll see continued growth of systems that support unstructured data as well as massive
volumes of data [11]. These systems will evolve and mature to operate well inside of enterprise IT systems and
standards, enabling both business users and data scientists to fully realize the value of big data. A number of
trends are discussion below. Note that most of the trends included in the discussion are stimulated by the need to
tackle the challenges covered in previous section.
5.1 The NoSQL Take 0ver
We noted the increasing adoption of NoSQL technologies, which are commonly associated with
unstructured data, as the conventional relational database management systems become inadequate. Going
forward, the shift to NoSQL databases becoming a leading piece of the enterprise IT landscape as the benefits of
schema-less database concepts become more pronounced. According to Gartner’s Magic Quadrant [12], the
developers of Operational Database Management Systems, which in the past were dominated by Oracle, IBM,
Microsoft and SAP, have shifted to more than a dozen NoSQL developers, including MongoDB, SQLFire,
Casandra, HBase, and Amazon Web Services (with DynamoDB).
5.2 Apache Spark Lights Up Big Data
Apache Spark has moved from being a component of the Hadoop ecosystem to the big data platform of
choice for a number of enterprises. Spark provides dramatically increased data processing speed compared to
Hadoop and is now the largest big data open source project, according to Spark originator and Databricks co-
founder, MateiZaharia [13]. We see more and more compelling enterprise use cases around Spark, such as at
Goldman Sachs where Spark has become the major platform of big data analytics.
5.3 Hadoop continues to evolve
In a recent survey of 2,200 Hadoop customers, only 3% of respondents anticipate they will be doing
less with Hadoop in the next 12 months. 76% of those who already use Hadoop plan on doing more within the
next 3 months and finally, almost half of the companies that haven’t deployed Hadoop say they will within the
next 12 months.
As further evidence to the growing trend of Hadoop becoming a core part of the enterprise IT
landscape, we’ll see investment grows in the components surrounding enterprise systems such as security.
Apache Sentry project provides a system for enforcing fine-grained, role based authorization to data and
metadata stored on a Hadoop cluster. These are the types of capabilities that customers expect from their
enterprise-grade RDBMS platforms and are now coming to the forefront of the emerging big data technologies,
thus eliminating one more barrier to enterprise adoption. We also see a growing demand from end users for the
same fast data exploration capabilities they’ve come to expect from traditional data warehouses.
5.4 The Number Of Options For Preparing Data Grows.
As the demand to reduce the cost of preparing raw data for analytics mounts, a number of
developments have been underway. Self-service data preparation tools are exploding in popularity. This is in
part due to the shift toward generated data discovery tools such as Tableau that reduce time to analyze data.
Business users also want to be able to reduce the time and complexity of preparing data for analysis, something
that is especially important in the world of big data when dealing with a variety of data types and formats.
We’ve seen a host of innovation in this space from companies focused on end user data preparation for Big Data
such as Alteryx, Trifacta, Paxata and Lavastorm.
5.5 Data Warehouse Growth Is Heating Up In The Cloud
Data warehouse have not been phasing out as were expected. But it’s no secret that growth in this
segment of the market has been slow. But we now see a major shift in the application of this technology to the
cloud where Amazon led the way with an on-demand cloud data warehouse in Redshift. Redshift was AWS’s
fastest growing service but it now has competition from Google with BigQuery. Offerings from long time data
warehouse power players such as Microsoft (with Azure SQL Data Warehouse) and Teradata along with new
start-ups such as Snowflake, winner of Strata + Hadoop World 2015 Startup Showcase, also are gaining
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adoption in this arena. Surveys indicate 90% of companies who have adopted Hadoop will also keep their data
warehouses. With these new cloud offerings, those customers can dynamically scale up or down the amount of
storage and compute resources in the data warehouse relative to the larger amounts of information stored in their
Hadoop data lake.
5.6 IoT, Cloud and Big Data Come Together.
The technology is still in its early days, but the data from devices in the Internet of Things will become
one of the “killer apps” for the cloud and a driver of petabyte scale data explosion. For this reason, we see
leading cloud and data companies such as Google, Amazon Web Services and Microsoft bringing Internet of
Things services to life where the data can move seamlessly to their cloud based analytics engines.
VI. JOB PERSPECTIVESRELATED TO BIG DATA
According to a Big Data Survey by Knowledgent, there are clear indicators that Big Data is moving out
of the experimental stage. Most respondents of this survey firmly believe that many organizations would be
utilizing Big Data in the production environment and over 60% of them responded that Big Data initiatives are
very or extremely important to their organizations. In 2015, 1.9 million jobs related to Big Data were created in
the United States to work on the IT-side of big data projects,but each of these jobs is supported by 3 new
jobs outside of IT.In general, several professionals with different skills are required to complete any project. For
example, as mentioned earlier,around 80 % of effort for big data project are spent on cleaning, integrating, and
transforming the data before it can be utilized.
EMC, IBM, Cisco, Oracle are just a few of the top companies who are looking for Big Data skills.
Table 2 is a distribution of job demands of the top Big Data employers today, according to Forbes [14]
Table 2: Big Data Jobs Demand from Major Employers
Jobs that require big data expertise pay well, too. The average salary, according to a report by Forbes last year,
is $124,000. Below is the list of 7 most common job titles related to big data [15].
Data Scientist
Sometimes referred as “the sexiest job in the 21 century”, data scientists are responsible for analyzing
data and extracting useful information out of it.Data scientists thrive on solving real world problems with real
data. They are very good at using different techniques for analyzing data from different sources to help business
make intelligent decisions. They need to have both skills of a software engineer and an applied scientist.
Data Analyst
The data analyst collects, processes and performs statistical data analyses. A data analyst needs to
understand R, Python, HTML, JavaScript, C/C++, SQL and NoSQL databases.
Data Architect
Data architects are responsible for the design, structure, and maintenance of data, ensuring the accuracy
and accessibility of data.
Data Engineer
Big data engineers are all-purpose workforce of a big data analytics operation. They often come from
programming backgrounds, and are experts in big data frameworks, such as Hadoop. They’re called on to ensure
that data pipelines are scalable, repeatable, and secure, and can serve multiple constituents in the enterprise.
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Statistician
Collect, analyze and interpret qualitative and quantitative data with statistical theories and methods.
Statisticians have to be mathematicians, who can churn out reports basis some data provided to them. The
second part is about being able to use a big data technology to automate data processing that provides insight on
a real-time basis.
Database Administrator
Big data and databases go hand-in-hand. A database administrator ensures that the database is
available, is performing properly, and is secure.
Business Analyst
Business analysts are primarily responsible for generating insights that convert big data into business
value. This can include translating insights and patterns embedded in data assets into language understood by
the business administrator.
VII. CONCLUSION
Big data technology has revolutionize the way organizations collect, manage and utilize the
explosive amount of data that has been generated in recent years, including but not limiting to audio,
video, log files, social media streams, and sensor data from the “internet of things”. This paper provides a
comprehensive view of current status of big data development. This paper has described and discussed the
5Vs of big data, the framework of Hadoop and MapReduce that enables the big data analytics, the
challenges of big data, the trend of big data development, and the job perspectives related to big data. This
paper will prove most beneficial to the planner of the organizations ready to adopt big data technology as
well as for the newly grads that are looking for jobs in the area of big data.
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