In recent past, big data opportunities have gained much momentum to enhance knowledge management in
organizations. However, big data due to its various properties like high volume, variety, and velocity can
no longer be effectively stored and analyzed with traditional data management techniques to generate
values for knowledge development. Hence, new technologies and architectures are required to store and
analyze this big data through advanced data analytics and in turn generate vital real-time knowledge for
effective decision making by organizations. More specifically, it is necessary to have a single infrastructure
which provides common functionality of knowledge management, and flexible enough to handle different
types of big data and big data analysis tasks. Cloud computing infrastructures capable of storing and
processing large volume of data can be used for efficient big data processing because it minimizes the
initial cost for the large-scale computing infrastructure demanded by big data analytics. This paper aims to
explore the impact of big data analytics on knowledge management and proposes a cloud-based conceptual
framework that can analyze big data in real time to facilitate enhanced decision making intended for
competitive advantage. Thus, this framework will pave the way for organizations to explore the relationship
between big data analytics and knowledge management which are mostly deemed as two distinct entities.
Use of big data technologies in capital marketsInfosys
What concerns capital market firms today is not the increase in data, but the volume of overall unstructured data. Capital market firms invest heavily in Big Data technologies despite the implementation costs involved. This article discusses the key transformations that capital market firms are undergoing to handle big data, drivers for use of big data technology in capital markets and relevant use cases.
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.
DATA MINING WITH CLUSTERING ON BIG DATA FOR SHOPPING MALL’S DATASETAM Publications
Big Data is the extremely large sets of data that their sizes are beyond the ability of capturing, managing, processing and storage by most software tools and people which is ever increasing day-by-day. In most enterprise scenarios the data is too big or it moves too fast that extremely exceeds current processing capacity. The term big data is also used by vendors, may refer to the technology which includes tools and processes that an organization requires to handle the large amounts of data and storage facilities. This advancement in technology leads to make relationship marketing a reality for today’s competitive world. But at the same time this huge amount of data cannot be analyzed in a traditional manner, by using manual data analysis. For this, technologies such as data warehousing and data mining have made customer relationship management as a new area where business firms can gain a competitive advantage for identifying their customer behaviors and needs. This paper mainly focuses on data mining technique that performs the extraction of hidden predictive information from large databases and organizations can identify valuable customers and predicts future user behaviors. This enables different organizations to make proactive, knowledge-driven decisions. Data mining tools answer business questions that in the past were too time-consuming, this makes customer relationship management possible. For this in this paper, we are trying explain the use of data mining technique to accomplish the goals of today’s customer relationship management and Decision making for different companies that deals with big data.
Use of big data technologies in capital marketsInfosys
What concerns capital market firms today is not the increase in data, but the volume of overall unstructured data. Capital market firms invest heavily in Big Data technologies despite the implementation costs involved. This article discusses the key transformations that capital market firms are undergoing to handle big data, drivers for use of big data technology in capital markets and relevant use cases.
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.
DATA MINING WITH CLUSTERING ON BIG DATA FOR SHOPPING MALL’S DATASETAM Publications
Big Data is the extremely large sets of data that their sizes are beyond the ability of capturing, managing, processing and storage by most software tools and people which is ever increasing day-by-day. In most enterprise scenarios the data is too big or it moves too fast that extremely exceeds current processing capacity. The term big data is also used by vendors, may refer to the technology which includes tools and processes that an organization requires to handle the large amounts of data and storage facilities. This advancement in technology leads to make relationship marketing a reality for today’s competitive world. But at the same time this huge amount of data cannot be analyzed in a traditional manner, by using manual data analysis. For this, technologies such as data warehousing and data mining have made customer relationship management as a new area where business firms can gain a competitive advantage for identifying their customer behaviors and needs. This paper mainly focuses on data mining technique that performs the extraction of hidden predictive information from large databases and organizations can identify valuable customers and predicts future user behaviors. This enables different organizations to make proactive, knowledge-driven decisions. Data mining tools answer business questions that in the past were too time-consuming, this makes customer relationship management possible. For this in this paper, we are trying explain the use of data mining technique to accomplish the goals of today’s customer relationship management and Decision making for different companies that deals with big data.
Big Data Impact on Purchasing and SCM - PASIA World Conference DiscussionBill Kohnen
The volume, velocity and variety of data available is almost unthinkable. 90% of the world’s data is less than 2 years old, we are able analyze less than 5% of it and 80% of what people generally are looking at is less than 6 weeks old. Harnessing this data for effective decision making is a goal for organizations worldwide and has created a 50Billion dollar industry to provide tools and consulting.
Even before “Big Data” Purchasing groups were swimming in data and struggled to put it to effective use. The success of Strategic Sourcing methodology had the effect of also identifying and standardizing the types and format of information that can be used to drive improvement.
This discussion will connect how big data sources and methodology can be used to develop specific and relevant spend analytics. Also presented will be an illustration of how you can use data and tools you already have - to get immediate results and make you better prepared to evaluate the need for more powerful analytic tools.
Finally will conclude with comments on how Big Data along with other disruptive digital trends will create a new required skill sets for Purchasing and Supply Chain Professionals and are transform how operate all ready.
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.
What is Big Data?
Big Data Laws
Why Big Data?
Industries using Big Data
Current process/SW in SCM
Challenges in SCM industry
How Big data can solve the problems?
Migration to Big data for an SCM industry
Data Mining: The Top 3 Things You Need to Know to Achieve Business Improvemen...Dr. Cedric Alford
While companies have been using various CRM and automation technologies for many years to capture and retain traditional business data, these existing technologies were not built to handle the massive explosion in data that is occurring today. The shift started nearly 10 years ago with expanding usage of the internet and the introduction of social media. But the pace has accelerated in the past five years following the introduction of smart phones and digital devices such as tablets and GPS devices. The continued rise in these technologies is creating a constant increase in complex data on a daily basis.
The result? Many companies don't know how to get value and insights from the massive amounts of data they have today. Worse yet, many more are uncertain how to leverage this data glut for business advantage tomorrow. In this white paper, we will explore three important things to know about big data and how companies can achieve major business benefits and improvements through effective data mining of their own big data.
Dr. Cedric Alford provides a roadmap for organizations seeking to understand how to make Big Data actionable.
Understanding big data and data analytics big dataSeta Wicaksana
Big Data helps companies to generate valuable insights. Companies use Big Data to refine their marketing campaigns and techniques. Companies use it in machine learning projects to train machines, predictive modeling, and other advanced analytics applications.
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.
STUDY OF ABSORPTION IN CARBON NANOTUBE COMPOSITES FROM 1HZ TO 40GHZjmicro
Absorption performances in High Density Polyethylene (HDPE) and polycarbonate (PC) polymer matrices
containing various loads of carbon nanotubes were analysed. It depends on electrical conductivity,
dielectric constant and thickness of the polymer composites. These parameters can be easily controlled.
Significant absorption, which can reach between 60 and 90%, hence occurs at particular combinations of
these last parameters (in a frequency range from 1Hz to GHz). These new results are really useful in
various applications, and are considered in low scale systems as a major technological solution against
electromagnetic interferences.
Big Data Impact on Purchasing and SCM - PASIA World Conference DiscussionBill Kohnen
The volume, velocity and variety of data available is almost unthinkable. 90% of the world’s data is less than 2 years old, we are able analyze less than 5% of it and 80% of what people generally are looking at is less than 6 weeks old. Harnessing this data for effective decision making is a goal for organizations worldwide and has created a 50Billion dollar industry to provide tools and consulting.
Even before “Big Data” Purchasing groups were swimming in data and struggled to put it to effective use. The success of Strategic Sourcing methodology had the effect of also identifying and standardizing the types and format of information that can be used to drive improvement.
This discussion will connect how big data sources and methodology can be used to develop specific and relevant spend analytics. Also presented will be an illustration of how you can use data and tools you already have - to get immediate results and make you better prepared to evaluate the need for more powerful analytic tools.
Finally will conclude with comments on how Big Data along with other disruptive digital trends will create a new required skill sets for Purchasing and Supply Chain Professionals and are transform how operate all ready.
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.
What is Big Data?
Big Data Laws
Why Big Data?
Industries using Big Data
Current process/SW in SCM
Challenges in SCM industry
How Big data can solve the problems?
Migration to Big data for an SCM industry
Data Mining: The Top 3 Things You Need to Know to Achieve Business Improvemen...Dr. Cedric Alford
While companies have been using various CRM and automation technologies for many years to capture and retain traditional business data, these existing technologies were not built to handle the massive explosion in data that is occurring today. The shift started nearly 10 years ago with expanding usage of the internet and the introduction of social media. But the pace has accelerated in the past five years following the introduction of smart phones and digital devices such as tablets and GPS devices. The continued rise in these technologies is creating a constant increase in complex data on a daily basis.
The result? Many companies don't know how to get value and insights from the massive amounts of data they have today. Worse yet, many more are uncertain how to leverage this data glut for business advantage tomorrow. In this white paper, we will explore three important things to know about big data and how companies can achieve major business benefits and improvements through effective data mining of their own big data.
Dr. Cedric Alford provides a roadmap for organizations seeking to understand how to make Big Data actionable.
Understanding big data and data analytics big dataSeta Wicaksana
Big Data helps companies to generate valuable insights. Companies use Big Data to refine their marketing campaigns and techniques. Companies use it in machine learning projects to train machines, predictive modeling, and other advanced analytics applications.
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.
STUDY OF ABSORPTION IN CARBON NANOTUBE COMPOSITES FROM 1HZ TO 40GHZjmicro
Absorption performances in High Density Polyethylene (HDPE) and polycarbonate (PC) polymer matrices
containing various loads of carbon nanotubes were analysed. It depends on electrical conductivity,
dielectric constant and thickness of the polymer composites. These parameters can be easily controlled.
Significant absorption, which can reach between 60 and 90%, hence occurs at particular combinations of
these last parameters (in a frequency range from 1Hz to GHz). These new results are really useful in
various applications, and are considered in low scale systems as a major technological solution against
electromagnetic interferences.
DEVELOPMENT AND IMPLEMENTATION OF MOBILE GEAR FOR SKIER TRAINING SYSTEMijseajournal
The purpose of this study is to develop a Skier Training System for monitoring skiing courses and analyzing
skier posture using smartphones. The system is composed of three elements: first, operating servers that
receive information on the skier’s exercise; second, Skier Training Apps developed using Java and C based
on Android SDK 4.2 or above; and third, Data Gathering Gear embedded with gyroscope sensors and GPS
for measuring skier stance. When a skier wearing our Data Gathering Gear runs down a slope, his or her
stance is uploaded to the operating servers. Then the servers calculate the recorded data, such as velocity
and turn trajectory, in order to suggest body angles that are optimized for the skier; subsequently, the skier
can improve his or her skills by checking his or her records, and then attempt to correct his or stances
based on the suggestions offered by the Skier Training Apps.
Médico Especialista Álvaro Miguel Carranza Montalvo, soy Médico General Alto, Rubio, de Piel Blanca, ojos claros , soy Atlético Simpático, me esmero a seguir Adelante solucionando los Problemas de las demás Personas para salvar su Vida en Salud y en Enfermedades. Internet, Networds….
Médico Especialista Álvaro Miguel Carranza Montalvo, la VIDA es una VIRTUD que cada Humano, Persona tiene es Valeroso y Digno lograr SALVAR la VIDA de una Persona que está en Peligro, cada Persona es una sóla Unidad único no hay nadie como esa persona somos distintos. Internet, Networds….
Médico Especialista Álvaro Miguel Carranza Montalvo, la NATURALEZA es Bella y Linda Vivirla al Aire Libre, con Agua, la Vegetación, los Bellos Animales en el Ecosistema la Biodiversidad hay que Valorar y Gozar lo que hay en el Mundo Vivirla y Disfrutarla. Internet, Networds….
Médico Especialista Álvaro Miguel Carranza Montalvo, ME GUSTA LO QUE SOY MI FORMA DE SER ME ENCANTA LO QUE SOY YÓ MI FÍSICO, MENTE, PENSAMIENTOS, ALMA Y CUERPO, FÍSICO. Y VIVIR LA VIDA, NATURALEZA LA BELLEZA. Web, Redes Sociales….
Médico Especialista Álvaro Miguel Carranza Montalvo, Me gusta la Naturaleza y la Vida. VIVIR LA VIDA RESPETANDO A LOS DEMÁS CHICAS Y CHICOS A TODAS LAS PERSONAS LES RESPETO Y ADMIRO PORQUE TIENEN SUS VALORES Y DONES. HACER EL BIEN NUNCA EL MAL A LA PERSONA TRATAR COMO A UNO LE GUSTARÍA QUE LE TRATEN. Web, Redes Sociales….
Médico Especialista Álvaro Miguel Carranza Montalvo, "creo que las artes marciales mixtas sirven principalmente para desarrollar la energía. A veces es necesario darse cuenta de un peligro y conocer el medio para salvar la vida. Web, Redes Sociales….
Médico Especialista Álvaro Miguel Carranza Montalvo, La Energía es Vital para lograr una Meta con Fuerza y Salud es lo más Importante en la Vida. ", Web, Internet….
Médico Especialista Álvaro Miguel Carranza Montalvo, "es necesario realizar ejercicios determinados en la columna, para proporcionar oxígeno al cerebro y ayudarle a descansar totalmente", Web, Internet….
Médico Especialista Álvaro Miguel Carranza Montalvo, "hay tres palabras que aprendemos a gritar que llevan consigo descanso y energía; fuerza, valor y convicción", Web, Internet….
ТПЛМ 1062 Обробка й виконання замовлень. О.М.Горяїнов (2009)Oleksiy Goryayinov
Презентація з дисципліни «Логістика» (для менеджерів). Наведено матеріал «6.2 Обробка й виконання замовлень» (шляхи і джерела отримання інформації, процедура обробки інформації, план-графік виконання замовлення та інше)
SOFTWARE DESIGN ANALYSIS WITH DYNAMIC SYSTEM RUN-TIME ARCHITECTURE DECOMPOSITIONijseajournal
Software re-engineering involves the studying of targeting system’s design and architecture. However,
enterprise legacy software systems tend to be large and complex, making the analysis of system design
architecture a difficult task. To solve this problem, the study proposes an approach that dynamically
decomposes software architecture using the run-time system information to reduce the complexity
associated with analyzing large scale architecture artifacts. The study demonstrates that dynamic
architecture decomposition is an efficient way to limit the complexity and risk associated with reengineering
activities of a large legacy system. This new approach divides the system into a collection of
meaningful modular parts with low coupling, high cohesion, and a minimal interface. This division
facilitates the design analysis and incremental software re-engineering process. This paper details two
major techniques to decompose legacy system architecture. The approach is also supported by automated
reverse engineering tools that were developed during the course of the study. The preliminary results
indicate that this novel approach is very promising.
COLLABORA: A COLLABORATIVE ARCHITECTURE FOR EVALUATING INDIVIDUALS PARTICIPAT...ijseajournal
The execution of collaborative activities enables interaction among its participants, however, the real
problem is to evaluate how much each subject contributed in the development of the activity. The
evaluation process allows to inform important aspects about the individual or the group, such as:
reliability, interdependence, flexibility, commitment, interpersonal relationship, productivity and
management strategies. This work proposes is based in domain based architecture and computer-supported
collaborative learning (CSCL) in order to measure individual and group contributions to the
accomplishment of its activities. The evaluation of the collaboration is made in a semi-automated way
using as criteria measures of collaboration present in the literature like counting the amount of meaningful
and valid words in conversations, which allows to evaluate its commitment. After the activity finalizes, a
collaboration score is given to the participant of the group. The proposed architecture was implemented in
the education domain. In addition to generate a set of exercises to the studied subject, the architecture
helped to provide statistic data related to the collaboration assessment among the peers during the
development of collaborative activities.
Médico Especialista Álvaro Miguel Carranza Montalvo, soy Médico General Alto, Rubio, de Piel Blanca, ojos claros , soy Atlético Simpático, me esmero a seguir Adelante solucionando los Problemas de las demás Personas para salvar su Vida en Salud y en Enfermedades. Internet, Networds….
Médico Especialista Álvaro Miguel Carranza Montalvo, la VIDA es una VIRTUD que cada Humano, Persona tiene es Valeroso y Digno lograr SALVAR la VIDA de una Persona que está en Peligro, cada Persona es una sóla Unidad único no hay nadie como esa persona somos distintos. Internet, Networds….
Médico Especialista Álvaro Miguel Carranza Montalvo, la NATURALEZA es Bella y Linda Vivirla al Aire Libre, con Agua, la Vegetación, los Bellos Animales en el Ecosistema la Biodiversidad hay que Valorar y Gozar lo que hay en el Mundo Vivirla y Disfrutarla. Internet, Networds….
Médico Especialista Álvaro Miguel Carranza Montalvo, ME GUSTA LO QUE SOY MI FORMA DE SER ME ENCANTA LO QUE SOY YÓ MI FÍSICO, MENTE, PENSAMIENTOS, ALMA Y CUERPO, FÍSICO. Y VIVIR LA VIDA, NATURALEZA LA BELLEZA. Web, Redes Sociales….
Médico Especialista Álvaro Miguel Carranza Montalvo, Me gusta la Naturaleza y la Vida. VIVIR LA VIDA RESPETANDO A LOS DEMÁS CHICAS Y CHICOS A TODAS LAS PERSONAS LES RESPETO Y ADMIRO PORQUE TIENEN SUS VALORES Y DONES. HACER EL BIEN NUNCA EL MAL A LA PERSONA TRATAR COMO A UNO LE GUSTARÍA QUE LE TRATEN. Web, Redes Sociales….
Médico Especialista Álvaro Miguel Carranza Montalvo, "creo que las artes marciales mixtas sirven principalmente para desarrollar la energía. A veces es necesario darse cuenta de un peligro y conocer el medio para salvar la vida. Web, Redes Sociales….
Médico Especialista Álvaro Miguel Carranza Montalvo, La Energía es Vital para lograr una Meta con Fuerza y Salud es lo más Importante en la Vida. ", Web, Internet….
Médico Especialista Álvaro Miguel Carranza Montalvo, "es necesario realizar ejercicios determinados en la columna, para proporcionar oxígeno al cerebro y ayudarle a descansar totalmente", Web, Internet….
Médico Especialista Álvaro Miguel Carranza Montalvo, "hay tres palabras que aprendemos a gritar que llevan consigo descanso y energía; fuerza, valor y convicción", Web, Internet….
Learning Resources Week 2
Frankfort-Nachmias, C., & Leon-Guerrero, A. (2020). Social statistics for a diverse society (9th ed.). Sage Publications.
· Chapter 2, “The Organization and Graphic Presentation Data” (pp. 27-74)
Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.
· Chapter 5, “Charts and Graphs”
· Chapter 11, “Editing Output”
Walden University Writing Center. (n.d.). General guidance on data displays. Retrieved from http://waldenwritingcenter.blogspot.com/2013/02/general-guidance-on-data-displays.html
Use this website to guide you as you provide appropriate APA formatting and citations for data displays.
Laureate Education (Producer). (2016j). Visual displays of data [Video file]. Baltimore, MD: Author.
Note: The approximate length of this media piece is 9 minutes.
In this media program, Dr. Matt Jones discusses frequency distributions. Focus on how his explanation might support your analysis in this week’s Assignment. (video attached separately)
sustainability
Case Report
Integrated Understanding of Big Data, Big Data
Analysis, and Business Intelligence: A Case Study
of Logistics
Dong-Hui Jin and Hyun-Jung Kim *
Seoul Business School, aSSIST, 46 Ewhayeodae 2-gil, Seodaemun-gu, Seoul 03767, Korea; [email protected]
* Correspondence: [email protected]; Tel.: +82-70-7012-2722
Received: 5 October 2018; Accepted: 17 October 2018; Published: 19 October 2018
����������
�������
Abstract: Efficient decision making based on business intelligence (BI) is essential to ensure
competitiveness for sustainable growth. The rapid development of information and communication
technology has made collection and analysis of big data essential, resulting in a considerable increase
in academic studies on big data and big data analysis (BDA). However, many of these studies are
not linked to BI, as companies do not understand and utilize the concepts in an integrated way.
Therefore, the purpose of this study is twofold. First, we review the literature on BI, big data,
and BDA to show that they are not separate methods but an integrated decision support system.
Second, we explore how businesses use big data and BDA practically in conjunction with BI through
a case study of sorting and logistics processing of a typical courier enterprise. We focus on the
company’s cost efficiency as regards to data collection, data analysis/simulation, and the results from
actual application. Our findings may enable companies to achieve management efficiency by utilizing
big data through efficient BI without investing in additional infrastructure. It could also give them
indirect experience, thereby reducing trial and error in order to maintain or increase competitiveness.
Keywords: business application; big data; big data analysis; business intelligence; logistics;
courier service
1. Introduction
A growing number of corporations depend on various and cont.
Learning Resources Week 2
Frankfort-Nachmias, C., & Leon-Guerrero, A. (2020). Social statistics for a diverse society (9th ed.). Sage Publications.
· Chapter 2, “The Organization and Graphic Presentation Data” (pp. 27-74)
Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.
· Chapter 5, “Charts and Graphs”
· Chapter 11, “Editing Output”
Walden University Writing Center. (n.d.). General guidance on data displays. Retrieved from http://waldenwritingcenter.blogspot.com/2013/02/general-guidance-on-data-displays.html
Use this website to guide you as you provide appropriate APA formatting and citations for data displays.
Laureate Education (Producer). (2016j). Visual displays of data [Video file]. Baltimore, MD: Author.
Note: The approximate length of this media piece is 9 minutes.
In this media program, Dr. Matt Jones discusses frequency distributions. Focus on how his explanation might support your analysis in this week’s Assignment. (video attached separately)
sustainability
Case Report
Integrated Understanding of Big Data, Big Data
Analysis, and Business Intelligence: A Case Study
of Logistics
Dong-Hui Jin and Hyun-Jung Kim *
Seoul Business School, aSSIST, 46 Ewhayeodae 2-gil, Seodaemun-gu, Seoul 03767, Korea; [email protected]
* Correspondence: [email protected]; Tel.: +82-70-7012-2722
Received: 5 October 2018; Accepted: 17 October 2018; Published: 19 October 2018
����������
�������
Abstract: Efficient decision making based on business intelligence (BI) is essential to ensure
competitiveness for sustainable growth. The rapid development of information and communication
technology has made collection and analysis of big data essential, resulting in a considerable increase
in academic studies on big data and big data analysis (BDA). However, many of these studies are
not linked to BI, as companies do not understand and utilize the concepts in an integrated way.
Therefore, the purpose of this study is twofold. First, we review the literature on BI, big data,
and BDA to show that they are not separate methods but an integrated decision support system.
Second, we explore how businesses use big data and BDA practically in conjunction with BI through
a case study of sorting and logistics processing of a typical courier enterprise. We focus on the
company’s cost efficiency as regards to data collection, data analysis/simulation, and the results from
actual application. Our findings may enable companies to achieve management efficiency by utilizing
big data through efficient BI without investing in additional infrastructure. It could also give them
indirect experience, thereby reducing trial and error in order to maintain or increase competitiveness.
Keywords: business application; big data; big data analysis; business intelligence; logistics;
courier service
1. Introduction
A growing number of corporations depend on various and cont.
Encroachment in Data Processing using Big Data TechnologyMangaiK4
Abstract—The nature of big data is now growing and information is present all around us in different kind of forms. The big data information plays crucial role and it provides business value for the firms and its benefits sectors by accumulating knowledge. This growth of big data around all the concerns is high and challenge in data processing technique because it contains variety of data in enormous volume. The tools which are built on the data mining algorithm provides efficient data processing mechanisms, but not fulfill the pattern of heterogeneous, so the emerging tools such like Hadoop MapReduce, Pig, SPARK, Cloudera, Impala and Enterprise RTQ, IBM Netezza and Apache Giraphe as computing tools and HBase, Hive, Neo4j and Apache Cassendra as storage tools useful in classifying, clustering and discovering the knowledge. This study will focused on the comparative study of different data processing tools, in big data analytics and their benefits will be tabulated.
DATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATAijseajournal
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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.
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LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCED DECISION MAKING IN ORGANIZATIONS
1. International Journal of Distributed and Parallel Systems (IJDPS) Vol.8, No.1, January 2017
DOI:10.5121/ijdps.2017.8101 1
LEVERAGING CLOUD BASED BIG DATA ANALYTICS
IN KNOWLEDGE MANAGEMENT FOR ENHANCED
DECISION MAKING IN ORGANIZATIONS
Mohammad Shorfuzzaman
Department of Computer Science, Taif University, Taif, Saudi Arabia
ABSTRACT
In recent past, big data opportunities have gained much momentum to enhance knowledge management in
organizations. However, big data due to its various properties like high volume, variety, and velocity can
no longer be effectively stored and analyzed with traditional data management techniques to generate
values for knowledge development. Hence, new technologies and architectures are required to store and
analyze this big data through advanced data analytics and in turn generate vital real-time knowledge for
effective decision making by organizations. More specifically, it is necessary to have a single infrastructure
which provides common functionality of knowledge management, and flexible enough to handle different
types of big data and big data analysis tasks. Cloud computing infrastructures capable of storing and
processing large volume of data can be used for efficient big data processing because it minimizes the
initial cost for the large-scale computing infrastructure demanded by big data analytics. This paper aims to
explore the impact of big data analytics on knowledge management and proposes a cloud-based conceptual
framework that can analyze big data in real time to facilitate enhanced decision making intended for
competitive advantage. Thus, this framework will pave the way for organizations to explore the relationship
between big data analytics and knowledge management which are mostly deemed as two distinct entities.
KEYWORDS
Knowledge management, cloud computing, big data, data analytics, competitive advantage, decision
making.
1. INTRODUCTION
Knowledge management (KM) is increasingly becoming crucial for enhanced decision making in
organizations. Hence, organizations are exploring ways to effectively accumulate and deal with
the data, information and knowledge that are accessible today. The earlier development of KM
was facilitated by the use of the Information and communications technology (ICT) in late
eighties. The goal was to manage the increasing amount of data, information and to ensure its
usage and flow across the organization. In the next stage, two pioneers of KM [2] proposed the
social, cognitive and business aspects of KM and regarded knowledge as an intellectual asset that
an organization should construct and monitor. The size of data and information that are handy
today is much more than what we could possibly envisage a decade ago. There is a pressing need
to investigate such big amount of data and establish its relationship with KM to enhance
organizational decision making and acquire competitive advantage.
The emergence of big data as described by the authors in [24] is due to the drastic increase of
processing power, the availability of data with high volume, velocity and variety, and the
combination of customary data management and open-sourced technologies and commodity
hardware. With the explosion of the Internet, mobile devices, and social media, the impact of big
data became apparent in numerous areas and industries. The competence of organizations to make
2. International Journal of Distributed and Parallel Systems (IJDPS) Vol.8, No.1, January 2017
2
use of big data to derive actionable insights has appeared as a vital policy to build competitive
advantages.
This big data due to its various properties like high volume, variety, and velocity can no longer be
effectively stored and analyzed with traditional data management techniques [4, 5, 6]. New
technologies and architectures are required to store and analyze this data and in turn generate vital
real-time information for decision making in organizations. This has opened the door for the
researchers to focus on big data analytics which are likely to play a radical role in the success of
organizations. The challenge is to collect, store, and analyze the enterprise big data at the right
speed from sources such as sales, supply chain, research, and customer relations to build the
knowledge base for effective decision making of the organizations. Recent studies also revealed
the fact that the utilization of big data has augmented notably in decision-making [3] and both
public and private organizations are reaping benefits from this emerging technology [1].
Thus, with the increasingly large amount of data, it is necessary to have a single infrastructure
which provides common functionality of big data management, and flexible enough to handle
different types of big data and big data analysis tasks. Lately, cloud computing has become an
attractive and mainstream solution for data storage, processing, and distribution [7]. It provides
on-demand and elastic computing and data storage resources without the large initial investments
usually required for the deployment of traditional data centers. Thus, the advent of cloud
computing meets the need for large-scale computing and storage infrastructures demanded by big
data storage and analytics. Our goal in this regard is to introduce an intelligent cloud-based
computing framework that can effectively analyze the big data captured from different enterprise
data sources and share this data in real time to facilitate knowledge creation and management
towards effective decision making in organizations. Hence, the novelty of approach is to present a
unified infrastructure for the organizations to benefit from cloud computing where big data
analytics is delivered as an on-demand cloud service and IT resources therein can be quickly
adjusted to meet changing demands. As such, the paper provides a detailed account of the
relationship between big data analytics and knowledge management from the perspective of
effective decision making given the opportunities and challenges faced by cloud computing
infrastructures.
The rest of the paper is ordered as follows. Section 2 provides background knowledge and related
literature. Requirements analysis for KM in the context of big data analytics is provided in
Section 3. Big data driven KM framework is presented in Section 4. Section 5 presents a relevant
use case study. Finally, conclusions are given in Section 6 with some potential future work.
2. BACKGROUND AND LITERATURE REVIEW
Exploring the role of big data analytics and its relationship with knowledge management is of
utmost importance. Hence, in this section, we present a detailed account of big data and
knowledge management and their key commonalities with regard to effective decision making in
organizations.
2.1. BIG DATA
In recent past, big data has become a popular IT phenomenon across business organizations.
Organizations are putting more emphasis on collecting the enormous amount of data that are
generated everyday due to the tremendous use of electronic devices and IT in product
development and dissemination. This huge amount of data is often referred to as “Big Data”.
Literature shows that there are copious indications suggesting the fact that the use of big data
processing and its analysis can harness great values for organizations in developing knowledge
3. International Journal of Distributed and Parallel Systems (IJDPS) Vol.8, No.1, January 2017
3
management [8]. Prior to finding out how this big data is transformed into knowledge, let us
comprehend this further.
There is no agreed upon definition of big data available in the literature. However, big data is
generally characterized by three entities such as volume, velocity and variety [9]. Volume
represents the large amount of data that are collected which generally range from terabytes to
exabytes. This is not adequate to express the real meaning of the concept. The big data presents a
great variety which extends beyond structured data, including semi-structured or unstructured
data of different types which is usually unsuitable for typical relational databases treatment. They
are text, audio, digital pictures and videos posted online, posts to social media sites, log files of
the users of social networks, search engines, machine generated data from sensor and device
networks, cell phone data to name a few. Finally, this big data is produced with great velocity and
must be captured and processed quickly (as in the case of making real time decisions). Figure 1
depicts a wide range of data types from various sources that constitute big data patterns. Some
researchers also propose a fourth ‘V’ which stands for veracity [9]. Veracity refers to the accuracy
and credibility of data and the method of analyzing that data. Yet other researchers [25, 26] see
this as the value of the data that are collected. For example, economic value of some data will
vary depending on the origin of the data and it is used.
Figure 1. Common sources of big data
Having said the availability of big data in organizations, the rising challenge for them is to
process this data to generate useful knowledge to support enhanced decision making. To this end,
big data analytics play a significant role in efficient knowledge management that in turns aid in
developing business strategic plan in the competitive market and particularly in product
development. This entails collection of data from the sources listed above and analyzing the data
with big data analytic tools and finally synthesizing the data with relevant enterprise data to
obtain constructive knowledge base for storing it and using it further across organizations. To get
the utmost benefit of big data, organizations have already initiated the change in their IT
infrastructures to deal with the fast rate of extorting and delivering enormous volume of data.
More particularly, the organizations with traditional systems would have to comprehend the
precedent data and how this can be joined together with their current IT infrastructures.
2.2. KNOWLEDGE MANAGEMENT
It is crucial to differentiate between data, information, and knowledge in present-day knowledge
literature as shown in Figure 2. Data refers to a set of values either quantitative or qualitative
obtained from some events. Information is gathered as a message upon data processing and is
4. International Journal of Distributed and Parallel Systems (IJDPS) Vol.8, No.1, January 2017
4
communicated generally in a document or audio-visual form. Finally, knowledge has a wider and
deeper meaning than data or information. It is created from the use, analysis, and productive
utilization of data and information. As shown in the Figure 2, there are two types of knowledge
typically covered in knowledge management. They are explicit knowledge and tacit knowledge.
Explicit knowledge refers to codified and external knowledge that can be stored in repositories
(i.e., as files), intranet, extranet, social media and so on. On the other hand, tacit knowledge exists
in the heads of people and in intangible form which is typically communicated and shared
through social interactions such as peer-to-peer discussions.
Figure 2. Flow of knowledge creation
The organizations can reap a lot of benefits when this knowledge is managed effectively such as
improving customer satisfaction, reducing product development cycle, enhancing product quality
and so on. Thus, effective knowledge management plays an important role in decision making
and correspondingly guiding the strategic plan for knowledge-based organizations in the context
of sustainable competitive advantage [10].
Prior to finding the relationship between big data and knowledge management, let us understand
this concept further. Knowledge management is a prescribed structured proposal to advance the
creation, distribution or use of knowledge in an organization. This is a formal process of turning
corporate knowledge into corporate value. It promotes ongoing business success through the
systematic acquisition, synthesis, sharing and use of information insights and experience. More
specifically, knowledge management also is the art of creating commercial value from intangible
assets. The key business functions of knowledge management is to create, preserve and share
knowledge, therefore intellectual capital is important for knowledge management. In a
technological sense, knowledge management is the process of acquiring, organizing, sharing and
efficiently using organizational knowledge in specific business process, such as production,
purchasing, marketing, etc. During sharing and applying, new knowledge is generated and these
should be captured and recorded over and over. For instance, in customer service, customer data
are captured and stored in appropriate database and then retrieved by the staffs to create service to
customers. Thus, new data are generated and then feedback is sent to the system.
Together with a cautious synthesis of knowledge management strategy and implementation of the
respective initiatives, an organization can leverage on knowledge management to deliver its value
to their customers and to the society. Thriving and sustainable execution of knowledge
management can produce many valuable benefits. Firstly, reducing costs and enhancing
productivity. Secondly, retaining knowledge from project teams, near retirement staff, and even
departed staff. This helps an organization make enhanced consistent decisions. It also facilitates
reduced product development or service development cycle. This prevents reinventing the wheel
and recommitting any mistakes that were previously made. All these are superb benefits for
organizations.
2.3. RELATIONSHIP BETWEEN BIG DATA AND KM
We are now in the innovative knowledge ecosystems based on cloud computing and big data.
Organizations are looking for means to effectively gather and process the data to create values for
improved knowledge management. Recent research has gained much momentum to aid industrial
organizations on how to deal with the various organizational and technical prospects and
challenges while working with knowledge management, big data, and analytics.
Data Information Knowledge
Explicit
Tacit
5. International Journal of Distributed and Parallel Systems (IJDPS) Vol.8, No.1, January 2017
5
Davenport et al. [10] has outlined a number of potential benefits that organizations can achieve by
means of using big data relating to knowledge management. Organizations focus on data flows as
opposed to stocks. It is also reported to have increasing reliance on data scientists and product
developers rather than data analysts. Finally, they are gradually detaching analytics away from IT
tasks and bringing into core businesses and operational functions. In this manner, organizations
can create precious knowledge and exploit it for improved knowledge management and
competitive market advantage. Thus, it can be inferred that big data and analytics contributes
towards real time knowledge management. Big data is also deemed as a knowledge asset and as
such state-of-the-art knowledge management has gained substantial impetus due to the use of big
data analytics for knowledge creation and management.
To better understand the relationship between big data and KM, we will first look at two
approaches to KM: conventional or traditional approach and big data-based approach. Traditional
approach typically focuses on conversion of tacit knowledge into explicit knowledge. The normal
flow is to capture people's know-how and good practices and then to codify them and put them
into repository. On the contrary, big data-based KM deals with discovering the rise of new
knowledge based on the huge volume of data that are amassed today. This data is mostly
collected from internal sources in addition to external sources, especially from the clouds that we
have access to. The focus of big data-based KM will be to do knowledge predictions, knowledge
navigation, and knowledge discovery to support enhanced operations and decision making in
organizations. These two approaches are in fact not mutually exclusive and can be applied to help
businesses and societies at the same time. The vast amount of data that are available in the cloud
will change the way companies organize and process business knowledge and provide
unparalleled opportunities in knowledge management. This articulates the relationship between
big data, knowledge management and cloud computing.
Now, we will closely look at some practical applications of big data in the real world business.
Manufacturing companies can use the huge amount of point-of-sale data from all sales outlets and
market research data to predict more accurately the fluctuations in demand. This will remove
overstock and enable a faster response to out-of-stock situations. Another instance is to make use
of sensors to large vehicles such as trucks. Sensors are attached in the engines and tires of a truck
fleet to collect data regarding their condition. This collected data are then sent back to a data
diagnosis centre for further action. In this manner, the location of the trucks and the wear and tear
of important parts are constantly observed in real time before any failure occurs. This can lower
downtime costs substantially due to sudden failures and non-delivery of services. Early
replacement cost can also be avoided.
The authors in [12] argue that one of the objectives of knowledge management is to assimilate
data from different perspectives and analyze them to extract value for effective decision making.
This is now a lot simpler to accumulate data from different big data sources and apply big data
analytics to generate value from it so that organizations can use it in decision making. For
example, expedia.com which is one of the foremost travel websites has invested a large amount of
money to use big data analytics to generate valuable insights from the huge amount of data that is
generated from everyday use of the site. They analyze the market strategies that attract the
customers who visit their website and establish a contributory relationship between their adopted
strategies and customers’ response. In this manner, the company generates useful insights by
analyzing the big data and decides on how to use this valuable information in improving business
strategy. This serves as an unambiguous instance of how big data is related to knowledge
management. The big challenge faced by industries today to come up with this type of strategic
information which help them to make prompt, accurate, and effective tactical decisions.
6. International Journal of Distributed and Parallel Systems (IJDPS) Vol.8, No.1, January 2017
6
Figure 3. Detailed knowledge creation in traditional and big data based KMS
Credit card companies through big data analytics of huge web monitoring and call center
activities data can make better decision regarding personalized customer offers and improved
business strategies. In this manner, they are exploiting the concept of real time knowledge
generation from big data analytics [11]. Research results obtained by Hair Jr. demonstrate that
predictive analytics and big data provide impressive support for emerging business including
product development and distribution [13]. The examples given above evidently offer an
indication of how big data and knowledge management are interlinked and industries are reaping
benefits of big data by generating valuable knowledge. Thus, it can be called as big data based
knowledge management that takes full advantage of big data analytics to enhance revenue
generation and sales and to reduce risk associated with incurred cost. Figure 3 demonstrates a
detailed knowledge creation flow in traditional and big data based knowledge management.
Furthermore, Table 1 compares these two types KMS based on a number of factors.
Table 1. Contrast between traditional and big data based knowledge management
Factor Traditional Knowledge
Management
Big Data based Knowledge
Management
Knowledge type Both tacit and codified knowledge Data is processed in real time to
extract useful knowledge
Necessary skills Apparently not necessary Analytical skills are highly
necessary
Orientation Highly people oriented Depends less on people more on
machine
Interaction Requires frequent face to face
interaction with people
Requires minimal face to face
interaction with people
Knowledge creation
and storage
Tacit knowledge repository is
mostly people’ brain. Tangible
storage of huge size.
Cloud storage is mostly used.
Knowledge is created through
perpetual flow and processing.
Data
Big Data Market trends,
competitor data,
customer data
Added
Context
Analytics
Information
Information
Tacit and
Explicit
Knowledge
Learning and
Experience
Intelligence
and Skills
Extracted
Knowledge/
Actionable
Insights
7. International Journal of Distributed and Parallel Systems (IJDPS) Vol.8, No.1, January 2017
7
3. REQUIREMENTS ANALYSIS FOR KM IN BIG DATA ANALYTICS CONTEXT
Recent developments in IT have given a boost to the usage of big data and analytics. At the same
time, data generation and consumption are driven by the factors such as data standards in
organizations, interchange formats of data, and databases used by the organizations [14]. This
has motivated organizations to analyze and benefit from the huge amount of data that are obtained
from web-based sources, sensors, and mobile devices. Accordingly, knowledge management
systems in organizations are receiving momentum by the introduction of big data generation and
analytics. To facilitate the processing of such enormous amount of both structured and
unstructured data highly optimized databases are necessary. Moreover, it is necessary to present
this data so that the user can easily comprehend it. Light-weight web oriented architecture can be
used to achieve this [15]. This will facilitate the knowledge discovery and will let the end users
realize the finishing outcome in terms of visual entities such as annotated graphs. Thus, to receive
the full advantage of big data and analytics, the ICT advancement plays a vital role in big data
based KMS. Knowledge sharing is also an important requirement as pointed out in the literature
[16] through which an enormous amount of data is produced and thereby knowledge economy
grows significantly.
Now, coming into the technology side, big data based KMS appreciates the power of cloud
computing. Lately, cloud computing has become an attractive and mainstream solution for data
storage, processing, and distribution [7]. It provides on-demand and elastic computing and data
storage resources without the large initial investments usually required for the deployment of
traditional data centers. Thus, the advent of cloud computing meets the need for large-scale
computing and storage infrastructures demanded by big data storage and analytics. The goal in
this regard is to introduce an intelligent cloud-based computing framework that can effectively
analyze the big data captured from diverse sources for creating knowledge and share this data in
real time to facilitate the implementation of a big data based KMS which is effective and fully
automated. Hence, organizations can benefit from cloud computing where big data analytics is
delivered as an on-demand cloud service and IT resources therein can be quickly adjusted to meet
changing demands.
Thus, cloud computing puts forward a vast cyberspace in which knowledge can be created,
shared, mined, and synthesized and all these can be done at an unprecedented speed. Big data
based KMS envisions to exploit these unprecedented opportunities offered by the cloud
computing. Thus, the proposed KMS framework addresses the convergent domain of cloud
computing and big data, that are at present two of the most prominent and popular ICT
paradigms. The adapted KMS framework hence needs changes that are fundamentally related to
transferring the traditional knowledge management system to the exploration of big data deployed
in the cloud through services in an effort to a support improved decision making. In addition to
storing huge amount of data, cloud computing provides lots of tools for operating on such data. In
a cloud, such data and systems are highly accessible and interoperable. Hence, the cloud is
appearing as a perfect environment for such experiments to be carried out, to discover new
knowledge, to analyze data in order to generate new ideas for organizations to act upon.
As mentioned earlier, the proposed big data based KMS exploits big data analytics for knowledge
extraction. The author in [17] suggested that a number of factors need to be considered while
taking advantage of big data analytics for successful knowledge creation, deployment, and
distribution. First, multidisciplinary team should be formed to supervise the overall process.
Second, an appropriate metrics needs to be defined for proper evaluation. Third but not the last,
perpetual motivation should be there in terms of special incentives for the work force. In addition,
the existing employees need to be trained to gain necessary skills to handle big data analytics in
the new system.
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The author in [18] explains that big data will be of little value if it cannot help in decision making
in organizations. Therefore, a number of key factors are discussed to be considered whenever big
data is analyzed for enhanced decision making, improved productivity and efficient operation of
the organizations. These will ensure big data to have significant values. First, data should be
relevant to a specific course of action. Second, data should be of high quality to ensure
dependable and steady support for decision making. Third, huge volume of data even though
having potential important information for decision making can result in ineffective outcome if
analyzed without vigilant analytic approaches. Fourth, the author in [19] suggests data collection
based on the problem at hand to solve. This will help determine the type of data to be gathered
based on a number of questions the decision makers will ask.
4. BIG DATA DRIVEN KM FRAMEWORK
The challenge in knowledge based systems using ICT oriented solutions lies in the assimilation of
data from different sources and processing the data to derive valuable information that is
delivered through services, consumed by common citizens, governments, and businesses.
However, the lack of a common and standardized platform to meet this challenge severely
restricts the creation and sharing of knowledge in organizations. Hence, we propose the
development of a unified framework based on cloud computing and big data technologies for the
collection, integration and analysis as well as the distribution of real-time data that come from
disparate sources to support decision making through automated knowledge management system.
To this end, a framework for big data based knowledge management which integrates big data
analytics, cloud computing, and knowledge management is illustrated in Figure 4.
We begin with an overview of the system architecture from a technological perspective. Then we
describe the architectural patterns for exploiting big data in a knowledge management system. We
also describe the functional view on big data, more particularly, the big data analytics technique
which is the crux of management system and related big data tools.
4.1.INFRASTRUCTURE LAYER
The bottom layer (infrastructure) consists of sensor networks, device networks, and data and
management interface. The network components are composed of various types of sensors,
actuators, software components, and other devices that collect data from (or actuate on) diverse
sources as shown in Figure 1. To manage the underlying network infrastructure and to deal with
the data generated therein, a generic control plane (interface) consisting of well-defined data and
management APIs (Application Programming Interface) are added. In addition to the big data
sources shown in Figure 1, we also consider customers’ participation as an important source of
data for their welfare, business planning, and decision-making which is generally known as
“crowdsourcing” [20]. Overall, this big data is produced with great velocity and must be captured
and processed quickly (as in the case of real time monitoring).
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Figure 4. A layered architecture of the framework
4.2.PLATFORM LAYER
At the heart of the architecture (as shown in Figure 4) is the second layer (platform) which
includes three basic building blocks, namely, big data analytics in the cloud with scalable storage
and computing resources, analytics application service, and operations management.
4.2.1. BIG DATA ANALYTICS IN THE CLOUD
The elastic and on-demand nature of resource provisioning makes a cloud platform attractive for
the execution of various applications, especially data and computation intensive ones [7]. We plan
to integrate cloud computing infrastructure in the proposed framework which includes scalable
storage and computation resources to store and analyze the data that are collected from the bottom
layer. Thus, big data analytics will be provided as a service by combining it with cloud
computing, not as a separate product solution. Instead of deploying self-developed servers, we
can actively use the cloud-based big data service to store and analyze the data. At the core of our
approach is a new highly scalable big data analytics algorithm that processes various types of data
in great volume to discover unknown patterns and relationship and other valuable information for
effective decision making in business planning.
PlatformInfrastructure
Device networks
Sensors Devices
Data API Management API
Sensor networks
Knowledge
Management
Knowledge
Storage and Use
Knowledge
Sharing
Knowledge
Creation
Analytics Application Service
Big Data Analytics
Data Storage
Hadoop
DFS
Configuration
Management
Platform
Management
Operations
Management
Cloud
Scalability
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Figure 5. Big data analytics architecture
As shown in Figure 5, the analysis of big data typically involves multiple steps. First, in the
acquisition phase, big data are sampled and recorded from various data sources as mentioned
above. Second, due to the diversity of the data collected, it’s necessary to automatically extract
and organize the data for archival, regardless of whether it is real-time data from sensors and
other inputs, or data from external systems. Organizing data is also referred to as data integration.
Due to the high volume of big data, it is essential to process data in the primary storage location
to maintain large throughput and to handle a large variety of data formats, from unstructured to
structured. After the above phases, data analysis and modeling will be conducted on the resulting
integrated and cleaned big data. The required infrastructure will thus be able to automate
decisions based on the analytical model. Finally, data interpretation and visualization will be done
since big data analytics alone is of limited value if users cannot understand the analysis results.
4.2.2. REQUIRED TOOLS
The most widely-used technology to handle big data is Hadoop, an open source project based on
Google’s MapReduce and Google File System [22, 23]. Hadoop is a distributed batch processing
infrastructure which consists of the Hadoop kernel, Hadoop Distributed File System (HDFS), and
MapReduce programming framework. In our framework, HDFS and/or key-value stores (NoSQL
database) will be used to acquire and store big data. They are optimized for exceedingly fast data
collection and simple query patterns. In addition, further processing of data will be performed by
implementing analytics algorithm using MapReduce framework.
4.2.3. ANALYTICS APPLICATION SERVICE
This service module utilizes the results from the big data analytics for developing domain specific
components and services. By this way, users will be able to interact with web-based analytics
services easily without worrying about the underlying data storage, management, and analyzing
procedures.
4.2.4. OPERATIONS MANAGEMENT
The configuration management module performs configuration of system components and
manages network resources. The platform management module provides administrative support
for the proposed framework.
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4.3.KNOWLEDGE MANAGEMENT LAYER
The top layer provides the knowledge management cycle for knowledge acquisition, creation,
distribution and sharing, storage, and utilization. Actionable insights are created in real-time to be
used in supporting on demand decision making. In particular, real-time analytic results are fed in
to the knowledge management layer as input to complete the knowledge management as
mentioned. Building on this framework, a community of stakeholders such as industries,
government and customers can better plan their business activities and engage in enhanced and
effective decision making.
4.4 IMPLEMENTATION CHALLENGES
Although the proposed big data based knowledge management framework envisions enjoying the
benefits of big data and cloud computing, there are relevant challenges and limitations that need
to be tackled. The foremost concern is to deal with technical challenges. Due to the growth of
state of the art technologies for collection and managing huge amount of data, the framework
needs to find ways to handle large volume of diverse data and time consuming data processing.
Often times data is too diverse to analyze and extract value for effective decision making.
Another related issue is the poor data governance which acts as an important factor in limiting the
efforts of extracting value from big data. At the same time, moving away from traditional data
management techniques and coming up with efficient analytical algorithms to process both
structured and unstructured data seems to be challenging.
On the other side, the practical implementation of the framework requires the expertise and new
tools in cloud based big data technologies. Researchers in the relevant field pointed out that there
is a lack of expertise and tools to create value from big data [28]. For instance, big data analytics
mostly require the expertise in statistics, machine learning, and data management to extract
valuable insight from organizational big data which is going to be a challenge. Hence, successful
implementation of the framework will produce new experts in the knowledge management field
that exploits these two emerging technologies as such big data and cloud computing.
5. A USE CASE STUDY
This section presents a case study conducted by Yahoo big data campaign [27] on predictive
modeling for advertising their own products and services. Yahoo represents one of the largest
web based companies. The survey statistics show that 80% of the internet users in the United
States use Yahoo for diverse internet services. It has experienced over 600 million users from all
over the world every month. The services and products offered ranges from miscellaneous
content, media, trade and commerce, search and access products. They possess over hundred
properties such as mail, television, news, shopping, finance, health, autos, games, movies, travel
and so on. The size of the collected data has reached over 25TB every day. This huge amount of
data represents the salient source to build content, category, consumer, and campaign actionable
insights for their primary content partners and major advertisers. Data processing is conducted in
major problem areas such as credit card, travel, stock exchange, retail purchase, and internet
usage. Yahoo data challenge thus exceeds other mainstream competitors by at least two orders of
magnitude.
Behavioral targeting is one of the important business strategies they adopt to facilitate enhanced
decision making intended for competitive advantage. They maintain a behavioral or interest
profile and profitability metrics for each consumer. The strategy works by targeting
advertisements to customers whose latest behavior or online activities signify which product or
service category is related to them. In this manner, the most relevant class of users is identified.
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For the sake of completeness, the following diagram illustrates how their predictive modeling
works at a very abstract level.
Figure 6. Yahoo predictive modeling cycle for target consumers
The predictive model starts analyzing purchase histories in cycles over hundred of thousands
product categories comprising huge volume of data for upfront processing. In each category of
these products, a behavior model is built to portray purchase of consumers that presumably
represent a huge volume of web click streams containing their responses to ads. In the next stage,
each consumer is labeled with a score for appropriate category of product every day. Consumers
are then ordered in terms of obtained scores in each category of products and the predictive
scheme selects consumers for targeting ads who receive highest relevance scores in the selected
category of products.
6. CONCLUSIONS
Organizations have realized the importance of huge amount of data that are collected to make
enhanced decisions. Research results suggest that organizations with data driven policies are more
prolific and profitable compared to their counterparts who are less data driven. Furthermore,
evidences demonstrate that big data can create new possibilities and immense opportunities for
the organizations to manage knowledge effectively. This paper attempts to investigate the role of
big data in knowledge management and make substantial contribution in linking these two
concepts. To this end, a cloud based framework is presented to extract the values from big data
through analytics for the development of knowledge management to support effective decision
making. In the proposed framework, big data platform will use cloud enabling technologies such
as HDFS and MapReduce that support distributed processing of data across a cluster of data
centers. In addition, NoSQL databases will be used to support real-time processing of big data.
The framework exemplifies the complete cycle of big data acquisition and analysis through
analytics to transform big data values into actionable insight to support knowledge management
process consisting of knowledge acquisition, distribution and sharing, presentation and storage,
and utilization. However, a number of challenges are faced in harnessing this new technology in
knowledge management such as availability of data scientists and maintaining data privacy and so
on which need to be addressed for successful implementation of the proposed framework.
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