Handbook of research on advanced data mining techniques and applications for business intelligence:
"This publication is a key resource on the latest advancements in business applications and the use of mining software solutions to achieve optimal decision-making and risk management results. Highlighting innovative studies on data warehousing, business activity monitoring, and text mining&quo...
Gespeichert in:
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| Format: | Buch |
| Sprache: | Englisch |
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Hershey, PA
IGI Global
[2017]
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| Schriftenreihe: | A volume in the Advances in Business Information Systems and Analytics (ABISA) book series
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| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029738370&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Zusammenfassung: | "This publication is a key resource on the latest advancements in business applications and the use of mining software solutions to achieve optimal decision-making and risk management results. Highlighting innovative studies on data warehousing, business activity monitoring, and text mining"... |
| Beschreibung: | Literaturverzeichnis Seite 397-426 |
| Umfang: | xxvi, 438 Seiten Diagramme |
| ISBN: | 9781522520313 |
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Datensatz im Suchindex
| _version_ | 1819271627144167424 |
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| adam_text | Table of Contents
Preface....................................................................................xxii
Acknowledgment.............................................................................xxvi
Section 1
Business Intelligence With Data Mining: Process and Applications
Chapter 1
An Introduction to Data Analytics; Its Types and Its Applications............................1
A. Sheik Abdullah, Thiagarajar College of Engineering, India
S. Selvakumar, G. K. M. College of Engineering and Technology, India
A. M. Abirami, Thiagarajar College of Engineering, India
Chapter 2
Data Mining and Statistics: Tools for Decision Making in the Age of Big Data...............15
Hirak Dasgupta, Symbiosis Institute of Management Studies, India
Chapter 3
Data Classification; Its Techniques and Big Data...........................................34
A. Sheik Abdullah, Thiagarajar College of Engineering, India
R. Suganya, Thiagarajar College of Engineering, India
S. Selvakumar, G. K. M. College of Engineering and Technology, India
S. Rajaram, Thiagarajar College of Engineering, India
Chapter 4
Secure Data Analysis in Clusters (Iris Database)...........................................52
Raghvendra Kumar, LNCT College, India
Prasant Kumar Pattnaik, KITT University, India
Priyanka Pandey, LNCT College, India
Chapter 5
Data Mining for Secure Online Payment Transaction..........................................62
Masoumeh Zareapoor, Shanghai Jiao Tong University, China
Pourya Shamsolmoali, CMCC, Italy
M. Afshar Alam, Jämiä Hamdard University, India
Chapter 6
The Integral of Spatial Data Mining in the Era of Big Data: Algorithms and Applications..90
Gebeyehu Belay Gebremeskel, Chongqing University, China
Chai Yi, Chongqing University, China
Zhongshi He, Chongqing University, China
Section 2
Social Media Analytics With Sentiment Analysis: Business Applications and Methods
Chapter 7
Social Media as Mirror of Society...........................................................128
Amir Manzoor, Bahria University, Pakistan
Chapter 8
Business Intelligence through Opinion Mining................................................142
T. K. Das, V1T University, India
Chapter 9
Sentiment Analysis..........................................................................162
A. M. Abirami, Thiagarajar College of Engineering, India
A. Sheik Abdullah, Thiagarajar College of Engineering, India
A. Askarunisa, KLN College of Information Technology, India
S. Selvakumar, G. K. M, College of Engineering and Technology, India
C. Mahalakshmi, Thiagarajar College of Engineering, India
Chapter 10
Aspect-Based Sentiment Analysis of Online Product Reviews................................175
Vinod Kumar Mishra, Bipin Tripathi Kumaon Institute of Technology, India
Himanshu Tiruwa, Bipin Tripathi Kumaon Institute of Technology, India
Chapter 11
Sentiment Analysis with Social Media Analytics, Methods, Process, and Applications.......192
Karteek Ramalinga Ponnuru, BML Munjal University, India
Rashik Gupta, BML Munjal University, India
Shrawan Kumar Trivedi, BML Munjal University, India
Chapter 12 ,
Organizational Issue for BI Success: Critical Success Factors for BI Implementations within the
Enterprise..........1....................................................................209
Sanjiva Shankar Dubey, BIMTECH Greater Nokia, India
Arunesh Sharan, AS Consulting, India
Chapter 13
Ethics of Social Media Research..........................................................225
Amir Manzoor, Bahria University, Pakistan
Section 3
Big Data Analytics: Its Methods and Applications
Chapter 14
Big Data Analytics in Health Care..........................................................240
Keerthi Suneetha, SVEC, Indict
Chapter 15
Mining Big Data for Marketing Intelligence.................................................250
Khadija Ali Vakeel, Indian Institute of Management Indore, India
Chapter 16
Predictive Analysis for Digital Marketing Using Big Data: Big Data for Predictive Analysis.259
Balamurugan Balusamy, VIT University, India
Priya Jha, VIT University, India
Tamizh Arasi, VIT University, India
Malathi Vein, VIT University, India
Chapter 17
Strategic Best-in-Class Performance for Voice to Customer: Is Big Data in Logistics a Perfect
Match?...................................................................................284
Supriyo Roy, Birla Institute of Technology, India
Kaushik Kumar, Birla Institute of Technology, India
Section 4
Advanced Data Analytics: Decision Models and Business Applications
Chapter 18
First Look on Web Mining Techniques to Improve Business Intelligence of E-Commerce
Applications...............................................................................298
G. Sreedhar, Rashtriya Sanskrit Vidyapeetha (Deemed University), India
A. Anandaraja Chari, Rayalaseema University, India
Chapter 19
Artificial Intelligence in Stochastic Multiple Criteria Decision Making....................315
Hanna Sawicka, Poznan University of Technology, Poland
Chapter 20
Joint Decision for Price Competitive Inventory Model with Time-Price and Credit Period
Dependent Demand...........................................................................341
Nita H. Shah, Gujarat University, India
Chapter 21
On Development of a Fuzzy Stochastic Programming Model with Its Application to Business
Management.................................................................................353
Animesh Biswas, University of Kälyäni, India
Amah Kumar De, Government College of Engineering and Textile Technology, India
Chapter 22
Ranking of Cloud Services Using Opinion Mining and Multi-Attribute Decision Making:
Ranking of Cloud Services Using Opinion Mining and MADM................................379
Srimanyu Timmaraju, Institute for Development and Research in Banking Technology, India
Vadlamani Ravi, Institute for Development and Research in Banking Technology, India
G. R. Gangadharan, Institute for Development and Research in Banking Technology, India
Compilation of References..............................................................397
About the Contributors.................................................................427
Index..................................................................................436
Ï
;
Detailed Table of Contents
Preface.......................................................................xxii
Acknowledgment................................................................xxvi
Section 1
Business Intelligence With Data Mining: Process and Applications
Chapter 1
An Introduction to Data Analytics: Its Types and Its Applications.............................1
A. Sheik Abdullah, Thiagarajar College of Engineering, India
S. Selvakumar, G, K. M. College of Engineering and Technology, India
A. M. Abirami, Thiagarajar College of Engineering, India
Data analytics mainly deals with the science of examining and investigating raw data to derive useful
patterns and inference. Data analytics has been deployed in many of the industries to make decisions
at proper levels. It focuses upon the assumption and evaluation of the method with the intention of
deriving a conclusion at various levels. Various types of data analytical techniques such as predictive
analytics, prescriptive analytics, descriptive analytics, text analytics, and social media analytics are used
by industrial organizations, educational institutions and by government associations. This context mainly
focuses towards the illustration of contextual examples for various types of analytical techniques and
its applications.
Chapter 2
Data Mining and Statistics: Tools for Decision Making in the Age of Big Data..................15
Hirak Dasgupta, Symbiosis Institute of Management Studies, India
In the age of information, the world abounds with data. In order to obtain an intelligent appreciation of
current developments, we need to absorb and interpret substantial amounts of data. The amount of data
collected has grown at a phenomenal rate over the past few years. The computer age has given us both
the power to rapidly process, summarize and analyse data and the encouragement to produce and store
more data. The aim of data mining is to make sense of large amounts of mostly unsupervised data, in
some domain. Data Mining is used to discover the patterns and relationships in data, with an emphasis
on large observational data bases. This chapter aims to compare the approaches and conclude that
Statisticians and Data miners can profit by studying each other’s methods by using the combination of
methods judiciously. The chapter also attempts to discuss data cleaning techniques involved in data mining.
Chapter 3
Data Classification: Its Techniques and Big Data................................................34
A. Sheik Abdullah, Thiagarajar College of Engineering, India
R, Suganya, Thiagarajar College of Engineering, India
S. Selvakumar, G. K, M. College of Engineering and Technology, India
S. Rajaram, Thiagarajar College of Engineering, India
Classification is considered to be the one of the data analysis technique which can be used over many
applications. Classification model predicts categorical continuous class labels. Clustering mainly deals
with grouping of variables based upon similar characteristics. Classification models are experienced by
comparing the predicted values to that of the known target values in a set of test data. Data classification has
many applications in business modeling, marketing analysis, credit risk analysis; biomedical engineering
and drag retort modeling. The extension of data analysis and classification makes the insight into big
data with an exploration to processing and managing large data sets. This chapter deals with various
techniques, methodologies that correspond to the classification problem in data analysis process and its
methodological impacts to big data.
Chapter 4
Secure Data Analysis in Clusters (Iris Database)................................................52
Raghvendra Kumar, LNCT College, India
Prasant Kumar Pattnaik, KIIT University, India
Priyanka. Pandey, LNCT College, India
This chapter used privacy preservation techniques (Data Modification) to ensure Privacy. Privacy
preservation is another important issue. A picture, where number of clients owning their clustered databases
(Iris Database) wish to run a data mining algorithm on the union of their databases, without revealing
any unnecessary information and requires the privacy of the privileged information. There are numbers
of efficient protocols are required for privacy preserving in data mining. This chapter presented various
privacy preserving protocols that are used for security in clustered databases. The Xln(X) protocol and
the secure sum protocol are used in mutual computing, which can defend privacy efficiently. Its focuses
on the data modification techniques, where it has been modified our distributed database and after that
sanded that modified data set to the client admin for secure data communication with zero percentage
of data leakage and also reduce the communication and computation complexity.
Chapter 5
Data Mining for Secure Online Payment Transaction...............................................62
Masoumeh Zareapoor, Shanghai Jiao Tong University, China
Pourya Shamsolmoali, CMCC, Italy
M. Afshar Alam, Jamia Hamdard University, India
}
The fraud detection method requires a holistic approach where the objective is to correctly classify the
transactions as legitimate or fraudulent. The existing methods give importance to detect all fraudulent
transactions since it results in money loss. For this most of the time, they have to compromise on some
genuine transactions. Thus, the major issue that the credit card fraud detection systems face today is that
a significant percentage of transactions labelled as fraudulent are in fact legitimate. These “false alarms”
delay the transactions and creates inconvenience and dissatisfaction to the customer. Thus, the objective
of this research is to develop an intelligent data mining based fraud detection system for secure online
payment transaction system. The performance evaluation of the proposed model is done on real credit
card dataset and it is found that the proposed model has high fraud detection rate and less false alarm
rate than other state-of-the-art classifiers.
Chapter 6
The Integral of Spatial Data Mining in the Era of Big Data: Algorithms and Applications.......90
Gebeyehu Belay Gebremeskel, Chongqing University, China
Chai Yi, Chongqing University, China
Zhongshi He, Chongqing University, China
Data Mining (DM) is a rapidly expanding field in many disciplines, and it is greatly inspiring to analyze
massive data types, which includes geospatial, image and other forms of data sets. Such the fast growths
of data characterized as high volume, velocity, variety, variability, value and others that collected and
generated from various sources that are too complex and big to capturing, storing, and analyzing and
challenging to traditional tools. The SDM is, therefore, the process of searching and discovering valu-
able information and knowledge in large volumes of spatial data, which draws basic principles from
concepts in databases, machine learning, statistics, pattern recognition and ‘soft’ computing. Using DM
techniques enables a more efficient use of the data warehouse. It is thus becoming an emerging research
field in Geosciences because of the increasing amount of data, which lead to new promising applica-
tions. The integral SDM in which we focused in this chapter is the inference to geospatial and GIS data.
Section 2
Social Media Analytics With Sentiment Analysis: Business Applications and Methods
Chapter 7
Social Media as Mirror of Society.............................................................128
Amir Manzoor, Bahria University, Pakistan
Over the last decade, social media use has gained much attention of scholarly researchers. One specific
reason of this interest is the use of social media for communication; a trend that is gaining tremendous
popularity. Every social media platform has developed its own set of application programming i nterface
(API). Through these APIs, the data available on a particular social media platform can be accessed.
However, the data available is limited and it is difficult to ascertain the possible conclusions that can
be drawn about society on the basis of this data. This chapter explores the ways social researchers and
scientists can use social media data to support their research and analysis.
Chapter 8
Business Intelligence through Opinion Mining..................................................142
T. K. Das, VIT University, India
Business organizations have been adopting different strategies to impress upon their customers and attract
them towards their products and services. On the other hand, the opinions of the customers gathered through
customer feedbacks have been a great source of information for companies to evolve business intelligence
to rightly place their products and services to meet the ever-changing customer requirements. In this work,
we present a new approach to integrate customers’ opinions into the traditional data warehouse model.
We have taken Twitter as the data source for this experiment. First, we have built a system which can be
used for opinion analysis on a product or a service. The second process is to model the opinion table so
obtained as a dimensional table and to integrate it with a central data warehouse schema so that reports
can be generated on demand. Furthermore, we have shown how business intelligence can be elicited from
online product reviews by using computational intelligence technique like rough set base data analysis.
Chapter 9
Sentiment Analysis..........................................................................162
A. M. Abirami, Thiagarajar College of Engineering, India
A. Sheik Abdullah, Thiagarajar College of Engineering, India
A. Askarunisa, KLN College of Information Technology, India
S. Selvakumar, G. K. M. College of Engineering and Technology, India
C. Mahalakshmi, Thiagarajar College of Engineering, India
It requires sophisticated streaming of big data processing to process the billions of daily social
conversations across millions of sources. Dataset needs information extraction from them and it requires
contextual semantic sentiment modeling to capture the intelligence through the complexity of online
social discussions. Sentiment analysis is one of the techniques to capture the intelligence from Social
Networks based on the user generated content. There are more and more researches evolving about
sentiment classification. Aspect extraction is the core task involved in aspect based sentiment analysis.
The proposed modeling uses Latent Semantic Analysis technique for aspect extraction and evaluates
senti-scores of various products under study.
Chapter 10
Aspect-Based Sentiment Analysis of Online Product Reviews...................................175
Vinod Kumar Mishra, Bipin Tripathi Kumaon Institute of Technology, India
Himanshu Tiruwa, Bipin Tripathi Kumaon Institute of Technology, India
Sentiment analysis is a part of computational linguistics concerned with extracting sentiment and emotion
from text. It is also considered as a task of natural language processing and data mining. Sentiment
analysis mainly concentrate on identifying whether a given text is subjective or objective and if it is
subjective, then whether it is negative, positive or neutral. This chapter provide an overview of aspect
based sentiment analysis with current and future trend of research on aspect based sentiment analysis.
This chapter also provide a aspect based sentiment analysis of online customer reviews of Nokia 6600.
To perform aspect based classification we are using lexical approach on eclipse platform which classify
the review as a positive, negative or neutral on the basis of features of product. The Sentiwordnet is used
as a lexical resource to calculate the overall sentiment score of each sentence, pos tagger is used for
part of speech tagging, frequency based method is used for extraction of the aspects/features and used
negation handling for improving the accuracy of the system.
h
Chapter 11 ՝
Sentiment Analysis with Social Media Analytics, Methods, Process, and Applications..........192
Karteek Ramalinga Ponnuru, BML Munjal University, India
Rashik Gupta, BML Munjal University, India
Shrawan Kumar Trivedi, BML Munjal University, India
Firms are turning their eye towards social media analytics to get to know what people are really talking
about their firm or their product. With the huge amount of buzz being created online about anything and
everything social media has become ‘the’ platform of the day to understand what public on a whole are
talking about a particular product and the process of converting all the talking into valuable information
is called Sentiment Analysis. Sentiment Analysis is a process of identifying and categorizing a piece of
text into positive or negative so as to understand the sentiment of the users. This chapter would take the
reader through basic sentiment classifiers like building word clouds, commonality clouds, dendrograms
and comparison clouds to advanced algorithms like K Nearest Neighbour, Naive Biased Algorithm and
Support Vector Machine.
Chapter 12
Organizational Issue for BI Success: Critical Success Factors for BI Implementations within the
Enterprise................................................................................209
Sanjiva Shankar Dubey, BIMTECH Greater Noida, India
Arunesh Sharan, AS Consulting, India
This chapter will focus on the transformative effect Business Intelligence (BI) brings to an organization
decision making, enhancing its performance, reducing overall cost of operations and improving its
competitive posture. This chapter will enunciate the key principles and practices to bridge the gap between
organization requirements vs. capabilities of any BI tool(s) by proposing a framework of organizational
factors such as user’s role, their analytical needs, access preferences and technical /analytical literacy
etc. Evaluation methodology to select best BI tools properly aligned to the organization infrastructure
will also be discussed. Softer issues and organizational change for successful implementation of BI will
be further explained.
Chapter 13
Ethics of Social Media Research...........................................................225
Amir Manzoor, Bahria University, Pakistan
Over the last decade, social media platforms have become a very popular channel of communication.
This popularity has sparked an increasing interest among researchers to investigate the social media
communication. Many studies have been done that collected the publicly available social media
communication data to unearth significant patterns. However, one significant concern raised over such
practice is the privacy of the individual’s social media communication data. As such it is important that
specific ethical guidelines are in place for future researches on social media sites. This chapter explores
various ethical issues related to researches related to social networking sites, The chapter also provides
a set of ethical guidelines that future researches on social media sites can use to address various ethical
issues.
Section 3
Big Data Analytics: Its Methods and Applications
Chapter 14
Big Data Analytics in Health Care........................................................240
Keerthi Suneetha, SVEC, India
With the arrival of technology and rising amount of data (Big Data) there is a need towards implementation
of effective analytical techniques (Big Data Analytics) in health sector which provides stakeholders with
new insights that have the potential to advance personalized care to improve patient outcomes and avoid
unnecessary costs. This chapter covers how to evaluate this big volume of data for unknown and useful
facts, associations, patterns, trends which can give birth to new line of handling of diseases and provide
high quality healthcare at lower cost to all citizens. This chapter gives a wide insight of introduction to
Big Data Analytics in health domain, processing steps of BDA, Challenges and Future scope of research
in healthcare,
Chapter 15
Mining Big Data for Marketing Intelligence...................................................250
KhadijaAli Vakeel, Indian Institute of Management Indore, India
This chapter elaborates on mining techniques useful in big data analysis. Specifically, it will elaborate
on how to use association rule mining, self organizing maps, word cloud, sentiment extraction, network
analysis, classification, and clustering for marketing intelligence. The application of these would be on
decisions related to market segmentation, targeting and positioning, trend analysis, sales, stock markets
and word of mouth. The chapter is divided in two sections of data collection and cleaning where we
elaborate on how twitter data can be extracted and mined for marketing decision making. Second part
discusses various techniques that can be used in big data analysis for mining content and interaction
network.
Chapter 16
Predictive Analysis for Digital Marketing Using Big Data: Big Data for Predictive Analysis...259
Balamurugan Balusamy, VIT University, India
Priya Jha, VIT University, India
Tamizh Arasi, VIT University, India
Malathi Velu, VIT University, India
Big data analytics in recent years had developed lightning fast applications that deal with predictive
analysis of huge volumes of data in domains of finance, health, weather, travel, marketing and more.
Business analysts take their decisions using the statistical analysis of the available data pulled in from
social media, user surveys, blogs and internet resources, Customer sentiment has to be taken into account
for designing, launching and pricing a product to be inducted into the market and the emotions of the
consumers changes and is influenced by several tangible and intangible factors. The possibility of using
Big data analytics to present data in a quickly viewable format giving different perspectives of the same
data is appreciated in the field of finance and health, where the advent of decision support system is
possible in all aspects of their working. Cognitive computing and artificial intelligence are making big
data analytical algorithms to think more on their own, leading to come out with Big data agents with
their own functionalities.
Chapter 17 y
Strategic Best-in-Class Performance for Voice to Customer: Is Big Data in Logistics a Perfect
Match?.......................................................................................284
Supriyo Roy, Birla Institute of Technology, India
Kaushik Kumar, Birla Institute of Technology, India
For any forward-looking perspective, organizational information which is typically historical, incomplete
and most of the time inaccurate, needs to be enriched with external information. However, traditional
systems and approaches are slow, inflexible and cannot handle new volume and complexity of information.
Big data, an evolving term, basically refers to voluminous amount of structured, semi-structured or
unstructured information in the form of data with a potential to be mined for ‘best in class information’.
Primarily, big data can be categorized by 3V’s: volume, variety and velocity. Recent hype around big
data concepts predicts that it will help companies to improve operations and makes faster and intelligent
decisions. Considering the complexities in realms of supply chain, in this study, an attempt has been
made to highlight the problems in storing data in any business, especially under Indian scenario where
logistics arena is most unstructured and complicated. Conclusion may be significant to any strategic
decision maker / manager working with distribution and logistics.
Section 4
Advanced Data Analytics: Decision Models and Business Applications
Chapter 18
First Look on Web Mining Techniques to Improve Business Intelligence of E-Commerce
Applications...................................................................................298
G. Sreedhar, Rashtriya Sanskrit Vidyapeetha (Deemed University), India
A. Anandaraja Chari, Rayalaseema University, India
Web Data Mining is the application of data mining techniques to extract useful knowledge from web data
like contents of web, hyperlinks of documents and web usage logs. There is also a strong requirement of
techniques to help in business decision in e-commerce. Web Data Mining can be broadly divided into
three categories: Web content mining, Web structure mining and Web usage mining. Web content data
are content availed to users to satisfy their required information. Web structure data represents linkage
and relationship of web pages to others. Web usage data involves log data collected by web server and
application server which is the main source of data. The growth of WWW and technologies has made
business functions to be executed fast and easier. As large amount of transactions are performed through
e-commerce sites and the huge amount of data is stored, valuable knowledge can be obtained by applying
the Web Mining techniques.
Chapter 19
Artificial Intelligence in Stochastic Multiple Criteria Decision Making........................315
Hanna Sawicka, Poznan University of Technology, Poland
This chapter presents the concept of stochastic multiple criteria decision making (MCDM) method to
solve complex ranking decision problems. This approach is composed of three main areas of research,
i.e. classical MCDM, probability theory and classification method. The most important steps of the idea
are characterized and specific features of the applied methods are briefly presented. The application
of Electre III combined with probability theory, and Promethee II combined with Bayes classifier are
described in details. Two case studies of stochastic multiple criteria decision making are presented. The
first one shows the distribution system of electrotechnical products, composed of 24 distribution centers
(DC), while the core business of the second one is the production and warehousing of pharmaceutical
products. Based on the application of presented stochastic MCDM method, different ways of improvements
of these complex systems are proposed and the final i.e. the best paths of changes are recommended.
Chapter 20
Joint Decision for Price Competitive Inventory Model with Time-Price and Credit Period
Dependent Demand.........................................................................341
Nita H. Shah, Gujarat University, India
The problem analyzes a supply chain comprised of two front-runner retailers and one supplier. The
retailers’ offer customers delay in payments to settle the accounts against the purchases which is received
by the supplier. The market demand of the retailer depends on time, retail price and a credit period offered
to the customers with that of the other retailer. The supplier gives items with same wholesale price and
credit period to the retailers. The joint and independent decisions are analyzed and validated numerically.
Chapter 21
On Development of a Fuzzy Stochastic Programming Model with Its Application to Business
Management...............................................................................353
Animesh Biswas, University ofKalyani, India
Arnab Kumar De, Government College of Engineering and Textile Technology, India
This chapter expresses efficiency of fuzzy goal programming for multiobjective aggregate production
planning in fuzzy stochastic environment. The parameters of the objectives are taken as normally
distributed fuzzy random variables and the chance constraints involve joint Cauchy distributed fuzzy
random variables. In model formulation process the fuzzy chance constrained programming model is
converted into its equivalent fuzzy programming using probabilistic technique, a-cut of fuzzy numbers
and taking expectation of parameters of the objectives. Defuzzification technique of fuzzy numbers
is used to find multiobjective linear programming model. Membership function of each objective is
constructed depending on their optimal values. Afterwards a weighted fuzzy goal programming model
is developed to achieve the highest degree of each of the membership goals to the extent possible by
minimizing group regrets in a multiobjective decision milking context. To explore the potentiality of the
proposed approach, production planning of a health drinks manufacturing company has been considered.
Chapter 22
Ranking of Cloud Services Using Opinion Mining and Multi-Attribute Decision Making:
Ranking of Cloud Services Using Opinion Mining and MADM..................................379
Srimanyu Titnmaraju, Institute for Development and Research in Banking Technology, India
Vadlamani Ravi, Institute for Development and Research in Banking Technology, India
G. R. Gangadharan, Institute for Development and Research in Banking Technology, India
Cloud computing has been a major focus of business organizations around the world. Many applications
are getting migrated to the cloud and many new applications are being developed to run on the cloud.
There are alreadyimore than 100 cloud service providers in the market offering various cloud services.
As the number of ¿loud services and providers is increasing in the market, it is very important to select
the right provider and service for deploying an application. This paper focuses on recommendation of
cloud services by ranking them with the help of opinion mining of users’ reviews and multi-attribute
decision making models (TOPSIS and FMADM were applied separately) in tandem on both quantitative
and qualitative data. Surprisingly, both TOPSIS and FMADM yielded the same rankings for the cloud
services.
Compilation of References............................................................397
About the Contributors...............................................................427
Index................................................................................436
|
| any_adam_object | 1 |
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| author_facet | Trivedi, Shrawan Kumar 1985- Dey, Shubhamoy Kumar, Anil 1980- Panda, Tapan Kumar |
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| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
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| dewey-sort | 3658.4 107202856312 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
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| id | DE-604.BV044335192 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T18:00:12Z |
| institution | BVB |
| isbn | 9781522520313 |
| language | English |
| lccn | 016052414 |
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| publishDate | 2017 |
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| publisher | IGI Global |
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| series2 | A volume in the Advances in Business Information Systems and Analytics (ABISA) book series |
| spellingShingle | Handbook of research on advanced data mining techniques and applications for business intelligence Datenverarbeitung Business intelligence Data processing Data mining Big data |
| subject_GND | (DE-588)4143413-4 |
| title | Handbook of research on advanced data mining techniques and applications for business intelligence |
| title_auth | Handbook of research on advanced data mining techniques and applications for business intelligence |
| title_exact_search | Handbook of research on advanced data mining techniques and applications for business intelligence |
| title_full | Handbook of research on advanced data mining techniques and applications for business intelligence Shrawan Kumar Trivedi, Shubhamoy Dey, Anil Kumar, and Tapan Kumar Panda |
| title_fullStr | Handbook of research on advanced data mining techniques and applications for business intelligence Shrawan Kumar Trivedi, Shubhamoy Dey, Anil Kumar, and Tapan Kumar Panda |
| title_full_unstemmed | Handbook of research on advanced data mining techniques and applications for business intelligence Shrawan Kumar Trivedi, Shubhamoy Dey, Anil Kumar, and Tapan Kumar Panda |
| title_short | Handbook of research on advanced data mining techniques and applications for business intelligence |
| title_sort | handbook of research on advanced data mining techniques and applications for business intelligence |
| topic | Datenverarbeitung Business intelligence Data processing Data mining Big data |
| topic_facet | Datenverarbeitung Business intelligence Data processing Data mining Big data Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029738370&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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