Pub Date : 1900-01-01DOI: 10.4018/978-1-5225-6210-8.CH002
Gopala Krishna Behara
This chapter covers the essentials of big data analytics ecosystems primarily from the business and technology context. It delivers insight into key concepts and terminology that define the essence of big data and the promise it holds to deliver sophisticated business insights. The various characteristics that distinguish big data datasets are articulated. It also describes the conceptual and logical reference architecture to manage a huge volume of data generated by various data sources of an enterprise. It also covers drivers, opportunities, and benefits of big data analytics implementation applicable to the real world.
{"title":"Big-Data-Based Architectures and Techniques","authors":"Gopala Krishna Behara","doi":"10.4018/978-1-5225-6210-8.CH002","DOIUrl":"https://doi.org/10.4018/978-1-5225-6210-8.CH002","url":null,"abstract":"This chapter covers the essentials of big data analytics ecosystems primarily from the business and technology context. It delivers insight into key concepts and terminology that define the essence of big data and the promise it holds to deliver sophisticated business insights. The various characteristics that distinguish big data datasets are articulated. It also describes the conceptual and logical reference architecture to manage a huge volume of data generated by various data sources of an enterprise. It also covers drivers, opportunities, and benefits of big data analytics implementation applicable to the real world.","PeriodicalId":365327,"journal":{"name":"Predictive Intelligence Using Big Data and the Internet of Things","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122108602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-5225-6210-8.CH005
V. Kakulapati, Mahender Reddy S.
Sensor data takes the microcontroller and sends it to doctors through the wi-fi network and provides real-time healthcare parameter monitoring. The clinician can analyze the sensor generated information. Patients provide their measures to the arrangement and identify their fitness status without human intervention. In this chapter, MapReduce algorithm is used to identify the patient health status. The controller is connected with the signal to alert the attendee about dissimilarity in sensor output data. If the situation is sever, an alert message is sent to the doctor through the IOT devices that can provide quick provisional medication to the ill person. The system improves usability of medical devices with less power consumption, simple setup, and high performance and response.
{"title":"Improved Usability of IOT Devices in Healthcare Using Big Data Analysis","authors":"V. Kakulapati, Mahender Reddy S.","doi":"10.4018/978-1-5225-6210-8.CH005","DOIUrl":"https://doi.org/10.4018/978-1-5225-6210-8.CH005","url":null,"abstract":"Sensor data takes the microcontroller and sends it to doctors through the wi-fi network and provides real-time healthcare parameter monitoring. The clinician can analyze the sensor generated information. Patients provide their measures to the arrangement and identify their fitness status without human intervention. In this chapter, MapReduce algorithm is used to identify the patient health status. The controller is connected with the signal to alert the attendee about dissimilarity in sensor output data. If the situation is sever, an alert message is sent to the doctor through the IOT devices that can provide quick provisional medication to the ill person. The system improves usability of medical devices with less power consumption, simple setup, and high performance and response.","PeriodicalId":365327,"journal":{"name":"Predictive Intelligence Using Big Data and the Internet of Things","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114154246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-5225-6210-8.CH001
Dharmpal Singh, Madhusmita Mishra, S. Sahana
Big-data-analyzed finding patterns derive meaning and make decisions on data to produce responses to the world with intelligence. It is an emerging area used in business intelligence (BI) for competitive advantage to analyze the structured, semi-structured, and unstructured data stored in different formats. As the big data technology continues to evolve, businesses are turning to predictive intelligence to deepen the engagement to customers with optimization in processes to reduce the operational costs. Predictive intelligence uses sets of advanced technologies that enable organizations to use data stored in real time that move from a historical and descriptive view to a forward-looking perspective of data. The comparison and other security issue of this technology is covered in this book chapter. The combination of big data technology and predictive analytics is sometimes referred to as a never-ending process and has the possibility to deliver significant competitive advantage. This chapter provides an extensive review of literature on big data technologies and its usage in the predictive intelligence.
{"title":"Big-Data-Based Techniques for Predictive Intelligence","authors":"Dharmpal Singh, Madhusmita Mishra, S. Sahana","doi":"10.4018/978-1-5225-6210-8.CH001","DOIUrl":"https://doi.org/10.4018/978-1-5225-6210-8.CH001","url":null,"abstract":"Big-data-analyzed finding patterns derive meaning and make decisions on data to produce responses to the world with intelligence. It is an emerging area used in business intelligence (BI) for competitive advantage to analyze the structured, semi-structured, and unstructured data stored in different formats. As the big data technology continues to evolve, businesses are turning to predictive intelligence to deepen the engagement to customers with optimization in processes to reduce the operational costs. Predictive intelligence uses sets of advanced technologies that enable organizations to use data stored in real time that move from a historical and descriptive view to a forward-looking perspective of data. The comparison and other security issue of this technology is covered in this book chapter. The combination of big data technology and predictive analytics is sometimes referred to as a never-ending process and has the possibility to deliver significant competitive advantage. This chapter provides an extensive review of literature on big data technologies and its usage in the predictive intelligence.","PeriodicalId":365327,"journal":{"name":"Predictive Intelligence Using Big Data and the Internet of Things","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114602991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-5225-6210-8.CH009
A. Bisht, Ravendra Singh, Rakesh Bhutiani, A. Bhatt
Predicting the water quality of rivers has attracted a lot of researchers all around the globe. A precise prediction of river water quality may benefit the water management bodies. However, due to the complex relationship existing among various factors, the prediction is a challenging job. Here, the authors attempted to develop a model for forecasting or predicting the water quality of the river Ganga using application of predictive intelligence based on machine learning approach called support vector machine (SVM). The monthly data sets of five water quality parameters from 2001 to 2015 were taken from five sampling stations from Devprayag to Roorkee in the Uttarakhand state of India. The experiments are conducted in Python 2.7.13 language (Anaconda2 4.3.1) using the radial basis function (RBF) as a kernel for developing the non-linear SVM-based classifier as a model for water quality prediction. The results indicated a prediction performance of 96.66% for best parameter combination which proved the significance of predictive intelligence in water quality forecasting.
{"title":"Application of Predictive Intelligence in Water Quality Forecasting of the River Ganga Using Support Vector Machines","authors":"A. Bisht, Ravendra Singh, Rakesh Bhutiani, A. Bhatt","doi":"10.4018/978-1-5225-6210-8.CH009","DOIUrl":"https://doi.org/10.4018/978-1-5225-6210-8.CH009","url":null,"abstract":"Predicting the water quality of rivers has attracted a lot of researchers all around the globe. A precise prediction of river water quality may benefit the water management bodies. However, due to the complex relationship existing among various factors, the prediction is a challenging job. Here, the authors attempted to develop a model for forecasting or predicting the water quality of the river Ganga using application of predictive intelligence based on machine learning approach called support vector machine (SVM). The monthly data sets of five water quality parameters from 2001 to 2015 were taken from five sampling stations from Devprayag to Roorkee in the Uttarakhand state of India. The experiments are conducted in Python 2.7.13 language (Anaconda2 4.3.1) using the radial basis function (RBF) as a kernel for developing the non-linear SVM-based classifier as a model for water quality prediction. The results indicated a prediction performance of 96.66% for best parameter combination which proved the significance of predictive intelligence in water quality forecasting.","PeriodicalId":365327,"journal":{"name":"Predictive Intelligence Using Big Data and the Internet of Things","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133338670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-5225-6210-8.CH003
Kamalendu Pal
Heterogeneous data types, widely distributed data sources, huge data volumes, and large-scale business-alliance partners describe typical global supply chain operational environments. Mobile and wireless technologies are putting an extra layer of data source in this technology-enriched supply chain operation. This environment also needs to provide access to data anywhere, anytime to its end-users. This new type of data set originating from the global retail supply chain is commonly known as big data because of its huge volume, resulting from the velocity with which it arrives in the global retail business environment. Such environments empower and necessitate decision makers to act or react quicker to all decision tasks. Academics and practitioners are researching and building the next generation of big-data-based application software systems. This new generation of software applications is based on complex data analysis algorithms (i.e., on data that does not adhere to standard relational data models). The traditional software testing methods are insufficient for big-data-based applications. Testing big-data-based applications is one of the biggest challenges faced by modern software design and development communities because of lack of knowledge on what to test and how much data to test. Big-data-based applications developers have been facing a daunting task in defining the best strategies for structured and unstructured data validation, setting up an optimal test environment, and working with non-relational databases testing approaches. This chapter focuses on big-data-based software testing and quality-assurance-related issues in the context of Hadoop, an open source framework. It includes discussion about several challenges with respect to massively parallel data generation from multiple sources, testing methods for validation of pre-Hadoop processing, software application quality factors, and some of the software testing mechanisms for this new breed of applications
{"title":"Quality Assurance Issues for Big Data Applications in Supply Chain Management","authors":"Kamalendu Pal","doi":"10.4018/978-1-5225-6210-8.CH003","DOIUrl":"https://doi.org/10.4018/978-1-5225-6210-8.CH003","url":null,"abstract":"Heterogeneous data types, widely distributed data sources, huge data volumes, and large-scale business-alliance partners describe typical global supply chain operational environments. Mobile and wireless technologies are putting an extra layer of data source in this technology-enriched supply chain operation. This environment also needs to provide access to data anywhere, anytime to its end-users. This new type of data set originating from the global retail supply chain is commonly known as big data because of its huge volume, resulting from the velocity with which it arrives in the global retail business environment. Such environments empower and necessitate decision makers to act or react quicker to all decision tasks. Academics and practitioners are researching and building the next generation of big-data-based application software systems. This new generation of software applications is based on complex data analysis algorithms (i.e., on data that does not adhere to standard relational data models). The traditional software testing methods are insufficient for big-data-based applications. Testing big-data-based applications is one of the biggest challenges faced by modern software design and development communities because of lack of knowledge on what to test and how much data to test. Big-data-based applications developers have been facing a daunting task in defining the best strategies for structured and unstructured data validation, setting up an optimal test environment, and working with non-relational databases testing approaches. This chapter focuses on big-data-based software testing and quality-assurance-related issues in the context of Hadoop, an open source framework. It includes discussion about several challenges with respect to massively parallel data generation from multiple sources, testing methods for validation of pre-Hadoop processing, software application quality factors, and some of the software testing mechanisms for this new breed of applications","PeriodicalId":365327,"journal":{"name":"Predictive Intelligence Using Big Data and the Internet of Things","volume":"12 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123451545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-5225-6210-8.CH008
J. Borah, K. K. Sarma, P. Gohain
Of late, home surveillance systems have been enhanced considerably by resorting to increased use of automated systems. The automation aspect has reduced human intervention and made such systems reliable and efficient. With the proliferation of wireless devices, networking among the connected devices is leading to the formation of internet of things (IoT). This has made it essential that home surveillance systems be also automate using IoT. The decision support system (DSS) in such platforms necessitates that automation be extensive. It necessitates the use of learning-aided systems. This chapter reports the design of IoT-driven learning-aided system for home surveillance application.
{"title":"Learning-Aided IoT Set-Up for Home Surveillance Applications","authors":"J. Borah, K. K. Sarma, P. Gohain","doi":"10.4018/978-1-5225-6210-8.CH008","DOIUrl":"https://doi.org/10.4018/978-1-5225-6210-8.CH008","url":null,"abstract":"Of late, home surveillance systems have been enhanced considerably by resorting to increased use of automated systems. The automation aspect has reduced human intervention and made such systems reliable and efficient. With the proliferation of wireless devices, networking among the connected devices is leading to the formation of internet of things (IoT). This has made it essential that home surveillance systems be also automate using IoT. The decision support system (DSS) in such platforms necessitates that automation be extensive. It necessitates the use of learning-aided systems. This chapter reports the design of IoT-driven learning-aided system for home surveillance application.","PeriodicalId":365327,"journal":{"name":"Predictive Intelligence Using Big Data and the Internet of Things","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116440086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-5225-6210-8.CH004
P. Guleria, M. Sood
Due to an increase in the number of digital transactions and data sources, a huge amount of unstructured data is generated by every interaction. In such a scenario, the concepts of data mining assume great significance as useful information/trends/predictions can be retrieved from this large amount of data, known as big data. Big data predictive analytics are making big inroads into the educational field because with the adoption of new technologies, new academic trends are being introduced into educational systems. This accumulation of large data of different varieties throws a new set of challenges to the learners as well as educational institutions in ensuring the quality of their education by improving strategic/operational decision-making capabilities. Therefore, the authors address this issue by proposing a support system that can guide the student to choose and to focus on the right course(s) based on their personal preferences. This chapter provides the readers with the requisite information about educational frameworks and related data mining.
{"title":"Big Data Analytics","authors":"P. Guleria, M. Sood","doi":"10.4018/978-1-5225-6210-8.CH004","DOIUrl":"https://doi.org/10.4018/978-1-5225-6210-8.CH004","url":null,"abstract":"Due to an increase in the number of digital transactions and data sources, a huge amount of unstructured data is generated by every interaction. In such a scenario, the concepts of data mining assume great significance as useful information/trends/predictions can be retrieved from this large amount of data, known as big data. Big data predictive analytics are making big inroads into the educational field because with the adoption of new technologies, new academic trends are being introduced into educational systems. This accumulation of large data of different varieties throws a new set of challenges to the learners as well as educational institutions in ensuring the quality of their education by improving strategic/operational decision-making capabilities. Therefore, the authors address this issue by proposing a support system that can guide the student to choose and to focus on the right course(s) based on their personal preferences. This chapter provides the readers with the requisite information about educational frameworks and related data mining.","PeriodicalId":365327,"journal":{"name":"Predictive Intelligence Using Big Data and the Internet of Things","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129251457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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