FSBD: A Framework for Scheduling of Big Data Mining in Cloud Computing

This is an age of big data. Big data is usually defined as a huge collection of unstructured information stored in multiple data sets that due to their size and complexity cannot be processed by using traditional database management systems or data mining/analysis applications. Despite it bring tremendous benefits to society, the rise of 'Big Data mining' in the private sector poses new challenges for privacy advocates. First, this article reviews a definition of big data mining. Second, this article will set out several essential technologies related to the big data mining, with particular reference to the cloud computing, and hydoop etc. Third, this article will address risks arising from big data mining, especially privacy harm.

Big data are products of human collective intelligence that are exponentially increasing in all facets of quantity, complexity, semantics, distribution, and processing costs in computer science, cognitive informatics, web-based computing, cloud computing, and computational intelligence. This paper presents fundamental big data analysis and mining technologies in the domain of social networks as a typical paradigm of big data engineering. A key principle of computational sociology known as the characteristic opinion equilibrium is revealed in social networks and electoral systems. A set of numerical and fuzzy models for collective opinion analyses is formally presented. Fuzzy data mining methodologies are rigorously described for collective opinion elicitation and benchmarking in order to enhance the conventional counting and statistical methodologies for big data analytics.

With the explosion of information and ever-increasing information resources, humanity has entered a brand-new cloud computing era. We are confronted with a brand-new problem: how to quickly and accurately extract the required information from vast information resources. It is even possible to argue that university educational administration is inextricably linked to university teaching accomplishments. The teaching data mining (DM) technology was created in order to extract the required information from vast information resources. People’s ability to find data using cloud computing technology (IT) has improved. This paper analyzes the division and optimal allocation of talent types in universities, in combination with the development strategy and HR characteristics of universities, and provides strategies for the rational allocation of talents in universities, as well as support for the information relationship of HR in universities, using DM technology research. The emergence of DM technology alleviates the problem of data accumulation-induced information explosion. In today’s universities, there are an increasing number of talents. In terms of talent allocation, the information-based talent allocation scheme offers new approaches to talent acquisition and university education development in the modern era.

As the number of patients treated in-home are increasing exponentially mainly in countries such as Japan, US A and Europe etc., these people often enter into a critical situation that may require help immediately (e.g. when facing an accident, or becoming depressed). Researches and Advances in computing areas and the Internet of Things (IOT) have provided efficient and cheap equipments including wireless communication and cameras, such as smart phones or embedded devices that enable the deployment of Health S mart Homes (HS H) that can provide medical treatment of patients in their homes. The images captured from these cameras can help nurses or caretakers of patients to provide timely help. The use of such patient images and emotional detection to assist patients and elderly people within home is provided in this article. Internet of Things and Cloud computing can work together, which can address to the Big Data problems. Big data is actually a term used for a huge data set that performs operations like storage of data, analysis, sharing, transfer, predictive analysis; updating etc. data set can grow rapidly. Data sets have different analytics on data that involve process of inspecting, transformation and modeling of data so as to discover new information, and to reach on some particular decision. There are some particular data analysis techniques from which data mining is a popular one which focuses on modeling and discovery of new facts. The discovered knowledge helps in predictive analysis purposes as well as text analysis. Further on data mining is performed on data set which involves discovery of patterns by applying different operations at databases, and using artificial intelligence, machine learning, statics etc. Keywords: Cloud computing, IOT, AI, face recognition, big data, data mining.

The healthcare big data industry is rapidly developing globally, and data mining and knowledge services in the healthcare field have become one of the core demands for its development. Data mining in healthcare is beneficial to improve the efficiency of diagnosis and treatment of patients, which is helpful to formulate more effective treatment plans and reduce medical costs. In this paper, we searched the core journals on China Knowledge Network and web of science by subject terms, and eliminated the irrelevant articles for literature counting. In this paper, the commonly used models and algorithms of data mining in healthcare are firstly elaborated; then the progress of the application of this technology in assisting medical tasks, optimizing resource allocation and improving health information services are respectively reviewed, summarizing the segmentation, classic algorithms and representative studies implied by each application. However, the application of data mining technology in healthcare also faces some problems, from data collection, to data cleaning, preprocessing, visualization, to the selection of algorithms and evaluation of results, each link is full of difficulties and challenges. Finally, this paper proposes future research directions such as diversifying data sources, strengthening security and privacy protection, developing visualization and analysis tools, accurately using big data to improve the service level of healthcare institutions, semanticizing electronic medical records mining, and improving cancer prevention. At the same time, data mining is deeply integrated with cloud computing, artificial intelligence and other fields to jointly promote scientific and technological progress in the field of health care.

Big data analysis and mining aims to discover implicit, previously unknown, and potentially useful information and knowledge from big databases that contain high volumes of valuable veracious data collected or generated at a high velocity from a wide variety of data sources. Among different big data mining tasks, this chapter focuses on big data analysis and mining for frequent patterns. By relying on the MapReduce programming model, researchers only need to specify the “map” and “reduce” functions to discover frequent patterns from (1) big databases of precise data in a breadth-first manner or in a depth-first manner and/or from (2) big databases of uncertain data. Such a big data analysis and mining process can be sped up. The resulting (constrained or unconstrained) frequent patterns mined from big databases provide users with new insights and a sound understanding of users' patterns. Such knowledge is useful is many real-life information science and technology applications.

Challenges of data mining in the medical industry in an era of big data are introduced herein.Combined with cloud computing, a frame of medical cloud data mining platform built in the ecological environment is proposed, with functions at various levels being introduced in detail, including the base level, the platform level, the function level and the service level.The research aims to provide useful information for big data analyses and mining in the medical industry.

This article addresses the importance of HaaS (Hadoop-as-a-Service) in cloud technologies, with specific reference to its usefulness in big data mining for environmental computing applications. The term environmental computing refers to computational analysis within environmental science and management, encompassing a myriad of techniques, especially in data mining and machine learning. As is well-known, the classical MapReduce has been adapted within many applications for big data storage and information retrieval. Hadoop based tools such as Hive and Mahout are broadly accessible over the cloud and can be helpful in data warehousing and data mining over big data in various domains. In this article, we explore HaaS technologies, mainly based on Apache's Hive and Mahout for applications in environmental computing, considering publicly available data on the Web. We dwell upon interesting applications such as automated text classification for energy management, recommender systems for ecofriendly products, and decision support in urban planning. We briefly explain the classical paradigms of MapReduce, Hadoop and Hive, further delve into data mining and machine learning over the MapReduce framework, and explore techniques such as Naïve Bayes and Random Forests using Apache Mahout with respect to the targeted applications. Hence, the paradigm of Hadoop-as-a-Service, popularly referred to as HaaS, is emphasized here as per its benefits in a domain-specific context. The studies in environmental computing, as presented in this article, can be useful in other domains as well, considering similar applications. This article can thus be interesting to professionals in web technologies, cloud computing, environmental management, as well as AI and data science in general.

Mining big graph data is an important problem in the graph mining research area. Although cloud computing is effective at solving traditional algorithm problems, mining frequent patterns of a massive graph with cloud computing still faces the three challenges: 1) the graph partition problem, 2) asymmetry of information, and 3) pattern-preservation merging. Therefore, this paper presents a new approach, the cloud-based SpiderMine (c-SpiderMine), which exploits cloud computing to process the mining of large patterns on big graph data. The proposed method addresses the above issues for implementing a big graph data mining algorithm in the cloud. We conduct the experiments with three real data sets, and the experimental results demonstrate that c-SpiderMine can significantly reduce execution time with high scalability in dealing with big data in the cloud.

Cloud computing and big data: Technologies and applications

In the ever-increasing competitive and customer-centered global market, the role of Customer Relationship Management (CRM) is essential to the performance, longevity, competitiveness and profitability of enterprises. The study aims to provide an overview of the impact of emerging technologies on traditional CRM and Social CRM. Therefore, through a critical review, this study explores the use of Artificial Intelligence (AI), Blockchain, Big Data Analytics (BDA), Data Mining and Machine Learning (ML) technologies in CRM. Based on the results, the above-mentioned technologies can significantly improve CRM. More specifically, their successful implementation enables enterprises to stay ahead of competition, to get to know their customers, improve their overall performance, Quality of Experience (QoE) and Quality of Service (QoS), increase profits, and reduce time consumption, risks and costs. Finally, by integrating these technologies, enterprises will offer more personalized and customized products and services and greatly improve customers' satisfaction, acquisition, relationships, loyalty, experience and retention.

Big data deals with data of very large data size, heterogeneous data types and from different sources. The data is very complex in nature and having growing data. Dealing with big data is one of the emerging areas of research which is expanding at a rapid rate in all domains of engineering and medical sciences. A major challenge imposes on the analysis of big data is originated from big data generation source, which generate data with very fast speed with varying data distribution due to which the classical methods are unable to process big data. This paper discusses the characteristics, challenges, and issues with big data mining. It also illustrates the examples taken from various fields like medical, finance, social networking sites, stock exchange, etc. to realize the application and importance of big data mining. This paper explains about the use of parallel computing in data mining security issues and how to deal with them. Furthermore, this paper also discusses challenges associated with big streaming data with concept drifts.

According to the contrastive perspective between traditional data mining and big data mining, this article discusses the connotation of big data mining and proposes the system frame of big data mining about cloud computing and mining service. Taking the multifunctional Hadoop big data mining platform as an example, this article analyses the internal workflow of big data mining and its advantages as well as challenges, which can provide a reference for users to cognize and apply the big data mining.

Cloud Computing and Big Data continue to be disruptive forces in computing and have made inroads in the Computer Science curriculum, with courses in Cloud Computing and Big Data being routinely offered at the graduate and undergraduate level. One major challenge in offering courses in Big Data and Cloud Computing is resources. The question is how to provide students with authentic experiences making use of current Cloud and Big Data resources and tools and do so in a cost effective manner. Historically, three options, namely physical clusters, virtual clusters and cloud-based clusters, have been used to support Big Data and Cloud Computing courses. Virtual clusters and cloud-based options are those that institutions have typically adopted and many arguments in favor of these options exist in the literature, citing cost and performance. Here we argue that teaching Big Data and Cloud Computing courses can be done making use of a physical cluster and that many of the existing arguments fail to take into account many important factors in their calculations. These factors include the flexibility and control of a physical cluster in responding to changes in industry, the ability to work with much larger datasets, and the synergy and broad applicability of an appropriately equipped physical cluster for courses such as Cloud Computing, Big Data and Data Mining. We present three possible configurations of a physical cluster which span the spectrum in terms of cost and provide cost comparisons of these configurations against virtual and cloud-based options, taking into account the unique requirements of an academic setting. While limitations do exist with a physical cluster and it is not an option for all situations, our analysis and experience indicates that there is great value in using a physical cluster to support teaching Cloud Computing and Big Data courses and it should not be dismissed.

Big data analysis is essential for modern applications in areas such as healthcare, assistive technology, intelligent transportation, environment and climate monitoring. Traditional algorithms in data mining and machine learning do not scale well with data size. Mining and learning from big data need time and memory efficient techniques, albeit the cost of possible loss in accuracy. We have developed a data aggregation structure to summarize data with large number of instances and data generated from multiple data sources. Data are aggregated at multiple resolutions and resolution provides a trade-off between efficiency and accuracy. The structure is built once, updated incrementally, and serves as a common data input for multiple mining and learning algorithms. Data mining algorithms are modified to accept the aggregated data as input. Hierarchical data aggregation serves as a paradigm under which novel data representations and algorithms work together for analysis and mining of big data. To evaluate its performance, we have implemented a multi-resolution Naive Bayes Classifier on the data aggregation structure. Experimental results show that the proposed structure helps the classifier to reduce computation time to 25% on average and reduce the memory usage while preserving the accuracy of results.