Discussion: Multimedia Big Data Analytics

Discussion: Multimedia Big Data Analytics ORDER NOW FOR CUSTOMIZED AND ORIGINAL ESSAY PAPERS ON Discussion: Multimedia Big Data Analytics I need to summarize 6 papers (attached) one page each one min and include: Discussion: Multimedia Big Data Analytics 1- Focus 2- usefulness 3- limitations 4- method of research 5- conclusion then write a literature review about the research (Big Data Multimedia Databases) .pdf .3281386.pdf .pdf .pdf caruccio2019_article_visualizationofmultimediadepen.pdf Multimedia Big Data Analytics: A Survey SAMIRA POUYANFAR, YIMIN YANG, and SHU-CHING CHEN, School of Computing & Info. Sciences, Florida International University MEI-LING SHYU, Department of Electrical and Computer Engineering, University of Miami S. S. IYENGAR, School of Computing & Info. Sciences, Florida International University With the proliferation of online services and mobile technologies, the world has stepped into a multimedia big data era. A vast amount of research work has been done in the multimedia area, targeting different aspects of big data analytics, such as the capture, storage, indexing, mining, and retrieval of multimedia big data. However, very few research work provides a complete survey of the whole pine-line of the multimedia big data analytics, including the management and analysis of the large amount of data, the challenges and opportunities, and the promising research directions. To serve this purpose, we present this survey, which conducts a comprehensive overview of the state-of-the-art research work on multimedia big data analytics. It also aims to bridge the gap between multimedia challenges and big data solutions by providing the current big data frameworks, their applications in multimedia analyses, the strengths and limitations of the existing methods, and the potential future directions in multimedia big data analytics. To the best of our knowledge, this is the first survey that targets the most recent multimedia management techniques for very large-scale data and also provides the research studies and technologies advancing the multimedia analyses in this big data era. Categories and Subject Descriptors: A.1 [General Literature]: Introductory and Survey; H.2.4 [Information Systems]: Database Management—Systems; H.2.8 [Information Systems]: Database Management— Database Applications; H.3.1 [Information Systems]: Information Storage and Retrieval—Content Analysis and Indexing; H.3.3 [Information Systems]: Information Storage and Retrieval—Information Search and Retrieval; H.3.4 [Information Systems]: Information Storage and Retrieval—Systems and Software; H.5.1 [Information Interfaces and Presentation]: Multimedia Information Systems General Terms: Algorithms, Design, Management, Performance Additional Key Words and Phrases: Big data analytics, multimedia analysis, 5V challenges, multimedia databases, indexing, retrieval, machine learning, data mining, mobile multimedia, survey ACM Reference format: Samira Pouyanfar, Yimin Yang, Shu-Ching Chen, Mei-Ling Shyu, and S. S. Iyengar. 2018. Multimedia Big Data Analytics: A Survey. ACM Comput. Surv. 51, 1, Article 10 (January 2018), 34 pages. https://doi.org/10.1145/3150226 Authors’ addresses: S. Pouyanfar, Y. Yang, S.-C. Chen, and S. S. Iyengar, School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA; emails: {spouy001, yyang010, chens, iyengar}@cs.fiu.edu; M.-L. Shyu, Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33124, USA; email: [email protected] Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected] © 2018 ACM 0360-0300/2018/01-ART10 $15.00 https://doi.org/10.1145/3150226 ACM Computing Surveys, Vol. 51, No. 1, Article 10. Publication date: January 2018. 10 10:2 S. Pouyanfar et al. 1 INTRODUCTION In the past few years, the fast and widespread use of multimedia data, including image, audio, video, and text, as well as the ease of access and availability of multimedia sources, have resulted in a big data revolution in multimedia management systems. Currently, multimedia sharing websites, such as Yahoo Flickr (Flickr.Com 2016), iCloud (iCloud.Com 2016), and YouTube (YouTube.Com 2016), and social networks such as Facebook (Facebook.Com 2016), Instagram (Instagram.Com 2016), and Twitter (Twitter.Com 2016), are considered as inimitable and valuable sources of multimedia big data (Zafarani et al. 2014). For example, to date, Instagram users have uploaded over 20 billion photos, YouTube users upload over 100h of videos every minute in a day, and 255 million active users of Twitter send approximately 500 million tweets every day (Smith 2015). Another statistic shows that the Internet traffic through multimedia sharing has reached 6,130 petabytes every month in 2016 (Statista.Com 2015). It is predicted that the digital data rate will exceed 40ZB by 2020, which means every person in the world will produce almost 5,200 gigabytes of data (Mearian 2012). With the emergence of new technologies and the advanced capabilities of smart phones and tablets, people, especially younger generations, spend a lot of time on the Internet and social networks to communicate with others to share information and to create multimedia data (Adler 2016). Discussion: Multimedia Big Data Analytics This rich source of information is considered “big data” due to its high volume and high variety. Unlike traditional data with only texts and numbers, multimedia data are usually unstructured and noisy. Handling this huge amount of complex data is not feasible using conventional data analysis. Therefore, more comprehensive and sophisticated solutions are required to manage such large and unstructured multimedia data (Shyu et al. 2007). Multimedia analytics addresses the issue of manipulating, managing, mining, understanding, and visualizing different types of data in effective and efficient ways to solve real-world challenges. The solutions include but are not limited to text analysis, image/vidoe processing, computer vision, audio/speech processing, and database management for a variety of applications such as healthcare, education, entertainment, and mobile devices. The big data concept is essentially used to describe extremely large datasets. However, different scientists and technological enterprises have various definitions for this term. For example, it is first used in a 1997 article published by NASA scientists explaining the computer system challenges (Cox and Ellsworth 1997). Later, Bryant et al. (2008) used the “Big-Data Computing” term in 2008. Finally, in 2010, it was defined as “datasets which could not be captured, managed, and processed by general computers within an acceptable scope” by Apache Hadoop (Chen et al. 2014). The main challenge of big data analystics is how to reduce the computational time and storage capacity, while maintaining the results as accurate as the ones from the small datasets. Parallel computing, i.e., processing a task using several computing resources at the same time, is one of the most significant steps in providing efficient analytics in a distributed environment. To reach this, several big data analytics platforms have been developed, including IBM Big Data Analytics (Zikopoulos et al. 2012), Microsoft Azure (Copeland et al. 2015), Oracle Big Data Analytics (Dijcks 2012), and so on, to analyze data in the most efficacious way. In the current big data era, new opportunities and challenges appear with high-diversity multimedia data together with the huge amount of social data. Hence, multimedia big data analytics has attracted a lot of attention in both academia and industry in recent years. It is considered an emerging and challenging topic due to its critical and valuable insights (Chen 2010; Smith 2013; Tian et al. 2015). In fact, multimedia big data explains what is happening in the world, emphasizes hot daily news, shows special events, and predicts people’s behavior and preferences. ACM Computing Surveys, Vol. 51, No. 1, Article 10. Publication date: January 2018. Multimedia Big Data Analytics: A Survey 10:3 1.1 Various Applications of Big Data in Multimedia Analytics At present, multimedia management systems are leveraging big data analytical techniques to manipulate multimedia data in a sensible and cost-effective way. Here, several popular multimedia big data applications are presented to show the significant role of big data in multimedia analytics. (1) Social networks: A flood of research studies have been published, and a tremendous amount of development has been released with respect to social media big data analysis (Cheung et al. 2015; Tufekci 2014; Wilson et al. 2012). Tufekci (2014), for instance, addresses the current challenges in this demanding field by analyzing human social activities and behaviors, specifically based on their Twitter hashtags. Tufekci chose Twitter due to the visibility of the data, its ease of access, and its enormous datasets. However, Wilson et al. (2012) conduct a review study on Facebook as a valuable social science resource. Social recommender system technology is another emerging topic, mainly utilizing multimedia information in social networks (Ma et al. 2011). For example, a YouTube video recommendation framework is presented in Davidson et al. (2010), which incorporates social context information into the video recommendation system and personalizes video sets based on the users’ social activities and preferences. (2) Smart phones: During recent years, smart phones have overtaken other electronic devices, such as laptops and PCs in people’s lives. Billions of individuals carry smart phones almost anywhere and any time. Due to the advanced capabilities and technologies of smart phones, like Bluetooth, GPS, camera, powerful CPUs, network connections, and so on, they can access and manipulate all multimedia data formats (e.g., audio, image, video, or text). Besides that, the explosion of innovative applications has made smart phones significant sources of multimedia big data (Do et al. 2011). Such advances over the past few decades have opened the doors for new studies investigating the smart phones data analyses. Lane et al. (2010) address the current open issues in smart phones, specifically in mobile sensing. In addition, different forms of user interaction challenges like sharing, personalized sensing, persuasion, and privacy, in these big sensor data, are discussed. Smart phones have also attracted attention in recommendation systems (Ricci 2010).Discussion: Multimedia Big Data Analytics In particular, a ubiquitous context-aware multimedia recommender system for smart phones is proposed (Yu et al. 2006), considering different kinds of context information. Other smart phone challenges, such as security (Wang et al. 2012), big data (Laurila et al. 2012), mobile commerce (Chang et al. 2009), and multimedia cloud computing (Khan and Ahirwar 2011) have been widely investigated in the literature. (3) Surveillance videos: Surveillance videos are one of the largest sources of multimedia data (Huang 2014). With the advent of innovative big data solutions for multimedia data, a major breakthrough has been made in surveillance video research. It is considered as multimedia big data due to the huge volume of surveillance data and its high value (e.g., criminal investigation, traffic control), high variety, and the fast velocity (Xu et al. 2015). One remarkable application of surveillance videos is how to automatically detect semantic concepts from videos (Chen et al. 2001b, 2003; Shyu et al. 2008). Smart city surveillance is another nascent application of multimedia big data, which is addressed in Dey et al. (2012). The authors leverage the cloud data stores to provide a reliable and scalable multimedia surveillance framework for small cities. (4) Other applications: Other applications of multimedia big data analytics can be categorized into computational health informatics (Fang et al. 2016), smart TVs (Fleites et al. 2015a, 2015b), disaster management systems (Yang et al. 2012, 2015), multimedia summarization (Bian et al. 2013), and the Internet of Things (IoT) (Zhou and Chao 2011). For ACM Computing Surveys, Vol. 51, No. 1, Article 10. Publication date: January 2018. 10:4 S. Pouyanfar et al. instance, healthcare and biomedicine data can be considered as one of the most significant multimedia big data sources. It includes a variety of data (structured or unstructured) such as medical images, physician notes, genomic sequencing, patient records, and radiographic films. Big data techniques are essential to handle such big, heterogeneous, and noisy data and to improve the quality of care in an efficient and effective way (Sukumar et al. 2015). In the past few years, comprehensive overviews on big data challenges, technologies, and opportunities in computational health informatics (Fang et al. 2016) and biomedicine (Bender 2015) have been presented in the literature. 1.2 Recent Survey Studies on Multimedia and Big Data Current big data analysis systems are generally narrowed to a single platform (e.g., one social network like Twitter) or a single data format (mostly text data). Related work can be divided into two main areas: multimedia analysis (Gao et al. 2017; Wang et al. 2017; Rawat et al. 2014; Akyildiz et al. 2007; Bhatt and Kankanhalli 2011; Hu et al. 2011) and big data analysis (Song et al. 2017; Siddiqa et al. 2016; Hashem et al. 2016; Agrawal et al. 2011; Che et al. 2013; Chen et al. 2014; Chen and Zhang 2014; Gandomi and Haider 2015; Hashem et al. 2015; Khan et al. 2014; Tsai et al. 2015). Table 1 lists the most recent surveys for both categories, as well as their areas of interest and pros and cons. Regarding the multimedia category, the surveys do not thoroughly cover the big data aspects and challenges of multimedia analyses. Moreover, only a specific aspect and application of multimedia analyses or a single format of data is targeted in those surveys. For instance, content-based image/video retrieval, one of the most significant topics in current multimedia research, is studied in Hu et al. (2011), Bhatt and Kankanhalli (2011) review the challenges and opportunities in multimedia data mining, and multimedia wireless sensor networks are also surveyed in Akyildiz et al. (2007), Rawat et al. (2014), and Wang et al. (2017). On the other hand, big data surveys mainly concentrate on big data challenges, techniques, and general applications (Siddiqa et al. 2016; Khan et al. 2014; Tsai et al. 2015). A recent study reviews next-generation big data techniques and challenges including storage, privacy and security, analysis, and applications (Song et al. 2017). Chen et al. (2014) present an extensive overview of big data analysis by providing a general context, and technical details of challenges and progression of four phases of big data, such as generation, acquisition, storage, and analysis of data. Another considerable overview of big data is presented in Chen and Zhang (2014), which not only discusses the state-of-the-art big data techniques but also presents several significant methodologies, such as cloud computing and quantum computing, to handle very large data. Big data mining techniques and challenges are surveyed in Che et al. (2013), discussing very large scale machine-learning algorithms using parallel platforms (e.g., Hadoop MapReduce). Discussion: Multimedia Big Data Analytics The research of Agrawal et al. (2011) and Hashem et al. (2015) further point out the importance of big data analytics, specifically on cloud computing. Nevertheless, quite a few studies have presented the problems of analyzing multimedia big data. Gandomi and Haider (2015) review big data analytics and statistical techniques for both structured data (e.g., predictive analytics) and unstructured data (e.g., text, audio, and video). However, generally speaking, surveys in this category lack technical discussions from multimedia’s point of view. As can be inferred from Table 1, most existing research in multimedia big data exclusively focuses on a specific area or challenge. Some surveys purely concentrate on big data management and related tools, while others discuss the multimedia challenges in a particular task and framework without considering the fast increase in the amount of unstructured multimedia data. In contrast, this survey not only provides a comprehensive study of multimedia research ACM Computing Surveys, Vol. 51, No. 1, Article 10. Publication date: January 2018. Multimedia Big Data Analytics: A Survey 10:5 Table 1. Recent Surveys Related to Multimedia and Big Data Analysis Category Multimedia Big Data Survey Area of Interest Pros Cons (Gao et al. 2017) – High-dimensional data – Feature analysis – Machine learning An overview on high-dimensional multimedia data and efficient machine-learning techniques – Lack of big data background and its technical challenges (Bhatt and Kankanhalli 2011) – Image, video, audio, and text mining An overview on multimedia data mining and open issues – Lack of big data background and its technical challenges (Akyildiz et al. 2007) (Rawat et al. 2014) (Wang et al. 2017) – Wireless sensor networks – Smart grid An overview on multimedia wireless sensor networks and its applications in smart grid – Lack of big data background and its technical challenges – Limited multimedia analytics (Hu et al. 2011) – Video retrieval An overview on content-based video indexing and retrieval – Discussing a single multimedia data type – Lack of big data background and its technical challenges (Song et al. 2017) – Online network – Mobile and IoT – Geography – Spatial temporal data An overview on big data analytics, storage, security and its applications – Limited overview on multimedia big data and data mining (Chen et al. 2014) – Enterprise management – Online social networks – Medial applications – Smart grid A comprehensive overview on big data technologies, acquisition, storage, and its applications – Lack of multimedia background, its technical challenges and multimedia data mining (Chen and Zhang 2014) – Cloud computing – Bio-inspired computing – Quantum computing – Granular computing An extensive overview on big data techniques and technologies, big data analysis and knowledge discovery – Lack of multimedia background, its technical challenges and multimedia data mining (Che et al. 2013) – Data mining A comprehensive overview on big data mining and challenges – Lack of multimedia background and its technical challenges (Gandomi and Haider 2015) – Social media – Text, audio, and video analytics – Predictive analytics An overview on structured and unstructured big data and efficient analytical methods -Limited overview on multimedia big data, challenges and data mining techniques (Agrawal et al. 2011) (Hashem et al. 2015) – Cloud computing A comprehensive overview on big data techniques, challenges and open issues in cloud computing – Discussing a single big data application – Lack of multimedia background and data mining (Hashem et al. 2016) – Smart city An overview on big data techniques, technologies, and future business models for smart cities – Discussing a single big data application – Lack of multimedia background and data mining (Khan et al. 2014) (Siddiqa et al. 2016) (Tsai et al. 2015) – Big data technologies A general overview of big data techniques, opportunities, and challenges – Limited overview on multimedia big data analytics ACM Computing Surveys, Vol. 51, No. 1, Article 10. Publication date: January 2018. 10:6 S. Pouyanfar et al. Fig. 1. Multimedia big data modules and challenges. contributions but also presents current big data solutions that can be leveraged in the multimedia data analysis. 1.3 Research Objectives The ultimate purpose of this survey is to present state-of-the-art multimedia big data research. The survey presents a comprehensive and organized overview of th … Get a 10 % discount on an order above $ 100 Use the following coupon code : NURSING10

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