Recent advances in social multimedia big data mining and applications

In the past decade, social media contributes significantly to the arrival of the Big Data era. Big Data has not only provided new solutions for social media mining and applications, but brought about a paradigm shift to many fields of data analytics. This special issue solicits recent related attempts in the multimedia community. We believe that the enclosed papers in this special issue provide a unique opportunity for multidisciplinary works connecting both the social media and big data contexts to multimedia computing.     

Computational optimization and applications for heterogeneous multimedia data

Multimedia Tools and Applications -     

Multiple categorizations of products: cognitive modeling of customers through social media data mining

As various forms of social media are spreading, we often witness that an idea of an individual user drives macroscopic changes. From the perspectives of product development and marketing, the opinions left by potential consumers in online social network can generate big ripple effects. This study analyzes the user opinions in online space to grasp preferences toward various products psychologically categorized by users. We also suggest an aspect of the market mentally configured by users using network modeling while following the framework of economic sociology. Existing analyses on online market place are mainly dealing with structural issues such as inter-actor relationships and status measurement. This study, however, analyzes complex preferences regarding diverse products and brands and derives a new model for inter-market connections. We expect that our study will provide important consequences on digital marketing and community design of corporations planning word of mouth effect in online space.     

A survey on sentiment analysis and opinion mining for social multimedia

Social media sentiment analysis (also known as opinion mining) which aims to extract people’s opinions, attitudes and emotions from social networks has become a research hotspot. Conventional sentiment analysis concentrates primarily on the textual content. However, multimedia sentiment analysis has begun to receive attention since visual content such as images and videos is becoming a new medium for self-expression in social networks. In order to provide a reference for the researchers in this active area, we give an overview of this topic and describe the algorithms of sentiment analysis and opinion mining for social multimedia. Having conducted a brief review on textual sentiment analysis for social media, we present a comprehensive survey of visual sentiment analysis on the basis of a thorough investigation of the existing literature. We further give a summary of existing studies on multimodal sentiment analysis which combines multiple media channels. We finally summarize the existing benchmark datasets in this area, and discuss the future research trends and potential directions for multimedia sentiment analysis. This survey covers 100 articles during 2008–2018 and categorizes existing studies according to the approaches they adopt.

     

Data science on multimedia data: Challenges and applications

Multimedia Tools and Applications -     

网络爬虫在Web信息搜索与数据挖掘中应用

分析了万维网不良网络信息对网络文化安全带来的挑战,提出了Web信息搜索与数据挖掘体系结构,并介绍了该体系结构中的关键技术和运行原理.分析了普通爬虫所实现的功能和不足之后,重点论述了该爬虫的工作原理、实现方式和性能分析以及该爬虫不同于其它爬虫的功能和在Web信息搜索与数据挖掘体系中应用.通过试验测试表明,该爬虫能够很好地获取万维网上的各种信息资源,有助于网络文化内容监测与管理.     

Folksonomy-based personalized search and ranking in social media services

In recent years, social Web users have been overwhelmed by the huge numbers of social media available. Consequentially, users have trouble finding social media suited to their needs. To help such users retrieve useful social media content, we propose a new model of tag-based personalized searches to enhance not only retrieval accuracy but also retrieval coverage. By leveraging social tagging as a preference indicator, we build two models: (i) a latent tag preference model that reflects how a certain user has assigned tags similar to a given tag and (ii) a latent tag annotation model that captures how users have tagged a certain tag to resources similar to a given resource. We then seamlessly map the tags onto items, depending on an individual user's query, to find the most desirable content relevant to the user's needs. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the art algorithms and show our method's feasibility for personalized searches in social media services.     

A model for specification and synchronization of data for distributed multimedia applications

As network technology provides the capability to handle multimedia traffic and the demand of multimedia services increases, protocols are required for effective communication of multimedia data in a distributed environment. Synchronization is one of the key issues in a multimedia system. Most of the current approaches do not support an integrated solution to the problem of synchronization. In this paper we propose a mechanism for synchronization of multimedia data in distributed environment where the accuracy of the protocol can be tailored to the application. The system model supports live and video-on-demand service. We present a scheme where the specification of the temporal requirements provided by the application can be directly mapped to obtain the information necessary to enforce the synchronization required. We present two examples of specifying the temporal requirements and process of obtaining the information and present performance results of our simulation studies.     

Interactive faceted search and exploration of open social media data on a touchscreen mobile phone

A central challenge of semantic ambient media applications is designing smart user interfaces that are able to dynamically deal with an a-priori unknown number of data categories and data instances received live from different Linked Open Data sources while at the same time being intuitive and easy to use. In the mobile world, this challenge is even more difficult as the mobile devices have limited interaction possibilities and smaller display size. In this paper, we tackle this challenge and present the user-centered, iterative design of a mobile application for faceted search and exploration of a large, multi-dimensional data set of open social media on a touchscreen mobile phone. The application is called Mobile Facets and provides live retrieval and interactive search and exploration of resources like places, persons, organizations, and events originating from different, integrated social media sources like DBpedia, Eventful, Upcoming, Flickr, and GeoNames. In contrast to existing work, we do not know in advance the number and type of data categories and data instances that will be received as the data is queried live from the sources. While developing Mobile Facets, we have applied a participatory design with a small group of five users. For the final prototype we have conducted a task-based, formative evaluation with 12 additional subjects to investigate the applicability and usability of our Mobile Facets application.     

Middleware for data mining applications on clusters and grids

This paper gives an overview of two middleware systems that have been developed over the last 6 years to address the challenges involved in developing parallel and distributed implementations of data mining algorithms. FREERIDE (FRamework for Rapid Implementation of Data mining Engines) focuses on data mining in a cluster environment. FREERIDE is based on the observation that parallel versions of several well-known data mining techniques share a relatively similar structure, and can be parallelized by dividing the data instances (or records or transactions) among the nodes. The computation on each node involves reading the data instances in an arbitrary order, processing each data instance, and performing a local reduction. The reduction involves only commutative and associative operations, which means the result is independent of the order in which the data instances are processed. After the local reduction on each node, a global reduction is performed. This similarity in the structure can be exploited by the middleware system to execute the data mining tasks efficiently in parallel, starting from a relatively high-level specification of the technique.     

Secure multimedia data processing scheme in medical applications

People communicate in a variety of ways via multimedia through the propagation of various techniques. Nowadays, variety of multimedia frameworks or techniques is used in various applications such as industries, software processing, vehicles and medical systems. The usage of multimedia frameworks in healthcare systems makes it possible to process, record and store huge amount of information generated by various medical records. However, the processing and management of huge records of every individual lead to overload the security risk and human efforts. The aim of this paper is to propose a secure and efficient technique that helps the medical organizations to process every record of individuals in a secure and efficient way. The proposed mechanism is validated against various security and processing metrics over conventional mechanisms such as Response Time, Message Alteration Record, Trusted Classification Accuracy and Record Accuracy. The analyzed results claim the significant improvement of proposed mechanism as compare to other schemes.

     

Multiresolution coding of motion capture data for real-time multimedia applications

Multimedia Tools and Applications - In this work, we present a novel and efficient method for coding of motion capture (MoCap) data obtained from recording of human actions. MoCap data is...     

Improving search results with data mining in a thematic search engine

The problem of obtaining relevant results in web searching has been tackled with several approaches. Although very effective techniques are currently used by the most popular search engines when no a priori knowledge on the user's desires beside the search keywords is available, in different settings it is conceivable to design search methods that operate on a thematic database of web pages that refer to a common body of knowledge or to specific sets of users. We have considered such premises to design and develop a search method that deploys data mining and optimization techniques to provide a more significant and restricted set of pages as the final result of a user search. We adopt a vectorization method based on search context and user profile to apply clustering techniques that are then refined by a specially designed genetic algorithm. In this paper we describe the method, its implementation, the algorithms applied, and discuss some experiments that has been run on test sets of web pages.     

High-performance secure multi-party computation for data mining applications

Secure multi-party computation (MPC) is a technique well suited for privacy-preserving data mining. Even with the recent progress in two-party computation techniques such as fully homomorphic encryption, general MPC remains relevant as it has shown promising performance metrics in real-world benchmarks. Sharemind is a secure multi-party computation framework designed with real-life efficiency in mind. It has been applied in several practical scenarios, and from these experiments, new requirements have been identified. Firstly, large datasets require more efficient protocols for standard operations such as multiplication and comparison. Secondly, the confidential processing of financial data requires the use of more complex primitives, including a secure division operation. This paper describes new protocols in the Sharemind model for secure multiplication, share conversion, equality, bit shift, bit extraction, and division. All the protocols are implemented and benchmarked, showing that the current approach provides remarkable speed improvements over the previous work. This is verified using real-world benchmarks for both operations and algorithms.     

Statistics to measure correlation for data mining applications

Correlation is usually used in the context of real-valued sequences but, in data mining, the values of fields may be of various types—real, nominal or ordinal. Techniques for measuring correlation between any two sequences of data are reviewed, regardless of their type. In particular, a new technique for measuring the correlation between real-valued data and nominal data is proposed. The technique relies on the definition of an assignment of the nominal values to real values and hence is called A-correlation (A for assignment). The proposed assignment is defined to be the most favourable of all such assignments and can be efficiently computed. Moreover, it is shown that the resulting correlation coefficient has a natural interpretation independent of the assignment. With nominal/nominal data, Cramer's V-statistic can be used or, alternatively, a new statistic based on matching. These can also be used in the ordinal/nominal case. With just ordinal data, correlations based on rank are appropriate and these can also be used in the ordinal/ordinal and the ordinal/real cases.     

WEB数据挖掘在电子商务中的研究应用

WEB数据挖掘旨在从大量的WEB数据信息中发现有用的模式和隐藏的信息,从而为决策者提供决策支持,优化市场策略,有效地解决当今互联网信息膨胀的问题。WEB数据挖掘的一个重要应用就是电子商务。电子商务是一个基于网络平台的现代化的商业模式,目前电子商务发展势头强劲,WEB数据挖掘在电子商务中必定有广阔的应用前景。本文将WEB数据挖掘与电子商务相结合,介绍了在电子商务平台中进行WEB数据挖掘的方法,从而为电子商务从业人员提供借鉴,以便更好地分析数据间的隐藏关系和模式,掌握用户喜好,为电子商务平台的市场决策提供决策支持,减少风险。     

A search space reduction methodology for data mining in large databases

Given the present need for Customer Relationship and the increased growth of the size of databases, many new approaches to large database clustering and processing have been attempted. In this work, we propose a methodology based on the idea that statistically proven search space reduction is possible in practice. Two clustering models are generated: one corresponding to the full data set and another pertaining to the sampled data set. The resulting empirical distributions were mathematically tested to verify a tight non-linear significant approximation.