An Architectural Paradigm for Collaborative Semantic Indexing of Multimedia Data Objects

2008 ◽  
pp. 216-226 ◽  
Author(s):  
Clement H. C. Leung ◽  
Jiming Liu ◽  
Alice W. S. Chan ◽  
Alfredo Milani
Keyword(s):  
Multimedia Data ◽  
Semantic Indexing ◽  
Data Objects

Multimedia Representation

2009 ◽  
pp. 995-1007
Author(s):  
Bo Yang
Keyword(s):  
Database Systems ◽  
Multimedia Applications ◽  
Multimedia Data ◽  
Multimedia Retrieval ◽  
Data Sources ◽  
Processing Power ◽  
Image Pixels ◽  
The Difference ◽  
Conducting Research ◽  
Data Objects

In recent years, the rapid expansion of multimedia applications, partly due to the exponential growth of the Internet, has proliferated over the daily life of computer users (Yang & Hurson, 2006). The integration of wireless communication, pervasive computing, and ubiquitous data processing with multimedia database systems has enabled the connection and fusion of distributed multimedia data sources. In addition, the emerging applications, such as smart classroom, digital library, habitat/environment surveillance, traffic monitoring, and battlefield sensing, have provided increasing motivation for conducting research on multimedia content representation, data delivery and dissemination, data fusion and analysis, and contentbased retrieval. Consequently, research on multimedia technologies is of increasing importance in computer society. In contrast with traditional text-based systems, multimedia applications usually incorporate much more powerful descriptions of human thought—video, audio, and images (Karpouzis, Raouzaiou, Tzouveli, Iaonnou, & Kollias, 2003; Liu, Bao, Yu, & Xu, 2005; Yang & Hurson, 2005). Moreover, the large collections of data in multimedia systems make it possible to resolve more complex data operations such as imprecise query or content-based retrieval. For instance, the image database systems may accept an example picture and return the most similar images of the example (Cox, Miller, & Minka, 2000; Hsu, Chua, & Pung, 2000; Huang, Chang, & Huang, 2003). However, the conveniences of multimedia applications come with challenges to the existing data management schemes: • Efficiency: Multimedia applications generally require more resources; however, the storage space and processing power are limited in many practical systems, for example, mobile devices and wireless networks (Yang & Hurson, 2005). Due to the large data volume and complicated operations of multimedia applications, new methods are needed to facilitate efficient representation, retrieval, and processing of multimedia data while considering the technical constraints. • Semantic Gap: There is a gap between user perception of multimedia entities and physical representation/access mechanism of multimedia data. Users often browse and desire to access multimedia data at the object level (“entities” such as human beings, animals, or buildings). However, the existing multimedia retrieval systems tend to access multimedia data based on their lower-level features (“characteristics” such as color patterns and textures), with little regard to combining these features into data objects. This representation gap often leads to higher processing cost and unexpected retrieval results. The representation of multimedia data according to human’s perspective is one of the focuses in recent research activities; however, few existing systems provide automated identification or classification of objects from general multimedia collections. • Heterogeneity: The collections of multimedia data are often diverse and poorly indexed. In a distributed environment, because of the autonomy and heterogeneity of data sources, multimedia data objects are often represented in heterogeneous formats. The difference in data formats further leads to the difficulty of incorporating multimedia data objects under a unique indexing framework. • Semantic Unawareness: The present research on content-based multimedia retrieval is based on feature vectors—features are extracted from audio/video streams or image pixels, empirically or heuristically, and combined into vectors according to the application criteria. Because of the application-specific multimedia formats, the feature-based paradigm lacks scalability and accuracy.

PPHA-Popularity Prediction Based High Data Availability for Multimedia Data Center

2019 ◽  
Vol 11 (1) ◽  
pp. 17-29
Author(s):  
Kuo-Chi Fang ◽  
Husnu S. Narman ◽  
Ibrahim Hussein Mwinyi ◽  
Wook-Sung Yoo
Keyword(s):  
Data Center ◽  
Service Providers ◽  
Data Access ◽  
Current Data ◽  
Multimedia Data ◽  
Data Availability ◽  
Full Data ◽  
Data Center Management ◽  
Popularity Prediction ◽  
Data Objects

Due to the growth of internet-connected devices and extensive data analysis applications in recent years, cloud computing systems are largely utilized. Because of high utilization of cloud storage systems, the demand for data center management has been increased. There are several crucial requirements of data center management, such as increase data availability, enhance durability, and decrease latency. In previous works, a replication technique is mostly used to answer those needs according to consistency requirements. However, most of the works consider full data, popular data, and geo-distance-based replications by considering storage and replication cost. Moreover, the previous data popularity based-techniques rely on the historical and current data access frequencies for replication. In this article, the authors approach this problem from a distinct aspect while developing replication techniques for a multimedia data center management system which can dynamically adapt servers of a data center by considering popularity prediction in each data access location. Therefore, they first label data objects from one to ten to track access frequencies of data objects. Then, they use those data access frequencies from each location to predict the future access frequencies of data objects to determine the replication levels and locations to replicate the data objects, and store the related data objects to close storage servers. To show the efficiency of the proposed methods, the authors conduct an extensive simulation by using real data. The results show that the proposed method has an advantage over the previous works in terms of data availability and increases the data availability up to 50%. The proposed method and related analysis can assist multimedia service providers to enhance their service qualities.

scholarly journals Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry, and Fusion

2021 ◽  
Vol 17 (1s) ◽  
pp. 1-25
Author(s):  
Yang Wang
Keyword(s):  
Data Analytics ◽  
Empirical Studies ◽  
Multimedia Data ◽  
Superior Performance ◽  
Individual Property ◽  
Research Attention ◽  
Modal Data ◽  
The Arts ◽  
Feature Spaces ◽  
Data Objects

With the development of web technology, multi-modal or multi-view data has surged as a major stream for big data, where each modal/view encodes individual property of data objects. Often, different modalities are complementary to each other. This fact motivated a lot of research attention on fusing the multi-modal feature spaces to comprehensively characterize the data objects. Most of the existing state-of-the-arts focused on how to fuse the energy or information from multi-modal spaces to deliver a superior performance over their counterparts with single modal. Recently, deep neural networks have been exhibited as a powerful architecture to well capture the nonlinear distribution of high-dimensional multimedia data, so naturally does for multi-modal data. Substantial empirical studies are carried out to demonstrate its advantages that are benefited from deep multi-modal methods, which can essentially deepen the fusion from multi-modal deep feature spaces. In this article, we provide a substantial overview of the existing state-of-the-arts in the field of multi-modal data analytics from shallow to deep spaces. Throughout this survey, we further indicate that the critical components for this field go to collaboration, adversarial competition, and fusion over multi-modal spaces. Finally, we share our viewpoints regarding some future directions in this field.

SOAF: Semantic Indexing System Based on Collaborative Tagging

10.28945/371 ◽  
2008 ◽  
Vol 4 ◽  
pp. 137-149 ◽  
Author(s):  
Doina Ana Cernea ◽  
Esther Del Moral-Pérez ◽  
Jose E. Labra Gayo
Keyword(s):  
Semantic Indexing ◽  
Collaborative Tagging ◽  
Indexing System
Sign in / Sign up

Export Citation Format

Share Document