Air Indexing for On-Demand XML Data Broadcast

XML data broadcast is an efficient way to disseminate semistructured information in wireless mobile environments. In this paper, we propose a novel two-tier index structure to facilitate the access of XML document in an on-demand broadcast system. It provides the clients with an overall image of all the XML documents available at the server side and hence enables the clients to locate complete result sets accordingly. A pruning strategy is developed to cut down the index size and a two-tier structure is proposed to further remove any redundant information. In addition, two index distribution strategies, namely naive distribution and partial distribution, have been designed to interleave the index information with the XML documents in the wireless channels. Theoretical analysis and simulation experiments are also put forward to show the benefits of our indexing methods

Effective scheduling algorithm for on-demand XML data broadcasts in wireless environments

The organization of data on wireless channels, which aims to reduce the access time of mobile clients, is a key problem in data broadcasts. Many scheduling algorithms have been designed to organize flat data on air. However, how to effectively schedule semi-structured information such as XML data on wireless channels is still a challenge. In this paper, we firstly propose a novel method to greatly reduce the tuning time by splitting query results into XML snippets and to achieve better access efficiency by combining similar ones. Then we analyze the data broadcast scheduling problem of on-demand XML data broadcasts and define the efficiency of a data item. Based on the definition, a Least Efficient Last (LEL) scheduling algorithm is also devised to effectively organize XML data on wireless channels. Finally, we study the performance of our algorithms through extensive experiments. The results show that our scheduling algorithms can reduce both access time and tuning time signifcantly when compared with existing work

Organizing XML data in a wireless broadcast system by exploiting structural similarities

Wireless data broadcast is an efficient way of delivering data of common interest to a large population of mobile devices within a proximate area, such as smart cities, battle fields, etc. In this work, we focus ourselves on studying the data placement problem of periodic XML data broadcast in mobile and wireless environments. This is an important issue, particularly when XML becomes prevalent in today’s ubiquitous and mobile computing devices and applications. Taking advantage of the structured characteristics of XML data, effective broadcast programs can be generated based on the XML data on the server only. An XML data broadcast system is developed and a theoretical analysis on the XML data placement on a wireless channel is also presented, which forms the basis of the novel data placement algorithm in this work. The proposed algorithm is validated through a set of experiments. The results show that the proposed algorithm can effectively place XML data on air and significantly improve the overall access efficiency

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Cloud Computing Strategies for Enhancing Smart Grid Performance in Developing Countries

In developing countries, the awareness and development of Smart Grids are in the introductory stage and the full realisation needs more time and effort. Besides, the partially introduced Smart Grids are inefficient, unreliable, and environmentally unfriendly. As the global economy crucially depends on energy sustainability, there is a requirement to revamp the existing energy systems. Hence, this research work aims at cost-effective optimisation and communication strategies for enhancing Smart Grid performance on Cloud platforms

Live Television in a Digital Library

Nowadays nearly everyone has access to digital television with a growing number of channels available for free. However due to the nature of broadcasting, this huge mass of information that reaches us is not, for the main part, organised—it is principally a succession of images and sound transmitted in a flow of data. Compare this with digital libraries which are powerful at organising a large but fixed set of documents. This project brings together these two concepts by concurrently capturing all the available live television channels, and segments them into files which are then imported into a digital video library. The system leverages off the information contained in the electronic program guide and the video recordings to generate metadata suitable for the digital library. By combining these two concepts together this way, the aim of this work is to look beyond what is currently available in the digital TV set top boxes on the market today and explore the full potential—unencumbered by commercial market constraints—to what the raw technology can provide

Robust methods for Chinese spoken document retrieval.

Hui Pui Yu.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 158-169).Abstracts in English and Chinese.Abstract --- p.2Acknowledgements --- p.6Chapter 1 --- Introduction --- p.23Chapter 1.1 --- Spoken Document Retrieval --- p.24Chapter 1.2 --- The Chinese Language and Chinese Spoken Documents --- p.28Chapter 1.3 --- Motivation --- p.33Chapter 1.3.1 --- Assisting the User in Query Formation --- p.34Chapter 1.4 --- Goals --- p.34Chapter 1.5 --- Thesis Organization --- p.35Chapter 2 --- Multimedia Repository --- p.37Chapter 2.1 --- The Cantonese Corpus --- p.37Chapter 2.1.1 --- The RealMedia´ёØCollection --- p.39Chapter 2.1.2 --- The MPEG-1 Collection --- p.40Chapter 2.2 --- The Multimedia Markup Language --- p.42Chapter 2.3 --- Chapter Summary --- p.44Chapter 3 --- Monolingual Retrieval Task --- p.45Chapter 3.1 --- Properties of Cantonese Video Archive --- p.45Chapter 3.2 --- Automatic Speech Transcription --- p.46Chapter 3.2.1 --- Transcription of Cantonese Spoken Documents --- p.47Chapter 3.2.2 --- Indexing Units --- p.48Chapter 3.3 --- Known-Item Retrieval Task --- p.49Chapter 3.3.1 --- Evaluation ´ؤ Average Inverse Rank --- p.50Chapter 3.4 --- Retrieval Model --- p.51Chapter 3.5 --- Experimental Results --- p.52Chapter 3.6 --- Chapter Summary --- p.53Chapter 4 --- The Use of Audio and Video Information for Monolingual Spoken Document Retrieval --- p.55Chapter 4.1 --- Video-based Segmentation --- p.56Chapter 4.1.1 --- Metric Computation --- p.57Chapter 4.1.2 --- Shot Boundary Detection --- p.58Chapter 4.1.3 --- Shot Transition Detection --- p.67Chapter 4.2 --- Audio-based Segmentation --- p.69Chapter 4.2.1 --- Gaussian Mixture Models --- p.69Chapter 4.2.2 --- Transition Detection --- p.70Chapter 4.3 --- Performance Evaluation --- p.72Chapter 4.3.1 --- Automatic Story Segmentation --- p.72Chapter 4.3.2 --- Video-based Segmentation Algorithm --- p.73Chapter 4.3.3 --- Audio-based Segmentation Algorithm --- p.74Chapter 4.4 --- Fusion of Video- and Audio-based Segmentation --- p.75Chapter 4.5 --- Retrieval Performance --- p.76Chapter 4.6 --- Chapter Summary --- p.78Chapter 5 --- Document Expansion for Monolingual Spoken Document Retrieval --- p.79Chapter 5.1 --- Document Expansion using Selected Field Speech Segments --- p.81Chapter 5.1.1 --- Annotations from MmML --- p.81Chapter 5.1.2 --- Selection of Cantonese Field Speech --- p.83Chapter 5.1.3 --- Re-weighting Different Retrieval Units --- p.84Chapter 5.1.4 --- Retrieval Performance with Document Expansion using Selected Field Speech --- p.84Chapter 5.2 --- Document Expansion using N-best Recognition Hypotheses --- p.87Chapter 5.2.1 --- Re-weighting Different Retrieval Units --- p.90Chapter 5.2.2 --- Retrieval Performance with Document Expansion using TV-best Recognition Hypotheses --- p.90Chapter 5.3 --- Document Expansion using Selected Field Speech and N-best Recognition Hypotheses --- p.92Chapter 5.3.1 --- Re-weighting Different Retrieval Units --- p.92Chapter 5.3.2 --- Retrieval Performance with Different Indexed Units --- p.93Chapter 5.4 --- Chapter Summary --- p.94Chapter 6 --- Query Expansion for Cross-language Spoken Document Retrieval --- p.97Chapter 6.1 --- The TDT-2 Corpus --- p.99Chapter 6.1.1 --- English Textual Queries --- p.100Chapter 6.1.2 --- Mandarin Spoken Documents --- p.101Chapter 6.2 --- Query Processing --- p.101Chapter 6.2.1 --- Query Weighting --- p.101Chapter 6.2.2 --- Bigram Formation --- p.102Chapter 6.3 --- Cross-language Retrieval Task --- p.103Chapter 6.3.1 --- Indexing Units --- p.104Chapter 6.3.2 --- Retrieval Model --- p.104Chapter 6.3.3 --- Performance Measure --- p.105Chapter 6.4 --- Relevance Feedback --- p.106Chapter 6.4.1 --- Pseudo-Relevance Feedback --- p.107Chapter 6.5 --- Retrieval Performance --- p.107Chapter 6.6 --- Chapter Summary --- p.109Chapter 7 --- Conclusions and Future Work --- p.111Chapter 7.1 --- Future Work --- p.114Chapter A --- XML Schema for Multimedia Markup Language --- p.117Chapter B --- Example of Multimedia Markup Language --- p.128Chapter C --- Significance Tests --- p.135Chapter C.1 --- Selection of Cantonese Field Speech Segments --- p.135Chapter C.2 --- Fusion of Video- and Audio-based Segmentation --- p.137Chapter C.3 --- Document Expansion with Reporter Speech --- p.137Chapter C.4 --- Document Expansion with N-best Recognition Hypotheses --- p.140Chapter C.5 --- Document Expansion with Reporter Speech and N-best Recognition Hypotheses --- p.140Chapter C.6 --- Query Expansion with Pseudo Relevance Feedback --- p.142Chapter D --- Topic Descriptions of TDT-2 Corpus --- p.145Chapter E --- Speech Recognition Output from Dragon in CLSDR Task --- p.148Chapter F --- Parameters Estimation --- p.152Chapter F.1 --- "Estimating the Number of Relevant Documents, Nr" --- p.152Chapter F.2 --- "Estimating the Number of Terms Added from Relevant Docu- ments, Nrt , to Original Query" --- p.153Chapter F.3 --- "Estimating the Number of Non-relevant Documents, Nn , from the Bottom-scoring Retrieval List" --- p.153Chapter F.4 --- "Estimating the Number of Terms, Selected from Non-relevant Documents (Nnt), to be Removed from Original Query" --- p.154Chapter G --- Abbreviations --- p.155Bibliography --- p.15