Building a Semantic Tendering System

In the new B2B e-commerce arena, applications such as auctions and data exchange are growing rapidly. However, Web content is currently designed for human consumption rather than computer manipulation. This limits the possibility of Web automation. Fortunately, the new development of the Semantic Web that allows Web pages to provide information not only in terms of their content, but also in terms of the properties of that content, can be used for automation. Electronic tendering systems are among the successfully commercial systems that can tremendously benefit from the availability of Semantic Web. This study proposes an e-tendering system that uses the Semantic Web to investigate the automatic negotiation process. The system is built in a P2P environment to simulate a two-player negotiation. It is found that the ontology of semantic information can be used to locate qualified suppliers and precede negotiation. The bargaining power of each party is then determined by the relative magnitude of the negotiators’ respective costs of haggling and the utility that varies with the degree of risk preference. Our experiments showed that applying automatic negotiation strategies to e-tendering system in semantic web can reflect the risk preference of the participants

A Parallel CBIR Implementation Using Perceptual Grouping Of Block-based Visual Patterns

ABSTRACT This paper proposes a parallel solution to retrieve images from distributed data sources using perceptual grouping of block-based visual patterns. The method of grouping visual patterns into image model based on generalized Hough transform is one of the most powerful techniques for image analysis. However, real-time applications of this method have been prohibited due to the computational intensity in similarity searching from a large centralized image collection. A query object is decomposed into non-overlapped blocks, where each of them is represented as a visual pattern obtained by detecting the line edge from the block using the moment-preserving edge detector. A voting scheme based on generalized Hough transform is proposed to provide object search method, which is invariant to the translation, rotation, scaling of image data. In this work, we describe a heterogeneous cluster-oriented CBIR implementation. First, the workload to perform an object search is analyzed, and then, a new load balancing algorithm for the CBIR system is presented. Simulation results show that the proposed method gives good performance and spans a new way to design a cost-effective CBIR system

Vernetzung virtueller Gemeinschaften mit P2P-Technologien

Mittelständische und große Unternehmen sehen sich durch die rasant fortschreitende Digitalisierung von Medien und Kommunikation mit dem Problem konfrontiert, ihre Daten konsistent und logisch strukturiert zu verwalten. Häufig wird dieses Problem durch verteilte Strukturen (Filialen, Zweigstellen, Zulieferer, mobile Mitarbeiter) noch verstärkt. Die häufig verwendeten zentralisierten Strukturen (Server, Datenbanken) sind zudem ein wesentlicher Angriffspunkt für die Systemsicherheit und erfordern großen Aufwand zu ihrer Wartung und Aktualisierung

A scalable approach for content based image retrieval in cloud datacenter

The emergence of cloud datacenters enhances the capability of online data storage. Since massive data is stored in datacenters, it is necessary to effectively locate and access interest data in such a distributed system. However, traditional search techniques only allow users to search images over exact-match keywords through a centralized index. These techniques cannot satisfy the requirements of content based image retrieval (CBIR). In this paper, we propose a scalable image retrieval framework which can efficiently support content similarity search and semantic search in the distributed environment. Its key idea is to integrate image feature vectors into distributed hash tables (DHTs) by exploiting the property of locality sensitive hashing (LSH). Thus, images with similar content are most likely gathered into the same node without the knowledge of any global information. For searching semantically close images, the relevance feedback is adopted in our system to overcome the gap between low-level features and high-level features. We show that our approach yields high recall rate with good load balance and only requires a few number of hops

Proceedings of the ECIR2010 workshop on information access for personal media archives (IAPMA2010), Milton Keynes, UK, 28 March 2010

Towards e-Memories: challenges of capturing, summarising, presenting, understanding, using, and retrieving relevant information from heterogeneous data contained in personal media archives. This is the proceedings of the inaugural workshop on “Information Access for Personal Media Archives”. It is now possible to archive much of our life experiences in digital form using a variety of sources, e.g. blogs written, tweets made, social network status updates, photographs taken, videos seen, music heard, physiological monitoring, locations visited and environmentally sensed data of those places, details of people met, etc. Information can be captured from a myriad of personal information devices including desktop computers, PDAs, digital cameras, video and audio recorders, and various sensors, including GPS, Bluetooth, and biometric devices. In this workshop research from diverse disciplines was presented on how we can advance towards the goal of effective capture, retrieval and exploration of e-memories

Content-based image retrieval: reading one's mind and helping people share.

Sia Ka Cheung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 85-91).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Problem Statement --- p.1Chapter 1.2 --- Contributions --- p.3Chapter 1.3 --- Thesis Organization --- p.4Chapter 2 --- Background --- p.5Chapter 2.1 --- Content-Based Image Retrieval --- p.5Chapter 2.1.1 --- Feature Extraction --- p.6Chapter 2.1.2 --- Indexing and Retrieval --- p.7Chapter 2.2 --- Relevance Feedback --- p.7Chapter 2.2.1 --- Weight Updating --- p.9Chapter 2.2.2 --- Bayesian Formulation --- p.11Chapter 2.2.3 --- Statistical Approaches --- p.12Chapter 2.2.4 --- Inter-query Feedback --- p.12Chapter 2.3 --- Peer-to-Peer Information Retrieval --- p.14Chapter 2.3.1 --- Distributed Hash Table Techniques --- p.16Chapter 2.3.2 --- Routing Indices and Shortcuts --- p.17Chapter 2.3.3 --- Content-Based Retrieval in P2P Systems --- p.18Chapter 3 --- Parameter Estimation-Based Relevance Feedback --- p.21Chapter 3.1 --- Parameter Estimation of Target Distribution --- p.21Chapter 3.1.1 --- Motivation --- p.21Chapter 3.1.2 --- Model --- p.23Chapter 3.1.3 --- Relevance Feedback --- p.24Chapter 3.1.4 --- Maximum Entropy Display --- p.26Chapter 3.2 --- Self-Organizing Map Based Inter-Query Feedback --- p.27Chapter 3.2.1 --- Motivation --- p.27Chapter 3.2.2 --- Initialization and Replication of SOM --- p.29Chapter 3.2.3 --- SOM Training for Inter-query Feedback --- p.31Chapter 3.2.4 --- Target Estimation and Display Set Selection for Intra- query Feedback --- p.33Chapter 3.3 --- Experiment --- p.35Chapter 3.3.1 --- Study of Parameter Estimation Method Using Synthetic Data --- p.35Chapter 3.3.2 --- Performance Study in Intra- and Inter- Query Feedback . --- p.40Chapter 3.4 --- Conclusion --- p.42Chapter 4 --- Distributed COntent-based Visual Information Retrieval --- p.44Chapter 4.1 --- Introduction --- p.44Chapter 4.2 --- Peer Clustering --- p.45Chapter 4.2.1 --- Basic Version --- p.45Chapter 4.2.2 --- Single Cluster Version --- p.47Chapter 4.2.3 --- Multiple Clusters Version --- p.51Chapter 4.3 --- Firework Query Model --- p.53Chapter 4.4 --- Implementation and System Architecture --- p.57Chapter 4.4.1 --- Gnutella Message Modification --- p.57Chapter 4.4.2 --- Architecture of DISCOVIR --- p.59Chapter 4.4.3 --- Flow of Operations --- p.60Chapter 4.5 --- Experiments --- p.62Chapter 4.5.1 --- Simulation Model of the Peer-to-Peer Network --- p.62Chapter 4.5.2 --- Number of Peers --- p.66Chapter 4.5.3 --- TTL of Query Message --- p.70Chapter 4.5.4 --- Effects of Data Resolution on Query Efficiency --- p.73Chapter 4.5.5 --- Discussion --- p.74Chapter 4.6 --- Conclusion --- p.77Chapter 5 --- Future Works and Conclusion --- p.79Chapter A --- Derivation of Update Equation --- p.81Chapter B --- An Efficient Discovery of Signatures --- p.82Bibliography --- p.8

Peer clustering and firework query model in peer-to-peer networks.

Ng, Cheuk Hang.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 89-95).Abstracts in English and Chinese.Abstract --- p.iiAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Problem Definition --- p.2Chapter 1.2 --- Main Contributions --- p.4Chapter 1.3 --- Thesis Organization --- p.5Chapter 2 --- Background --- p.6Chapter 2.1 --- Background of Peer-to-Peer --- p.6Chapter 2.2 --- Background of Content-Based Image Retrieval System --- p.9Chapter 2.3 --- Literature Review of Peer-to-Peer Application --- p.10Chapter 2.4 --- Literature Review of Discovery Mechanisms for Peer-to-Peer Applications --- p.13Chapter 2.4.1 --- Centralized Search --- p.13Chapter 2.4.2 --- Distributed Search - Flooding --- p.15Chapter 2.4.3 --- Distributed Search - Distributed Hash Table --- p.21Chapter 3 --- Peer Clustering and Firework Query Model --- p.25Chapter 3.1 --- Peer Clustering --- p.26Chapter 3.1.1 --- Peer Clustering - Simplified Version --- p.27Chapter 3.1.2 --- Peer Clustering - Single Cluster Version --- p.29Chapter 3.1.3 --- "Peer Clustering - Single Cluster, Multiple Layers of Con- nection Version" --- p.34Chapter 3.1.4 --- Peer Clustering - Multiple Clusters Version --- p.35Chapter 3.2 --- Firework Query Model Over Clustered Network --- p.38Chapter 4 --- Experiments and Results --- p.43Chapter 4.1 --- Simulation Model of Peer-to-Peer Network --- p.43Chapter 4.2 --- Performance Metrics --- p.45Chapter 4.3 --- Experiment Results --- p.47Chapter 4.3.1 --- Performances in different Number of Peers in P2P Network --- p.47Chapter 4.3.2 --- Performances in different TTL value of query packet in P2P Network --- p.52Chapter 4.3.3 --- "Performances in different different data sets, synthetic data and real data" --- p.55Chapter 4.3.4 --- Performances in different number of local clusters of each peer in P2P Network --- p.58Chapter 4.4 --- Evaluation of different clustering algorithms --- p.64Chapter 5 --- Distributed COntent-based Visual Information Retrieval (DIS- COVIR) --- p.67Chapter 5.1 --- Architecture of DISCOVIR and Functionality of DISCOVIR Components --- p.68Chapter 5.2 --- Flow of Operations --- p.72Chapter 5.2.1 --- Preprocessing (1) --- p.73Chapter 5.2.2 --- Connection Establishment (2) --- p.75Chapter 5.2.3 --- "Query Message Routing (3,4,5)" --- p.75Chapter 5.2.4 --- "Query Result Display (6,7)" --- p.78Chapter 5.3 --- Gnutella Message Modification --- p.78Chapter 5.4 --- DISCOVIR EVERYWHERE --- p.81Chapter 5.4.1 --- Design Goal of DISCOVIR Everywhere --- p.82Chapter 5.4.2 --- Architecture and System Components of DISCOVIR Ev- erywhere --- p.83Chapter 5.4.3 --- Flow of Operations --- p.84Chapter 5.4.4 --- Advantages of DISCOVIR Everywhere over Prevalent Web-based Search Engine --- p.86Chapter 6 --- Conclusion --- p.87Bibliography --- p.8