PHIRST: A distributed architecture for P2P information retrieval

Recent progress in peer to peer (P2P) search algorithms has presented viable structured and unstructured approaches for full-text search. We posit that these existing approaches are each best suited for different types of queries. We present PHIRST, the first system to facilitate effective full-text search within P2P databases. PHIRST works by effectively leveraging between the relative strengths of these approaches. Similar to structured approaches, agents first publish terms within their stored documents. However, frequent terms are quickly identified and not exhaustively stored, resulting in a significant reduction in the system's storage requirements. During query lookup, agents use unstructured search to compensate for the lack of fully published terms. Additionally, they explicitly weigh between the costs involved in structured and unstructured approaches, allowing for a significant reduction in query costs. Finally, we address how node failures can be effectively addressed through storing multiple copies of selected data. We evaluated the effectiveness of our approach using both real-world and artificial queries. We found that in most situations our approach yields near perfect recall. We discuss the limitations of our system, as well as possible compensatory strategies.     

A comprehensive agent-based architecture for intelligent information retrieval in a distributed heterogeneous environment

The Internet has become the global infrastructure supporting information acquisition and retrieval from many heterogeneous data sources containing high-speed text and rich multimedia images, audio, and video. AgentRAIDER is an ongoing research project at Texas Tech University designed to develop a comprehensive architecture for an intelligent information retrieval system with distributed heterogeneous data sources. The system is designed to support intelligent retrieval and integration of information from the Internet. Current systems of this nature focus only on specific aspects of the distributed heterogeneous problem such as database queries or information filtering. Consequently, these concepts and others have never been successfully integrated into a unified, cohesive architecture. This paper discusses the design and implementation of the AgentRAIDER system and identifies areas for applications of the system in various domains.     

A timed mobile agent planning approach for distributed information retrieval in dynamic network environments

The number of mobile agents and total execution time are two factors used to represent the system overhead that must be considered as part of mobile agent planning (MAP) for distributed information retrieval. In addition to these two factors, the time constraints at the nodes of an information repository must also be taken into account when attempting to improve the quality of information retrieval. In previous studies, MAP approaches could not consider dynamic network conditions, e.g., variable network bandwidth and disconnection, such as are found in peer-to-peer (P2P) computing. For better performance, mobile agents that are more sensitive to network conditions must be used. In this paper, we propose a new MAP approach that we have named Timed Mobile Agent Planning (Tmap). The proposed approach minimizes the number of mobile agents and total execution time while keeping the turnaround time to a minimum, even if some nodes have a time constraint. It also considers dynamic network conditions to reflect the dynamic network condition more accurately. Moreover, we incorporate a security and fault-tolerance mechanism into the planning approach to better adapt it to real network environments.     

Using cooperative mobile agents to monitor distributed and dynamic environments

Monitoring the changes in data values obtained from the environment (e.g., locations of moving objects) is a primary concern in many fields, as for example in the pervasive computing environment. The monitoring task is challenging from a double perspective. First and foremost, the environment can be highly dynamic in terms of the rate of data changes. Second, the monitored data are often not available from a single computer/device but are distributed; moreover, the set of data providers can change along the course of time. Therefore, obtaining a global snapshot of the environment and keeping it up-to-date is not easy, especially if the conditions (e.g., network delays) change.In this article, a decentralized, loose, and fault-tolerant monitoring approach based on the use of mobile agents is described. Mobile agents allow easy tracking of the involved computers, carrying the monitoring tasks to wherever they are needed. A deadline-based mechanism is used to coordinate the cooperative agents, which strive to perform their continuous tasks in time while considering data as recent as possible, constantly adapting themselves to new environmental conditions (changing communication and processing delays). This approach has been successfully used in a real environment and experiments were carried out to prove its feasibility and benefits.     

Consensus-based evaluation framework for distributed information retrieval systems

Multi-agent systems have been attacking the challenges of information retrieval tasks on distributed environment. In this paper, we propose a consensus choice selection method based framework to evaluate the performance of cooperative information retrieval tasks of the multiple agents. Thereby, two well-known measurements, precision and recall, are extended to handle consensual closeness (i.e., local and global consensus) between the sets of retrieved results. We show that in a motivating example the proposed criteria are prone to solve the rigidity problem of classical precision and recall. More importantly, the retrieved results can be ranked with respect to the consensual score, and the ranking mechanism has been verified to be more reasonable.

Energy-efficient wireless information retrieval

In this paper, we propose an adaptive application-driven power management (AADPM) protocol for wireless information retrieval applications within the IEEE 802.11b infrastructure WLAN environment. Our goal is to minimize energy consumption while achieving low round trip time delay. We discuss the protocol and evaluate its effectiveness using the network simulator NS2. We also draw horizontal comparisons among a variety of PM methods reported in the literature. Experimental results show that, compared to the power save mode supported by 802.11b, AADPM reduces the network interface card energy consumption by 52% while only introducing 3% RTT delay.     

Design information retrieval: a thesauri-based approach for reuse of informal design information

Information is integral to the engineering design process, and gaining access to design knowledge is critical to effective design decision-making. This paper considers the indexing and retrieval of informal, unstructured information captured from electronic design logbooks. One of the key observations of informal design information is its evolutionary nature over time. While this characteristic makes informal information a rich source for reuse, it also makes it difficult to employ traditional information retrieval (IR) approaches. The work described in this paper is based on a framework developed specifically for the information handling requirements of designers. This manual method for indexing information is adapted to meet the evolutionary nature of design through the development of thesauri for design context. Several approaches to building thesauri are examined, including manual and automated methods. It is found that manual methods provide a high level of IR performance, but also have high overhead requirements. Machine methods, however, may provide a viable, low overhead alternative.

Multiagent systems and information retrieval our experience with X.MAS

Autonomous agents and multiagent systems have been successfully applied to a number of problems and have been largely used in different application fields. In particular, in this paper we are interested in information retrieval. In fact, in this field multiagent solutions are very useful and effective since they decouple the problem in a network of software agents that interact to solve problems that are beyond the individual capabilities or knowledge. In so doing, multiagent systems allow to overwhelm typical problems of single agent and centralized approaches. To discuss the lesson learnt in using the multiagent technology in the field of information retrieval, in this paper, we present our experience in using X.MAS, a generic multiagent architecture aimed at retrieving, filtering and reorganizing information according to user interests. To this end, after presenting X.MAS, we illustrate six applications built upon it. Our conclusion is that multiagent technology is quite effective to design and realize concrete information retrieval applications.     

关于信息管理与信息系统专业《信息存储与检索》课程的研究

本文从信息管理与信息系统的专业目标开始分析,确立《信息存储与检索》课程在该专业中的地位,然后阐述《信息存储与检索》课程的跨多学科的特点,分析当前大学的主要教材,选择最适合信息管理与信息系统专业的教材,针对所选教材阐述了该课程的教学内容及教学方式、方法。     

Collection-integral source selection for uncooperative distributed information retrieval environments

We propose a new integral-based source selection algorithm for uncooperative distributed information retrieval environments. The algorithm functions by modeling each source as a plot, using the relevance score and the intra-collection position of its sampled documents in reference to a centralized sample index. Based on the above modeling, the algorithm locates the collections that contain the most relevant documents. A number of transformations are applied to the original plot, in order to reward collections that have higher scoring documents and dampen the effect of collections returning an excessive number of documents. The family of linear interpolant functions that pass through the points of the modified plot is computed for each available source and the area that they cover in the rank-relevance space is calculated. Information sources are ranked based on the area that they cover. Based on this novel metric for collection relevance, the algorithm is tested in a variety of testbeds in both recall and precision oriented settings and its performance is found to be better or at least equal to previous state-of-the-art approaches, overall constituting a very effective and robust solution.     

Event graphs for information retrieval and multi-document summarization

With the number of documents describing real-world events and event-oriented information needs rapidly growing on a daily basis, the need for efficient retrieval and concise presentation of event-related information is becoming apparent. Nonetheless, the majority of information retrieval and text summarization methods rely on shallow document representations that do not account for the semantics of events. In this article, we present event graphs, a novel event-based document representation model that filters and structures the information about events described in text. To construct the event graphs, we combine machine learning and rule-based models to extract sentence-level event mentions and determine the temporal relations between them. Building on event graphs, we present novel models for information retrieval and multi-document summarization. The information retrieval model measures the similarity between queries and documents by computing graph kernels over event graphs. The extractive multi-document summarization model selects sentences based on the relevance of the individual event mentions and the temporal structure of events. Experimental evaluation shows that our retrieval model significantly outperforms well-established retrieval models on event-oriented test collections, while the summarization model outperforms competitive models from shared multi-document summarization tasks.     

Mobile merchandise evaluation service using novel information retrieval and image recognition technology

Consumers’ purchasing behavior has obviously changed in recent years with developments in social economics. This change has been evident in the decreased ratio of planned purchases but not in the increase of planned (or spontaneous) purchases. This act of spontaneous or otherwise unplanned purchasing is called “impulse buying”. However, buying under these conditions costs more money always comes with negative responses, such as complaints and regret. Therefore, we propose and have designed a new merchandise recommendation system, the Mobile Merchandise Evaluation Service Platform (MMESP). This is a three-tier system composed of Real-time Merchandise Identifying System (RMIS), Real-time Merchandise Evaluation System (RMES), and Real-time Merchandise Recommendation System (RMRS). With this system, Mobile Users (MUs) take pictures of merchandise and send them to MMESP, RMIS integrates Region Adjacency Graph (RAG) and Self-Organizing Maps (SOM) to gather information on the merchandise through those photographs, and. RMES and RMRS provide Intelligence Agents (IAs) and Multiple Document Summarization (MDS) to summarize recommendations on merchandise for MUs, all in real time.     

一种Internet移动代理系统的黄页服务模型

提出了一种基于信度推理网络的移动代理系统黄页服务模型,为自主迁移的移动代理提供迁移决策支持。与现有的移动代理系统黄页服务模型相比,该模型较好地解决了信息局部性和信息更新的问题,其结构具有良好的可扩展性和灵活性,能更好地适应Ｉｎｔｅｒｎｅｔ动态的网络环境。     

Exploiting concept clusters for content-based information retrieval

Current approaches to index weighting for information retrieval from texts are based on statistical analysis of the texts' contents. A key shortcoming of these indexing schemes, which consider only the terms in a document, is that they cannot extract semantically exact indexes that represent the semantic content of a document. To address this issue, we proposed a new indexing formalism that considers not only the terms in a document, but also the concepts. In the proposed method, concepts are extracted by exploiting clusters of terms that are semantically related, referred to as concept clusters. Through experiments on the TREC-2 collection of Wall Street Journal documents, we show that the proposed method outperforms an indexing method based on term frequency (TF), especially in regard to the highest-ranked documents. Moreover, the index term dimension was 53.3% lower for the proposed method than for the TF-based method, which is expected to significantly reduce the document search time in a real environment.     

Forward multidimensional search with application to information retrieval systems

A new architecture for information retrieval systems is presented. If it was implemented, this architecture would allow the system to process retrieval statements that are equivalent to fuzzily defined queries. The philosophy on which the centerpiece of this system is based—the document search module—is fully explained in this paper. The emphasis is placed on the quick elimination of irrelevant references. A new technique, that takes into account the user's knowledge to discriminate between documents before they are actually retrieved from the data base, was developed. The search technique uses simple computations to select or eliminate potential candidates for retrieval. This technique does not have, qualitatively, the shortcomings of, not only conventional retrieval techniques, but also retrieval systems that accept relevance feedback from the user, in order to refine the search process. No implementation details have been included in this article and system performance figures are not discussed.     

A trust-aware,self-organizing system for large-scale federations of utility computing infrastructures

On-demand computing environments, like Cloud/Grid systems, consist of nodes that individually manage local resources intended to be served to clients. When a client needs some resources, it has the problem of finding the most suitable nodes capable of providing them. In addition, a provider node too may be in need to efficiently locate resources for itself, given the emerging, highly competitive, context of large-scale federations. Indeed, a node competes, with the other federated ones, to obtain the assignment of available tasks. To this end, it may decide to publish a set of resources/ services wider than the one it has currently available. Should such a node be assigned a job for which its actual resources are insufficient, it could end up requiring the collaboration of other nodes.Hence the crucial problem, for nodes and clients alike, is to determine the most promising collaborators. For this purpose, in the competitive and demanding scenarios considered, we advocate taking into account the trustworthiness of nodes in declaring their capabilities, i.e., to help it making an effective selection of possible collaborators, each node should be provided with a trust model for accurately evaluating the trustworthiness of its interlocutors.In this paper, a trust-based approach for large-scale federations Utility Computing infrastructures is proposed. The proposed model is designed to allow any node to find the most suitable collaborators in an efficient way, avoiding exploration of the whole node space. A fully decentralized approach is employed, which allows nodes of a federation to be organized in an overlay network on the basis of suitable criteria. This enables any customer or provider in need of collaborators to determine a suitable set of candidate nodes within which to search.     

Automatic authoring and construction of hypermedia for information retrieval

This paper describes the complete process and a tool for the automatic construction of a multimedia hypertext starting from a large collection of multimedia documents. Through the use of an authoring methodology, the document collection is automatically authored, and the result is a multimedia hypertext, also called a hypermedia, written in hypertext mark-up language (HTML), almost a standard among hypermedia mark-up languages. The resulting hypermedia can be browsed and queried with Mosaic, an interface developed in the framework of the World Wide Web Project. In particular, the set of methods and techniques used for the automatic construction of hypermedia is described in this paper, and their relevance in the context of multimedia information retrieval is highlighted.