A Multithreaded Java Framework for Information Extraction in the Context of Enterprise Application Integration

In this paper, we present a new multithreaded framework for information extraction with Java in heterogeneous enterprise application environments, which frees the developer from having to deal with the error-prone task of low-level thread programming. The power of this framework is demonstrated by an example of extracting product prices from web sites, but the framework is useful for numerous other purposes, too. Strong points of the framework are its performance, continuous feedback, and adherence to maximum response times. The description of the framework uses UML modeling techniques for visualizing multithreading. Moreover, we tackle Java problems of stopping running threads.

On MAS Scalability

In open dynamic multi-agent environments the number of agents can vary significantly within very short periods of time. Very few (if any) current multi-agent systems have, however, been designed to cope with large-scale distributed applications. Scalability requires increasing numbers of new agents and resources to have no noticeable effect on performance nor to increase administrative complexity. In this paper a number of implications for techniques and management are discussed. Current research on agent middleware is briefly described.

Shopbots: A Syntactic Present, A Semantic Future

Despite high expectations, shopbots have yet to significantly facilitate a richer, more satisfying online shopping experience for users. By taking advantage of Semantic Web and Web services technologies, however, researchers can overcome current technological limitations and finally realize the shopbot's significant potential. © 2006 IEEE

An Expressive Language and Efficient Execution System for Software Agents

Software agents can be used to automate many of the tedious, time-consuming information processing tasks that humans currently have to complete manually. However, to do so, agent plans must be capable of representing the myriad of actions and control flows required to perform those tasks. In addition, since these tasks can require integrating multiple sources of remote information ? typically, a slow, I/O-bound process ? it is desirable to make execution as efficient as possible. To address both of these needs, we present a flexible software agent plan language and a highly parallel execution system that enable the efficient execution of expressive agent plans. The plan language allows complex tasks to be more easily expressed by providing a variety of operators for flexibly processing the data as well as supporting subplans (for modularity) and recursion (for indeterminate looping). The executor is based on a streaming dataflow model of execution to maximize the amount of operator and data parallelism possible at runtime. We have implemented both the language and executor in a system called THESEUS. Our results from testing THESEUS show that streaming dataflow execution can yield significant speedups over both traditional serial (von Neumann) as well as non-streaming dataflow-style execution that existing software and robot agent execution systems currently support. In addition, we show how plans written in the language we present can represent certain types of subtasks that cannot be accomplished using the languages supported by network query engines. Finally, we demonstrate that the increased expressivity of our plan language does not hamper performance; specifically, we show how data can be integrated from multiple remote sources just as efficiently using our architecture as is possible with a state-of-the-art streaming-dataflow network query engine

A New Approach of Intelligent Data Retrieval Paradigm

What is a real time agent, how does it remedy ongoing daily frustrations for users, and how does it improve the retrieval performance in World Wide Web? These are the main question we focus on this manuscript. In many distributed information retrieval systems, information in agents should be ranked based on a combination of multiple criteria. Linear combination of ranks has been the dominant approach due to its simplicity and effectiveness. Such a combination scheme in distributed infrastructure requires that the ranks in resources or agents are comparable to each other before combined. The main challenge is transforming the raw rank values of different criteria appropriately to make them comparable before any combination. Different ways for ranking agents make this strategy difficult. In this research, we will demonstrate how to rank Web documents based on resource-provided information how to combine several resources raking schemas in one time. The proposed system was implemented specifically in data provided by agents to create a comparable combination for different attributes. The proposed approach was tested on the queries provided by Text Retrieval Conference (TREC). Experimental results showed that our approach is effective and robust compared with offline search platforms

Enabling Global Price Comparison through Semantic Integration of Web Data

“Sell Globally” and “Shop Globally” have been seen as a potential benefit of web-enabled electronic business. One important step toward realizing this benefit is to know how things are selling in various parts of the world. A global price comparison service would address this need. But there have not been many such services. In this paper, we use a case study of global price dispersion to illustrate the need and the value of a global price comparison service. Then we identify and discuss several technology challenges, including semantic heterogeneity, in providing a global price comparison service. We propose a mediation architecture to address the semantic heterogeneity problem, and demonstrate the feasibility of the proposed architecture by implementing a prototype that enables global price comparison using data from web sources in several countries

Semantic eBroker

The world of e-commerce presents ample opportunity to fully utilise the capability of intelligent Agents. The highly dynamic, fast- moving and information-rich environment can often be overwhelming for the human participant. Agents can intelligently assist users by mimicking human behaviour and adapting themselves to their client’s specification. This thesis presents an e-commerce framework that would introduce negotiation techniques which allows sellers and buyers to trade using Case Base Reasoning techniques as well as being proactive in remembering users’ requests and autonomously monitoring vendor sites for new items that might match the users’ needs and preferences. It observes the users whilst shopping and learns their preferences with respect to various features that characterise shopping items.peer-reviewe