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

Real Time Web Search Framework for Performing Efficient Retrieval of Data

With the rapidly growing amount of information on the internet, real-time system is one of the key strategies to cope with the information overload and to help users in finding highly relevant information. Real-time events and domain-specific information are important knowledge base references on the Web that frequently accessed by millions of users. Real-time system is a vital to product and a technique must resolve the context of challenges to be more reliable, e.g. short data life-cycles, heterogeneous user interests, strict time constraints, and context-dependent article relevance. Since real-time data have only a short time to live, real-time models have to be continuously adapted, ensuring that real-time data are always up-to-date. The focal point of this manuscript is for designing a real-time web search approach that aggregates several web search algorithms at query time to tune search results for relevancy. We learn a context-aware delegation algorithm that allows choosing the best real-time algorithms for each query request. The evaluation showed that the proposed approach outperforms the traditional models, in which it allows us to adapt the specific properties of the considered real-time resources. In the experiments, we found that it is highly relevant for most recently searched queries, consistent in its performance, and resilient to the drawbacks faced by other algorithms

Architecture of a Commercialized Search Engine Using Mobile Agents

Shopping Search Engine (SSE) implies a unique challenge for validating distinct items available online in market place. For sellers, having a user finding relevant search results on top is very difficult. Buyers tend to click on and buy from the listings which appear first. Search engine optimization devotes that goal to influence such challenges. In current shopping search platforms, lots of irrelevant items retrieved from their indices; e.g. retrieving accessories of exact items rather than retrieving the items itself, regardless the price of item were considered or not. Also, users tend to move from shoppers to another searching for appropriate items where the time is crucial for consumers. In our proposal, we exploit the drawbacks of current shopping search engines, and the main goal of this research is to combine and merge multiple search results retrieved from some highly professional shopping sellers in the commercial market. Experimental results showed that our approach is more efficient and robust for retrieving a complete list of desired and relevant items with respect to all query space.CCS CONCEPTSInformation systems - Commercial-specific retrieva