6001645
Towards a highly-scalable and effective metasearch engine
A metasearch engine is a system that supports uni ed access to multiple local search engines. Database selection is one of the main challenges in building a large-scale metasearch engine. The problem is to eAEciently and accurately determine a small number of potentially useful local search engines to invoke for each user query. In order to enable accurate selection, metadata that re ect the contents of each search engine need to be collected and used. In this paper, we propose a highly scalable and accurate database selection method. This method has several novel features. First, the metadata for representing the contents of all search engines are organized into a single integrated representative. Such a representative yields both computation eAEciency and storage eAEciency. Second, our selection method is based on a theory for ranking search engines optimally. Experimental results indicate that this new method is very e ective. An operational prototype system has been built based on the proposed approach.
15933707
Automatic extraction of web search interfaces for interface schema integration
This paper provides an overview of a technique for extracting information from the Web search interfaces of e-commerce search engines that is useful for supporting automatic search interface integration. In particular, we discuss how to group elements and labels on a search interface into attributes and how to derive certain meta-information for each attribute.
9755561
A Treemap-Based Result Interface for Search Engine Users
Search engines and metasearch engines on the World Wide Web typically display representative information such as the result document titles and snippets in a one-dimensional ranked list. Alternatively, a few others such as Clusty and Kartoo cluster their results so that users can, to a certain extent, interact with documents, keywords and/or clusters. The number of documents that can be effectively presented in one screen is usually limited to between 10 and 20. In this paper, we propose a method based on treemap visualization that substantially improve the information compactness. In addition, it provides a few unique post-search interaction methods that enable a user to manage a large number of results. An online prototype system is built. Various experiments are designed and done to evaluate the efficiency, effectiveness and usability.
2888926
AllInOneNews: development and evaluation of a large-scale news metasearch engine
AllInOneNews is the largest news metasearch engine in the world, connecting to over 1,000 news sites over 150 countries. Implementing a large-scale metasearch engine like AllInOneNews needs to overcome unique challenges not faced by building small metasearch engines such as developing highly scalable search engine selection techniques. In this paper, we discuss these unique challenges and our solutions to these challenges. We also discuss some novel features of AllInOneNews such as highly automated solution and semantic query match. This paper also reports the results of a comparative evaluation of three commercial news search systems, one search engine - Google News and two metasearch engines - Mamma News and AllInOneNews. Several measures such as effectiveness, diversity and time-sensitivity are used to perform the comparison. Another contribution of this paper is that we introduce a novel scheme to compare multiple news search systems in a combined measure that takes both relevance and time-sensitivity of retrieved information into consideration.
3399913
WISE-Integrator: A System for Extracting and Integrating Complex Web Search Interfaces of the Deep Web
We demonstrate WISE-Integrator - an automatic search interface extraction and integration tool. The basic research issues behind this tool will also be explained.
16923777
Identifying redundant search engines in a very large scale metasearch engine context
For a given set of search engines, a search engine is redundant if its searchable contents can be found from other search engines in this set. In this paper, we propose a method to identify redundant search engines in a very large-scale metasearch engine context. The general problem is equivalent to an NP hard problem -- the set-covering problem. Due to the large number of search engines that need to be considered and the large sizes of these search engines, approximate solutions must be developed. In this paper, we propose a general methodology to tackle this problem and within the context of this methodology, we propose several new heuristic algorithms for solving the set-covering problem.
7233121
PARMA: a PHY/MAC aware routing metric for ad-hoc wireless networks with multi-rate radios
Ad-hoc wireless networks with multi-rate radios (such as 802.11a, b, g) require a new class of MAC/PHY aware metrics that take into account factors such as physical-layer link speed and MAC-layer channel congestion. Conventional "layer 3" ad-hoc routing algorithms typically make routing decisions based on the minimum hop-count (MH). Use of the MH metric leads to the selection of paths with few hops, but one or more of these hops may turn out to be low-speed radio links due to adaptive rate selection at the physical layer We investigate a new cross-layer routing metric that takes into account both physical layer link speed and estimated channel congestion, thus aiming to minimize end-to-end delay that includes both transmission and access times. The proposed "PARMA" routing metric thus helps to spread the traffic across the "good links and nodes" in the network, increasing network capacity and reducing packet loss and delay. The paper presents the design and implementation of the proposed PARMA metric for proactive ad-hoc routing protocols, such as DSDV. DSDV modifications for incorporating the MAC/PHY aware metric into an ns-2 simulation model are given. Simulation results for typical multi-rate 802.11 ad-hoc network scenarios show that the proposed cross-layer PHY/MAC aware metric achieves significantly higher network throughput and decreases network congestion by selecting paths with high bit-rate links, while also avoiding areas of MAC congestion.
1209241
Spectral Energy Minimization for Semi-supervised Learning
Data mining problems often involve a large amount of unlabeled data and there is often very limited known information on the dataset. In such scenario, semi-supervised learning can often improve classification performance by utilizing unlabeled data for learning. In this paper, we proposed a novel approach to semi-supervised learning as as an optimization of both the classification energy and cluster compactness energy in the unlabeled dataset. The resulting integer programming problem is relaxed by a semi-definite relaxation where efficient solution can be obtained. Furthermore, the spectral graph methods provide improved energy minimization via the incorporation of additional criteria. Results on UCI datasets show promising results.
1394886
An approach for optimizing binding lifetime with mobile IPv6
In this paper an analytical model is presented in order to provide useful insight into the performance behaviour of mobile IPv6 in the context of a binding lifetime setting. By using this model, the optimal binding lifetime can be estimated in terms of a user mobility, traffic workload, and network structure. An algorithm for dynamically setting the binding lifetime in mobile IPv6 networks is proposed based on the analytical model. The numerical results of the system simulation are also demonstrated.
6719824
Performance modeling of multicast groups for multiplayer games in peer-to-peer networks
A computer game is usually considered as a finite state machine. For a distributed multiplayer game, state information has to be exchanged among its players when they move and interact in a virtual space. If the number of players is very large, only a small subset of the game entities, controlled by their players, is interested and forms a multicast group so as to reduce the information dissemination over the network. In this paper, analytical models are proposed for evaluating the performance of multicast groups with multiplayer games in peer-to-peer networks. The system modeling is based on two stochastic processes and their possible solutions in terms of game type, player activity, and entity vision size, mode of user input processing, and grouping strategy. The cost of game operations on consumed resources is introduced to characterize the performance measure. Numerical examples are demonstrated, which provide useful insights of the behaviors of such systems.
17480069
Similarity-based Web service matchmaking
With the increasing growth in popularity of Web services, matchmaking of relevant Web services becomes a significant challenge. Commonly, Web service is described by WSDL and published on UDDI registers. UDDI provides limited search facilities allowing only a keyword-based search of businesses, services, and the so called tModels based on names and identifiers. This category-based keyword-browsing method is clearly insufficient. Semantic Web service uses DAML-S instead of WSDL to represent capabilities of Web services. This improvement enables software agents or search engines to automatically find appropriate Web services via ontologies and reasoning algorithm enriched methods. However, the high cost of formally defining to the heavy and complicated services makes this improvement widespread adoption unlikely. To cope with these limitations, we have developed a suite of methods which assesses the similarity of Web services to achieve matchmaking. In particular, we present a conceptual model which classifies properties of Web services into four categories. For each category, a similarity assessment method has been given. In Web service matchmaking process, these similarity assessment methods can be used together or individually. Experiments highlight complementary contributions that our work makes to facilitate Web service matchmaking.