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.

In this paper, we describe MySearchView, a system for assembling search engines into metasearch engines. With this system, any user can create a metasearch engine by simply letting the system know the URLs of the search engines the user wants to be included and the metasearch engine will be built fully automatically. In this paper, the main steps of building metasearch engines will be sketched. We will also outline our plan to demonstrate all the features of MySearchView.

Given a large number of search engines on the Internet, it is difficult for a person to determine which search engines could serve his/her information needs. A common solution is to construct a metasearch engine on top of the search engines. Upon receiving a user query, the metasearch engine sends it to those underlying search engines which are likely to return the desired documents for the query. The selection algorithm used by a metasearch engine to determine whether a search engine should be sent the query typically makes the decision based on the search-engine representative, which contains characteristic information about the database of a search engine. However, an underlying search engine may not be willing to provide the needed information to the metasearch engine. This paper shows that the needed information can be estimated from an uncooperative search engine with good accuracy. Two pieces of information which permit accurate search engine selection are the number of documents indexed by the search engine and the maximum weight of each term. In this paper, we present techniques for the estimation of these two pieces of information.

Searching desired data on the Internet is one of the most common ways the Internet is used. No single search engine is capable of searching all data on the Internet. The approach that provides an interface for invoking multiple search engines for each user query has the potential to satisfy more users. When the number of search engines under the interface is large, invoking all search engines for each query is often not cost effective because it creates unnecessary network traffic by sending the query to a large number of useless search engines and searching these useless search engines wastes local resources. The problem can be overcome if the usefulness of every search engine with respect to each query can be predicted. We present a statistical method to estimate the usefulness of a search engine for any given query. For a given query, the usefulness of a search engine in this paper is defined to be a combination of the number of documents in the search engine that are sufficiently similar to the query and the average similarity of these documents. Experimental results indicate that our estimation method is much more accurate than existing methods.

This paper presents a methodology for finding the n most similar documents across multiple text databases for any given query and for any positive integer n. This methodology consists of two steps. First, the contents of databases are indicated approximately by database representatives. Databases are ranked using their representatives with respect to the given query. We provide a necessary and sufficient condition to rank the databases optimally. In order to satisfy this condition, we provide three estimation methods. One estimation method is intended for short queries; the other two are for all queries. Second, we provide an algorithm, OptDocRetrv, to retrieve documents from the databases according to their rank and in a particular way. We show that if the databases containing the n most similar documents for a given query are ranked ahead of other databases, our methodology will guarantee the retrieval of the n most similar documents for the query. When the number of databases is large, we propose to organize database representatives into a hierarchy and employ a best-search algorithm to search the hierarchy. It is shown that the effectiveness of the best-search algorithm is the same as that of evaluating the user query against all database representatives.

Given a large number of search engines on the Internet, it is difficult for a person to determine which search engines could serve his/her information needs. A common solution is to construct a metasearch engine on top of the search engines. Upon receiving a user query, the metasearch engine sends it to those underlying search engines which are likely to return the desired documents for the query. The selection algorithm used by a metasearch engine to determine whether a search engine should be sent the query typically makes the decision based on the search-engine representative, which contains characteristic information about the database of a search engine. However, an underlying search engine may not be willing to provide the needed information to the metasearch engine. This paper shows that the needed information can be estimated from an uncooperative search engine with good accuracy. Two pieces of information which permit accurate search engine selection are the number of documents indexed by the search engine and the maximum weight of each term. In this paper, we present techniques for the estimation of these two pieces of information.

Linkages among documents have a significant impact on the importance of documents, as it can be argued that important documents are pointed to by many documents or by other important documents. Metasearch engines can be used to facilitate ordinary users for retrieving information from multiple local sources (text databases). There is a search engine associated with each database. In a large-scale metasearch engine, the contents of each local database is represented by a representative. Each user query is evaluated against he set of representatives of all databases in order to determine the appropriate databases (search engines) to search (invoke) In previous word, the linkage information between documents has not been utilized in determining the appropriate databases to search. In this paper, such information is employed to determine the degree of relevance of a document with respect to a given query. Specifically, the importance (rank) of each document as determined by the linkages is integrated in each database representative to facilitate the selection of databases for each given query. We establish a necessary and sufficient condition to rank databases optimally, while incorporating the linkage information. A method is provided to estimate the desired quantities stated in the necessary and sufficient condition. The estimation method runs in time linearly proportional to the number of query terms. Experimental results are provided to demonstrate the high retrieval effectiveness of the method.

Two typical situations in which it is of practical interest to determine the similarities of text documents to a query due to a search engine are: (1) a global search engine, constructed on top of a group of local search engines, wishes to retrieve the set of local documents globally most similar to a given query; and (2) an organization wants to compare the retrieval performance of search engines. The dot-product function is a widely used similarity function. For a search engine using such a function, we can determine its similarity computations if how the search engine sets the weights of terms is known, which is usually not the case. In this paper, techniques are presented to discover certain mathematical expressions of these formulas and the values of embedded constants when the dot-product similarity function is used. Preliminary results from experiments on the WebCrawler search engine are given to illustrate our techniques. 1 1 Introduction Documents of interest are often found...

Metasearch engines can be used to facilitate ordinary users for retrieving information from multiple local sources (text databases). In a metasearch engine, the contents of each local database is represented by a representative. Each user query is evaluated against the set of representatives of all databases in order to determine the appropriate databases to search. When the number of databases is very large, say in the order of tens of thousands or more, then a traditional metasearch engine may become inefficient as each query needs to be evaluated against too many database representatives. Furthermore, the storage requirement on the site containing the metasearch engine can be very large. In this paper, we propose to use a hierarchy of database representatives to improve the efficiency. We provide an algorithm to search the hierarchy. We show that the retrieval effectiveness of our algorithm is the same as that of evaluating the user query against all database representatives. We also show that our algorithm is efficient. In addition, we propose an alternative way of allocating representatives to sites so that the storage burden on the site containing the metasearch engine is much reduced.

As the number of text retrieval systems (search engines) grows rapidly on the World Wide Web, there is an increasing need to build search brokers (metasearch engines) on top of them. Often, the task of building an effective and efficient metasearch engine is hindered by the heterogeneities among the underlying local search engines. We first analyze the impact of various heterogeneities on building a metasearch engine. We then present some techniques that can be used to detect the most prominent heterogeneities among multiple search engines. Applications of utilizing the detected heterogeneities in building better metasearch engines are provided.

In this paper, we present a statistical method to estimate the usefulness of a search engine for any given query. The estimates can be used by a metasearch engine to choose local search engines to invoke. For a given query, the usefulness of a search engine in this paper is defined to be a combination of the number of documents in the search engine that are sufficiently similar to the query and the average similarity of these documents. Experimental results indicate that the proposed estimation method is quite accurate.

A.P. Sistla et al. (1997) designed a similarity-based video retrieval system. Queries were specified in a language called the Hierarchical Temporal Language (HTL). In this paper, we present several extensions of HTL. These extensions include queries that can have the negation operator and any other logical and temporal operators such as disjunction. Efficient algorithms for processing queries in the extended language are also presented.

Proximity queries that involve multiple object types are very common. In this paper, we present a parallel algorithm for answering proximity queries of one kind over object instances that lie in a one-dimensional metric space. The algorithm exploits a specialized hardware, the Dynamic Associative Access Memory chip. In most proximity queries of this kind, the number of object types is less than or equal to three and the distance d, within which object instances are required to locate to satisfy a given proximity condition, is small (dd=80e = 1). The execution time for such queries is linearly proportional to the number of object types and is independent of the size of the database. This allows numerous concurrent users to be serviced. The algorithm is extended to process 2-dimensional proximity queries efficiently. This research is supported in part by NASA under NAGW-4080 and ARO under BMDO grant DAAH04-0024. 1 1