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This paper introduces a novelkeyword searching paradigm in relational databases (DBs), where the result of a search is a ranked set of object summaries (OSs). An OS summarizes all data held about a data subject (DS) in the database. More precisely, it is a tree with a tuple containing the keyword as a root and
spatial keyword query because many objects are not always valid. For example, visitors may plan their trips according to the opening time of attractions. In this paper, we identify and solve a novel problem, i.e., the time-aware Boolean spatial keyword query (TABSKQ), which returns the
adequate to handle approximate keyword matching in space database efficiently. This means they are not error-tolerant for users. In this paper we propose a novel approach for Approximate SK queries-ASK queries, whose motivation is to find the spatial objects with their textual attributes similar to the user-specified keyword
relevancy of spatial objects, and KSR refines intermediate results by considering both spatial and keyword constraints with the spatial keyword ranker. In addition, we design novel algorithms for evaluating SKkNN and SKR queries. These algorithms employ the inverted index technique, shortest path search algorithms, and network
Identifying prospective customers is an important aspect of marketing research. In this paper, we provide support for a new type of query, the Reverse Top-k Geo-Social Keyword (RkGSK) query. This query takes into account spatial, textual, and social information, and finds prospective customers for geotagged objects
Finding relevant documents in digital libraries has been a well studied problem in information retrieval. It is not uncommon to see users browsing digital collections without having a clear idea of the keyword search that they should perform. However, we believe that such initial query search is not totally
Office applications are becoming a major pillar of today's organizations since they are used to edit a vast amount of digital documents. Finding these office documents in large databases that fit users' needs is becoming increasingly important. Traditional search tools that employ keyword and phrase matching between
We present an index structure to support the approximate keyword search in text databases. In an approximate keyword search query, the user presents a query word Q and a tolerance value k (kges0), and wishes to find all documents in the database that contain the query word Q or any other word in the vocabulary that
Along with the fast developing of network technology, the number of Web page and user of network search become very enormous. In order to solve the problem of inefficiency and low precision in the search that users have different demand and knowledge background, this paper presents a new text model called vocabulary
In this paper, we propose the ldquoaddedrdquo use of proximity search to a Web search query for narrowing down the set of documents returned as answers to a keyword based search query. This approach adds value to Web search query results by allowing users to better express what they are looking for. Most of the
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