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An individual's problem space has been identified as important in problem solving. A problem space is a person's inner representation of the task after extracting critical components in the external problem task. This paper proposes a study to probe whether there are different problem spaces for efficient and inefficient Web information searchers. The questions will be answered quantitatively using...
In this paper, we focus on the issue of the m-closest keywords (mCK) query over spatial data in the Web. The mCK query is a problem to find the optimal set of records in the sense that they are the spatially-closest records that satisfy m user-given keywords. The mCK query was proposed by Zhang et al[1]. They assumed
Currently keyword search is a prominent data retrieval method for the Web because the simple and efficient nature of the keyword processing allows it to process a large amount of information with fast response. However, keyword search approaches do not formally capture the clear meaning of a keyword query and fail to
audiences and website's competitors when analyzing keywords; (2) insert keywords into web text that will appear on search engine results pages, and (3) involve their web content and websites with other web content creators. Implications: Because successful search engine optimization requires considerable time, professional
We have become able to get enough approvable images of a target object just by submitting its object-name to a conventional keyword-based Web image search engine. However, because the search results rarely include its uncommon images, we can often get only its common images and cannot easily get exhaustive knowledge
Most researches on Image Retrieval (IR) have aimed at clearing away noisy images and allowing users to search only acceptable images for a target object specified by its object-name. We have become able to get enough acceptable images of a target object just by submitting its object-name to a conventional keyword
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