document: Answering bounded continuous search queries in the world wide web Search queries applied to extract relevant information from the World Wide Web over a period of time may be denoted as continuous search queries . The improvement of continuous search queries may concern not only the quality of retrieved results but also the freshness of results , i.e. the time between the availability of a respective data object on the Web and the notification of a user by the search engine . In some cases a user should be notified immediately since the value of the respective information decreases quickly , as e.g. news about companies that affect the value of respective stocks , or sales offers for products that may no longer be available after a short period of time . In the document filtering literature , the optimization of such queries is usually based on threshold classification . Documents above a quality threshold are returned to a user . The threshold is tuned in order to optimize the quality of retrieved results . The disadvantage of such approaches is that the amount of information returned to a user may hardly be controlled without further user-interaction . In this paper , we consider the optimization of bounded continuous search queries where only the estimated best k elements are returned to a user . We present a new optimization method for bounded continuous search queries based on the optimal stopping theory and compare the new method to methods currently applied by Web search systems . The new method provides results of significantly higher quality for the cases where very fresh results have to be delivered .
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