In an online banner advertising scenario, an advertiser expects that the banner advertisement should be displayed to certain percentage of web site visitors. In this context, to generate more revenue for a given web site, the publisher has to meet the demands of several advertisers by providing appropriate sets of web pages. To help the publishers and advertisers, in this paper, we propose a model of coverage patterns and a methodology to extract potential coverage patterns by analyzing click stream data. Given web pages of a site, a coverage pattern is a set of web pages visited by a certain percentage of visitors. The proposed approach has the potential to enable the publisher in meeting the demands of several advertisers. The efficiency and advantages of the proposed approach is shown by conducting experiments on real world data sets.
India Meteorological Department (IMD) has started block-level level agromet advisory (AA) service from the year 2015 and is currently operating in a few blocks of each state across India. In a block-level AA service, on every Tuesday and Friday, AA is being prepared for each block based on the block-level Medium Range weather Forecast (MRF). In this paper, we propose a framework to improve the preparation of blocklevel AA by modeling a weather situation as “Category-based Weather Condition (CWC)” and exploiting both “temporal reuse” and “spatial reuse” of AA based on the similarity among CWCs. The weather data analysis for 12 blocks of Telangana by considering the phenophase-specific CWCs of Rice crop showed that there is a scope to improve the efficiency of block-level AA bulletin preparation process by exploiting reuse.
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