Since today's television can receive more and more programs, and televisions are often viewed by groups of people, such as a family or a student dormitory, this paper proposes a TV program recommendation strategy for multiple viewers based on user profile merging. This paper first introduces three alternative strategies to achieve program recommendation for multiple television viewers, discusses, and analyzes their advantages and disadvantages respectively, and then chooses the strategy based on user profile merging as our solution. The selected strategy first merges all user profiles to construct a common user profile, and then uses a recommendation approach to generate a common program recommendation list for the group according to the merged user profile. This paper then describes in detail the user profile merging scheme, the key technology of the strategy, which is based on total distance minimization. The evaluation results proved that the merging result can appropriately reflect the preferences of the majority of members within the group, and the proposed recommendation strategy is effective for multiple viewers watching TV together.
As scientific instruments and computer simulations produce more and more data, the task of locating the essential information to gain insight becomes increasingly difficult. FastBit is an efficient software tool to address this challenge. In this article, we present a summary of the key techniques, namely bitmap compression, encoding and binning. The advances in these techniques have led to a search tool that can answer structured (SQL) queries orders of magnitude faster than popular database systems. To illustrate how FastBit is used in applications, we present three examples involving a high-energy physics experiment, a combustion simulation, and an accelerator simulation. In each case, FastBit significantly reduces the response time and enables interactive exploration on terabytes of data.
The amount of scientific data generated by simulations or collected from large scale experiments have reached levels that cannot be stored in the researcher's workstation or even in his/her local computer center. Such data are vital to large scientific collaborations dispersed over wide-area networks. In the past, the concept of a Grid infrastructure [1] mainly emphasized the computational aspect of supporting large distributed computational tasks, and optimizing the use of the network by using bandwidth reservation techniques. In this paper we discuss the concept of Storage Resource Managers (SRMs) as components that complement this with the support for the storage management of large distributed datasets. The access to data is becoming the main bottleneck in such "data intensive" applications because the data cannot be replicated in all sites. SRMs can be used to dynamically optimize the use of storage resource to help unclog this bottleneck.
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