In most of the business areas, many organizations are running similar trades and maintaining comparable databases. These organizations have noticed the importance of analyzing results obtained from the union of databases owned by different organizations. However, they do not want to disclose their contents to others since some of the contents are sensitive and private. Recently, preference-based queries have drawn massive attention in the database community. Particularly, the top-k dominating queries have been studied extensively, which selects the k objects that are better than other objects based on the `domination score'. In this paper, we have considered the top-k dominating queries on the combined databases of different organizations. We propose a secure framework for multi-party top-k dominating queries, where individual organizations do not need to expose their private databases to others. We analyze the privacy of our proposed framework and also evaluate its performance for various settings.

Recommendation systems address the "information overload" problem by filtering out items that customers may not be interested. Collaborative Filtering (CF) is the most popular technique which recommends items to a user based on similar users' preferences and/or similar items records by utilizing the user-item rating matrix. However, such a large amount of explicit feedbacks are not always available. Furthermore, user preferences are changing over time. The Conventional CF cannot capture the temporal dynamics of recommendations well. In this paper, we apply Deep Recurrent Neural Networks (DRNNs) to CF and generate dynamic, personalized recommendations by utilizing user purchase history big data. Experiments on the MovieLens dataset show relative improvements over previously reported results.

Selection of good locations in a map is an indispensable function in many applications. In order to select specific locations, we have to specify detailed selection criteria. However, it is not easy especially for users of mobile devices. Therefore, we used an idea of skyline queries, which are known to be easy and effective to retrieve interesting data from a database. In our previous work, we have proposed area skyline query that selects good locations in a map. However, the query is not fast enough for handling “big data”. We simplify and revise the algorithm of the query in this paper by using MapReduce framework so that we can use it for big data. Experiments' results demonstrate that the performance and scalability are superior to previous area skyline algorithm and are able to handle big data.