The prediction of time series data, such as stock prices, is difficult since there exist many factors that affect the prediction model. Also, the influence of different factors on a stock price may be linear or nonlinear. The generation of good models for stock prices challenge the researchers in recent years. Long Short-Term Memory (LSTM) is a variation of Recurrent Neural Network (RNN), which can capture temporal sequence and have gained great success on time series prediction. Also, Convolutional Neural Network (CNN) is superior for extracting features from multi-dimensional sequences. In this paper, we propose a CNN-LSTM hybrid neural network with multiple factors to predict stock prices. Moreover, we add an attention mechanism to improve the scalability and the accuracy of the CNN-LSTM model. In the experiments, we compare our proposed model with different approaches in two real stock datasets. The results confirm the efficiency and scalability of our proposed method.

Selecting representative objects from a large-scale database is an essential task to understand the database. A skyline query is one of the popular methods for selecting representative objects. It retrieves a set of non-dominated objects. In this paper, we consider a distributed algorithm for computing skyline, which is efficient enough to handle “big data”. We have noticed the importance of “big data” and want to use it. On the other hand, we must take care of its privacy. In conventional distributed algorithms for computing a skyline query, we must disclose the sensitive values of each object of a private database to another for comparison. Therefore, the privacy of the objects is not preserved. However, such disclosures of sensitive information in conventional distributed database systems are not allowed in the modern privacy-aware computing environment. Recently several privacy-preserving skyline computation frameworks have been introduced. However, most of them use computationally expensive secure comparison protocol for comparing homomorphically encrypted data. In this work, we propose a novel and efficient approach for computing the skyline in a secure multi-party computing environment without disclosing the individual attributes’ value of the objects. We use a secure multi-party sorting protocol that uses the homomorphic encryption in the semi-honest adversary model for transforming each attribute value of the objects without changing their order on each attribute. To compute skyline we use the order of the objects on each attribute for comparing the dominance relationship among the objects. The security analysis confirms that the proposed framework can achieve multi-party skyline computation without leaking the sensitive attribute value to others. Besides that, our experimental results also validate the effectiveness and scalability of the proposed privacy-preserving skyline computation framework.

Nowadays, the management and analyses of ‘big data’ are becoming indispensable for numerous organizations all over the world. In many cases, multiple organizations want to perform data analyses on their combined databases. Skyline query is one of the popular operations for selecting representative objects from a large database, where any other object within the database does not dominate each of the representative objects, called ‘skyline’. Like other data analytics operations, the multi-party skyline query can provide benefits to the participating organizations by retrieving the skyline objects from their combined databases. Such a multi-party skyline query demands the disclosure of individual parties’ objects to others during the computation. But, owing to the data privacy and security concern of the present IT era, such disclosure of the individual parties’ databases is strictly prohibited. Considering this issue, we are proposing a new framework for the privacy-preserving multi-party skyline query, exploiting additive homomorphic encryption along with data anonymization, perturbation, and randomization techniques. The underlying protocols within our proposed framework ensure that every participating party can identify its multi-party skyline objects without revealing the objects to others during the multi-party skyline query. The detailed privacy and security analyses show that the proposed framework can achieve the desired computation goal without privacy leakage. Besides, the performance evaluation through complexity analyses, extensive simulations, and comprehensive comparison also demonstrate the utility and the efficiency of the proposed framework.

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.

Location recommendation is essential for various map-based mobile applications. However, it is not easy to generate location-based recommendations with the changing contexts and locations of mobile users. Skyline operation is one of the most well-established techniques for location-based services. Our previous work proposed a new query method, called “area skyline query”, to select areas in a map. However, it is not efficient for large-scale data. In this paper, we propose a parallel algorithm for processing the area skyline using MapReduce. Intensive experiments on both synthetic and real data confirm that our proposed algorithm is sufficiently efficient for large-scale data.