Recently, how to provide better POI recommendation performance by exploiting representation learning frame work is still one challenging task in LBSNs. Most existing methods either focus on modeling few limited information without considering other important information such as social influence and sentiment factor, or only rely on traditional shallow models to combine different factors, lacking effective integrating way to learn latent representations by fully utilizing multi-aspect interaction relations. To address these issues, we propose a novel sentiment-aware POI recommendation framework, dubbed as Senti2LSTM, which is capable of learning more comprehensive representations of users and POIs with fusion of multi-relations. Specifically, we first employ dual LSTMs to capture different sentimental embeddings for users and POIs respectively from emotional comments in LBSNs, and then we integrate them into the aggregation of propagation embeddings for users and POIs when learning their latent representations from user-POI bipartite graph and social link graph in LBSNs. Additionally, we also consider discriminating the importance of different social neighbors by leveraging social based attention mechanism, which makes social friends with common sentiments have more similar preferences.Finally,extensive experimental results conducted on two real-world datasets, e.g., Foursquare-NYC and Yelp2018, have demonstrated the effectiveness of our proposed Senti2LSTM in sentiment learning, and significantly outperforming the state-of-the-art POI recommendation methods

The discovering of frequent XML query patterns gains its focus due to its many applications in XML data management, and several algorithms have been proposed to discover frequent query patterns using the frequent structure mining techniques. In this paper we consider the problem of incremental mining of frequent XML query patterns. We propose a method to minimize the I/O and computation requirements for handling incremental updates.

Caching the results of frequent query patterns can improve the performance of query evaluation. This paper describes a 2-pass mining algorithm called 2PXMiner to discover frequent XML query patterns. We design 3 data structures to expedite the mining process. Experiments results indicate that 2PXMiner is both efficient and scalable.

As XML prevails over the Internet, the efficient retrieval of XML data becomes important. Research to improve query response times has been largely concentrate on indexing XML documents and processing regular path expressions. Another approach is to discover frequent query patterns since the answers to these queries can be stored and indexed. Mining frequent query patterns requires more than simple tree matching since the XML queries involves special characters such as "*" or "//". In addition, the matching process can be expensive since the search space is exponential to the size of XML schema. In this paper, we present two mining algorithms, XQPMiner and XQPMinerTID, to discover frequent query pattern frees from a large collection of XML queries efficiently. Both algorithms exploit schema information to guide the enumeration of candidate subtrees, thus eliminating unnecessary node expansions. Experiments results show that the proposed methods are efficient and have good scalability.

It is increasingly important to develop scalable integration techniques for the growing number of XML data sources. A practical starting point for the integration of large numbers of Document Type Definitions (DTDs) of XML sources would be to first find clusters of DTDs that are similar in structure and semantics. Reconciling similar DTDs within such a cluster will be an easier task than reconciling DTDs that are different in structure and semantics as the latter would involve more restructuring. We introduce XClust, a novel integration strategy that involves the clustering of DTDs. A matching algorithm based on the semantics, immediate descendents and leaf-context similarity of DTD elements is developed. Our experiments to integrate real world DTDs demonstrate the effectiveness of the XClust approach.