With the popularity of social networks, Twitter has become one of the dominant providers of massive quantities of information.Exploring the distributions and correlations from Twitter data helps accurate personalized recommendations.Online Analytical Processing, or OLAP, provides an intuitive form that is suitable for exploring Twitter data.Unfortunately, the traditional OLAP approaches can only deal with structured data, not unstructured textual data like tweets.The key to applying OLAP to Twitter data is to mine and build a dimension hierarchy of tweeter interests.However, the current methods can extract tweeter interests from Twitter data on a single level, but fail to obtain a hierarchy of tweeter interests with different granularities.To address this problem, we propose a LDA-based model, called MS-LDA, which combines tweeters' social relationships and tweets to extract and build the tweeters' interest dimension hierarchy.Such a dimension hierarchy can be further employed to apply OLAP techniques to Twitter data.In addition, we employ Word2vec to obtain the linguistic similarity of words in tweets, to improve its effectiveness.The extensive experiments demonstrate that our method can effectively extract the dimension hierarchy of tweeters' interests for multidimensional analysis.

Online Analytical Processing, or OLAP, is an approach to answering multidimensional analytical (MDA) queries in an interactive way. However, the traditional OLAP approaches can only deal with structured data, but not unstructured textual data like tweets. To address this problem, we propose a Latent Dirichlet Allocation (LDA)-based model, called Multilayered Semantic LDA (MS-LDA), which detects the hidden layered interests from Twitter data based on LDA. The layered dimension of interests can be further used to apply OLAP techniques to Twitter data. Furthermore, MS-LDA employs the semantic similarity among words of tweets based on word2vec, and also the social relationship among twitters, to improve its effectiveness. The extensive experiments demonstrate that MS-LDA can effectively extract the dimension hierarchy of tweeters' interests for OLAP.

While extensive efforts have been devoted to providing optimization based, distributed congestion control schemes for efficient bandwidth utilization and fair allocation in both wireline and wireless networks, a common assumption therein is fixed link capacities. This unfortunately will limit the application scope in multi-hop wireless networks where channels are ever changing. In this paper, we explicitly model link capacities to be time varying and investigate congestion control problems in multi-hop wireless networks. In particular we propose a primal-dual congestion control algorithm which is proved to be trajectory stable in the absence of feedback delay. Different from system stability around a single equilibrium point, trajectory stability Guarantees the system is stable around a time varying reference trajectory. Moreover, we obtain sufficient conditions for the scheme to be locally stable in the presence of delay. Our key technique is to model time variations of capacities as perturbations to a constant link. Furthermore, to study the robustness of the algorithm against capacity variations, we investigate the sensitivity of the control scheme and through simulations to study the tradeoff between stability and sensitivity.