Yinan Jing

Energy-efficient shortest path query processing on air

Wireless broadcast provides a scalable and secure spatial data dissemination approach for geographical applications in wireless mobile environments. Among various location-based services, the shortest path query on road networks is one of the most popular and essential services in our daily life. In this paper, we propose an energy-efficient scheme for on air shortest path query processing on road networks, which leverages an elaborate air index called BagIndex based upon the novel Hilbert-based heuristic tree decomposition for the road networks. Experimental results show that the proposed approach incurs less energy consumption on both communication and computation than the previous schemes.

On Efficient Aggregate Nearest Neighbor Query Processing in Road Networks

An aggregate nearest neighbor (ANN) query returns a point of interest (POI) that minimizes an aggregate function for multiple query points. In this paper, we propose an efficient approach to tackle ANN queries in road networks. Our approach consists of two phases: searching phase and pruning phase. In particular, we first continuously compute the nearest neighbors (NNs) for each query point in some specific order to obtain the candidate POIs until all query points find a common POI. Second, we filter out the unqualified POIs based on the pruning strategy for a given aggregate function. The two-phase process is repeated until there remains only one candidate POI, and the remained one is returned as the final result. In addition, we discuss the partition strategies for query points and the approximate ANN query for the case where the number of query points is huge. Extensive experiments using real datasets demonstrate that our proposed approach outperforms its competitors significantly in most cases.

iExplore: Accelerating Exploratory Data Analysis by Predicting User Intention

Exploratory data analysis over large datasets has become an increasingly prevalent use case. However, users are easily overwhelmed by the data and might take a long time to find interesting facts. In this paper, we design a system called iExplore to assist users in doing this time-consuming data exploration task through predicting user intention. Moreover, we propose an intention model to help the iExplore system have a comprehensive understanding of user’s intention. Thus, the exploratory process can be accelerated by the intention-driven recommendation and prefetching mechanisms. Extensive experiments demonstrate that the intention-driven iExplore system can significantly lighten the burden of users and facilitate the exploratory process.

Online Subset Topic Modeling for Interactive Documents Exploration

Data exploration over text databases is an important problem. In an exploration scenario, users would find something useful without previously knowing what exactly they are looking for, until the time they identify them. Therefore, labor-intensive efforts are often required, since users have to review the overview (or detail) results of ad-hoc queries and adjust the queries (e.g., zoom or filter) continuously. Probabilistic topic models are often adopted as a solution to provide the overview for a given text collection, since it could discover the underlying thematic structures of unstructured text data. However, training a topic model for a selected document collection is time consuming. Moreover, frequent model retraining would be introduced by continuous query-adjusting, which leads to large amount of time wasting and therefore is unsuitable for online exploration. To remedy this problem, this paper presents STMS, an algorithm for constructing topic structures in document subsets efficiently. STMS accelerates the process of subset modeling by leveraging global precomputation and applying an efficient sampling-based inference algorithm. The experiments on real world datasets show that STMS achieves orders of magnitude speed-ups than standard topic model, while remaining comparable in terms of modeling quality.