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Full-Text Articles in Databases and Information Systems
A Survey Of Typical Attributed Graph Queries, Yanhao Wang, Yuchen Li, Ju Fan, Chang Ye, Mingke Chai
A Survey Of Typical Attributed Graph Queries, Yanhao Wang, Yuchen Li, Ju Fan, Chang Ye, Mingke Chai
Research Collection School Of Computing and Information Systems
Graphs are commonly used for representing complex structures such as social relationships, biological interactions, and knowledge bases. In many scenarios, graphs not only represent topological relationships but also store the attributes that denote the semantics associated with their vertices and edges, known as attributed graphs. Attributed graphs can meet demands for a wide range of applications, and thus a variety of queries on attributed graphs have been proposed. However, these diverse types of attributed graph queries have not been systematically investigated yet. In this paper, we provide an extensive survey of several typical types of attributed graph queries. We propose …
Efficient Algorithms For Solving Aggregate Keyword Routing Problems, Qize Jiang, Weiwei Sun, Baihua Zheng, Kunjie Chen
Efficient Algorithms For Solving Aggregate Keyword Routing Problems, Qize Jiang, Weiwei Sun, Baihua Zheng, Kunjie Chen
Research Collection School Of Computing and Information Systems
With the emergence of smart phones and the popularity of GPS, the number of point of interest (POIs) is growing rapidly and spatial keyword search based on POIs has attracted significant attention. In this paper, we study a more sophistic type of spatial keyword searches that considers multiple query points and multiple query keywords, namely Aggregate Keyword Routing (AKR). AKR looks for an aggregate point m together with routes from each query point to m. The aggregate point has to satisfy the aggregate keywords, the routes from query points to the aggregate point have to pass POIs in order to …
Semantic And Influence Aware K-Representative Queries Over Social Streams, Yanhao Wang, Yuchen Li, Kianlee Tan
Semantic And Influence Aware K-Representative Queries Over Social Streams, Yanhao Wang, Yuchen Li, Kianlee Tan
Research Collection School Of Computing and Information Systems
Massive volumes of data continuously generated on social platforms have become an important information source for users. A primary method to obtain fresh and valuable information from social streams is social search. Although there have been extensive studies on social search, existing methods only focus on the relevance of query results but ignore the representativeness. In this paper, we propose a novel Semantic and Influence aware k-Representative (k-SIR) query for social streams based on topic modeling. Specifically, we consider that both user queries and elements are represented as vectors in the topic space. A k-SIR query retrieves a set of …
Distributed K-Nearest Neighbor Queries In Metric Spaces, Xin Ding, Yuanliang Zhang, Lu Chen, Yunjun Gao, Baihua Zheng
Distributed K-Nearest Neighbor Queries In Metric Spaces, Xin Ding, Yuanliang Zhang, Lu Chen, Yunjun Gao, Baihua Zheng
Research Collection School Of Computing and Information Systems
Metric k nearest neighbor (MkNN) queries have applications in many areas such as multimedia retrieval, computational biology, and location-based services. With the growing volumes of data, a distributed method is required. In this paper, we propose an Asynchronous Metric Distributed System (AMDS), which uniformly partitions the data with the pivot-mapping technique to ensure the load balancing, and employs publish/subscribe communication model to asynchronously process large scale of queries. The employment of asynchronous processing model also improves robustness and efficiency of AMDS. In addition, we develop an efficient estimation based MkNN method using AMDS to improve the query efficiency. Extensive experiments …
On Processing Reverse K-Skyband And Ranked Reverse Skyline Queries, Yunjun Gao, Qing Liu, Baihua Zheng, Mou Li, Gang Chen, Qing Li
On Processing Reverse K-Skyband And Ranked Reverse Skyline Queries, Yunjun Gao, Qing Liu, Baihua Zheng, Mou Li, Gang Chen, Qing Li
Research Collection School Of Computing and Information Systems
In this paper, for the first time, we identify and solve the problem of efficient reverse k-skyband (RkSB) query processing. Given a set P of multi-dimensional points and a query point q, an RkSB query returns all the points in P whose dynamic k-skyband contains q. We formalize RkSB retrieval, and then propose five algorithms for computing the RkSB of an arbitrary query point efficiently. Our methods utilize a conventional data-partitioning index (e.g., R-tree) on the dataset, and employ pre-computation, reuse and pruning techniques to boost the query efficiency. In addition, we extend our solutions to tackle an interesting variant …