In this paper, we study the skyline group problem over a data stream. An object can dominate another object if it is not worse than the other object on all attributes and is better than the other object on at least on...In this paper, we study the skyline group problem over a data stream. An object can dominate another object if it is not worse than the other object on all attributes and is better than the other object on at least one attribute. If an object cannot be dominated by any other object, it is a skyline object. The skyline group problem involves finding k-item groups that cannot be dominated by any other k-item group. Existing algorithms designed to find skyline groups can only process static data. However, data changes as a stream with time in many applications,and algorithms should be designed to support skyline group queries on dynamic data. In this paper, we propose new algorithms to find skyline groups over a data stream. We use data structures, namely a hash table, dominance graph, and matrix, to store dominance information and update results incrementally. We conduct experiments on synthetic datasets to evaluate the performance of the proposed algorithms. The experimental results show that our algorithms can efficiently find skyline groups over a data stream.展开更多
Performance and scalability are two issues that are becoming increasingly pressing as the resource descrip- tion framework （RDF） data model is applied to real-world ap- plications. Because neither vertical nor flat ...Performance and scalability are two issues that are becoming increasingly pressing as the resource descrip- tion framework （RDF） data model is applied to real-world ap- plications. Because neither vertical nor flat structures of RDF storage can handle frequent schema updates and meanwhile avoid possible long-chain joins, there is no clear winner be- tween the two typical structures. In this paper, we propose an alternative open user schema. The open user schema con- sists of flat tables automatically extracted from RDF query streams. A query is divided into two parts and conquered on the fiat tables in the open user schema and on the vertical ta- ble stored in a backend storage. At the core of this divide and conquer architecture with open user schema, an efficient iso- morphic decision algorithm is introduced to guide a query to related flat tables in the open user schema. Our proposal in essence departs from existing methods in that it can accom- modate schema updates without possible long-chain joins. We implement our approach and provide empirical evalua- tions to demonstrate both the efficiency and effectiveness of our approach in evaluating complex RDF queries.展开更多
基金supported by the Fundamental Research Funds for the Central Universities (Nos. FRF-TP-14025A1 and FRF-TP-15-025A2)supported by the Key Technologies Research and Development Program of 12th Five-Year Plan of China (No.2013BAI13B06)
文摘In this paper, we study the skyline group problem over a data stream. An object can dominate another object if it is not worse than the other object on all attributes and is better than the other object on at least one attribute. If an object cannot be dominated by any other object, it is a skyline object. The skyline group problem involves finding k-item groups that cannot be dominated by any other k-item group. Existing algorithms designed to find skyline groups can only process static data. However, data changes as a stream with time in many applications,and algorithms should be designed to support skyline group queries on dynamic data. In this paper, we propose new algorithms to find skyline groups over a data stream. We use data structures, namely a hash table, dominance graph, and matrix, to store dominance information and update results incrementally. We conduct experiments on synthetic datasets to evaluate the performance of the proposed algorithms. The experimental results show that our algorithms can efficiently find skyline groups over a data stream.
文摘Performance and scalability are two issues that are becoming increasingly pressing as the resource descrip- tion framework （RDF） data model is applied to real-world ap- plications. Because neither vertical nor flat structures of RDF storage can handle frequent schema updates and meanwhile avoid possible long-chain joins, there is no clear winner be- tween the two typical structures. In this paper, we propose an alternative open user schema. The open user schema con- sists of flat tables automatically extracted from RDF query streams. A query is divided into two parts and conquered on the fiat tables in the open user schema and on the vertical ta- ble stored in a backend storage. At the core of this divide and conquer architecture with open user schema, an efficient iso- morphic decision algorithm is introduced to guide a query to related flat tables in the open user schema. Our proposal in essence departs from existing methods in that it can accom- modate schema updates without possible long-chain joins. We implement our approach and provide empirical evalua- tions to demonstrate both the efficiency and effectiveness of our approach in evaluating complex RDF queries.
