TY - GEN
T1 - A Scalable Query Pricing Framework for Incomplete Graph Data
AU - Hou, Huiwen
AU - Qiao, Lianpeng
AU - Yuan, Ye
AU - Chen, Chen
AU - Wang, Guoren
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - With the rapid growth of data, how to make full use of their value becomes a critical issue. In the past few years, it has been a popular method to buy and sell data through the data market. Meanwhile, a variety of data pricing mechanisms have been proposed. However, since most of them concentrate on relational data, little is known about graph data pricing, particularly incomplete graph data. In this paper, we mainly focus on the pricing problem for queries over incomplete graph data. We take data provenance as the key idea behind our pricing mechanism and assign a base price to each edge in the graph. Considering the arbitrage-free property of query price, and the lack of some potential answers due to data incompleteness, we propose two practical pricing functions for incomplete graph query respectively. Furthermore, we design feasible pricing algorithms based on subgraph matching to derive each type of query price. Extensive experiments on real graph datasets demonstrate the effectiveness and efficiency of our solutions.
AB - With the rapid growth of data, how to make full use of their value becomes a critical issue. In the past few years, it has been a popular method to buy and sell data through the data market. Meanwhile, a variety of data pricing mechanisms have been proposed. However, since most of them concentrate on relational data, little is known about graph data pricing, particularly incomplete graph data. In this paper, we mainly focus on the pricing problem for queries over incomplete graph data. We take data provenance as the key idea behind our pricing mechanism and assign a base price to each edge in the graph. Considering the arbitrage-free property of query price, and the lack of some potential answers due to data incompleteness, we propose two practical pricing functions for incomplete graph query respectively. Furthermore, we design feasible pricing algorithms based on subgraph matching to derive each type of query price. Extensive experiments on real graph datasets demonstrate the effectiveness and efficiency of our solutions.
KW - Arbitrage-free
KW - Data pricing
KW - Incomplete graph data
UR - https://www.scopus.com/pages/publications/85161666783
U2 - 10.1007/978-3-031-30637-2_7
DO - 10.1007/978-3-031-30637-2_7
M3 - Conference contribution
AN - SCOPUS:85161666783
SN - 9783031306365
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 97
EP - 113
BT - Database Systems for Advanced Applications - 28th International Conference, DASFAA 2023, Proceedings
A2 - Wang, Xin
A2 - Sapino, Maria Luisa
A2 - Han, Wook-Shin
A2 - El Abbadi, Amr
A2 - Dobbie, Gill
A2 - Feng, Zhiyong
A2 - Shao, Yingxiao
A2 - Yin, Hongzhi
PB - Springer Science and Business Media Deutschland GmbH
T2 - 28th International Conference on Database Systems for Advanced Applications, DASFAA 2023
Y2 - 17 April 2023 through 20 April 2023
ER -