时态图上的最坏情况最优基本图模式
Worst-Case Optimal BGPs on Temporal Graphs
浏览论文内容
中文总结 AI 辅助
研究时态图上最坏情况最优评估基本图模式的方法，提出灵活查询语言，构建需O(N)空间的索引结构，能在O(Q* m log N)时间内评估扩展BGPs，实验证明该方法能在毫秒内低开销回答实际查询。
中文摘要 AI 辅助
我们研究如何在带时间标记的图上以最坏情况最优（wco）方式评估基本图模式（BGPs），其中边具有时间有效性区间。我们采用一种灵活的查询语言，用户用常量或变量指定形式为（主体，属性，对象，时间）的m个四元组。时间分量表示特定边有效的时刻，用户还可包含时间常量或变量之间的顺序关系。答案是所有有效的变量赋值集合，包括时间。我们描述了一种索引结构，对于有N条边的时态图，需要O(N)空间，能在wco时间O(Q* m log N)内评估扩展BGPs，其中Q*表示在具有相同边有效时刻数的任何时态图上查询Q的最大解数。我们用该索引使Leapfrog Triejoin适应任何变量评估顺序下的时态图设置。我们的索引还为相关查询类型提供wco保证，包括快照评估、版本查询和其他时态变体。在真实世界数据集上的实验表明，我们的方法以低空间开销在毫秒内回答实际查询。
英文摘要
We study how to evaluate basic graph patterns (BGPs) in a worst-case-optimal (wco) manner over {\em temporal} labeled graphs, where edges have an interval of temporal validity. We adopt a flexible query language in which users specify m quads of the form (subject, property, object, time), using constants or variables. The time component denotes the instant at which a particular edge is valid, and users may also include order relations between temporal constants or variables. The answer is the set of all valid variable assignments, including time. We describe an index structure that, for a temporal graph with N edges, requires O(N) space and can evaluate extended BGPs in wco time O(Q* m log N), where Q* represents the maximum number of solutions for query Q over any temporal graph with the same number of instants of edge validity. We use our index to adapt Leapfrog Triejoin to the temporal graph setting under any variable evaluation ordering. Our index further yields wco guarantees for related query types, including snapshot evaluation, version queries, and other temporal variants. Experiments on real-world datasets show that our approach answers realistic queries in milliseconds with low space overhead.
