We are living in an ever more connected world, where data recording the interactions between people, software systems, and the physical world is becoming increasingly prevalent. These data often take the form of a temporally evolving graph, where entities are the vertices and the interactions between them are the edges. We call such graphs interaction graphs. Various domains, including telecommunications, transportation, and social media, depend on analytics performed on interaction graphs. The ability to efficiently support historical analysis over interaction graphs requires effective solutions for the problem of data layout on disk. This paper presents an adaptive disk layout called the railway layout for optimizing disk block storage for interaction graphs. The key idea is to divide blocks into one or more sub-blocks. Each sub-block contains the entire graph structure, but only a subset of the attributes. This improves query I/O, at the cost of increased storage overhead. We introduce optimal integer linear program (ILP) formulations for partitioning disk blocks into sub-blocks with overlapping and nonoverlapping attributes. Additionally, we present greedy heuristics that can scale better compared to the ILP alternatives, yet achieve close to optimal query I/O. We provide an implementation of the railway layout as part of RailwayDB—an open-source graph database we have developed. To demonstrate the benefits of the railway layout, we provide an extensive experimental evaluation, including model-based as well as empirical results comparing our approach to baseline alternatives.
The VLDB Journal – Springer Journals
Published: Apr 1, 2016
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