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Novel Approach to Minimize the Memory Requirements of Frequent Subgraph Mining Techniques

Chinese Journal of Electronics 2021年第2期30卷 258-267页

作者： B?LG?N Turgay Tugay O?UZ Murat Department of Computer Engineering Bursa Technical University Microsoft Corporation Turkey Branch

Frequent subgraph mining(FSM) is a subset of the graph mining domain that is extensively used for graph classification and clustering. Over the past decade, many efficient FSM algorithms have been developed with improvements generally focused on reducing the time complexity by changing the algorithm structure or using parallel programming techniques. FSM algorithms also require high memory consumption, which is another problem that should be solved. In this paper, we propose a new approach called Predictive dynamic sized structure packing(PDSSP) to minimize the memory needs of FSM algorithms. Our approach redesigns the internal data structures of FSM algorithms without making algorithmic modifications. PDSSP offers two contributions. The first is the Dynamic Sized Integer Type, a newly designed unsigned integer data type, and the second is a data structure packing technique to change the behavior of the compiler. We examined the effectiveness and efficiency of the PDSSP approach by experimentally embedding it into two state-of-the-art algorithms, g Span and *** compared our implementations to the performance of the originals. Nearly all results show that our proposed implementation consumes less memory at each support level, suggesting that PDSSP extensions could save memory, with peak memory usage decreasing up to 38% depending on the dataset.

关键词： data structures algorithmic modifications high memory consumption graph mining domain Dynamic Sized Integer Type Predictive dynamic sized structure packing internal data structures graph classification parallel programming peak memory usage memory requirements PDSSP extensions finite state machines graph theory PDSSP approach algorithm structure newly designed unsigned integer data type data mining data structure packing technique efficient FSM algorithms clustering frequent subgraph mining techniques

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