SOC: A Succinct Adaptive Semantic OLAP Caching

SOC: A Succinct Adaptive Semantic OLAP Caching

연구 분야: Strategies

학회: Data Science and Engineering

In big data analysis, a large quantity of OLAP and aggregate queries exists, which have much stronger semantic context relationships (e.g. drill down and roll up) than generic SQL queries. Caching query results in memory playing an important role in accelerating data queries. Nevertheless, traditional query caching schemata neither fully utilize the features of OLAP, such as drill down and roll up semantics, nor compress the cached results, as the memory space is limited. In this paper, we propose a succinct, adaptive semantic OLAP caching, where the cache items are the cube lattice equivalence classes with only the bounds in a class stored. With further queries, the bound ranges are extended or expanded, indicating more query-answering ability which is assessed by the proposed covering capacity. The bounds of equivalence classes that more covering capacity are preferentially preserved in caching. We further empower our cache with some inference ability to derive more new data cells without posing extra queries and develop efficient query and update algorithms. The extensive experimental evaluation is conducted on synthetic and real data sets with various parameter settings. Our cache outperforms the common caching like LRU and LFU. Furthermore, it is robust to the non-repeated-pattern queries, still with a 30% hit ratio.