| Title | Adaptive Cache Replacement in Efficiently Querying Semantic Big Data |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Akhtar, U., Lee, S. |
| Conference Name | 2018 IEEE International Conference on Web Services (ICWS) |
| Keywords | ACR policy, adaptive cache replacement policy, Big Data, Cache replacement, cache storage, Conferences, exponential smoothing, forecast method, knowledge based systems, knowledge bases querying, least frequently used, least recently used, LFU, Linked data, LRU, Metrics, pubcrawl, query log, query processing, query response, RDBMS, RDF Caching, relational databases, Resiliency, Scalability, semantic Big Data querying, triplestore, Web Caching, web services |
| Abstract | This paper addresses the problem of querying Knowledge bases (KBs) that store semantic big data. For efficiently querying data the most important factor is cache replacement policy, which determines the overall query response. As cache is limited in size, less frequently accessed data should be removed to provide more space to hot triples (frequently accessed). So, to achieve a similar performance to RDBMS, we proposed an Adaptive Cache Replacement (ACR) policy that predict the hot triples from query log. Moreover, performance bottleneck of triplestore, makes realworld application difficult. To achieve a closer performance similar to RDBMS, we have proposed an Adaptive Cache Replacement (ACR) policy that predict the hot triples from query log. Our proposed algorithm effectively replaces cache with high accuracy. To implement cache replacement policy, we have applied exponential smoothing, a forecast method, to collect most frequently accessed triples. The evaluation result shows that the proposed scheme outperforms the existing cache replacement policies, such as LRU (least recently used) and LFU (least frequently used), in terms of higher hit rates and less time overhead. |
| DOI | 10.1109/ICWS.2018.00063 |
| Citation Key | akhtar_adaptive_2018 |
Adaptive Cache Replacement in Efficiently Querying Semantic Big Data