A Cross-Platform Benchmark Framework for Mobile Semantic Web Reasoning Engines

Abstract

Semantic Web technologies are used in a variety of domains for their ability to facilitate data integration, as well as enabling expressive, standards-based reasoning. Deploying Semantic Web reasoning processes directly on mobile devices has a number of advantages, including robustness to connectivity loss, more timely results, and reduced infrastructure requirements. At the same time, a number of challenges arise as well, related to mobile platform heterogeneity and limited computing resources. To tackle these challenges, it should be possible to benchmark mobile reasoning performance across different mobile platforms, with rule-and datasets of varying scale and complexity and existing reasoning process flows. To deal with the current heterogeneity of rule formats, a uniform rule-and data-interface on top of mobile reasoning engines should be provided as well. In this paper, we present a cross-platform benchmark framework that supplies 1) a generic, standards-based Semantic Web layer on top of existing mobile reasoning engines; and 2) a benchmark engine to investigate and compare mobile reasoning performance.

Benchmarking semantic reasoning on mobile platforms: Towards optimization using OWL2 RL

“…To better align benchmarks with real-world use cases, MobiBench supports several process flows, which dictate the times at which operations (e.g., load data, execute rules / perform reasoning) are performed. From previous work [54], [55], and in line with our choice for materializing inferences, we identified two useful process flows:…”

“…For the IRIS and PocketKrHyper engines, rules are first loaded (e.g., to build the Datalog KB), after which the dataset is loaded and reasoning is performed (LoadRulesDataExecute). For more details, we refer to previous work [54].…”

“…We note that, although the proposed OWL2 RL subset selections were construed and evaluated in the context of resource-constrained platforms, they may be applied in any kind of computing environment. This paper is built on previous work, which presented a clinical benchmark [55] and an initial version of the Mobile Benchmark Framework [54], which only supplied an API, restricted benchmarking to rule-based reasoning, and did not attempt optimizations or applications of OWL2 RL.…”