An approximate search framework for big data

1. 2020 – 2021
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An Approximate Search Framework for Big Data
Abstract:
In the age of big data, a traditional scanning search pattern is gradually becoming unfit for a
satisfying user experience due to its lengthy computing process. In this paper, we propose a
sampling-based approximate search framework called Hermes, to meet user's query demand
for both accurate and efficient results. A novel metric, (ε,δ)-approximation, is presented to
uniformly measure accuracy and efficiency for a big data search service, which enables
Hermes to work out a feasible searching job. Based on this, we employ the bootstrapping
technique to further speed up the search process. Moreover, an incremental sampling
strategy is investigated to process homogeneous queries; in addition, the reuse theory of
historical results is also studied for the scenario of appending data. Theoretical analyses and
experiments on a real-world dataset demonstrate that Hermes is capable of producing
approximate results meeting the preset query requirements with both high accuracy and
efficiency.