Trustworthy answers for top-k queries on uncertain Big Data in decision making

Effectively extracting reliable and trustworthy information from Big Data has become crucial for large business enterprises. Obtaining useful knowledge to enable better decisions to be made in order to improve business performance is not a trivial task. The most fundamental challenge for Big Data extraction is to handle the uncertainty of data to meet emerging business needs, such as marketing analysis, future prediction and decision making. In this paper, we firstly propose a novel approach called Dominating Top-k Aggregate Query (DA-Topk) to provide trustworthy and reliable informative knowledge from uncertain Big Data by combining the techniques of skyline and top-k queries. Then, we design a number of pruning rules to reduce the search space and terminate the ranking process as early as possible. Next, we provide a deeper analysis regarding the satisfaction of the six ranking properties (i.e. exact-k, containment, unique-rank, value-invariance, stability and faithfulness) between our approach and existing approaches to demonstrate that our method is the only one which satisfied all of these properties. Extensive experiments with both real and synthetic data sets have been conducted to verify the efficiency and effectiveness of our proposed approach compared to the state-of-the-art approaches. Our approach can help managers make strategic decisions quickly and accurately in competitive market places.