2,887 research outputs found
Efficient Processing of k Nearest Neighbor Joins using MapReduce
k nearest neighbor join (kNN join), designed to find k nearest neighbors from
a dataset S for every object in another dataset R, is a primitive operation
widely adopted by many data mining applications. As a combination of the k
nearest neighbor query and the join operation, kNN join is an expensive
operation. Given the increasing volume of data, it is difficult to perform a
kNN join on a centralized machine efficiently. In this paper, we investigate
how to perform kNN join using MapReduce which is a well-accepted framework for
data-intensive applications over clusters of computers. In brief, the mappers
cluster objects into groups; the reducers perform the kNN join on each group of
objects separately. We design an effective mapping mechanism that exploits
pruning rules for distance filtering, and hence reduces both the shuffling and
computational costs. To reduce the shuffling cost, we propose two approximate
algorithms to minimize the number of replicas. Extensive experiments on our
in-house cluster demonstrate that our proposed methods are efficient, robust
and scalable.Comment: VLDB201
Study on Buyback Contract in Supply Chain With a Loss-Averse Supplier and Multiple Loss-Averse Retailers Under Stockout Loss Situation
According to the prospect theory and the loss-aversion function, this paper developers the buyback contract model in a two-stage supply chain with a loss-averse supplier and multiple loss-averse retailers. Under the stockout loss setting, we analyze the effect of the loss aversion on the behavior from the retailers and the supplier, and then the buyback contract has been shown to be able to coordinate the supply chain. Furthermore, the number of retailers and loss aversion coefficient meet a certain range, there will be a unique optimal buyback price to achieve supply chain coordination
Evaluation of Performance of Bus Lanes on Urban Expressway Using Paramics Micro-simulation Model
AbstractUrban expressway, as the main skeleton of the road network, is the aorta between urban regions and urban external traffic communication, but also bears the commuter channel. It makes a large amount of traffic flow into the expressway, resulting the congestion in the expressway in many big cities, including Beijing. Taking the Beijing southwest third ring expressway for example, a simulation model was built using Paramics. The simulation model was pre-evaluated before and after the bus lanes set, and the model was post-evaluated to verify the validity of the model after the bus lanes were implemented, it has important theoretical and practical value
Enabling Quality Control for Entity Resolution: A Human and Machine Cooperation Framework
Even though many machine algorithms have been proposed for entity resolution,
it remains very challenging to find a solution with quality guarantees. In this
paper, we propose a novel HUman and Machine cOoperation (HUMO) framework for
entity resolution (ER), which divides an ER workload between the machine and
the human. HUMO enables a mechanism for quality control that can flexibly
enforce both precision and recall levels. We introduce the optimization problem
of HUMO, minimizing human cost given a quality requirement, and then present
three optimization approaches: a conservative baseline one purely based on the
monotonicity assumption of precision, a more aggressive one based on sampling
and a hybrid one that can take advantage of the strengths of both previous
approaches. Finally, we demonstrate by extensive experiments on real and
synthetic datasets that HUMO can achieve high-quality results with reasonable
return on investment (ROI) in terms of human cost, and it performs considerably
better than the state-of-the-art alternatives in quality control.Comment: 12 pages, 11 figures. Camera-ready version of the paper submitted to
ICDE 2018, In Proceedings of the 34th IEEE International Conference on Data
Engineering (ICDE 2018
Manipulation of gas-liquid-liquid systems in continuous flow microreactors for efficient reaction processes
Gas-liquid-liquid flow in microreactors holds great potential towards process intensification of operation in multiphase systems, particularly by a precise control over the three-phase contact patterns and the associated mass transfer enhancement. This work reviews the manipulation of gas-liquid-liquid three-phase flow in microreactors for carrying out efficient reaction processes, including gas-liquid-liquid reactions with catalysts residing in either liquid phase, coupling of a gas-liquid reaction with the liquid-liquid extraction, inert gas assisted liquid-liquid reactions and particle synthesis under three-phase flow. Microreactors are shown to be able to provide well-defined flow patterns and enhanced gas-liquid/liquid-liquid mass transfer rates towards the optimized system performance. The interplay between hydrodynamics and mass transfer, as well as its influence on the overall microreactor system performance is discussed. Meanwhile, future perspectives regarding the scale-up of gas-liquid-liquid microreactors in order to meet the industrial needs and their potential applications especially in biobased chemicals and fuels synthesis are further addressed
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