Paper Info
Title
Knowledge Extraction and Improved Data Fusion for Sales Prediction in Local Agricultural Markets.
Abstract
In this paper, a monitoring system of agricultural production is modeled as a Data Fusion System (data from local fairs and meteorological data). The proposal considers the particular information of sales in agricultural markets for knowledge extraction about the associations among them. This association knowledge is employed to improve predictions of sales using a spatial prediction technique, as shown with data collected from local markets of the Andean region of Ecuador. The commercial activity in these markets uses Alternative Marketing Circuits (CIALCO). This market platform establishes a direct relationship between producer and consumer prices and promotes direct commercial interaction among family groups. The problem is presented first as a general fusion problem with a network of spatially distributed heterogeneous data sources, and is then applied to the prediction of products sales based on association rules mined in available sales data. First, transactional data is used as the base to extract the best association rules between products sold in different local markets, knowledge that allows the system to gain a significant improvement in prediction accuracy in the spatial region considered.
Year
DOI
Venue
2019
10.3390/s19020286
SENSORS
Keywords
Field
DocType
predictive analysis,data mining,alternative circuits of commercialization,association rules,time series,spatial prediction,kriging and co-kriging
Econometrics,Producer–consumer problem,Spatial prediction,Electronic engineering,Sensor fusion,Association rule learning,Agriculture,Knowledge extraction,Engineering,Transaction data,Agricultural productivity
Journal
Volume
Issue
ISSN
19
2
1424-8220
Citations 
PageRank 
References 
0
0.34
6
Authors
3
Name
Order
Citations
PageRank
Washington R Padilla100.34
Jesús García223830.37
José M. Molina360467.82