Popularity Driven Data Integration
This addresses data integration challenges for organizations dealing with large-scale analytics, though it appears incremental as it builds on existing concepts of data reuse.
The paper tackles the problem of high costs and low reusability in integrating data from multiple sources for large-scale analytics, proposing the iTelos methodology which treats data based on popularity to reduce preprocessing costs and increase backward compatibility and future sharing.
More and more, with the growing focus on large scale analytics, we are confronted with the need of integrating data from multiple sources. The problem is that these data are impossible to reuse as-is. The net result is high cost, with the further drawback that the resulting integrated data will again be hardly reusable as-is. iTelos is a general purpose methodology aiming at minimizing the effects of this process. The intuition is that data will be treated differently based on their popularity: the more a certain set of data have been reused, the more they will be reused and the less they will be changed across reuses, thus decreasing the overall data preprocessing costs, while increasing backward compatibility and future sharing