As a result of the development in Industry 4.0, the data generated within the Industries are increasing rapidly every day to attain the innovative environment within the industry through maximal asset utilization. Meanwhile, the redundancy rate in the server is also increasing, which has an impact on the storage as well as in the analysis of data. Most existing de-duplication techniques partition the data with respect to memory. However if the time period is considered for partition, time-series analysis would be achieved during the de-duplication process. To address the above issue, the proposed work presents the Index Based De-duplication technique with Categorized Region Method for computing time-series data. The Merkle Tree with a super feature called reckoning of occurrence is combined in the proposed system to rapidly identify the existence of similar data in the distributed system with an accurate existence count that significantly helps in predicting the future drifts of the industrial environment. Finally, the proposed system also concludes with optimal transportation cost to reach the storage nodes in the cloud using MODI method. The experimental results reveal that the proposed model is efficient since it facilitates less memory and less computation overhead. The proposed technique achieves space reduction by 98%, reduces the computation overhead during analysis by 55%, and increases the efficacy of cloud storage by 60%.