Logistics is a data-intensive industry. The information systems used by logistics companies generate massive volume of data which the companies store to perform different types of analysis. In addition, the advent of Big Data technologies and Internet of Things paradigm have given logistics companies an opportunity to use external data stemming from a wide variety of sources including sensors (e.g., GPS), social media and traffic controlling systems. The logistics companies aim to leverage the power of these external data and perform rigorous analysis in real-time to discover intelligence such as unpredictable delay. However, there are different challenges involved. One of the core challenges is integrating and processing a wide variety of data coming from heterogeneous sources. To the best of our knowledge, there is no off-the shelf solution which can address this challenge. In this paper, we present a framework called ProLoD which performs pre-processing and processing tasks with different types of data. Our framework relies on machine learning algorithms, for processing data; however, we found that the ready to use algorithms are not adequate to guarantee processing efficiency. Therefore, we extended an algorithm called Hierarchical Clustering Algorithm. We evaluated ProLoD by comparing its performance with the HCL algorithm found in the widely-adopted machine learning tool called WEKA. We found that ProLoD is performing reasonably better than WEKA in terms of producing optimal number of clusters.