High utility pattern mining has emerged to overcome the limitation of frequent pattern mining where only frequency is taken as importance without considering the actual importance of items. Existing algorithms for mining high utility patterns over a data stream are two-phase algorithms that are not scalable due to the large number of candidates generation in the first phase, particularly when the minimum utility threshold is low. Moreover, in the second phase, the algorithm needs to scan the database again to find out actual utility for candidates. In this paper, we propose a novel algorithm SOHUPDS to mine high utility patterns over a data stream with the sliding window technique using the projected database approach. In addition, we propose a data structure IUDataListSW, which stores utility and upper-bound values of the items in the current sliding window. Moreover, IUDataListSW stores position of items in the transaction to get the initial projected database of items efficiently. Furthermore, we propose an update strategy to utilize mined high utility patterns from the previous sliding window to update high utility patterns in the current sliding window. Therefore, SOHUPDS is able to mine high utility patterns over a data stream in a single pass and one phase. Experimental results illustrate that SOHUPDS is more efficient than the state-of-the-art algorithms in terms of execution time as well as memory usage.