Present day, mining of high utility itemsets especially from transactional databases is required task to process many transactional operations quick. There are many methods that are presented for mining high utility itemsets from transactional datasets are subjected to some serious limitations such as performance of this methods needs to be investigated in low memory based systems for mining high utility itemsets from large transactional datasets and hence needs to address further as well. Further limitation includes these methods cannot overcome the screenings as well as overhead of null transactions; hence, performance degrades eventually. We are analyzing the new approaches to overcome these limitations such as distributed programming model for mining business-oriented transactional datasets, which overcomes the limitations and main memory-based computing, but also unexpectedly highly scalable in terms of increasing database size. We have used this approach with existing UP-Growth and UP-Growth+ with aim of improving their performances further.