Several real-time applications rely on dynamic graphs to model and store data arriving from multiple streams. Providing both high ingestion rate and efficient analytics with transactional guarantees is challenging, even more so when updates may be received out-of-order at the database. In this work, we propose HAL, a novel in-memory dynamic graph database design, addressing these challenges. HAL outperforms comparable systems by a factor of up to 73× in terms of update processing throughput and up to 357× for analytics, while being the first to support out-of-order updates.

Frequent itemsets mining is an active research problem in the domain of data mining and knowledge discovery. With the advances in database technology and an exponential increase in data to be stored, there is a need for efficient approaches that can quickly extract useful information from such large datasets. Frequent Itemsets (FIs) mining is a data mining task to find itemsets in a transactional database which occur together above a certain frequency. Finding these FIs usually requires multiple passes over the databases; therefore, making efficient algorithms crucial for mining FIs. This work presents a graph-based approach for representing a complete transactional database. The proposed graph-based representation enables the storing of all relevant information (for extracting FIs) of the database in one pass. Later, an algorithm that extracts the FIs from the graph-based structure is presented. Experimental results are reported comparing the proposed approach with 17 related FIs mining methods using six benchmark datasets. Results show that the proposed approach performs better than others in terms of time.