Multimodal dialog system has attracted increasing attention from both academia and industry over recent years. Although existing methods have achieved some progress, they are still confronted with challenges in the aspect of question understanding (i.e., user intention comprehension). In this paper, we present a relational graph-based context-aware question understanding scheme, which enhances the user intention comprehension from local to global. Specifically, we first utilize multiple attribute matrices as the guidance information to fully exploit the product-related keywords from each textual sentence, strengthening the local representation of user intentions. Afterwards, we design a sparse graph attention network to adaptively aggregate effective context information for each utterance, completely understanding the user intentions from a global perspective. Moreover, extensive experiments over a benchmark dataset show the superiority of our model compared with several state-of-the-art baselines.

Recent advances in flash memory show great potential to replace traditional hard drives (HDDs) with flash-based solid state drives (SSDs) from personal computing to distributed systems. However, it is still a long way to go before completely using SSDs for enterprise data storage. Considering the cost, performance, and reliability of SSDs, a practical solution is to combine both SSDs and HDDs together. This paper proposes a hybrid storage system named PASS (Performance-dAta Synchronization - hybrid storage System) to tradeoff between I/O performance and data discrepancy between SSDs and HDDs. PASS includes a high-performance SSD and a traditional HDD to store mirrored data for reliability. All of the I/O requests are redirected to the primary SSD first and then the updated data blocks are copied to the backup HDD asynchronously. In order to hide the latency of copying operations, we use an I/O window to coalesce write requests and maintain an ordered I/O queue to shorten the HDD seek and rotation times. Depending on the charateristics of different I/O workloads, we develop an adaptive policy to dynamically balance the foreground I/O processing and background mirroring. We implement a prototype system of PASS by developing a Linux device driver and conduct experiments on the IoMeter, PostMark, and TPCC benchmarks. Our results show that PASS can achieve up to 12 times the performance of a RAID1 storage system for the IoMeter and PostMark workloads while tolerating less than 2% data discrepancy between the primary SSD and the backup HDD. More interestingly, while PASS does not produce any performance benefit for the TPC-C benchmark, it does allow the system to scale to larger sizes than when using an HDD-based RAID system alone.