Zoned Namespace (ZNS) SSDs are the latest evolution of host-managed flash storage, enabling improved performance at a lower cost-per-byte than traditional block interface (conventional) SSDs. To date, there is no support for arranging these new devices in arrays that offer increased throughput and reliability (RAID). We identify key challenges in designing redundant ZNS SSD arrays, such as managing metadata updates and persisting partial stripe writes in the absence of overwrite support from the device. We present RAIZN, a logical volume manager that exposes a ZNS interface and stripes data and parity across ZNS SSDs. RAIZN provides more stable throughput and lower tail latencies than an mdraid array of conventional SSDs based on the same hardware platform. RAIZN achieves superior performance because device-level garbage collection slows down conventional SSDs. We confirm that the benefits of RAIZN translate to higher layers by adapting the F2FS file system, RocksDB key-value store, and MySQL database to work with ZNS and leverage its benefits by closely controlling garbage collection. Compared to arrays of conventional SSDs experiencing on-device garbage collection, RAIZN leverages the ZNS interface to maintain consistent performance with up to 14× higher throughput and lower tail latency.

Optical Character Recognition (OCR) systems have been widely used in various applications for extracting semantic information from images. To give the user more control over their privacy, an on-device solution is needed. The current state of the art models are too heavy and complex to be deployed on-device. We develop an efficient lightweight scene text recognition (STR) system, which has only 0.88M parameters and performs real-time text recognition. Attention modules tend to boost the accuracy of STR networks but are generally slow and not optimized for device inference. So, we propose the use of convolution attention modules to the text recognition networks, which aims to provide channel and spatial attention information to the LSTM module by adding very minimal computational cost. It boosts our word accuracy on ICDAR 13 dataset by almost 2%. We also introduce a novel orientation classifier module, to support the simultaneous recognition of both horizontal and vertical text. The proposed model surpasses on-device metrics of inference time and memory footprint and achieves comparable accuracy when compared to the leading commercial and other open-source OCR engines. We deploy the system on-device with an inference speed of 2.44 ms per word on the Exynos 990 chipset device and achieve an accuracy of 88.4% on ICDAR-13 dataset.

Designing and implementation of an intrusion detection system in any database environment has emerged as an absolute necessity in the recent years. Detection of both, the outsider attack and privilege abuse from within the organisation, has become a fundamental need for maintenance of dynamic, scalable and reinforced databases. Proposed advanced approach, malicious query detection using fuzzy and cluster analysis (MQDFCA) operates in a seamless manner and efficaciously performs detection and prevention of transactions that are intrusive in nature, within a database environment, thus shielding the vital data stored in a database from any unauthorised/malicious access or modifications. The method utilises concepts of machine learning like fuzzy logic, association rule mining and clustering algorithms at various stages to validate a newly generated transaction at role segment, profile segment and the rule validation segment. The degree of adherence of user supplied queries within a transaction to the previously generated user roles, transaction profiles and extracted rules is used to categorise the transaction as non-malicious or malicious. The efficaciousness of proposed methodology in detection of intrusions is exemplified from the results of the experiments conducted on the synthetic dataset yielding recall and precision values of 93% and 98% respectively.