Innovation is vital to find new solutions to problems, increase quality, and improve profitability. Big open linked data (BOLD) is a fledgling and rapidly evolving field that creates new opportunities for innovation. However, none of the existing literature has yet considered the interrelationships between antecedents of innovation through BOLD. This research contributes to knowledge building through utilising interpretive structural modelling to organise nineteen factors linked to innovation using BOLD identified by experts in the field. The findings show that almost all the variables fall within the linkage cluster, thus having high driving and dependence powers, demonstrating the volatility of the process. It was also found that technical infrastructure, data quality, and external pressure form the fundamental foundations for innovation through BOLD. Deriving a framework to encourage and manage innovation through BOLD offers important theoretical and practical contributions.@en

While FPGAs have seen prior use in database systems, in recent years interest in using FPGA to accelerate databases has declined in both industry and academia for the following three reasons. First, specifically for in-memory databases, FPGAs integrated with conventional I/O provide insufficient bandwidth, limiting performance. Second, GPUs, which can also provide high throughput, and are easier to program, have emerged as a strong accelerator alternative. Third, programming FPGAs required developers to have full-stack skills, from high-level algorithm design to low-level circuit implementations. The good news is that these challenges are being addressed. New interface technologies connect FPGAs into the system at main-memory bandwidth and the latest FPGAs provide local memory competitive in capacity and bandwidth with GPUs. Ease of programming is improving through support of shared coherent virtual memory between the host and the accelerator, support for higher-level languages, and domain-specific tools to generate FPGA designs automatically. Therefore, this paper surveys using FPGAs to accelerate in-memory database systems targeting designs that can operate at the speed of main memory.@en

While in-memory databases have largely removed I/O as a bottleneck for database operations, loading the data from storage into memory remains a significant limiter to end-to end performance. Snappy is a widely used compression algorithm in the Hadoop ecosystem and in database systems and is an option in often-used file formats such as Parquet and ORC. Compression reduces the amount of data that must be transferred from/to the storage saving both storage space and storage bandwidth. While it is easy for a CPU Snappy decompressor to keep up with the bandwidth of a hard disk drive, when moving to NVMe devices attached with high bandwidth connections such as PCIe Gen4 or OpenCAPI, the decompression speed in a CPU is insufficient. We propose an FPGA-based Snappy decompressor that can process multiple tokens in parallel and operates on each FPGA block ram independently. Read commands are recycled until the read data is valid dramatically reducing control complexity. One instance of our decompression engine takes 9% of the LUTs in the XCKU15P FPGA, and achieves up to 3GB/s (5GB/s) decompression rate from the input (output) side, about an order of magnitude faster than a CPU (single thread). Parquet allows for independent decompression of multiple pages and instantiating eight of these units on a XCKU15P FPGA can keep up with the highest performance interface bandwidths.@en