In this monograph, we survey recent research on using reconfigurable hardware accelerators, namely, Field Programmable Gate Arrays (FPGAs), to accelerate analytical processing. Such accelerators are being adopted as a way of overcoming the recent stagnation in CPU performance because they can implement algorithms differently from traditional CPUs, breaking traditional trade-offs. As such, it is timely to discuss their benefits in the context of analytical processing, both as an accelerator within a single node database and as part of distributed data analytics pipelines. We present guidelines for accelerator design in both scenarios, as well as, examples of integration within full-fledged Relational Databases. We do so through the prism of recent research projects that explore how emerging compute-intensive operations in databases can benefit from FPGAs. Finally, we highlight future research challenges in programmability and integration, and cover architectural trends that are propelling the rapid adoption of accelerators in datacenters and the cloud.

Due to the growing need to process large graph and network datasetscreated by modern applications, recent years have witnessed a surginginterest in developing big graph platforms. Tens of such big graphsystems have already been developed, but there lacks a systematic categorizationand comparison of these systems. This article provides atimely and comprehensive survey of existing big graph systems, andsummarizes their key ideas and technical contributions from variousaspects. In addition to the popular vertex-centric systems which espousea think-like-a-vertex paradigm for developing parallel graph applications,this survey also covers other programming and computationmodels, contrasts those against each other, and provides a vision forthe future research on big graph analytics platforms. This survey aimsto help readers get a systematic picture of the landscape of recent biggraph systems, focusing not just on the systems themselves, but alsoon the key innovations and design philosophies underlying them.

In recent years, we have witnessed a revival of the use of recursive queries in a variety of emerging application domains such as data integration and exchange, information extraction, networking, and program analysis. A popular language used for expressing these queries is Datalog. This paper surveys for a general audience the Datalog language, recursive query processing, and optimization techniques. This survey differs from prior surveys written in the eighties and nineties in its comprehensiveness of topics, its coverage of recent developments and applications, and its emphasis on features and techniques beyond classical Datalog which are vital for practical applications. Specifically, the topics covered include the core Datalog language and various extensions, semantics, query optimizations, magic-sets optimizations, incremental view maintenance, aggregates, negation, and types. We conclude the paper with a survey of recent systems and applications that use Datalog and recursive queries.