Design and Implementation of a Data Stream Management System

The amount of data stored by companies has grown exponentially over the last decade. Of late, data is being continuously collected for various purposes - click stream analysis, credit card transactions for fraud detection, weather monitoring, stock tickers in nancial services, link statistics in networking, user logins and web surng statistics, highway trac congestion analysis and so on. The data that is being collected is in the form of a stream - arrives continuously, at a variable rate, and can occupy potentially innite storage. As organizations have realized that fast and ecient processing of this data can help in protable predictions, there exists a need for developing systems to handle this collected data eectively. We present in this thesis, the architecture of a generic Database Stream Management System (DBSMS) to handle streaming data. While literature has provided insights into Data Stream Management Systems (DSMS), the DBSMS is a dierent approach that tries to integrate a DSMS with the traditional Database Management Systems (DBMS). We discuss the need for such a generic DBSMS and present the system that we have implemented using the discussed architecture. We also present the performance of our system, in terms of space taken, time taken to answer a query and the accuracy of the result compared to a DBMS. Finally, we conclude with brief discussion on certain goals and open challenges that are of interest and which still need to be addressed by the system

Automatic parallelization of array-oriented programs for a multi-core machine

Abstract We present the work on automatic parallelization of array-oriented programs for multi-core machines. Source programs written in standard APL are translated by a parallelizing APL-to-C compiler into parallelized C code, i.e. C mixed with OpenMP directives. We describe techniques such as virtual operations and datapartitioning used to effectively exploit parallelism structured around array-primitives. We present runtime performance data, showing the speedup of the resulting parallelized code, using different numbers of threads and different problem sizes, on a 4-core machine, for several examples

Inferring Symbolic Automata

We study the learnability of symbolic finite state automata, a model shown useful in many applications in software verification. The state-of-the-art literature on this topic follows the query learning paradigm, and so far all obtained results are positive. We provide a necessary condition for efficient learnability of SFAs in this paradigm, from which we obtain the first negative result. The main focus of our work lies in the learnability of SFAs under the paradigm of identification in the limit using polynomial time and data. We provide a necessary condition and a sufficient condition for efficient learnability of SFAs in this paradigm, from which we derive a positive and a negative result

FPT: a Fixed-Point Accelerator for Torus Fully Homomorphic Encryption

Fully Homomorphic Encryption is a technique that allows computation on encrypted data. It has the potential to change privacy considerations in the cloud, but computational and memory overheads are preventing its adoption. TFHE is a promising Torus-based FHE scheme that relies on bootstrapping, the noise-removal tool invoked after each encrypted logical/arithmetical operation. We present FPT, a Fixed-Point FPGA accelerator for TFHE bootstrapping. FPT is the first hardware accelerator to exploit the inherent noise present in FHE calculations. Instead of double or single-precision floating-point arithmetic, it implements TFHE bootstrapping entirely with approximate fixed-point arithmetic. Using an in-depth analysis of noise propagation in bootstrapping FFT computations, FPT is able to use noise-trimmed fixed-point representations that are up to 50% smaller than prior implementations. FPT is built as a streaming processor inspired by traditional streaming DSPs: it instantiates directly cascaded high-throughput computational stages, with minimal control logic and routing networks. We explore throughput-balanced compositions of streaming kernels with a user-configurable streaming width in order to construct a full bootstrapping pipeline. Our approach allows 100% utilization of arithmetic units and requires only a small bootstrapping key cache, enabling an entirely compute-bound bootstrapping throughput of 1 BS / 35us. This is in stark contrast to the classical CPU approach to FHE bootstrapping acceleration, which is typically constrained by memory and bandwidth. FPT is implemented and evaluated as a bootstrapping FPGA kernel for an Alveo U280 datacenter accelerator card. FPT achieves two to three orders of magnitude higher bootstrapping throughput than existing CPU-based implementations, and 2.5x higher throughput compared to recent ASIC emulation experiments.Comment: ACM CCS 202

A Synchronisation Facility for a Stream Processing Coordination Language

In this thesis we present the AstraKahn project that aims to provide environment for stream processing applications with an automatic resource and concurrency management based on communication demand. At the moment the work on the thesis started, the project was at an early stage and there existed no software implementation. The aim of my work is to implement a stream synchronisation facility called synchroniser which is a part of the AstraKahn concept. Synchronisers are programmed in a dedicated language. The thesis focuses on the implementation of the language compiler to be integrated into the runtime system prototype being developed in parallel. AstraKahn structures streaming networks using a xed set of wiring patterns, including the serial replication. This pattern dynamically replicates its operand network conceptually in nitely many times and wires the replicas in a chain. AstraKahn provides an approach to extract messages from the chain based on the concept of xed point. The thesis explores the role of synchronisers in forming from a serial replication pipeline

Point cloud data compression

The rapid growth in the popularity of Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) experiences have resulted in an exponential surge of three-dimensional data. Point clouds have emerged as a commonly employed representation for capturing and visualizing three-dimensional data in these environments. Consequently, there has been a substantial research effort dedicated to developing efficient compression algorithms for point cloud data. This Master's thesis aims to investigate the current state-of-the-art lossless point cloud geometry compression techniques, explore some of these techniques in more detail and then propose improvements and/or extensions to enhance them and provide directions for future work on this topic