Persistent Data Structures for Incremental Join Indices

Join indices are used in relational databases to make join operations faster. Join indices essentially materialise the results of join operations and so accrue maintenance cost, which makes them more suitable for use cases where modifications are rare and joins are performed frequently. To make the maintenance cost lower incrementally updating existing indices is to be preferred. The usage of persistent data structures for the join indices were explored. Motivation for this research was the ability of persistent data structures to construct multiple partially different versions of the same data structure memory efficiently. This is useful, because there can exist different versions of join indices simultaneously due to usage of multi-version concurrency control (MVCC) in a database. The techniques used in Relaxed Radix Balanced Trees (RRB-Trees) persistent data structure were found promising, but none of the popular implementations were found directly suitable for the use case. This exploration was done from the context of a particular proprietary embedded in-memory columnar multidimensional database called FastormDB developed by RELEX Solutions. This focused the research into Java Virtual Machine (JVM) based data structures as the implementation of FastormDB is in Java. Multiple persistent data-structures made for the thesis and ones from Scala, Clojure and Paguro were evaluated with Java Microbenchmark Harness (JMH) and Java Object Layout (JOL) based benchmarks and their results analysed via visualisations

Proceedings of the Fifth Workshop on Information Theoretic Methods in Science and Engineering

These are the online proceedings of the Fifth Workshop on Information Theoretic Methods in Science and Engineering (WITMSE), which was held in the Trippenhuis, Amsterdam, in August 2012

Security Implications of Insecure DNS Usage in the Internet

The Domain Name System (DNS) provides domain-to-address lookup-services used by almost all internet applications. Because of this ubiquitous use of the DNS, attacks against the DNS have become more and more critical. However, in the past, studies of DNS security have been mostly conducted against individual protocols and applications. In this thesis, we perform the first comprehensive evaluation of DNS-based attacks against a wide range of internet applications, ranging from time-synchronisation via NTP over internet resource management to security mechanisms. We show how to attack those applications by exploiting various weaknesses in the DNS. These attacks are based on both, already known weaknesses which are adapted to new attacks, as well as previously unknown attack vectors which have been found during the course of this thesis. We evaluate our attacks and provide the first taxonomy of DNS applications, to show how adversaries can systematically develop attacks exploiting the DNS. We analyze the attack surface created by our attacks in the internet and find that a significant number of applications and systems can be attacked. We work together with the developers of the vulnerable applications to develop patches and general countermeasures which can be applied by various parties to block our attacks. We also provide conceptual insights into the root causes allowing our attacks to help with the development of new applications and standards. The findings of this thesis are published in in 4 full-paper publications and 2 posters at international academic conferences. Additionally, we disclose our finding to developers which has lead to the registration of 8 Common Vulnerabilities and Exposures identifiers (CVE IDs) and patches in 10 software implementations. To raise awareness, we also presented our findings at several community meetings and via invited articles

Proceedings of the Fifth Workshop on Information Theoretic Methods in Science and Engineering (WITMSE-2012)

Peer reviewe

Methods for Epigenetic Analyses from Long-Read Sequencing Data

Epigenetics, particularly the study of DNA methylation, is a cornerstone field for our understanding of human development and disease. DNA methylation has been included in the "hallmarks of cancer" due to its important function as a biomarker and its contribution to carcinogenesis and cancer cell plasticity. Long-read sequencing technologies, such as the Oxford Nanopore Technologies platform, have evolved the study of structural variations, while at the same time allowing direct measurement of DNA methylation on the same reads. With this, new avenues of analysis have opened up, such as long-range allele-specific methylation analysis, methylation analysis on structural variations, or relating nearby epigenetic modalities on the same read to another. Basecalling and methylation calling of Nanopore reads is a computationally expensive task which requires complex machine learning architectures. Read-level methylation calls require different approaches to data management and analysis than ones developed for methylation frequencies measured from short-read technologies or array data. The 2-dimensional nature of read and genome associated DNA methylation calls, including methylation caller uncertainties, are much more storage costly than 1-dimensional methylation frequencies. Methods for storage, retrieval, and analysis of such data therefore require careful consideration. Downstream analysis tasks, such as methylation segmentation or differential methylation calling, have the potential of benefiting from read information and allow uncertainty propagation. These avenues had not been considered in existing tools. In my work, I explored the potential of long-read DNA methylation analysis and tackled some of the challenges of data management and downstream analysis using state of the art software architecture and machine learning methods. I defined a storage standard for reference anchored and read assigned DNA methylation calls, including methylation calling uncertainties and read annotations such as haplotype or sample information. This storage container is defined as a schema for the hierarchical data format version 5, includes an index for rapid access to genomic coordinates, and is optimized for parallel computing with even load balancing. It further includes a python API for creation, modification, and data access, including convenience functions for the extraction of important quality statistics via a command line interface. Furthermore, I developed software solutions for the segmentation and differential methylation testing of DNA methylation calls from Nanopore sequencing. This implementation takes advantage of the performance benefits provided by my high performance storage container. It includes a Bayesian methylome segmentation algorithm which allows for the consensus instance segmentation of multiple sample and/or haplotype assigned DNA methylation profiles, while considering methylation calling uncertainties. Based on this segmentation, the software can then perform differential methylation testing and provides a large number of options for statistical testing and multiple testing correction. I benchmarked all tools on both simulated and publicly available real data, and show the performance benefits compared to previously existing and concurrently developed solutions. Next, I applied the methods to a cancer study on a chromothriptic cancer sample from a patient with Sonic Hedgehog Medulloblastoma. I here report regulatory genomic regions differentially methylated before and after treatment, allele-specific methylation in the tumor, as well as methylation on chromothriptic structures. Finally, I developed specialized methylation callers for the combined DNA methylation profiling of CpG, GpC, and context-free adenine methylation. These callers can be used to measure chromatin accessibility in a NOMe-seq like setup, showing the potential of long-read sequencing for the profiling of transcription factor co-binding. In conclusion, this thesis presents and subsequently benchmarks new algorithmic and infrastructural solutions for the analysis of DNA methylation data from long-read sequencing