Constructing minimal acyclic deterministic finite automata

This thesis is submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Ph.D) in the FASTAR group of the Department of Computer Science, University of Pretoria, South Africa. I present a number of algorithms for constructing minimal acyclic deterministic finite automata (MADFAs), most of which I originally derived/designed or co-discovered. Being acyclic, such automata represent finite languages and have proven useful in applications such as spellchecking, virus-searching and text indexing. In many of those applications, the automata grow to billions of states, making them difficult to store without using various compression techniques — the most important of which is minimization. Results from the late 1950’s show that minimization yields a unique automaton (for a given language), and later results show that minimization of acyclic automata is possible in time linear in the number of states. These two results make for a rich area of algorithmics research; automata and algorithmics research are relatively old fields of computing science and the discovery/invention of new algorithms in the field is an exciting result. I present both incremental and nonincremental algorithms. With nonincremental techniques, the unminimized acyclic deterministic finite automaton (ADFA) is first constructed and then minimized. As mentioned above, the unminimized ADFA can be very large indeed — often even too large to fit within the virtual memory space of the computer. As a result, incremental techniques for minimization (i.e. the ADFA is minimized during its construction) become interesting. Incremental algorithms frequently have some overhead: if the unminimized ADFA fits easily within physical memory, it may still be faster to use nonincremental techniques. The presentation used in this thesis has a few unusual characteristics: Few other presentations follow a correctness-by-construction style for presenting and deriving algorithms. The presentations given here include correctness arguments or sketches thereof. The presentation is taxonomic — emphasizing the similarities and differences between the algorithms at a fundamental level. While it is possible to present these algorithms in a formal-language-theoretic setting, this thesis remains somewhat closer to the actual implementation issues. In several chapters, new algorithms and interesting new variants of existing algorithms are presented. It gives new presentations of many existing algorithms — all in a common format with common examples. There are extensive links to the existing literature. Thesis (PhD)--University of Pretoria, 2010.Computer Scienceunrestricte

Symbolic Tree Automata

Abstract We introduce symbolic tree automata as a generalization of finite tree automata with a parametric alphabet over any given background theory. We show that symbolic tree automata are closed under Boolean operations, and that the operations are effectively uniform in the given alphabet theory. This generalizes the corresponding classical properties known for finite tree automata

Symbolic Solving of Extended Regular Expression Inequalities

This paper presents a new solution to the containment problem for extended regular expressions that extends basic regular expressions with intersection and complement operators and consider regular expressions on infinite alphabets based on potentially infinite character sets. Standard approaches deciding the containment do not take extended operators or character sets into account. The algorithm avoids the translation to an expression-equivalent automaton and provides a purely symbolic term rewriting systems for solving regular expressions inequalities. We give a new symbolic decision procedure for the containment problem based on Brzozowski's regular expression derivatives and Antimirov's rewriting approach to check containment. We generalize Brzozowski's syntactic derivative operator to two derivative operators that work with respect to (potentially infinite) representable character sets.Comment: Technical Repor

MergedTrie: Efficient textual indexing

The accessing and processing of textual information (i.e. the storing and querying of a set of strings) is especially important for many current applications (e.g. information retrieval and social networks), especially when working in the fields of Big Data or IoT, which require the handling of very large string dictionaries. Typical data structures for textual indexing are Hash Tables and some variants of Tries such as the Double Trie (DT). In this paper, we propose an extension of the DT that we have called MergedTrie. It improves the DT compression by merging both Tries into a single and by segmenting the indexed term into two fixed length parts in order to balance the new Trie. Thus, a higher overlapping of both prefixes and suffixes is obtained. Moreover, we propose a new implementation of Tries that achieves better compression rates than the Double-Array representation usually chosen for implementing Tries. Our proposal also overcomes the limitation of static implementations that does not allow insertions and updates in their compact representations. Finally, our MergedTrie implementation experimentally improves the efficiency of the Hash Tables, the DTs, the Double-Array, the Crit-bit, the Directed Acyclic Word Graphs (DAWG), and the Acyclic Deterministic Finite Automata (ADFA) data structures, requiring less space than the original text to be indexed.This study has been partially funded by the SEQUOIA-UA (TIN2015-63502-C3-3-R) and the RESCATA (TIN2015-65100-R) projects of the Spanish Ministry of Economy and Competitiveness (MINECO)

Symbolic Model Learning: New Algorithms and Applications

In this thesis, we study algorithms which can be used to extract, or learn, formal mathematical models from software systems and then using these models to test whether the given software systems satisfy certain security properties such as robustness against code injection attacks. Specifically, we focus on studying learning algorithms for automata and transducers and the symbolic extensions of these models, namely symbolic finite automata (SFAs). In a high level, this thesis contributes the following results: 1. In the first part of the thesis, we present a unified treatment of many common variations of the seminal L* algorithm for learning deterministic finite automata (DFAs) as a congruence learning algorithm for the underlying Nerode congruence which forms the basis of automata theory. Under this formulation the basic data structures used by different variations are unified as different ways to implement the Nerode congruence using queries. 2. Next, building on the new formulation of L*-style algorithms we proceed to develop new algorithms for learning transducer models. Firstly, we present the first algorithm for learning deterministic partial transducers. Furthermore, we extend my algorithm into non-deterministic models by introducing a novel, generalized congruence relation over string transformations which is able to capture a subclass of string transformations with regular lookahead. We demonstrate that this class is able to capture many practical string transformation from the domain of string sanitizers in Web applications. 3. Classical learning algorithms for automata and transducers operate over finite alphabets and have a query complexity that scales linearly with the size of the alphabet. However, in practice, this dependence on the alphabet size hinders the performance of the algorithms. To address this issue, we develop the MAT* algorithm for learning symbolic finite state automata (SFAs) which operate over infinite alphabets. In practice, the MAT* learning algorithm allow us to plug custom transition learning algorithms which will efficiently infer the predicates in the transitions of the SFA without querying the whole alphabet set. 4. Finally, we use our learning algorithm toolbox as the basis for the development of a set of black-box testing algorithms. More specifically, we present Grammar Oriented Filter Auditing (GOFA), a novel technique which allows one to utilize my learning algorithms to evaluate the robustness of a string sanitizer or filter against a set of attack strings given as a context-free grammar. Furthermore, because such grammars are many times unavailable, we developed sfadiff a differential testing technique based on symbolic automata learning which can be used in order to perform differential testing of two different parser implementations using SFA learning algorithms and we demonstrate how our algorithm can be used to develop program fingerprints. We evaluate our algorithms against state-of-the-art Web Application Firewalls and discover over 15 previously unknown vulnerabilities which result in evading the firewalls and performing code injection attacks in the backend Web application. Finally, we show how our learning algorithms can uncover vulnerabilities which are missed by other black-box methods such as fuzzing and grammar-based testing

Stream Processing using Grammars and Regular Expressions

In this dissertation we study regular expression based parsing and the use of grammatical specifications for the synthesis of fast, streaming string-processing programs. In the first part we develop two linear-time algorithms for regular expression based parsing with Perl-style greedy disambiguation. The first algorithm operates in two passes in a semi-streaming fashion, using a constant amount of working memory and an auxiliary tape storage which is written in the first pass and consumed by the second. The second algorithm is a single-pass and optimally streaming algorithm which outputs as much of the parse tree as is semantically possible based on the input prefix read so far, and resorts to buffering as many symbols as is required to resolve the next choice. Optimality is obtained by performing a PSPACE-complete pre-analysis on the regular expression. In the second part we present Kleenex, a language for expressing high-performance streaming string processing programs as regular grammars with embedded semantic actions, and its compilation to streaming string transducers with worst-case linear-time performance. Its underlying theory is based on transducer decomposition into oracle and action machines, and a finite-state specialization of the streaming parsing algorithm presented in the first part. In the second part we also develop a new linear-time streaming parsing algorithm for parsing expression grammars (PEG) which generalizes the regular grammars of Kleenex. The algorithm is based on a bottom-up tabulation algorithm reformulated using least fixed points and evaluated using an instance of the chaotic iteration scheme by Cousot and Cousot

Programming Using Automata and Transducers

Automata, the simplest model of computation, have proven to be an effective tool in reasoning about programs that operate over strings. Transducers augment automata to produce outputs and have been used to model string and tree transformations such as natural language translations. The success of these models is primarily due to their closure properties and decidable procedures, but good properties come at the price of limited expressiveness. Concretely, most models only support finite alphabets and can only represent small classes of languages and transformations. We focus on addressing these limitations and bridge the gap between the theory of automata and transducers and complex real-world applications: Can we extend automata and transducer models to operate over structured and infinite alphabets? Can we design languages that hide the complexity of these formalisms? Can we define executable models that can process the input efficiently? First, we introduce succinct models of transducers that can operate over large alphabets and design BEX, a language for analysing string coders. We use BEX to prove the correctness of UTF and BASE64 encoders and decoders. Next, we develop a theory of tree transducers over infinite alphabets and design FAST, a language for analysing tree-manipulating programs. We use FAST to detect vulnerabilities in HTML sanitizers, check whether augmented reality taggers conflict, and optimize and analyze functional programs that operate over lists and trees. Finally, we focus on laying the foundations of stream processing of hierarchical data such as XML files and program traces. We introduce two new efficient and executable models that can process the input in a left-to-right linear pass: symbolic visibly pushdown automata and streaming tree transducers. Symbolic visibly pushdown automata are closed under Boolean operations and can specify and efficiently monitor complex properties for hierarchical structures over infinite alphabets. Streaming tree transducers can express and efficiently process complex XML transformations while enjoying decidable procedures

An Investigation into Performance-related Issues of Regular Expression Matching

Computer Scienc

Flexible finite automata-based algorithms for detecting microsatellites in DNA

Apart from contributing to Computer Science, this research also contributes to Bioinformatics, a subset of the subject discipline Computational Biology. The main focus of this dissertation is the development of a data-analytical and theoretical algorithm to contribute to the analysis of DNA, and in particular, to detect microsatellites. Microsatellites, considered in the context of this dissertation, refer to consecutive patterns contained by genomic sequences. A perfect tandem repeat is defined as a string of nucleotides which is repeated at least twice in a sequence. An approximate tandem repeat is a string of nucleotides repeated consecutively at least twice, with small differences between the instances. The research presented in this dissertation was inspired by molecular biologists who were discovered to be visually scanning genetic sequences in search of short approximate tandem repeats or so called microsatellites. The aim of this dissertation is to present three algorithms that search for short approximate tandem repeats. The algorithms comprise the implementation of finite automata. Thus the hypothesis posed is as follows: Finite automata can detect microsatellites effectively in DNA. "Effectively" includes the ability to fine-tune the detection process so that redundant data is avoided, and relevant data is not missed during search. In order to verify whether the hypothesis holds, three theoretical related algorithms have been proposed based on theorems from finite automaton theory. They are generically referred to as the FireìSat algorithms. These algorithms have been implemented, and the performance of FireìSat2 has been investigated and compared to other software packages. From the results obtained, it is clear that the performance of these algorithms differ in terms of attributes such as speed, memory consumption and extensibility. In respect of speed performance, FireìSat outperformed rival software packages. It will be seen that the FireìSat algorithms have several parameters that can be used to tune their search. It should be emphasized that these parameters have been devised in consultation with the intended user community, in order to enhance the usability of the software. It was found that the parameters of FireìSat can be set to detect more tandem repeats than rival software packages, but also tuned to limit the number of detected tandem repeats. CopyrightDissertation (MSc)--University of Pretoria, 2010.Computer Scienceunrestricte