Large-Scale Pattern-Based Information Extraction from the World Wide Web

Extracting information from text is the task of obtaining structured, machine-processable facts from information that is mentioned in an unstructured manner. It thus allows systems to automatically aggregate information for further analysis, efficient retrieval, automatic validation, or appropriate visualization. This work explores the potential of using textual patterns for Information Extraction from the World Wide Web

Mining for Frequent Events in Time Series

While much work has been done in mining nominal sequential data much less has been done on mining numeric time series data. This stems primarily from the problems of relating numeric data, which likely contains error or other variations which make directly relating values difficult. To handle this problem, many algorithms first convert data into a sequence of events. In some cases these events are known a priori, but in others they are not. Our work evaluates a set of time series data instances in order to determine likely candidates for unknown underlying events. We use the concept of bounding envelopes to represent the area around a numeric time series in which the unknown noise-free points could exist. We then use an algorithm similar to Apriori to build up sets of envelope intersections. The areas created by these intersections represent common patterns found throughout the data

DESQ: Frequent Sequence Mining with Subsequence Constraints

Frequent sequence mining methods often make use of constraints to control which subsequences should be mined. A variety of such subsequence constraints has been studied in the literature, including length, gap, span, regular-expression, and hierarchy constraints. In this paper, we show that many subsequence constraints---including and beyond those considered in the literature---can be unified in a single framework. A unified treatment allows researchers to study jointly many types of subsequence constraints (instead of each one individually) and helps to improve usability of pattern mining systems for practitioners. In more detail, we propose a set of simple and intuitive "pattern expressions" to describe subsequence constraints and explore algorithms for efficiently mining frequent subsequences under such general constraints. Our algorithms translate pattern expressions to compressed finite state transducers, which we use as computational model, and simulate these transducers in a way suitable for frequent sequence mining. Our experimental study on real-world datasets indicates that our algorithms---although more general---are competitive to existing state-of-the-art algorithms.Comment: Long version of the paper accepted at the IEEE ICDM 2016 conferenc

Large-Scale Pattern-Based Information Extraction from the World Wide Web

Extracting information from text is the task of obtaining structured, machine-processable facts from information that is mentioned in an unstructured manner. It thus allows systems to automatically aggregate information for further analysis, efficient retrieval, automatic validation, or appropriate visualization. This work explores the potential of using textual patterns for Information Extraction from the World Wide Web

Large-Scale Pattern-Based Information Extraction from the World Wide Web

Extracting information from text is the task of obtaining structured, machine-processable facts from information that is mentioned in an unstructured manner. It thus allows systems to automatically aggregate information for further analysis, efficient retrieval, automatic validation, or appropriate visualization. This thesis explores the potential of using textual patterns for Information Extraction from the World Wide Web

Extraction of patterns in selected network traffic for a precise and efficient intrusion detection approach

This thesis investigates a precise and efficient pattern-based intrusion detection approach by extracting patterns from sequential adversarial commands. As organisations are further placing assets within the cyber domain, mitigating the potential exposure of these assets is becoming increasingly imperative. Machine learning is the application of learning algorithms to extract knowledge from data to determine patterns between data points and make predictions. Machine learning algorithms have been used to extract patterns from sequences of commands to precisely and efficiently detect adversaries using the Secure Shell (SSH) protocol. Seeing as SSH is one of the most predominant methods of accessing systems it is also a prime target for cyber criminal activities. For this study, deep packet inspection was applied to data acquired from three medium interaction honeypots emulating the SSH service. Feature selection was used to enhance the performance of the selected machine learning algorithms. A pre-processing procedure was developed to organise the acquired datasets to present the sequences of adversary commands per unique SSH session. The preprocessing phase also included generating a reduced version of each dataset that evenly and coherently represents their respective full dataset. This study focused on whether the machine learning algorithms can extract more precise patterns efficiently extracted from the reduced sequence of commands datasets compared to their respective full datasets. Since a reduced sequence of commands dataset requires less storage space compared to the relative full dataset. Machine learning algorithms selected for this study were the Naïve Bayes, Markov chain, Apriori and Eclat algorithms The results show the machine learning algorithms applied to the reduced datasets could extract additional patterns that are more precise, compared to their respective full datasets. It was also determined the Naïve Bayes and Markov chain algorithms are more efficient at processing the reduced datasets compared to their respective full datasets. The best performing algorithm was the Markov chain algorithm at extracting more precise patterns efficiently from the reduced datasets. The greatest improvement in processing a reduced dataset was 97.711%. This study has contributed to the domain of pattern-based intrusion detection by providing an approach that can precisely and efficiently detect adversaries utilising SSH communications to gain unauthorised access to a system

Novel algorithms for protein sequence analysis

Each protein is characterized by its unique sequential order of amino acids, the so-called protein sequence. Biology__s paradigm is that this order of amino acids determines the protein__s architecture and function. In this thesis, we introduce novel algorithms to analyze protein sequences. Chapter 1 begins with the introduction of amino acids, proteins and protein families. Then fundamental techniques from computer science related to the thesis are briefly described. Making a multiple sequence alignment (MSA) and constructing a phylogenetic tree are traditional means of sequence analysis. Information entropy, feature selection and sequential pattern mining provide alternative ways to analyze protein sequences and they are all from computer science. In Chapter 2, information entropy was used to measure the conservation on a given position of the alignment. From an alignment which is grouped into subfamilies, two types of information entropy values are calculated for each position in the MSA. One is the average entropy for a given position among the subfamilies, the other is the entropy for the same position in the entire multiple sequence alignment. This so-called two-entropies analysis or TEA in short, yields a scatter-plot in which all positions are represented with their two entropy values as x- and y-coordinates. The different locations of the positions (or dots) in the scatter-plot are indicative of various conservation patterns and may suggest different biological functions. The globally conserved positions show up at the lower left corner of the graph, which suggests that these positions may be essential for the folding or for the main functions of the protein superfamily. In contrast the positions neither conserved between subfamilies nor conserved in each individual subfamily appear at the upper right corner. The positions conserved within each subfamily but divergent among subfamilies are in the upper left corner. They may participate in biological functions that divide subfamilies, such as recognition of an endogenous ligand in G protein-coupled receptors. The TEA method requires a definition of protein subfamilies as an input. However such definition is a challenging problem by itself, particularly because this definition is crucial for the following prediction of specificity positions. In Chapter 3, we automated the TEA method described in Chapter 2 by tracing the evolutionary pressure from the root to the branches of the phylogenetic tree. At each level of the tree, a TEA plot is produced to capture the signal of the evolutionary pressure. A consensus TEA-O plot is composed from the whole series of plots to provide a condensed representation. Positions related to functions that evolved early (conserved) or later (specificity) are close to the lower left or upper left corner of the TEA-O plot, respectively. This novel approach allows an unbiased, user-independent, analysis of residue relevance in a protein family. We tested the TEA-O method on a synthetic dataset as well as on __real__ data, i.e., LacI and GPCR datasets. The ROC plots for the real data showed that TEA-O works perfectly well on all datasets and much better than other considered methods such as evolutionary trace, SDPpred and TreeDet. While positions were treated independently from each other in Chapter 2 and 3 in predicting specificity positions, in Chapter 4 multi-RELIEF considers both sequence similarity and distance in 3D structure in the specificity scoring function. The multi-RELIEF method was developed based on RELIEF, a state-of-the-art Machine-Learning technique for feature weighting. It estimates the expected __local__ functional specificity of residues from an alignment divided in multiple classes. Optionally, 3D structure information is exploited by increasing the weight of residues that have high-weight neighbors. Using ROC curves over a large body of experimental reference data, we showed that multi-RELIEF identifies specificity residues for the seven test sets used. In addition, incorporating structural information improved the prediction for specificity of interaction with small molecules. Comparison of multi-RELIEF with four other state-of-the-art algorithms indicates its robustness and best overall performance. In Chapter 2, 3 and 4, we heavily relied on multiple sequence alignment to identify conserved and specificity positions. As mentioned before, the construction of such alignment is not self-evident. Following the principle of sequential pattern mining, in Chapter 5, we proposed a new algorithm that directly identifies frequent biologically meaningful patterns from unaligned sequences. Six algorithms were designed and implemented to mine three different pattern types from either one or two datasets using a pattern growth approach. We compared our approach to PRATT2 and TEIRESIAS in efficiency, completeness and the diversity of pattern types. Compared to PRATT2, our approach is faster, capable of processing large datasets and able to identify the so-called type III patterns. Our approach is comparable to TEIRESIAS in the discovery of the so-called type I patterns but has additional functionality such as mining the so-called type II and type III patterns and finding discriminating patterns between two datasets. From Chapter 2 to 5, we aimed to identify functional residues from either aligned or unaligned protein sequences. In Chapter 6, we introduce an alignment-independent procedure to cluster protein sequences, which may be used to predict protein function. Traditionally phylogeny reconstruction is usually based on multiple sequence alignment. The procedure can be computationally intensive and often requires manual adjustment, which may be particularly difficult for a set of deviating sequences. In cheminformatics, constructing a similarity tree of ligands is usually alignment free. Feature spaces are routine means to convert compounds into binary fingerprints. Then distances among compounds can be obtained and similarity trees are constructed via clustering techniques. We explored building feature spaces for phylogeny reconstruction either using the so-called k-mer method or via sequential pattern mining with additional filtering and combining operations. Satisfying trees were built from both approaches compared with alignment-based methods. We found that when k equals 3, the phylogenetic tree built from the k-mer fingerprints is as good as one of the alignment-based methods, in which PAM and Neighborhood joining are used for computing distance and constructing a tree, respectively (NJ-PAM). As for the sequential pattern mining approach, the quality of the phylogenetic tree is better than one of the alignment-based method (NJ-PAM), if we set the support value to 10% and used maximum patterns only as descriptors. Finally in Chapter 7, general conclusions about the research described in this thesis are drawn. They are supplemented with an outlook on further research lines. We are convinced that the described algorithms can be useful in, e.g., genomic analyses, and provide further ideas for novel algorithms in this respect.Leiden University, NWO (Horizon Breakthrough project 050-71-041) and the Dutch Top Institute Pharma (D1-105)UBL - phd migration 201

Query and mining in biological databases

Ph.DDOCTOR OF PHILOSOPH