Handling Massive N-Gram Datasets Efficiently

This paper deals with the two fundamental problems concerning the handling of large n-gram language models: indexing, that is compressing the n-gram strings and associated satellite data without compromising their retrieval speed; and estimation, that is computing the probability distribution of the strings from a large textual source. Regarding the problem of indexing, we describe compressed, exact and lossless data structures that achieve, at the same time, high space reductions and no time degradation with respect to state-of-the-art solutions and related software packages. In particular, we present a compressed trie data structure in which each word following a context of fixed length k, i.e., its preceding k words, is encoded as an integer whose value is proportional to the number of words that follow such context. Since the number of words following a given context is typically very small in natural languages, we lower the space of representation to compression levels that were never achieved before. Despite the significant savings in space, our technique introduces a negligible penalty at query time. Regarding the problem of estimation, we present a novel algorithm for estimating modified Kneser-Ney language models, that have emerged as the de-facto choice for language modeling in both academia and industry, thanks to their relatively low perplexity performance. Estimating such models from large textual sources poses the challenge of devising algorithms that make a parsimonious use of the disk. The state-of-the-art algorithm uses three sorting steps in external memory: we show an improved construction that requires only one sorting step thanks to exploiting the properties of the extracted n-gram strings. With an extensive experimental analysis performed on billions of n-grams, we show an average improvement of 4.5X on the total running time of the state-of-the-art approach.Comment: Published in ACM Transactions on Information Systems (TOIS), February 2019, Article No: 2

Efficient Indexing for Structured and Unstructured Data

The collection of digital data is growing at an exponential rate. Data originates from wide range of data sources such as text feeds, biological sequencers, internet traffic over routers, through sensors and many other sources. To mine intelligent information from these sources, users have to query the data. Indexing techniques aim to reduce the query time by preprocessing the data. Diversity of data sources in real world makes it imperative to develop application specific indexing solutions based on the data to be queried. Data can be structured i.e., relational tables or unstructured i.e., free text. Moreover, increasingly many applications need to seamlessly analyze both kinds of data making data integration a central issue. Integrating text with structured data needs to account for missing values, errors in the data etc. Probabilistic models have been proposed recently for this purpose. These models are also useful for applications where uncertainty is inherent in data e.g. sensor networks. This dissertation aims to propose efficient indexing solutions for several problems that lie at the intersection of database and information retrieval such as joining ranked inputs, full-text documents searching etc. Other well-known problems of ranked retrieval and pattern matching are also studied under probabilistic settings. For each problem, the worst-case theoretical bounds of the proposed solutions are established and/or their practicality is demonstrated by thorough experimentation

Approximate String Matching With Dynamic Programming and Suffix Trees

The importance and the contribution of string matching algorithms to the modern society cannot be overstated. From basic search algorithms such as spell checking and data querying, to advanced algorithms such as DNA sequencing, trend analysis and signal processing, string matching algorithms form the foundation of many aspects in computing that have been pivotal in technological advancement. In general, string matching algorithms can be divided into the categories of exact string matching and approximate string matching. We study each area and examine some of the well known algorithms. We probe into one of the most intriguing data structure in string algorithms, the suffix tree. The lowest common ancestor extension of the suffix tree is the key to many advanced string matching algorithms. With these tools, we are able to solve string problems that were, until recently, thought intractable by many. Another interesting and relatively new data structure in string algorithms is the suffix array, which has significant breakthroughs in its linear time construction in recent years. Primarily, this thesis focuses on approximate string matching using dynamic programming and hybrid dynamic programming with suffix tree. We study both approaches in detail and see how the merger of exact string matching and approximate string matching algorithms can yield synergistic results in our experiments

CONDENSER: A custom tool for capturing and summarizing network traffic for AVALANCHE and ISEAGE

The Cyber-world is becoming an environment that is increasingly plagued by botnets and other programs adept at creating distributed and flooding-based attacks. These forms of assault are currently some of the most feared types of attacks being faced by networks.;The Internet Scale Event and Attack Generation Environment (ISEAGE) at Iowa State University, has been established to help researchers study actions and events that happen on the Internet. ISEAGE uses a custom tool, AVALANCHE, to simulate distributed and flooding-based attacks. While AVALANCHE is able to launch large quantities of attack packets, its ability to react and handle the enormity of responses from targeted hosts comes from the functionality afforded by the CONDENSER.;The CONDENSER uses multi-threaded artificial intelligence methods for efficiently catching the return traffic generated by targeted hosts, storing the payloads of captured packets, calculating statistics about the collection, reducing the captured payloads into a single representative-packet, and facilitating communication with remote command/control clients. These capabilities allow researchers to gain important insights into defensive measures and devices needed to stop distributed and flooding-based attacks

High-Performance Computing Algorithms for Constructing Inverted Files on Emerging Multicore Processors

Current trends in processor architectures increasingly include more cores on a single chip and more complex memory hierarchies, and such a trend is likely to continue in the foreseeable future. These processors offer unprecedented opportunities for speeding up demanding computations if the available resources can be effectively utilized. Simultaneously, parallel programming languages such as OpenMP and MPI have been commonly used on clusters of multicore CPUs while newer programming languages such as OpenCL and CUDA have been widely adopted on recent heterogeneous systems and GPUs respectively. The main goal of this dissertation is to develop techniques and methodologies for exploiting these emerging parallel architectures and parallel programming languages to solve large scale irregular applications such as the construction of inverted files. The extraction of inverted files from large collections of documents forms a critical component of all information retrieval systems including web search engines. In this problem, the disk I/O throughput is the major performance bottleneck especially when intermediate results are written onto disks. In addition to the I/O bottleneck, a number of synchronization and consistency issues must be resolved in order to build the dictionary and postings lists efficiently. To address these issues, we introduce a dictionary data structure using a hybrid of trie and B-trees and a high-throughput pipeline strategy that completely avoids the use of disks as temporary storage for intermediate results, while ensuring the consumption of the input data at a high rate. The high-throughput pipelined strategy produces parallel parsed streams that are consumed at the same rate by parallel indexers. The pipelined strategy is implemented on a single multicore CPU as well as on a cluster of such nodes. We were able to achieve a throughput of more than 262MB/s on the ClueWeb09 dataset on a single node. On a cluster of 32 nodes, our experimental results show scalable performance using different metrics, significantly improving on prior published results. On the other hand, we develop a new approach for handling time-evolving documents using additional small temporal indexing structures. The lifetime of the collection is partitioned into multiple time windows, which guarantees a very fast temporal query response time at a small space overhead relative to the non-temporal case. Extensive experimental results indicate that the overhead in both indexing and querying is small in this more complicated case, and the query performance can indeed be improved using finer temporal partitioning of the collection. Finally, we employ GPUs to accelerate the indexing process for building inverted files and to develop a very fast algorithm for the highly irregular list ranking problem. For the indexing problem, the workload is split between CPUs and GPUs in such a way that the strengths of both architectures are exploited. For the list ranking problem involved in the decompression of inverted files, an optimized GPU algorithm is introduced by reducing the problem to a large number of fine grain computations in such a way that the processing cost per element is shown to be close to the best possible

An intelligent user interface model for contact centre operations

Contact Centres (CCs) are at the forefront of interaction between an organisation and its customers. Currently, 17 percent of all inbound calls are not resolved on the first call by the first agent attending to that call. This is due to the inability of the contact centre agents (CCAs) to diagnose customer queries and find adequate solutions in an effective and efficient manner. The aim of this research is to develop an intelligent user interface (IUI) model to support and improve CC operations. A literature review of existing IUI architectures, modelbased design and existing CC software together with a field study of CCs has resulted in the design of an IUI model for CCs. The proposed IUI model is described in terms of its architecture, component-level design and interface design. An IUI prototype has been developed as a proof of concept of the proposed IUI model. The IUI prototype was evaluated in order to determine to what extent it supports problem identification and query resolution. User testing, incorporating the use of eye tracking and a post-test questionnaire, was used in order to determine the usability and usefulness of the prototype. The results of this evaluation show that the users were highly satisfied with the task support and query resolution assistance provided by the IUI prototype. This research resulted in the design of an IUI model for the domain of CCs. This model can be used to assist the development of CC applications incorporating IUIs. Use of the proposed IUI model is expected to support and enhance the effectiveness and efficiency of CC operations. Further research is needed to conduct a longitudinal study to determine the impact of IUIs in the CC domain

These are not the k-mers you are looking for: efficient online k-mer counting using a probabilistic data structure

K-mer abundance analysis is widely used for many purposes in nucleotide sequence analysis, including data preprocessing for de novo assembly, repeat detection, and sequencing coverage estimation. We present the khmer software package for fast and memory efficient online counting of k-mers in sequencing data sets. Unlike previous methods based on data structures such as hash tables, suffix arrays, and trie structures, khmer relies entirely on a simple probabilistic data structure, a Count-Min Sketch. The Count-Min Sketch permits online updating and retrieval of k-mer counts in memory which is necessary to support online k-mer analysis algorithms. On sparse data sets this data structure is considerably more memory efficient than any exact data structure. In exchange, the use of a Count-Min Sketch introduces a systematic overcount for k-mers; moreover, only the counts, and not the k-mers, are stored. Here we analyze the speed, the memory usage, and the miscount rate of khmer for generating k-mer frequency distributions and retrieving k-mer counts for individual k-mers. We also compare the performance of khmer to several other k-mer counting packages, including Tallymer, Jellyfish, BFCounter, DSK, KMC, Turtle and KAnalyze. Finally, we examine the effectiveness of profiling sequencing error, k-mer abundance trimming, and digital normalization of reads in the context of high khmer false positive rates. khmer is implemented in C++ wrapped in a Python interface, offers a tested and robust API, and is freely available under the BSD license at github.com/ged-lab/khmer