Multi-Stage Search Architectures for Streaming Documents

The web is becoming more dynamic due to the increasing engagement and contribution of Internet users in the age of social media. A more dynamic web presents new challenges for web search--an important application of Information Retrieval (IR). A stream of new documents constantly flows into the web at a high rate, adding to the old content. In many cases, documents quickly lose their relevance. In these time-sensitive environments, finding relevant content in response to user queries requires a real-time search service; immediate availability of content for search and a fast ranking, which requires an optimized search architecture. These aspects of today's web are at odds with how academic IR researchers have traditionally viewed the web, as a collection of static documents. Moreover, search architectures have received little attention in the IR literature. Therefore, academic IR research, for the most part, does not provide a mechanism to efficiently handle a high-velocity stream of documents, nor does it facilitate real-time ranking. This dissertation addresses the aforementioned shortcomings. We present an efficient mech- anism to index a stream of documents, thereby enabling immediate availability of content. Our indexer works entirely in main memory and provides a mechanism to control inverted list con- tiguity, thereby enabling faster retrieval. Additionally, we consider document ranking with a machine-learned model, dubbed "Learning to Rank" (LTR), and introduce a novel multi-stage search architecture that enables fast retrieval and allows for more design flexibility. The stages of our architecture include candidate generation (top k retrieval), feature extraction, and docu- ment re-ranking. We compare this architecture with a traditional monolithic architecture where candidate generation and feature extraction occur together. As we lay out our architecture, we present optimizations to each stage to facilitate low-latency ranking. These optimizations include a fast approximate top k retrieval algorithm, document vectors for feature extraction, architecture- conscious implementations of tree ensembles for LTR using predication and vectorization, and algorithms to train tree-based LTR models that are fast to evaluate. We also study the efficiency- effectiveness tradeoffs of these techniques, and empirically evaluate our end-to-end architecture on microblog document collections. We show that our techniques improve efficiency without degrading quality

Approximate Data Analytics Systems

Today, most modern online services make use of big data analytics systems to extract useful information from the raw digital data. The data normally arrives as a continuous data stream at a high speed and in huge volumes. The cost of handling this massive data can be significant. Providing interactive latency in processing the data is often impractical due to the fact that the data is growing exponentially and even faster than Moore’s law predictions. To overcome this problem, approximate computing has recently emerged as a promising solution. Approximate computing is based on the observation that many modern applications are amenable to an approximate, rather than the exact output. Unlike traditional computing, approximate computing tolerates lower accuracy to achieve lower latency by computing over a partial subset instead of the entire input data. Unfortunately, the advancements in approximate computing are primarily geared towards batch analytics and cannot provide low-latency guarantees in the context of stream processing, where new data continuously arrives as an unbounded stream. In this thesis, we design and implement approximate computing techniques for processing and interacting with high-speed and large-scale stream data to achieve low latency and efficient utilization of resources. To achieve these goals, we have designed and built the following approximate data analytics systems: • StreamApprox—a data stream analytics system for approximate computing. This system supports approximate computing for low-latency stream analytics in a transparent way and has an ability to adapt to rapid fluctuations of input data streams. In this system, we designed an online adaptive stratified reservoir sampling algorithm to produce approximate output with bounded error. • IncApprox—a data analytics system for incremental approximate computing. This system adopts approximate and incremental computing in stream processing to achieve high-throughput and low-latency with efficient resource utilization. In this system, we designed an online stratified sampling algorithm that uses self-adjusting computation to produce an incrementally updated approximate output with bounded error. • PrivApprox—a data stream analytics system for privacy-preserving and approximate computing. This system supports high utility and low-latency data analytics and preserves user’s privacy at the same time. The system is based on the combination of privacy-preserving data analytics and approximate computing. • ApproxJoin—an approximate distributed joins system. This system improves the performance of joins — critical but expensive operations in big data systems. In this system, we employed a sketching technique (Bloom filter) to avoid shuffling non-joinable data items through the network as well as proposed a novel sampling mechanism that executes during the join to obtain an unbiased representative sample of the join output. Our evaluation based on micro-benchmarks and real world case studies shows that these systems can achieve significant performance speedup compared to state-of-the-art systems by tolerating negligible accuracy loss of the analytics output. In addition, our systems allow users to systematically make a trade-off between accuracy and throughput/latency and require no/minor modifications to the existing applications

Low-latency, query-driven analytics over voluminous multidimensional, spatiotemporal datasets

2017 Summer.Includes bibliographical references.Ubiquitous data collection from sources such as remote sensing equipment, networked observational devices, location-based services, and sales tracking has led to the accumulation of voluminous datasets; IDC projects that by 2020 we will generate 40 zettabytes of data per year, while Gartner and ABI estimate 20-35 billion new devices will be connected to the Internet in the same time frame. The storage and processing requirements of these datasets far exceed the capabilities of modern computing hardware, which has led to the development of distributed storage frameworks that can scale out by assimilating more computing resources as necessary. While challenging in its own right, storing and managing voluminous datasets is only the precursor to a broader field of study: extracting knowledge, insights, and relationships from the underlying datasets. The basic building block of this knowledge discovery process is analytic queries, encompassing both query instrumentation and evaluation. This dissertation is centered around query-driven exploratory and predictive analytics over voluminous, multidimensional datasets. Both of these types of analysis represent a higher-level abstraction over classical query models; rather than indexing every discrete value for subsequent retrieval, our framework autonomously learns the relationships and interactions between dimensions in the dataset (including time series and geospatial aspects), and makes the information readily available to users. This functionality includes statistical synopses, correlation analysis, hypothesis testing, probabilistic structures, and predictive models that not only enable the discovery of nuanced relationships between dimensions, but also allow future events and trends to be predicted. This requires specialized data structures and partitioning algorithms, along with adaptive reductions in the search space and management of the inherent trade-off between timeliness and accuracy. The algorithms presented in this dissertation were evaluated empirically on real-world geospatial time-series datasets in a production environment, and are broadly applicable across other storage frameworks

Digital provenance - models, systems, and applications

Data provenance refers to the history of creation and manipulation of a data object and is being widely used in various application domains including scientific experiments, grid computing, file and storage system, streaming data etc. However, existing provenance systems operate at a single layer of abstraction (workflow/process/OS) at which they record and store provenance whereas the provenance captured from different layers provide the highest benefit when integrated through a unified provenance framework. To build such a framework, a comprehensive provenance model able to represent the provenance of data objects with various semantics and granularity is the first step. In this thesis, we propose a such a comprehensive provenance model and present an abstract schema of the model. ^ We further explore the secure provenance solutions for distributed systems, namely streaming data, wireless sensor networks (WSNs) and virtualized environments. We design a customizable file provenance system with an application to the provenance infrastructure for virtualized environments. The system supports automatic collection and management of file provenance metadata, characterized by our provenance model. Based on the proposed provenance framework, we devise a mechanism for detecting data exfiltration attack in a file system. We then move to the direction of secure provenance communication in streaming environment and propose two secure provenance schemes focusing on WSNs. The basic provenance scheme is extended in order to detect packet dropping adversaries on the data flow path over a period of time. We also consider the issue of attack recovery and present an extensive incident response and prevention system specifically designed for WSNs

Indexation et analyse de grandes collections de séquençages via des matrices de k-mers

The 21st century is bringing a tsunami of data in many fields, especially in bioinformatics. This paradigm shift requires the development of new processing methods capable of scaling up on such data. This work consists mainly in considering massive tera-scaled datasets from genomic sequencing. A common way to process these data is to represent them as a set of words of a fixed size, called k-mers. The k-mers are widely used as building blocks by many sequencing data analysis techniques. The challenge is to be able to represent the k-mers and their abundances in a large number of datasets. One possibility is the k-mer matrix, where each row is a k-mer associated with a vector of abundances and each column corresponds to a sample. Some k-mers are erroneous due to sequencing errors and must be discarded. The usual technique consists in discarding low-abundant k-mers. On complex datasets such as metagenomes, such a filter is not efficient and discards too many k-mers. The holistic view of abundances across samples allowed by the matrix representation also enables a new procedure for error detection on such datasets. In summary, we explore the concept of k-mer matrix and show its scalability in various applications, from indexing to analysis, and propose different tools for this purpose. On the indexing side, our tools have allowed indexing a large metagenomic dataset from the Tara Ocean project while keeping additional k-mers, usually discarded by the classical k-mer filtering technique. The next and important step is to make the index publicly available. On the analysis side, our matrix construction technique enables to speed up a differential k-mer analysis of a state-of-the-art tool by an order of magnitude.Le 21ème siècle subit un tsunami de données dans de nombreux domaines, notamment en bio-informatique. Ce changement de paradigme nécessite le développement de nouvelles méthodes de traitement capables de passer à l’échelle sur de telles données. Ce travail consiste principalement à considérer des jeux de données massifs provenant du séquençage génomique. Une façon courante de traiter ces données est de les représenter comme un ensemble de mots de taille fixe, appelés k-mers. Les k-mers sont très largement utilisés comme éléments de bases par de nombreuses méthodes d’analyses de données de séquençages. L’enjeu est de pouvoir représenter les k-mers et leurs abondances dans un grand nombre de jeux de données. Une possibilité est la matrice de k-mers, où chaque ligne est un k-mer associé à un vecteur d’abondances. Ces k-mers sont erronées en raison des erreurs de séquençage et doivent être filtrés. La technique habituelle consiste à écarter les k-mers peu abondants. Sur des ensembles de données complexes comme les métagénomes, un tel filtre n’est pas efficace et élimine un trop grand nombre de k-mers. La vision des abondances à travers les échantillons permise par la représentation matricielle permet également une nouvelle procédure de détection des erreurs dans les jeux de données complexes. En résumé, nous explorons le concept de matrice de k-mer et montrons ses capacités en termes de passage à l’échelle au travers de diverses applications, de l’indexation à l’analyse, et proposons différents outils à cette fin. Sur le plan de l’indexation, nos outils ont permis d’indexer un grand ensemble métagénomique du projet Tara Ocean tout en conservant des k-mers rares, habituellement écartés par les techniques de filtrage classiques. En matière d’analyse, notre technique de construction de matrices permet d’accélérer d’un ordre de grandeur l’analyse différentielle de k-mers

Letter from the Special Issue Editor

Editorial work for DEBULL on a special issue on data management on Storage Class Memory (SCM) technologies

Efficient algorithms for passive network measurement

Network monitoring has become a necessity to aid in the management and operation of large networks. Passive network monitoring consists of extracting metrics (or any information of interest) by analyzing the traffic that traverses one or more network links. Extracting information from a high-speed network link is challenging, given the great data volumes and short packet inter-arrival times. These difficulties can be alleviated by using extremely efficient algorithms or by sampling the incoming traffic. This work improves the state of the art in both these approaches. For one-way packet delay measurement, we propose a series of improvements over a recently appeared technique called Lossy Difference Aggregator. A main limitation of this technique is that it does not provide per-flow measurements. We propose a data structure called Lossy Difference Sketch that is capable of providing such per-flow delay measurements, and, unlike recent related works, does not rely on any model of packet delays. In the problem of collecting measurements under the sliding window model, we focus on the estimation of the number of active flows and in traffic filtering. Using a common approach, we propose one algorithm for each problem that obtains great accuracy with significant resource savings. In the traffic sampling area, the selection of the sampling rate is a crucial aspect. The most sensible approach involves dynamically adjusting sampling rates according to network traffic conditions, which is known as adaptive sampling. We propose an algorithm called Cuckoo Sampling that can operate with a fixed memory budget and perform adaptive flow-wise packet sampling. It is based on a very simple data structure and is computationally extremely lightweight. The techniques presented in this work are thoroughly evaluated through a combination of theoretical and experimental analysis.Postprint (published version

Flexible Application-Layer Multicast in Heterogeneous Networks

This work develops a set of peer-to-peer-based protocols and extensions in order to provide Internet-wide group communication. The focus is put to the question how different access technologies can be integrated in order to face the growing traffic load problem. Thereby, protocols are developed that allow autonomous adaptation to the current network situation on the one hand and the integration of WiFi domains where applicable on the other hand