Papers.

Quantifying eventual consistency with PBS

The wisdom of ad-hoc crowds

Buon appetito

Bringing data science to the speakers of every language

LASTA: large scale topic assignment on multiple social networks

Millions of people use social networks everyday to talk about a variety of subjects, publish opinions and share information. Understanding this data to infer user's topical interests is a challenging problem with applications in various data-powered products. In this paper, we present 'LASTA' (Large Scale Topic Assignment), a full production system used at Klout, Inc., which mines topical interests from five social networks and assigns over 10,000 topics to hundreds of millions of users on a daily basis. The system continuously collects streams of user data and is reactive to fresh information, updating topics for users as interests shift. LASTA generates over 50 distinct features derived from signals such as user generated posts and profiles, user reactions such as comments and retweets, user attributions such as lists, tags and endorsements, as well as signals based on social graph connections. We show that using this diverse set of features leads to a better representation of a user's topical interests as compared to using only generated text or only graph based features. We also show that using cross-network information for a user leads to a more complete and accurate understanding of the user's topics, as compared to using any single network. We evaluate LASTA's topic assignment system on an internal labeled corpus of 32,264 user-topic labels generated from real users.

Shallow semantic parsing of product offering titles (for better automatic hyperlink insertion)

With billions of database-generated pages on the Web where consumers can readily add priced product offerings to their virtual shopping cart, several opportunities will become possible once we can automatically recognize what exactly is being offered for sale on each page. We present a case study of a deployed data-driven system that first chunks individual titles into semantically classified sub-segments, and then uses this information to improve a hyperlink insertion service. To accomplish this process, we propose an annotation structure that is general enough to apply to offering titles from most e-commerce industries while also being specific enough to identify useful semantics about each offer. To automate the parsing task we apply the best-practices approach of training a supervised conditional random fields model and discover that creating separate prediction models for some of the industries along with the use of model-ensembles achieves the best performance to date. We further report on a real-world application of the trained parser to the task of growing a lexical dictionary of product-related terms which critically provides background knowledge to an affiliate-marketing hyperlink insertion service. On a regular basis we apply the parser to offering titles to produce a large set of labeled terms. From these candidates we select the most confidently predicted novel terms for review by crowd-sourced annotators. The agreed on terms are then added into a dictionary which significantly improves the performance of the link-insertion service. Finally, to continually improve system performance, we retrain the model in an online fashion by performing additional annotations on titles with incorrect predictions on each batch.

Large-Scale High-Precision Topic Modeling on Twitter

An industrial-scale topic modelling system built and deployed at Twitter, tuned for precision rather than perplexity so that the topics can drive production products.

Dissecting Darknets

The curse of the excluded middle

"Mostly functional" programming does not work.

Bundle recommendation in ecommerce

Scalability and efficiency challenges in large-scale web search engines

Evaluation of DysWebxia

Cleaning inconsistencies in information extraction via prioritized repairs

The population of a predefined relational schema from textual content, commonly known as Information Extraction (IE), is a pervasive task in contemporary computational challenges associated with Big Data. Since the textual content varies widely in nature and structure (from machine logs to informal natural language), it is notoriously difficult to write IE programs that extract the sought information without any inconsistencies (e.g., a substring should not be annotated as both an address and a person name). Dealing with inconsistencies is hence of crucial importance in IE systems. Industrial-strength IE systems like GATE and IBM SystemT therefore provide a built-in collection of cleaning operations to remove inconsistencies from extracted relations. These operations, however, are collected in an ad-hoc fashion through use cases. Ideally, we would like to allow IE developers to declare their own policies. But existing cleaning operations are defined in an algorithmic way and, hence, it is not clear how to extend the built-in operations without requiring low-level coding of internal or external functions. We embark on the establishment of a framework for declarative cleaning of inconsistencies in IE, though principles of database theory. Specifically, building upon the formalism of document spanners for IE, we adopt the concept of prioritized repairs, which has been recently proposed as an extension of the traditional database repairs to incorporate priorities among conflicting facts. We show that our framework captures the popular cleaning policies, as well as the POSIX semantics for extraction through regular expressions. We explore the problem of determining whether a cleaning declaration is unambiguous (i.e., always results in a single repair), and whether it increases the expressive power of the extraction language. We give both positive and negative results, some of which are general, and some of which apply to policies used in practice.

Are we experiencing a big data bubble?

Fine-grained partitioning for aggressive data skipping

Storm@twitter

HAWQ

GenBase: a complex analytics genomics benchmark

PLANET: Making Progress with Commit Processing in Unpredictable Environments

Delite

Developing high-performance software is a difficult task that requires the use of low-level, architecture-specific programming models (e.g., OpenMP for CMPs, CUDA for GPUs, MPI for clusters). It is typically not possible to write a single application that can run efficiently in different environments, leading to multiple versions and increased complexity. Domain-Specific Languages (DSLs) are a promising avenue to enable programmers to use high-level abstractions and still achieve good performance on a variety of hardware. This is possible because DSLs have higher-level semantics and restrictions than general-purpose languages, so DSL compilers can perform higher-level optimization and translation. However, the cost of developing performance-oriented DSLs is a substantial roadblock to their development and adoption. In this article, we present an overview of the Delite compiler framework and the DSLs that have been developed with it. Delite simplifies the process of DSL development by providing common components, like parallel patterns, optimizations, and code generators, that can be reused in DSL implementations. Delite DSLs are embedded in Scala, a general-purpose programming language, but use metaprogramming to construct an Intermediate Representation (IR) of user programs and compile to multiple languages (including C++, CUDA, and OpenCL). DSL programs are automatically parallelized and different parts of the application can run simultaneously on CPUs and GPUs. We present Delite DSLs for machine learning, data querying, graph analysis, and scientific computing and show that they all achieve performance competitive to or exceeding C++ code.

Wikipedia as a time machine

The 4th temporal web analytics workshop (TempWeb'14)

Analyzing temporal characteristics of check-in data

Detecting trending topics using page visitation statistics

Scalability and efficiency challenges in large-scale web search engines

Privacy-preserving publication of provenance workflows

Improving the efficiency of multi-site web search engines

Hiding user content interest while preserving P2P performance

Large-scale message synchronization in challenged networks

LittleD: a SQL database for sensor nodes and embedded applications.

Adventures in hackademia: leveraging humanitarian free/open source software development in the class room (abstract only)

Teaching computing with the IPython notebook (abstract only)

The IPython Notebook is an interactive browser-based environment where you can combine code execution, text, mathematics, plots, and rich media into a single document. Originally designed for use as an electronic lab notebook for computational science, it is increasingly being used in teaching as well, and a rich ecosystem of open source plugins and extensions for teaching is growing around it. The first half of this hands-on workshop will introduce the Notebook and present examples of lessons and instructional materials built around it. In the second half, attendees will explore future directions for the Notebook as a teaching platform. For more information, please view our GitHub repository online at https://github.com/gvwilson/sigcse2014-ipython-workshop.

Informational structure of an emerging communication system is shaped by its environment

Characterization of online groups along space, time, and social dimensions

FRI0201 Are Rheumatic Disease Patient Reported Outcomes Collected Passively and Directly through Smart Phones Feasible? Early Results from A Nation-Wide Pilot Study

Research and applications: Induced lexico-syntactic patterns improve information extraction from online medical forums

OBJECTIVE To reliably extract two entity types, symptoms and conditions (SCs), and drugs and treatments (DTs), from patient-authored text (PAT) by learning lexico-syntactic patterns from data annotated with seed dictionaries. BACKGROUND AND SIGNIFICANCE Despite the increasing quantity of PAT (eg, online discussion threads), tools for identifying medical entities in PAT are limited. When applied to PAT, existing tools either fail to identify specific entity types or perform poorly. Identification of SC and DT terms in PAT would enable exploration of efficacy and side effects for not only pharmaceutical drugs, but also for home remedies and components of daily care. MATERIALS AND METHODS We use SC and DT term dictionaries compiled from online sources to label several discussion forums from MedHelp (http://www.medhelp.org). We then iteratively induce lexico-syntactic patterns corresponding strongly to each entity type to extract new SC and DT terms. RESULTS Our system is able to extract symptom descriptions and treatments absent from our original dictionaries, such as 'LADA', 'stabbing pain', and 'cinnamon pills'. Our system extracts DT terms with 58-70% F1 score and SC terms with 66-76% F1 score on two forums from MedHelp. We show improvements over MetaMap, OBA, a conditional random field-based classifier, and a previous pattern learning approach. CONCLUSIONS Our entity extractor based on lexico-syntactic patterns is a successful and preferable technique for identifying specific entity types in PAT. To the best of our knowledge, this is the first paper to extract SC and DT entities from PAT. We exhibit learning of informal terms often used in PAT but missing from typical dictionaries.

Three crocodilian genomes reveal ancestral patterns of evolution among archosaurs

Classification of group behaviors in social media via social behavior grammars

Image processing for drawing recognition

DeepQA Jeopardy! Gamification: A Machine-Learning Perspective

DeepQA is a large-scale natural language processing (NLP) question-and-answer system that responds across a breadth of structured and unstructured data, from hundreds of analytics that are combined with over 50 models, trained through machine learning. After the 2011 historic milestone of defeating the two best human players in the Jeopardy! game show, the technology behind IBM Watson, DeepQA, is undergoing gamification into real-world business problems. Gamifying a business domain for Watson is a composite of functional, content, and training adaptation for nongame play. During domain gamification for medical, financial, government, or any other business, each system change affects the machine-learning process. As opposed to the original Watson Jeopardy!, whose class distribution of positive-to-negative labels is 1:100, in adaptation the computed training instances, question-and-answer pairs transformed into true-false labels, result in a very low positive-to-negative ratio of 1:100 000. Such initial extreme class imbalance during domain gamification poses a big challenge for the Watson machine-learning pipelines. The combination of ingested corpus sets, question-and-answer pairs, configuration settings, and NLP algorithms contribute toward the challenging data state. We propose several data engineering techniques, such as answer key vetting and expansion, source ingestion, oversampling classes, and question set modifications to increase the computed true labels. In addition, algorithm engineering, such as an implementation of the Newton-Raphson logistic regression with a regularization term, relaxes the constraints of class imbalance during training adaptation. We conclude by empirically demonstrating that data and algorithm engineering are complementary and indispensable to overcome the challenges in this first Watson gamification for real-world business problems.

Modeling and processing of time interval data for data-driven decision support

Architecture Fault Modeling with the AADL Error-Model Annex

Safety-Critical systems, as used in the automotive, avionics, or aerospace domains, are becoming increasingly software-reliant to the extent that the system cannot function without the software. On one hand the software system provides an integrated set of functionality to operate the system and manage failure and unsafe conditions. Current best safety engineering practices, such as DO178B/C or SAE ARP4761, are labor intensive and are only performed as part of the system engineering process. At the same time increased interaction complexity of the embedded software with the hardware platform and mechanical system has resulted in the software to be a major source of defects with potentially fatal consequences. To address these issues, the SAE Architecture Analysis & Design Language (AADL) standard has been extended with an Error Model Annex to support architecture fault modeling and automated safety analysis. In this paper we introduce the concepts of the revised Error Model (EMV2) Annex and a fault propagation ontology to support such architecture fault models at three levels of abstraction focusing on fault propagation, failure behavior of individual components, and composite failure behavior of a system in terms of its components. Such specifications reflect fault tolerance strategies assumptions made by fault impact, fault tree and reliability analysis about the safety system component. We illustrate their use on a dual redundant flight guidance system and discuss the automation of different safety analysis methods in use by the SAE ARP4761, emphasizing on automation benefits.

Distributed Searchable Symmetric Encryption

100 Million DHT replies

4P: Performant private peer-to-peer file sharing

Giving Text Analytics a Boost

The amount of textual data has reached a new scale and continues to grow at an unprecedented rate. IBM's SystemT software is a powerful text-analytics system that offers a query-based interface to reveal the valuable information that lies within these mounds of data. However, traditional server architectures are not capable of analyzing so-called big data efficiently, despite the high memory bandwidth that is available. The authors show that by using a streaming hardware accelerator implemented in reconfigurable logic, the throughput rates of the SystemT's information extraction queries can be improved by an order of magnitude. They also show how such a system can be deployed by extending SystemT's existing compilation flow and by using a multithreaded communication interface that can efficiently use the accelerator's bandwidth.

Recommending Documents for Complex Question Exploration by Analyzing Browsing Behavior

Using off-the-Shelf Medical Devices for Biomedical Signal Monitoring in a Telemedicine System for Emergency Medical Services

The SPRIGHT algorithm for robust sparse Hadamard Transforms

In this paper, we consider the problem of computing a K-sparse N-point Hadamard Transforms (HT) from noisy time domain samples, where K = O(Nα) scales sub-linearly in N for some α ∈ (0; 1). The SParse Robust Iterative Graph-based Hadamard Transform (SPRIGHT) algorithm is proposed to recover the sparse HT coefficients in a stable manner that is robust to additive Gaussian noise. In particular, it is shown that the K-sparse HT of the signal can be reconstructed from noisy time domain samples with a vanishing error probability using the same sample complexity O(K logN) as in the noiseless case of [1] and computational complexity1O(N logN). Last but not least, given the complexity orders of the SPRIGHT algorithm, our numerical experiments further validate that the big-Oh constants in the complexity are small.

Combined task and motion planning through an extensible planner-independent interface layer