Papers.

Illustrating the AADL error modeling annex (v.2) using a simple safety-critical medical device

Developing and certifying safety-critical and highly reliable systems almost always includes significant emphasis on hazard analysis and risk assessment. There have been substantial improvements in automation and formalization of other aspects of critical system engineering including model-driven development, analysis of source code and models, and verification techniques. However, hazard analysis and risk assessment are still largely manual and informal activities, tool support is limited (which for both development and auditing, increases time and effort and reduces accuracy and correctness), and artifacts are not integrated with architectural descriptions, system interfaces, high-level behavioral descriptions or code.

Your server as a function

Consistency without borders

Discretized streams: fault-tolerant streaming computation at scale

Many "big data" applications must act on data in real time. Running these applications at ever-larger scales requires parallel platforms that automatically handle faults and stragglers. Unfortunately, current distributed stream processing models provide fault recovery in an expensive manner, requiring hot replication or long recovery times, and do not handle stragglers. We propose a new processing model, discretized streams (D-Streams), that overcomes these challenges. D-Streams enable a parallel recovery mechanism that improves efficiency over traditional replication and backup schemes, and tolerates stragglers. We show that they support a rich set of operators while attaining high per-node throughput similar to single-node systems, linear scaling to 100 nodes, sub-second latency, and sub-second fault recovery. Finally, D-Streams can easily be composed with batch and interactive query models like MapReduce, enabling rich applications that combine these modes. We implement D-Streams in a system called Spark Streaming.

Sparrow: distributed, low latency scheduling

Large-scale data analytics frameworks are shifting towards shorter task durations and larger degrees of parallelism to provide low latency. Scheduling highly parallel jobs that complete in hundreds of milliseconds poses a major challenge for task schedulers, which will need to schedule millions of tasks per second on appropriate machines while offering millisecond-level latency and high availability. We demonstrate that a decentralized, randomized sampling approach provides near-optimal performance while avoiding the throughput and availability limitations of a centralized design. We implement and deploy our scheduler, Sparrow, on a 110-machine cluster and demonstrate that Sparrow performs within 12% of an ideal scheduler.

Good fonts for dyslexia

An iOS reader for people with dyslexia

Networked user engagement

Instant pickles: generating object-oriented pickler combinators for fast and extensible serialization

Miniboxing: improving the speed to code size tradeoff in parametric polymorphism translations

Improved type specialization for dynamic scripting languages

Orthogonal query recommendation

PredictionIO: a distributed machine learning server for practical software development

Online multitasking and user engagement

Is There Space for Theory in Modern Commercial Search Engines?

Analyzing multi-dimensional networks within MediaWikis

Bridging islands of specialized code using macros and reified types

Scala macros: let our powers combine!: on how rich syntax and static types work with metaprogramming

WTF: The Who to Follow Service at Twitter

Presents WTF ('Who to Follow'), Twitter's user recommendation service: the Cassovary in-memory graph processing engine, the SALSA-based recommendation algorithm, and the production architecture behind it.

Design and prototyping of a social media observatory

Measuring web quality

Timelines as summaries of popular scheduled events

Online social networks

Scaling matrix factorization for recommendation with randomness

Hadoop

The business impact of deep learning

GraphX: a resilient distributed graph system on Spark

Semantic models for answer re-ranking in question answering

The task of Question Answering (QA) is to find correct answers to users' questions expressed in natural language. In the last few years non-factoid QA received more attention. It focuses on causation, manner and reason questions, where the expected answer has the form of a passage of text. The presence of question and answers corpora allows the adoption of Learning to Rank (MLR) algorithms in order to out- put a sensible ranking of the candidate answers. The importance and effectiveness of linguistically motivated features, obtained from syntax, lexical semantics and semantic role labeling, was shown in literature [2-4], but there are still several different possible semantic features that have not been taken into account so far and our goal is to find out if their use could lead to performance improvement. In particular features coming from Semantic Models (SM) like Distributional Semantic Models (DSMs), Explicit Semantic Analysis (ESA), Latent Dirichlet Allocation (LDA) induced topics have never been applied to the task so far. Based on the usefulness that those models show in other tasks, we think that SM can have a significant role in improving current state-of-the-art systems' performance in answer re-ranking.

Scalability and efficiency challenges in commercial web search engines

Reducing data transfer for charts on adaptive web sites.

Bolt-on causal consistency

CHIC: a combination-based recommendation system

PBS at work

The big data ecosystem at LinkedIn

Spanners: a formal framework for information extraction

DysWebxia 2.0!

Simplify or help?

Eventual Consistency Today: Limitations, Extensions, and Beyond

MDCC: Multi-Data Center Consistency

Eventual consistency today

Using version control in the classroom (abstract only)

Truthy

(big) usage data in web search

Exploiting social relations for sentiment analysis in microblogging

Optimizing data structures in high-level programs

High level data structures are a cornerstone of modern programming and at the same time stand in the way of compiler optimizations. In order to reason about user- or library-defined data structures compilers need to be extensible. Common mechanisms to extend compilers fall into two categories. Frontend macros, staging or partial evaluation systems can be used to programmatically remove abstraction and specialize programs before they enter the compiler. Alternatively, some compilers allow extending the internal workings by adding new transformation passes at different points in the compile chain or adding new intermediate representation (IR) types. None of these mechanisms alone is sufficient to handle the challenges posed by high level data structures. This paper shows a novel way to combine them to yield benefits that are greater than the sum of the parts.

The Discrete Fundamental Group of the Associahedron, and the Exchange Module

The associahedron is an object that has been well studied and has numerous applications, particularly in the theory of operads, the study of non-crossing partitions, lattice theory and more recently in the study of cluster algebras. We approach the associahedron from the point of view of discrete homotopy theory. We study the abelianization of the discrete fundamental group, and show that it is free abelian of rank . We also find a combinatorial description for a basis of this rank. We also introduce the exchange module of the type An cluster algebra, used to model the relations in the cluster algebra. We use the discrete fundamental group to the study of exchange module, and show that it is also free abelian of rank .

Dynamic contrast-enhanced MRI-based biomarkers of therapeutic response in triple-negative breast cancer.

Automating phonetic measurement: The case of voice onset time

Approximations for Partial Differential Equations Appearing in Li-Ion Battery Models

Closed-Loop Control of SI-HCCI Mode Switch Using Fuel Injection Timing