Big Data Now, 2015 Edition

Now in its fifth year, O’Reilly’s annual Big Data Now report recaps the trends, tools, applications, and forecasts we’ve talked about over the past year. For 2015, we’ve included a collection of blog posts, authored by leading thinkers and experts in the field, that reflect a unique set of themes we’ve identified as gaining significant attention and traction. Our list of 2015 topics include: Data-driven cultures Data science Data pipelines Big data architecture and infrastructure The Internet of Things and real time Applications of big data Security, ethics, and governance Is your organization on the right track? Get a hold of this free report now and stay in tune with the latest significant developments in big data

Flamingo: a visual language model for few-shot learning

Building models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research. We introduce Flamingo, a family of Visual Language Models (VLM) with this ability. We propose key architectural innovations to: (i) bridge powerful pretrained vision-only and language-only models, (ii) handle sequences of arbitrarily interleaved visual and textual data, and (iii) seamlessly ingest images or videos as inputs. Thanks to their flexibility, Flamingo models can be trained on large-scale multimodal web corpora containing arbitrarily interleaved text and images, which is key to endow them with in-context few-shot learning capabilities. We perform a thorough evaluation of our models, exploring and measuring their ability to rapidly adapt to a variety of image and video tasks. These include open-ended tasks such as visual question-answering, where the model is prompted with a question which it has to answer, captioning tasks, which evaluate the ability to describe a scene or an event, and close-ended tasks such as multiple-choice visual question-answering. For tasks lying anywhere on this spectrum, a single Flamingo model can achieve a new state of the art with few-shot learning, simply by prompting the model with task-specific examples. On numerous benchmarks, Flamingo outperforms models fine-tuned on thousands of times more task-specific data

Scalable and fault-tolerant data stream processing on multi-core architectures

With increasing data volumes and velocity, many applications are shifting from the classical “process-after-store” paradigm to a stream processing model: data is produced and consumed as continuous streams. Stream processing captures latency-sensitive applications as diverse as credit card fraud detection and high-frequency trading. These applications are expressed as queries of algebraic operations (e.g., aggregation) over the most recent data using windows, i.e., finite evolving views over the input streams. To guarantee correct results, streaming applications require precise window semantics (e.g., temporal ordering) for operations that maintain state. While high processing throughput and low latency are performance desiderata for stateful streaming applications, achieving both poses challenges. Computing the state of overlapping windows causes redundant aggregation operations: incremental execution (i.e., reusing previous results) reduces latency but prevents parallelization; at the same time, parallelizing window execution for stateful operations with precise semantics demands ordering guarantees and state access coordination. Finally, streams and state must be recovered to produce consistent and repeatable results in the event of failures. Given the rise of shared-memory multi-core CPU architectures and high-speed networking, we argue that it is possible to address these challenges in a single node without compromising window semantics, performance, or fault-tolerance. In this thesis, we analyze, design, and implement stream processing engines (SPEs) that achieve high performance on multi-core architectures. To this end, we introduce new approaches for in-memory processing that address the previous challenges: (i) for overlapping windows, we provide a family of window aggregation techniques that enable computation sharing based on the algebraic properties of aggregation functions; (ii) for parallel window execution, we balance parallelism and incremental execution by developing abstractions for both and combining them to a novel design; and (iii) for reliable single-node execution, we enable strong fault-tolerance guarantees without sacrificing performance by reducing the required disk I/O bandwidth using a novel persistence model. We combine the above to implement an SPE that processes hundreds of millions of tuples per second with sub-second latencies. These results reveal the opportunity to reduce resource and maintenance footprint by replacing cluster-based SPEs with single-node deployments.Open Acces

Lemmas: Generation, Selection, Application

Noting that lemmas are a key feature of mathematics, we engage in an investigation of the role of lemmas in automated theorem proving. The paper describes experiments with a combined system involving learning technology that generates useful lemmas for automated theorem provers, demonstrating improvement for several representative systems and solving a hard problem not solved by any system for twenty years. By focusing on condensed detachment problems we simplify the setting considerably, allowing us to get at the essence of lemmas and their role in proof search

Helmholtz Portfolio Theme Large-Scale Data Management and Analysis (LSDMA)

The Helmholtz Association funded the "Large-Scale Data Management and Analysis" portfolio theme from 2012-2016. Four Helmholtz centres, six universities and another research institution in Germany joined to enable data-intensive science by optimising data life cycles in selected scientific communities. In our Data Life cycle Labs, data experts performed joint R&D together with scientific communities. The Data Services Integration Team focused on generic solutions applied by several communities

Distributed Load Testing by Modeling and Simulating User Behavior

Modern human-machine systems such as microservices rely upon agile engineering practices which require changes to be tested and released more frequently than classically engineered systems. A critical step in the testing of such systems is the generation of realistic workloads or load testing. Generated workload emulates the expected behaviors of users and machines within a system under test in order to find potentially unknown failure states. Typical testing tools rely on static testing artifacts to generate realistic workload conditions. Such artifacts can be cumbersome and costly to maintain; however, even model-based alternatives can prevent adaptation to changes in a system or its usage. Lack of adaptation can prevent the integration of load testing into system quality assurance, leading to an incomplete evaluation of system quality. The goal of this research is to improve the state of software engineering by addressing open challenges in load testing of human-machine systems with a novel process that a) models and classifies user behavior from streaming and aggregated log data, b) adapts to changes in system and user behavior, and c) generates distributed workload by realistically simulating user behavior. This research contributes a Learning, Online, Distributed Engine for Simulation and Testing based on the Operational Norms of Entities within a system (LODESTONE): a novel process to distributed load testing by modeling and simulating user behavior. We specify LODESTONE within the context of a human-machine system to illustrate distributed adaptation and execution in load testing processes. LODESTONE uses log data to generate and update user behavior models, cluster them into similar behavior profiles, and instantiate distributed workload on software systems. We analyze user behavioral data having differing characteristics to replicate human-machine interactions in a modern microservice environment. We discuss tools, algorithms, software design, and implementation in two different computational environments: client-server and cloud-based microservices. We illustrate the advantages of LODESTONE through a qualitative comparison of key feature parameters and experimentation based on shared data and models. LODESTONE continuously adapts to changes in the system to be tested which allows for the integration of load testing into the quality assurance process for cloud-based microservices

Provenance Management for Collaborative Data Science Workflows

Collaborative data science activities are becoming pervasive in a variety of communities, and are often conducted in teams, with people of different expertise performing back-and-forth modeling and analysis on time-evolving datasets. Current data science systems mainly focus on specific steps in the process such as training machine learning models, scaling to large data volumes, or serving the data or the models, while the issues of end-to-end data science lifecycle management are largely ignored. Such issues include, for example, tracking provenance and derivation history of models, identifying data processing pipelines and keeping track of their evolution, analyzing unexpected behaviors and monitoring the project health, and providing the ability to reason about specific analysis results. We address these challenges by ingesting, managing, and analyzing rich provenance information generated during data science projects, and using it to enable users to easily publish, share, and discover data analytics projects. We first describe the design of our unified provenance and metadata management system, called ProvDB. We adopt a schema-later approach and use a flexible graph-based provenance representation model that combines the core concepts in version control and provenance management. We describe several ingestion mechanisms for this provenance model and show how heterogeneous data analysis environments can be served with natural extensions to this framework. We also describe a set of novel features of the system including graph queries for retrospective provenance, fileviews for data transformations, introspective queries for debugging, and continuous monitoring queries for anomaly detection. We then illustrate how to support deep learning modeling lifecycle via the extensibility mechanism in ProvDB. We describe techniques to compactly store and efficiently query the rich set of data artifacts generated during deep learning modeling lifecycle. We also describe a high-level domain specific language that helps raise the abstraction level during model exploration and enumeration and accelerate the modeling process. Lastly, we propose graph query operators and develop efficient evaluation techniques to address the verbose and evolving nature of such provenance graphs. First, we introduce a graph segmentation operator, which queries the provenance of a collection of user-given vertices (e.g., versioned files, author names) via flexible boundary criteria. Second, we propose a graph summarization operator to aggregate the results of multiple segmentation operations, and allow multi-resolution interaction with the aggregation result to understand similar and abnormal behaviors in those segments

The Ecology and Sociology of the Mission-Aransas Estuary : An Estuarine and Watershed Profile

watershed profileThe Mission-Aransas National Estuarine Research Reserve (NERR) is one of 28 national estuarine reserves created to promote the responsible use and management of the nation's estuaries through a program combining scientific research, education, and stewardship. The purpose of this document is to provide researchers and resource managers with an adequate basis of knowledge to further development of scientific studies and applied management investigations. This document describes the different physical ecosystem components, ecological processes, habitats, and watersheds of the Reserve. The Mission-Aransas NERR is a complex of wetland, terrestrial, and marine environments. The land is primarily coastal prairie with unique oak motte habitats. The wetlands include riparian habitat, and freshwater and salt water marshes. Within the water areas, the bays are large, open, and include extensive wind tidal flats, seagrass meadows, mangroves, and oyster reefs. This site profile describes each habitat by their location, type, distribution, abundance, current status and trends, issues of concerns, and future research plans. Research within the Mission-Aransas NERR seeks to improve the understanding of the Texas coastal zone ecosystems structure and function. Current research includes: nutrient loading and transformation, estimates of community metabolism, water quality monitoring, freshwater inflow, climate change and fishery habitat. Harmful algal blooms, zooplankton, coliform bacteria, submerged aquatic vegetation, and marsh grass are monitored through the System- Wide Monitoring Program (SWMP). This document also describes the climate, hydrography and oceanography, geology, water quality, and endangered species within the Mission-AransasUniversity of Texas Marine Science InstituteMarine Scienc

Software Engineering Laboratory Series: Proceedings of the Twenty-Second Annual Software Engineering Workshop

The Software Engineering Laboratory (SEL) is an organization sponsored by NASA/GSFC and created to investigate the effectiveness of software engineering technologies when applied to the development of application software. The activities, findings, and recommendations of the SEL are recorded in the Software Engineering Laboratory Series, a continuing series of reports that includes this document