Paul Ramirez

Unlocking the grid

The grid has emerged as a novel paradigm that supports seamless cooperation of distributed, heterogeneous computing resources in addressing highly complex computing and data management tasks. A number of software technologies have emerged to enable ”grid computing”. However, their exact nature, underlying principles, requirements, and architecture are still not fully understood and remain under-specified. In this paper, we present the results of a study whose goal was to try to identify the key underlying requirements and shared architectural traits of grid technologies. We then used these requirements and architecture in assessing five existing, representative grid technologies. Our studies show a fair amount of deviation by the individual technologies from the widely cited baseline grid architecture. Our studies also suggest a core set of critical requirements that must be satisfied by grid technologies, and highlight a key distinction between ”computational” and ”data” grids in terms of the identified requirements.

Time-bound analytic tasks on large datasets through dynamic configuration of workflows

Domain experts are often untrained in big data technologies and this limits their ability to exploit the data they have available. Workflow systems hide the complexities of high-end computing and software engineering by offering pre-packaged analytic steps combined into multi-step methods commonly used by experts. A current limitation of workflow systems is that they do not take into account user deadlines: they run workflows selected by the user, but take their time to do so. This is impractical when large datasets are at stake, since users often prefer to see an answer faster even if it has lower precision or quality. In this paper, we present an extension to workflow systems that enables them to take into account user deadlines by automatically generating alternative workflow candidates and ranking them according to performance estimates. The system makes these estimates based on workflow performance models created from workflow executions, and uses semantic technologies to reason about workflow options. Possible workflow candidates are presented to the user in a compact manner, and are ranked according to their runtime estimates. We have implemented this approach in the WOOT system, which combines and extends capabilities from the WINGS semantic workflow system and the Apache OODT Object Oriented Data Technology and workflow execution system.

A component framework supporting peer services for space data management

Knowledge discovery and data correlation require a unified approach to basic data management. However, achieving such an approach is nearly impossible with hundreds of disparate data sources, legacy systems and data formats. This problem is pervasive in the space science community where data models, taxonomies and data management systems are locally implemented and limited metadata has been collected and organized. Technology developed by the Object Oriented Data Technology (OODT) task at NASAs Jet Propulsion Laboratory (JPL) has been exploring component frameworks for managing, locating and exchanging data residing within a geographically distributed network. OODT has taken a novel approach towards solving this problem by exploiting Web technologies usually dedicated to e-commerce, combined with a rich, metadata-based environment. The components developed by OODT create a set of distributed peer-to-peer services that allow for data managed by a peer to be searched and returned as part of an integrated data management system. This paper discusses the approach taken to develop a software framework, and two prototype development efforts for the Planetary Data System (PDS) and the Mission and Ground Asset Database.

Understanding Open Source Software at NASA

To provide a framework for comparing and understanding open source software at NASA, the authors describe a set of relevant dimensions and decision points that NASA and other government agencies can use in formulating an open source strategy.

Big Data Challenges in Climate Science: Improving the next-generation cyberinfrastructure

The knowledge we gain from research in climate science depends on the generation, dissemination, and analysis of high-quality data. This work comprises technical practice as well as social practice, both of which are distinguished by their massive scale and global reach. As a result, the amount of data involved in climate research is growing at an unprecedented rate. Some examples of the types of activities that increasingly require an improved cyberinfrastructure for dealing with large amounts of critical scientific data are climate model intercomparison (CMIP) experiments; the integration of observational data and climate reanalysis data with climate model outputs, as seen in the Observations for Model Intercomparison Projects (Obs4MIPs), Analysis for Model Intercomparison Projects (Ana4MIPs), and Collaborative Reanalysis Technical Environment-Intercomparison Project (CREATE-IP) activities; and the collaborative work of the Intergovernmental Panel on Climate Change (IPCC). This article provides an overview of some of climate sciences big data problems and the technical solutions being developed to advance data publication, climate analytics as a service, and interoperability within the Earth System Grid Federation (ESGF), which is the primary cyberinfrastructure currently supporting global climate research activities.

Ensemble Maximum Entropy Classification and Linear Regression for Author Age Prediction

The evolution of the Internet has created an abundance of unstructured data on the web, a significant part of which is textual. The task of author profiling seeks to find the demographics of people solely from their linguistic and content-based features in text. The ability to describe traits of authors clearly has applications in fields such as security and forensics, as well as marketing. Instead of seeing age as just a classification problem, we also frame age as a regression one, but use an ensemble chain method that incorporates the power of both classification and regression to learn the authors exact age.

Abstract 5282: A cloud-enabled open source data management platform supporting a federated research and development organization

Biopharmaceutical RD 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5282.

Abstract 5283: Shangri-Docs: a browser based tool for document exploration and automatic knowledge extraction from unstructured biomedical text

Biomedical information is available to research and development scientists as unstructured text in the form of scientific manuscripts and reports published in the literature and elsewhere. Scientists focused on specific research programs are burdened with surveying vast numbers of publications and reports to acquire information relevant to their efforts. Employing technology as a research aid provides a mechanism to cope with information overload that characterizes the RD however, such reader tools are typically desktop applications limited to specific platforms and data sources so they cannot easily support broad based integrated scientific search needs for a dispersed RD 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5283.

A Reference Architecture for Space Information Management

We describe a reference architecture for space information management systems that elegantly overcomes the rigid design of common information systems in many domains. The reference architecture consists of a set of flexible, reusable, independent models and software components that function in unison, but remain separately managed entities. The main guiding principle of the reference architecture is to separate the various models of information (e.g., data, metadata, etc.) from implemented system code, allowing each to evolve independently. System modularity, systems interoperability, and dynamic evolution of information system components are the primary benefits of the design of the architecture. The architecture requires the use of information models that are substantially more advanced than those used by the vast majority of information systems. These models are more expressive and can be more easily modularized, distributed and maintained than simpler models e.g., configuration files and data dictionaries. Our current work focuses on formalizing the architecture within a CCSDS Green Book and evaluating the architecture within the context of the C3I initiative.