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From Chris Mattmann <>
Subject Re: [DISCUSS] Accept Science Data Analytics Platform (SDAP) into Apache Incubator
Date Thu, 12 Oct 2017 17:29:38 GMT
Very interesting project Lewis and Thomas and glad to see this coming to the Incubator!

On 10/11/17, 11:22 AM, "lewis john mcgibbney" <> wrote:

    Hi Folks,
    I would like to open a DISCUSS thread on the topic of accepting the Science
    Data Analytics Platform (SDAP) <> Project into the Incubator.
    I am CC'ing Thomas Huang from NASA JPL who I have been working with to
    build community around a kick-ass set of software projects under the SDAP
    At this stage we would very much appreciate critical feedback from general@
    community. We are also open to mentors who may have an interest in the
    project proposal.
    The proposal is pasted below.
    Thanks in advance,
    = Abstract =
    The Science Data Analytics Platform (SDAP) establishes an integrated data
    analytic center for Big Science problems. It focuses on technology
    integration, advancement and maturity.
    = Proposal =
    SDAP currently represents a collaboration between NASA Jet Propulsion
    Laboratory (JPL), Florida State University (FSU), the National Center for
    Atmospheric Research (NCAR), and George Mason University (GMU). SDAP brings
    together a number of big data technologies including a NASA funded
    OceanXtremes (Anomaly detection and ocean science), NEXUS (Deep data
    analytic platform), DOMS (Distributed in-situ to satellite matchup), MUDROD
    (Search relevancy and discovery) and VQSS (Virtualized Quality Screening
    Service) under a single umbrella. Within the original Incubator proposal,
    VQSS will not be included however it is anticipated that a future source
    code donation will cover VQSS.
    = Background and Rationale =
    SDAP is a technology software solution currently geared to better enable
    scientists involved in advancing the study of the Earth's physical
    oceanography. With increasing global temperature, warming of the ocean, and
    melting ice sheets and glaciers, the impacts can be observed from changes
    in anomalous ocean temperature and circulation patterns, to increasing
    extreme weather events and stronger/more frequent hurricanes, sea level
    rise and storm surges affecting coastlines, and may involve drastic changes
    and shifts in marine ecosystems. Ocean science communities are relying on
    data distributed through data centers such as the JPL's Physical
    Oceanographic Data Active Archive Center (PO.DAAC) to conduct their
    research. In typical investigations, oceanographers follow a traditional
    workflow for using datasets: search, evaluate, download, and apply tools
    and algorithms to look for trends. While this workflow has been working
    very well historically for the oceanographic community, it cannot scale if
    the research involves massive amount of data. NASA's Surface Water and
    Ocean Topography (SWOT) mission, scheduled to launch in April of 2021, is
    expected to generate over 20PB data for a nominal 3-year mission. This will
    challenge all existing NASA Earth Science data archival/distribution
    paradigms. It will no longer be feasible for Earth scientists to download
    and analyze such volumes of data. SDAP was therefore developed primarily as
    a Web-service platform for big ocean data science at the PO.DAAC with open
    source solutions used to enable fast analysis of oceanographic data. SDAP
    has been developed collaboratively between JPL, FSU, NCAR, and GMU and is
    rapidly maturing to become the generic platform for the next generation of
    big science data solutions. The platform is an orchestration of several
    previously funded NASA big ocean data solutions using cloud technology,
    which include data analysis (NEXUS), anomaly detection (OceanXtremes),
    matchup (DOMS), subsetting, discovery (MUDROD), and visualization (VQSS).
    SDAP will enable web-accessible, fast data analysis directly on huge
    scientific data archives to minimize data movement and provide access,
    including subset, only to the relevant data.
    = Science Data Analytics Platform Project Overview =
    SDAP consists of several loosely coupled, independently functioning
    sub-projects. The graphic below displays an overview of how these
    sub-projects fuse together. N.B., although the graphic uses terminology
    relating to OceanWorks, essentially the SDAP architecture is identical.
    == OceanXtremes ==
    Oceanographic Data-Intensive Anomaly Detection and Analysis Portal. An
    application that allows you to view imagery and perform analysis on sea
    level rise data.
    Develop an anomaly detection system which identifies items, events or
    observations which do not conform to an expected pattern.
     * Mature and test domain-specific, multi-scale anomaly and feature
    detection algorithms.
     * Identify unexpected correlations between key measured variables.
    Demonstrate value of technologies in this service:
     * Adapted Map-Reduce data mining.
     * Algorithm profiling service.
     * Shared discovery and exploration search tools.
     * Automatic notification of events of interest.
    == NEXUS ==
    NEXUS is an emerging technology developed at JPL
     * A Cloud-based/Cluster-based data platform that performs scalable
    handling of observational parameters analysis designed to scale horizontally
     * Leveraging high-performance indexed, temporal, and geospatial search
     * Breaks data products into small chunks and stores them in a Cloud-based
    data store
    ''Data Volumes Exploding''
     * SWOT mission is coming
     * File I/O is slow
    ''Scalable Store & Compute is Available''
     * NoSQL cluster databases
     * Parallel compute, in-memory map-reduce
     * Bring Compute to Highly-Accessible Data (using Hybrid Cloud)
    ''Pre-Chunk and Summarize Key Variables''
     * Easy statistics instantly (milliseconds)
     * Harder statistics on-demand (in seconds)
     * Visualize original data (layers) on a map quickly
    == DOMS ==
    The Distributed Oceanographic Match-Up Service
    DOMS is designed to reconcile satellite and in situ datasets in support of
    NASA's Earth Science mission. The service will provide a mechanism for
    users to input a series of geospatial references for satellite observations
    and receive the in situ observations that are matched to the satellite data
    within a selectable temporal and spatial domain. DOMS includes several
    characteristic in situ and satellite observation datasets - with an initial
    focus on salinity, sea temperature, and winds. DOMS will be used by the
    marine and satellite research communities to support a range of activities
    and several use cases will be described. The service is designed to provide
    a community-accessible tool that dynamically delivers matched data and
    allows the scientist to only work with the subset of data where the matches
    == MUDROD ==
    Mining and Utilizing Dataset Relevancy from Oceanographic Datasets to
    Improve Data Discovery and Access
    Data discovery accuracy is a challenging topic for both Earth science and
    other domains. It is especially true for scientific data sets that are not
    as popular as Amazon or Google data. MUDROD is focused on mining oceanic
    knowledge from the PO.DAAC user log files to improve the end user data
    discovery experience at PO.DAAC. There are three steps in the research: a)
    the oceanographic semantics were extracted from three resources of SWEET,
    GCMD ontology, and the keywords used by end users for searching PO.DAAC
    datasets, b) mining the linkage among different vocabularies based on user
    data discvoery sessions, and c) build the linkage among vocabularies based
    on a comprehensive approach by considering domain de facto standard, e.g.,
    SWEET and GCMD, and the knowledge mined from the log files. The semantics
    is used to improve data discovery for ranking results, navigating among
    vocabularies, and recommending data based on user searchers.
    = Current Status =
    All components of SDAP were originally designed and developed under grants
    from the NASA-funded Advanced Information Systems and Technologies (AIST)
    program. The initiative to bring them the components together under the
    SDAP umbrella was granted through an AIST-funded follow-on grant which will
    run for another ~18 or so months.
    Currently no projects have made official releases so outside of community
    building, this will be our primary Incubating goal. All SDAP source code is
    currently publicly available and licensed under the ALv2.0.
    = Meritocracy =
    The current developers are familiar with meritocratic open source
    development at Apache. The SDAP team consumes Apache products heavily with
    members being part of several Apache user communities. SDAP itself has
    critical dependencies upon Apache products. Lewis McGibbney (JPL employee),
    a Member of the ASF and V.P. of Apache Any23, Gora PMC Nutch, Tika, OODT,
    OCW, etc., is championing the effort to bring SDAP into and through the
    Apache Incubator and has been evangelizing the Apache Way to the current
    SDAP contributors such that the meritocratic process is well understood and
    followed. Apache was chosen specifically because we want to encourage this
    style of community development for the project and for it to sustain SDAP
    forward to become the generic platform for the next generation of big
    science data solutions
    = Community =
    The SDAP project is a fairly new effort and our community is not yet
    fully/firmly established. Initial committers comprising the SDAP roster
    have only recently fully come together as a unified team however there is a
    large degree of synergy between constituent members at JPL, FSU, NCAR, and
    GMU. Therefore, community building and publicity continues to be a major
    thrust. With the activity and exposure regularly attained by several
    community members, we hope to grow the SDAP presence in and across several
    (scientific) forums. The SDAP technology is generating interest within
    communities such as the Earth Science Information Partnership (ESIP),
    American Geophysical Union (AGU) and plethora or science meetings around
    the globe. This in effect, we hope, will further contribute towards the
    possibility of SDAP being used across Government Agencies such as NASA,
    NOAA, USGS, EPA, DOI, etc. as well as by researchers and students in
    academic institutions around the globe.
    During incubation, we will explicitly seek to increase our adoption, with
    SDAP already being featured on the agenda for several high profile globally
    significant scientific conferences and meetings.
    = Core Developers =
    The current set of core developers is relatively small, including full-time
    and students from across JPL, FSU, NCAR, and GMU. Initial community
    management and participation will be distributed across the entire team,
    most of which have been involved with the constituent projects for <2
    = Alignment =
    All SDAP code is licensed under Apache v2.0.
    = Known Risks =
    == Orphaned products ==
    There are currently no orphaned products. Each component of SDAP has
    dedicated personnel leading and participating in its ongoing development.
    Additionally, there is substantial collaboration between projects
    facilitated by regular project meetings which are specific the the initial
    member entities and focused on advancing physical oceanographic science.
    == Inexperience with Open Source ==
    JPL (in particular Lewis McGibbney) has been part of several efforts to
    transition to and grow projects communities at Apache e.g. Apache OODT,
    Apache Open Climate Workbench, Apache Joshua (Incubating), Apache SensSoft
    (Incubating), Apache DRAT (Incubating). Most of the code developed under
    the SDAP umbrella was and is open source prior to the Incubator effort so
    we are well familiarized with the nuances of open source software.
    = Relationships with Other Apache Products =
    SDAP has strong dependency upon a number of high profile and smaller
    profile Apache products. Examples can be seen in the breakdown of External
    Dependencies. As we continue to grow SDAP within the Incubator, we will
    make efforts to share community stories, software advancements and possible
    improvements in our use of our Apache dependencies back to those project
    = Developers =
    The SDAP project and hence developers is currently funded through a NASA
    AIST follow-on grant with funding secured for the next ~18 months. There
    are currently no 100% time dedicated developers, however, the same core
    team that does work currently will continue to work on the project
    throughout the next current funding period and after. There is currently no
    business strategy aligned with SDAP however it is perceived that future,
    yet unsecured funding may by directed to further feature advancement and
    project evangelism.
    = Documentation =
    Documentation is currently available in a number of locations e.g. Github
    wiki, Github pages, etc. with each repository under the oceanworks-aist
    Github Org maintaining documentation available through wiki’s attached to
    the repositories. Additionally, most of the SDAP sub-projects have been
    extensively documented within plethora of formal academic publications
    across several academic communities. It would be our intention, certainly
    atleast to unify the Github wiki ad Github pages documentation most likely
    to make up the Website content.
    = Initial Source =
    Current source resides in several locations Github:
     * (NEXUS, OceanXtremes, DOMS)
     * (EDGE)
     * (MUDROD)
     * (DOMS)
    = External Dependencies =
    Each component of the Science Data Analytics Platform has its own
    dependencies. Documentation will be available for integrating them.
    == MUDROD ==
    '''Core''' gson 2.5 compile
    jar false
    org.jdom jdom 2.0.2 compile
    jar false
    org.elasticsearch elasticsearch 5.2.0 compile
    jar false
    org.elasticsearch elasticsearch-spark-20_2.11 5.2.0 compile
    jar false
    joda-time joda-time 2.9.4 compile
    jar false
    com.carrotsearch hppc 0.7.1 compile
    jar false
    org.apache.spark spark-core_2.11 2.1.0 compile
    jar false
    org.apache.spark spark-sql_2.11 2.1.0 compile
    jar false
    org.apache.spark spark-mllib_2.11 2.1.0 compile
    jar false
    org.scala-lang scala-library 2.11.8 compile
    jar false
    org.codehaus.jettison jettison 1.3.8 compile
    jar false
    commons-cli commons-cli 1.2 compile
    jar false
    net.sf.opencsv opencsv 2.3 compile
    jar false
    org.apache.jena jena-core 3.3.0 compile
    jar false
    junit junit 4.12 test
    jar false
    gov.nasa.jpl.mudrod mudrod-core 0.0.1-SNAPSHOT compile
    jar false
    javax.servlet javax.servlet-api 3.1.0 provided
    jar false gson 2.5 compile
    jar false
     * AngularJS - MIT License
     * BootstrapJS - MIT License
     * jQueryJS - MIT License
     * Underscore JS - MIT License
    == DOMS ==
     * Apache Solr version 5.5.1
     * EDGE
     * NetCDF4
     * Python 3.5 (NOTE: only partial support for py2.7)
    Non stdlib Python dependencies:
     * Jinja2==2.9.5
     * python-dateutil==2.6.0
     * cython==0.25.2
     * numpy==1.12.0
     * scipy==0.18.1
     * netCDF4==1.2.7
     * solrpy3
     * siphon==0.4.0
     * neo4j-driver==1.1.0
     * matplotlib==2.0.0
     * requests==2.13.0
     * shapely==1.5.17
     * flask==0.12
     * networkx==1.11
     * pyproj==
     * blist==1.3.6
    == NEXUS ==
     * matplotlib
     * numpy
     * netCDF4
     * pathos (
     * Just a collection of scripts/vagrant file used to stand up a developer
    instance of nexus ingestion. No dependencies to report
     * Collection of Groovy scripts that can be used as part of data ingestion.
    They only rely on the standard Groovy library and the ‘nexus-messages’
     * only python standard libraries are used
    = Required Resources =
    Mailing Lists
    Git Repos
    Issue Tracking
     * JIRA Science Data Analytics Platform (SDAP)
    Continuous Integration
     * Jenkins builds on
     * wiki at
    = Initial Committers =
    The following is a list of the planned initial Apache committers (the
    active subset of the committers for the current repository on Github).
     * Lewis John McGibbney (
     * Vardis M. Tsontos (
     * Joseph C. Jacob (
     * Ed Armstrong (
     * Frank Greguska (
     * Brian Wilson (
     * Chaowe Phil Yang (
     * Yongyao Jiang (
     * Yun Li (
     * Shawn R. Smith (
     * Jocelyn Elya (
     * Mark Bourassa (
     * Thomas Cram (
     * Thomas Huang (
     * Steven Worley (
     * Zaihua Ji (
    = Affiliations =
     * Lewis John McGibbney (
     * Vardis M. Tsontos (
     * Joseph C. Jacob (
     * Ed Armstrong (
     * Frank Greguska (
     * Thomas Huang (
     * Brian Wilson (
    George Mason University
     * Chaowe Phil Yang (
     * Yongyao Jiang (
     * Yun Li (
    Center for Ocean-Atmospheric Prediction Studies, Florida State University
     * Shawn R. Smith (
     * Jocelyn Elya (
     * Mark Bourassa (
    Computational Information Systems Laboratory (CISL) / National Center for
    Atmospheric Research (NCAR)
     * Thomas Cram (
     * Zaihua Ji (
     * Steven Worley (
    = Sponsors =
    = Champion =
    * Lewis McGibbney (NASA/JPL)
    = Nominated Mentors =
     * TBD
     * TBD
     * TBD
    = Sponsoring Entity =
    The Apache Incubator

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