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From "Huang, Thomas (398G)" <>
Subject Re: [DISCUSS] Accept Science Data Analytics Platform (SDAP) into Apache Incubator
Date Thu, 12 Oct 2017 19:00:29 GMT
Thanks Chris.  We are learning from the master ;-)

Big thanks to Lewis for getting us here!


Thomas Huang
Jet Propulsion Laboratory
4800 Oak Grove Drive, Mail Stop 158-242, Pasadena, CA 91109
Phone: 818.354.2747, Email:
DISCLAIMER: All personal and professional opinions presented herein are my own and do not,
in any way, represent the opinion or policy of JPL, NASA or Caltech.

On 10/12/17, 10:29 AM, "Chris Mattmann" <> wrote:

    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 =
        NASA JPL
         * 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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