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From lewis john mcgibbney <>
Subject [DISCUSS] Accept Science Data Analytics Platform (SDAP) into Apache Incubator
Date Wed, 11 Oct 2017 18:22:20 GMT
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)
 * (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
 * 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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