MUDROD - Mining and Utilizing Dataset Relevancy from Oceanographic Dataset Metadata, Usage Metrics, and User Feedback to Improve Data Discovery and Access

1. 1
Mining and Utilizing Dataset Relevancy
from Oceanographic Dataset
(MUDROD) Metadata, Usage Metrics,
and User Feedback to Improve Data
Discovery and Access
Dr. Chaowei (Phil) Yang (PI, George Mason University)
Mr. Edward M Armstrong (Co-I, Jet Propulsion Laboratory, NASA)
AIST-14-82, NNX15AM85G Final Review
June 22, 2017

2. 2
Mining and Utilizing Dataset Relevancy from Oceanographic Data (MUDROD)
PI: Chaowei (Phil) Yang, George Mason University
TRLin = 5
4/09
Objective
• Improve data discovery, selection and access to NASA
Observational Data.
• Intuitive interface to federated data holdings.
• Enable new user communities to discover and access
data for their projects.
• Reduce time for scientists to discover, download and
reformat data.
• Implement extensible ontology framework.
• Improve discovery accuracy of oceanographic data
• Foundation for Managing Big Data.
• Demonstrate MUDROD at PO.DAAC.
Approach
• Setup collaboration, testing environment.
• Design MUDROD knowledge base system.
• Reconstruct sessions from web logs
• Calculate vocabulary similarity from logs and metadata.
• Update semantic search and conduct alpha testing.
• Integrate MUDROD alpha into P.O.DAAC.
• Enhance knowledge base, to include GCMD.
• Demonstrate Prototype.
03/16
Key Milestones
AIST-14-82 NNX15AM85G
CoIs: E. Armstrong, T. Huang, D. Moroni, JPL;
• Start 06/15
• Identify Use cases 01/16
• Design search, query, reasoning engine 03/16
• Ontological System Implementation 07/16
• Complete Beta test at P.O. DAAC 12/16
• Integrated test 02/17
• PO.DAAC metadata discovery Demo (TRL 7) 05/17
MUDROD Engine Architecture

3. 3
Mining and Utilizing Dataset Relevancy from Oceanographic Data (MUDROD)
PI: Chaowei (Phil) Yang, George Mason University
TRLin = 5 TRLout = 7
4/09
Objective
• Improve data discovery, selection and access to NASA
Observational Data.
• Intuitive interface to federated data holdings.
• Enable new user communities to discover and access
data for their projects.
• Reduce time for scientists to discover, download and
reformat data.
• Implement extensible ontology framework.
• Improve discovery accuracy of oceanographic data
• Foundation for Managing Big Data.
• Demonstrate MUDROD at PO.DAAC.
Accomplishments
• Developed framework
• Analyze web logs to discover user knowledge (query and
data relationships)
• Construct knowledge base by combining semantics and
profile analyzer
• Improve data discover
• Resulted in
• better ranking;
• recommendation;
• ontology navigation
03/16 AIST-14-0082
CoIs: T. Huang, D. Moroni, E. Armstrong, JPL;

4. 4
Mr. Edward M Armstrong
(Senior Data Engineer , NASA Jet
Propulsion Laboratory)
Mr. David Moroni
(Data Engineer, NASA Jet
Propulsion Laboratory)
Team Members: Investigators
Dr. Chaowei Phil Yang
(Professor and Director, NSF
Spatiotemporal Innovation Center,
George Mason University)
Mr. Thomas Huang
(Principal Scientist, NASA Jet
Propulsion Laboratory)

5. 5
Team Members: Students and Post-Docs
Ms. Yun Li
(Ph.D. student, George Mason
University; Research Assistant ).
Mr. Yongyao Jiang
(Ph.D. student, George Mason
University; Research Assistant ).
Dr. Lewis J. McGibbney
(Data Scientist, NASA Jet
Propulsion Laboratory).
Mr. Chris Finch,
(Software Engineer, NASA Jet
Propulsion Laboratory).
Mr. Frank Greguska,
(Software Engineer, NASA Jet Propulsion
Laboratory) .
Mr. Gary Chen

6. 6
Presentation Contents
• Background / Objectives / Technical Advancements
• TRL assessment
• Summary of Accomplishments and Plans
• Schedule
• Financials
• Publications
• List of Acronyms

7. 7
Background / Objectives / Tech Advance (1)
• Keyword-based matching (traditional search engines)
– User query: ocean wind
– Final query: ocean AND wind
• Reveal the real intent of user query
– ocean wind = “ocean wind” OR “greco” OR
“surface wind” OR “mackerel breeze” …
• PO.DAAC UWG Recommendation 2014-07
• NASA ESDSWG Search Relevance Recommendations 2016 & 2017
Data Discovery Problems

8. 8
Background / Objectives / Tech Advance (2)
• Analyze web logs to discover user knowledge (query and data
relationships)
• Construct knowledge base by combining semantics and profile
analyzer
• Improve data discovery by 1) better ranking; 2) recommendation; 3)
ontology navigation
Objectives

9. 9
Background / Objectives / Tech Advance (2)
• Web log preprocessing
• Semantic analysis of user queries & Navigation
• Machine learning based search ranking
• Data Recommendation
Tech Advance (four technological modules)

10. 10
Presentation Contents
• Background and Objectives
• TRL assessment
• Summary of Accomplishments and Plans
• Schedule
• Financials
• Publications
• List of Acronyms

11. 11
Accomplishments Since last Review
TRL Assessment (1)
• Performance improvement
• Improved log mining performance using cloud computing
• Hybrid Recommendation algorithm
• Stem MUDROD ontologies from a number of sources
• create an OWL manifestation based on NASA GCMD-DIF data model
• create a PO.DAAC-specific extension of the SWEET concept
• Ingest PO.DAAC extractions into ESIP Ontology Portal
• Improve web UI
• Test MUDROD and ingest latest PO.DAAC logs
• Deploy MUDROD on JPL server

12. 12
Current TRL Rational
TRL Assessment (2)
Component Current TRL Project end TRL Description
Semantic search engine
Search Dispatcher 7 7
Translating a user search query into a
set of new semantic queries
Similarity calculator 7 7
Calculating the semantic similarity from
weblogs, metadata, and ontology
Recommendation module 7 7
Recommending similar datasets to the
clicked dataset
Ranking module 7 7
Re-ranking the search results based on
RankSVM ML algorithm
Knowledge base
Ontology 7 7
Extensions from SWEET ontology for
earth science data
Triple Store 7 7 ESIP ontology repository
Vocabulary linkage discovery engine
Profile analyzer 7 7
Extracting user browsing pattern from
raw web logs
Web services/GUI
Ranking service/presenter 7 7
Providing and presenting the ranked
results
Recommendation service/presenter 7 7
Providing and presenting the related
datasets
Ontology navigation service/presenter 7 7
Providing and presenting related
searches

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Presentation Contents
• Background and Objectives
• TRL assessment
• Summary of Accomplishments and Plans
• Schedule
• Financials
• Publications
• List of Acronyms

14. 14
Overview
• Web log preprocessing
• Semantic analysis of user queries & Navigation
• Machine learning based search ranking
• Data Recommendation
Functions/Modules

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Web log processing

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• Requests sent from client e.g. browser, cmd line tool, etc. recorded by
server
• Log files provided by PO.DAAC (HTTP(S), FTP)
Client IP: 68.180.228.99
Request date/time: [31/Jan/2015:23:59:13 -0800]
Request: " GET /datasetlist/... HTTP/1.1 "
HTTP Code: 200
Bytes returned: 84779
Referrer/previous page: “/ghrsst/"
User agent/browser: "Mozilla/5.0 ...
68.180.228.99 - - [31/Jan/2015:23:59:13 -0800] "GET /datasetlist/... HTTP/1.1" 200
84779 "/ghrsst/" "Mozilla/5.0 ..."
Web logs

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Goal: reconstruct user browsing pattern
(search history & clickstream) from a set
of raw logs
Web logs
User identification
Crawler detection
Structure
reconstruction
Session
identification
Search
history
Clickstrea
m
Additional steps include: word
normalization, stop words removal, and
stemming
Data preprocess

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Reconstructed session structure

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Data preprocess results
1. User search history 2. Clickstream
Jiang, Y., Y. Li, C. Yang, E. M. Armstrong, T. Huang & D. Moroni (2016) “Reconstructing Sessions from Data Discovery
and Access Logs to Build a Semantic Knowledge Base for Improving Data Discovery” ISPRS International Journal of
Geo-Information, 5, 54.

20. 20
Semantic similarity

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Existing ontology (SWEET)
• SWEET (Raskin and Pan
2003)
• Focus on only two relations
• The closer, the more similar

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User search history
• Create query – user matrix
• Calculate binary cosine similarity
Conceptual example

23. 23
Clickstream
• Hypothesis: similar queries can result in similar clicking behavior
• If two queries are similar, the data that get clicked after they are
searched would be more likely to be similar
Query b
Data
Query a
Similar?

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Metadata
• Hypothesis: semantically related terms tend to appear in the same
metadata more frequently
• Essentially the same as the clickstream analysis
• Perform Latent Semantic Analyses (LSA) over the term – metadata
matrix
Query b
Metadata
Query a
Similar?

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Integration
• All four results could be converted to
• Problem:
– None of them are perfect (uncertainty in data, hypothesis and
method)
– Metadata and ontology might have unknown terms to search
engine end users
– Sometimes, similarity values from different methods are
inconsistent
Concept
A
Concept
B
Similarity

26. 26
Integration
• The maximum similarity of all of the components (large similarity
appears to be more reliable)
• The adjustment increment becomes larger when the similarity exists in
more sources

27. 27
Semantic Similarity Calculation Workflow

28. 28
Results and evaluation
Query
Search history Clickstream Metadata
SWEE
T
Integrated list
ocean
temperature
sea surface
temperature(0.66), sea
surface topography(0.56),
ocean wind(0.56),
aqua(0.49)
sea surface
temperature(0.94),
sst(0.94), group high
resolution sea surface
temperature dataset(0.89),
ghrsst(0.87)
sst(0.96), ghrsst(0.77), sea
surface temperature(0.72),
surface temperature(0.63),
reynolds(0.58)
None
sst(1.0), sea surface temperature(1.0),
ghrsst(1.0), group high resolution sea
surface temperature dataset(0.99),
reynolds sea surface temperature(0.74)
Sample group Overall accuracy
Most popular 10 queries 88%
Least popular 10 queries 61%
Randomly selected 10 queries 83%
By domain experts
Jiang, Y., Y. Li, C. Yang, K. Liu, E. M. Armstrong, T.
Huang & D. Moroni (2017) A Comprehensive
Approach to Determining the Linkage Weights
among Geospatial Vocabularies - An Example with
Oceanographic Data Discovery. International
Journal of Geographical Information Science (minor
revision)

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Search ranking

30. 30
Background
• Ranking is a long-standing problem in geospatial data discovery
• Typically, hundreds, even thousands of matches
• Can get larger as more Earth observation data is being collected

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Objective and Methods
• Put the most desired data to the top of the result list
• What features can represent users’ search preferences for
geospatial data?
• How can the ranking function reach a balance of all these features?
• Identified eleven features from
– Geospatial metadata attributes
– Query – metadata content
overlap
– User behavior from web logs

32. 32
Ranking features – Metadata attributes
Features Description
Release date The date when the data was published
Processing level (PL) The processing level of image products, ranging from
level 0 to level 4.
Version number The publish version of the data
Spatial resolution The spatial resolution of the data
Temporal resolution The temporal resolution of the data
• Five metadata features
• Verified by domains experts
• Query-independent: static, depends on the data itself, won’t
change with the query

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Spatial query-metadata overlap
• Spatial similarity between query area and the coverage of a particular
data
• Overlap area normalized by the original area of query and data

34. 34
Ranking features – User behavior
• All-time, monthly, user popularity, and semantic popularity (retrieved
from web logs)
• Semantic popularity: the number of times that the data has been
clicked after searching a particular query and its highly related ones
(query-dependent)

35. 35
RankSVM
• One of the well-recognized ML ranking algorithm
• Convert a ranking problem into a classification problem that a
regular SVM algorithm can solve
• 3 main steps
 1) Standardize: mean = 0, std = 1
o SVM is not scale invariant
o Over-optimized
o Longer to train
 2) For any pair of training data, calculate the difference
 3) A ranking problem becomes a binary classification problem,
where SVM is applied to find the optimal decision boundary

36. 36
Architecture
• All of these (except for
training) can be
finished within 2
seconds
• None of the open
source mainstream
ML library provide any
ranking algorithm
• Implemented it by
ourselves with the aid
of Spark MLlib
Index
User query
Semantic
query
Top K
retrieval
Ranking
model
Learning
algorithm
Training
data
Re-ranked
results
Feature
extractor
Weblogs
User clicksKnowledge
base

37. 37
NDCG (K) for five different ranking
methods at varying K (1-40)
Jiang, Y., Y. Li, C.
Yang, K. Liu, E.
M. Armstrong, T.
Huang, D.
Moroni & L. J.
McGibbney
(2017) Towards
intelligent
geospatial
discovery: a
machine learning
ranking
framework.
International
Journal of Digital
Earth (minor
revision)

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Data recommendation

39. 39
How to recommend geospatial data?
• Use geospatial metadata for content-based recommendation
-Metadata spatiotemporal similarity
-Metadata attribute similarity
-Metadata content similarity
• Leverage user behaviors data for CF recommendation
-Session-based co-occurrence of data

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Attribute type Attribute name Attribute description
Spatiotemporal attributes DatasetCoverage-EastLon The East longitude of the bounding rectangle
DatasetCoverage-WestLon The West longitude of the bounding rectangle
DatasetCoverage-NorthLat The North latitude of the bounding rectangle
DatasetCoverage-SouthLat The South latitude of the bounding rectangle
DatasetCoverage-StartTimeLong The start time of the data
DatasetCoverage-StopTimeLong The end time of the data
Categorical geographic
attributes
DatasetRegion-Region Region of dataset. Such as global, Atlantic
Dataset-ProjectionType Project type like cylindrical lat-lon
Dataset-ProcessingLevel Data processing level
DatasetPolicy-DataFormat Data format e.g. HDF, NetCDF
DatasetSource-Sensor-ShortName Short name of sensor
Ordinal geographic
attributes
Dataset-TemporalResolution Temporal resolution of dataset
Dataset-TemporalRepeat Temporal resolution of dataset
Dataset-SpatialResolution Spatial resolution of dataset
Descriptive attributes Dataset-description Describe the content of the dataset
Geographic metadata

41. 41
• Spatial variables: NorthLat, SouthLat, WestLon, EastLon
• Temporal variables: DatasetCoverage-StartTimeLong, StopTimeLong
• Use volume overlap ratio to calculate similarity
𝑠𝑝𝑎𝑡𝑖𝑜𝑡𝑒𝑚𝑝𝑜𝑟𝑎𝑙 𝑠𝑖𝑚 𝑟 𝑖,𝑟 𝑗
=
𝑣𝑜𝑙𝑢𝑚𝑒 𝑟𝑖 ∩ 𝑟𝑗
𝑣𝑜𝑙𝑢𝑚𝑒 𝑟𝑖
+
𝑣𝑜𝑙𝑢𝑚𝑒 𝑟𝑖 ∩ 𝑟𝑗
𝑣𝑜𝑙𝑢𝑚𝑒 𝑟𝑗
∗ 0.5
𝑣𝑜𝑙𝑢𝑚𝑒 𝑟 = 𝑒𝑎𝑠𝑡𝑙𝑜𝑛 − 𝑤𝑒𝑠𝑡𝑙𝑜𝑛 ∗ 𝑠𝑜𝑢𝑡ℎ𝑙𝑎𝑡 − 𝑛𝑜𝑟𝑡ℎ𝑙𝑎𝑡 ∗ 𝑒𝑛𝑑𝑡𝑖𝑚𝑒 − 𝑠𝑡𝑎𝑟𝑡𝑖𝑚𝑒
Spatiotemporal similarity

42. 42
• Fixed number of values
• No intrinsic ordering
• sensor-name: "AMSR-E", "MODIS", "AVHRR-3" and "WindSat"
𝑐𝑎𝑡𝑒𝑔𝑜𝑟𝑖𝑐𝑎𝑙_𝑣𝑎𝑟_𝑠𝑖𝑚 𝑣𝑖, 𝑣𝑗 =
𝑣 𝑖 ∩ 𝑣 𝑗
𝑣 𝑖 ∪ 𝑣 𝑗
Categorical similarity

43. 43
Ordinal attribute is similar to categorical attribute but its values has a
clear order, e.g. spatial resolution
• Converted into rank from 1 to R
• Nominalize these ranks for similarity calculation
𝑜𝑟𝑑𝑖𝑛𝑎𝑙 𝑣𝑎𝑟
𝑠𝑖𝑚 𝑣 𝑖,𝑣 𝑗
= 1 − 𝑛𝑜𝑟𝑚 𝑟𝑎𝑛𝑘 𝑣 𝑖
− 𝑛𝑜𝑟𝑚 𝑟𝑎𝑛𝑘 𝑣 𝑗
𝑛𝑜𝑟𝑚_𝑟𝑎𝑛𝑘 𝑣𝑖 =
𝑅𝑎𝑛𝑘𝑣 𝑖+1
𝑅+1
Ordinal similarity

44. 44
Original text
Aquarius Level 3 sea surface salinity (SSS) standard mapped image
data contains gridded 1 degree spatial resolution SSS averaged over
daily, 7 day, monthly, and seasonal time scales. This particular data
set is the seasonal climatology, Ascending sea surface salinity
product for version 4.0 of the Aquarius data set, which is the official
end of prime mission public data release from the AQUARIUS/SAC-D
mission. Only retrieved values for Ascending passes have been used
to create this product. The Aquarius instrument is onboard the
AQUARIUS/SAC-D satellite, a collaborative effort between NASA and
the Argentinian Space Agency Comision Nacional de Actividades
Espaciales (CONAE). The instrument consists of three radiometers in
push broom alignment at incidence angles of 29, 38, and 46 degrees
incidence angles relative to the shadow side of the orbit. Footprints
for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x
120 km and 96km x 156 km, yielding a total cross-track swath of 370
km. The radiometers measure brightness temperature at 1.413 GHz
in their respective horizontal and vertical polarizations (TH and TV). A
scatterometer operating at 1.26 GHz measures ocean backscatter in
each footprint that is used for surface roughness corrections in the
estimation of salinity. The scatterometer has an approximate 390km
swath.
Extracted terms
Radiometers Measure Brightness Temperature,AQUARIUS/SAC
Mission,Image Data,Broom Alignment,Resolution
SSS,AQUARIUS/SAC
Satellite,Scatterometer,Scatterometer,Aquarius Data,Argentinian
Space Agency Comision Nacional,Incidence Angles,Time
Scales,Actividades Espaciales,Cross-track Swath,Official
End,Aquarius Instrument,Shadow Side,Ascending Sea Surface
Salinity Product,Level,Level,Surface Roughness
Corrections,Data Release,Salinity,Density, Salinity,Density,
AQUARIUS,L3,SSS,SMIA,SEASONAL-CLIMATOLOGY,V4
Step 1: Phrase extraction
1. Extract term candidates from metadata description with POS (part of speech) Tagging
2. Introduce “occurrence” and “strength” to filter out terms from candidates.
“occurrence”: occurrences number of terms
“strength”: the number of words in a term
Descriptive similarity

45. 45
Step 2: Represent metadata in the phrase vector space (The
dimension lower than word feature space)
Term 1 Term 2 Term 3 Term 4 Term 5 Term 6 Term 7 … Term N
dataser1 1 0 1 0 0 0 0 1
dataser2 0 0 0 1 1 0 0 0
…
dataset k 1 0 1 0 0 0 0 1
Step 3: Calculate cosine similarity
Metadata abstract semantic similarity

46. 46
Session
1
Session
2
…
Session
N
Data 1 1 0 1
Data 2 0 0 0
…
Data k 1 0 1
Calculate metadata similarity based on session level co-occurrence
Similarity (i,j) =
𝑁(𝑖ᴖ𝑗)
𝑁(𝑖)∗ 𝑁(𝑗)
N(i): The number of sessions in which dataset i was viewed or download
N(j): The number of sessions in which dataset j was viewed or download
N(𝑖ᴖ𝑗): The number of sessions in which both dataset i and j were viewed or download
Session based recommendation

47. 47
Recommendation
method
Pros Cons Strategy
Descriptive
similarity
1. Natural language
processing methods
can be adopted to
find latent semantic
relationship
1. Many datasets has nearly
same abstract with few words/values
changed
2. It is hard to extract detailed
attributed from description
Used as the basic
method of
recommendation
algorithm
Attribute similarity
(spatiotemporal,
ordinal, categorical)
1. As structured
data, geographic
metadata have many
variables.
1. Variable values may be null or
wrong
2. The quality depends on the weight
assigned to every variable
As supplement to
semantic similarity
Session
concurrence
1. Reflect users’
preference
1. Cold start problem: Newly
published data don’t have usage
data
Fine-tune
recommendation
list
Recommend (i) = 𝑊 𝑠𝑠 ∗ 𝐷𝑒𝑠𝑐𝑟𝑖𝑝𝑡𝑖𝑣𝑒𝑐 𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑖 + ∑𝑊 𝑐𝑣 ∗ 𝐶𝑎𝑡𝑒𝑔𝑜𝑟𝑖𝑎𝑙 𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦(𝑖) + ∑𝑊 𝑜𝑣 ∗
𝑂𝑟𝑑𝑖𝑎𝑛𝑙 𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦(𝑖) + 𝑊𝑠𝑡𝑣 ∗ 𝑆𝑝𝑎𝑡𝑖𝑜𝑇𝑒𝑚𝑝𝑜𝑟𝑎𝑙𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 + 𝑊𝑠𝑜 ∗ 𝑆𝑒𝑠𝑠𝑖𝑜𝑛𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦
Hybrid recommendation

48. 48
Quantitative Evaluation
Hybrid similarity
outperform other
similarities since it
integrates metadata
attributes and user
preference.
Y. Li, Jiang, Y., C. Yang, K. Liu, E. M. Armstrong, T. Huang, D. Moroni & L. J. McGibbney (2017) A Geospatial Data
Recommender System based on Metadata and User Behaviour (revision)

49. 49
Plan Forward
• Add more features (e.g., temporal similarity)
• Create training data from web logs for RankSVM
• Develop a query understanding module to better interpret user’s search
intent (e.g. “ocean wind level 3” -> “ocean wind” AND “level 3”)
• Support Solr
• Support near real-time data ingestion to dynamically update knowledge
base
• Integration with DOMS and OceanXtremes to support an ocean science
analytics center
• Leverage advanced computing techniques to speed up the process

50. 50
What has been done
• Offline/sequential log analysis
– Time and computation intensive
• Ranking
– Use manually labeled data as train data
– RankSVM
• Offline recommendation
– Cannot respond to what user clicked in the last few minutes
• Offline Similarity calculation
– Cannot be updated in real-time
– Cannot parse multi-concept queries

51. 51
Software status
• Java 8
• Git
• Apache Maven 3.X
• Elasticsearch v5.X
• Kibana v4
• Apache Spark v2.0.0
• Apache Tomcat 7.X
• https://github.com/mudrod/mudrod

52. 52
What remains to be done
• Support for Solr
• Add faceted search to the UI and backend
• Support for real-time ranking, recommendation,
similarity calculation
• Support for DOMS and OceanXstreams

53. 53
Real-time ranking

54. 54
Real-time recommendation
User clicked
metadata
Knowledge base
Metadata based
similar datasets
Top-k datasets
Hybrid similarity
calculator
Recommendation results
sequential clicked
datasets in a session
Weblogs
Train
model

55. 55
Proposed timeline
55
Sept. –
Oct.
Nov. –
Dec.
Jan. –
Feb.
March -
April
May -
June
July –
Aug.
Support for Solr
Add faceted search
Support for DOMS &
OceanXstreams
Real-time ranking
Real-time
recommendation
Real-time similarity

56. 56
Research points
• Semantic similarity
– Online matrix analysis
– Query parsing
• Ranking
– Generate training data from user clicks
– Online deep learning algorithm
• Near real-time log ingesting
– Spark Streaming APIs
– Real-time sessionization
• High performance log mining
– Use parallel computing to speed up
• Recommendation
– Session-based real-time recommendation
– Personalized recommendation

57. 57
Summary
• Log mining enables a data portal integrating implicit user
preferences
• Word similarity retrieved by data mining tasks expands any given
query to improve search recall and precision.
• The rich set of ranking features and the ML algorithm provide
substantial advantages over using other ranking methods
• The recommendation algorithm can discover latent data relevancy
• The proposed architecture enables the loosely coupled software
structure of a data portal and avoids the cost of replacing the existing
system

58. 58
Presentation Contents
• Background and Objectives
• TRL assessment
• Summary of Accomplishments and Plans Forward
• Schedule
• Financials
• Publications
• List of Acronyms

59. 59
Project Schedule

60. 60
Jun'1
5
Jul'15
Aug
'15
Sep'1
5
Oct
'16
Nov
'16
Dec
'16
Jan
'16
Feb
'16
Mar
'16
Apr
'16
May
'16
Jun
'16
Jul
'16
Aug
'16
Sep'1
6
Oct
'17
Nov
'17
Dec
'17
Jan
'17
Feb
'17
Mar
'17
Apr
'17
May
'17
Jun
'17
Funding Plan 208 208 208 208 208 208 208 208 208 208 208 208 392 392 392 392 392 392 392 392 392 392 392 392 392
Cum Obligation Plan 2 3 6 8 10 14 23 30 33 41 48 67 91 120 144 167 200 230 253 273 292 343 370 390 392
Cum Actuals 2 3 6 8 10 14 23 30 33 41 48 67 84.3 106.7 132.1 141.2 159.7 179.7 200.1 226.1 255.1 285.5 339.1 366.7
Variance 0 0 0 0 0 0 0 0 0 0 0 0 -7 -13 -12 -26 -40 -50 -53 -47 -37 -58 -31 -23
0
50
100
150
200
250
300
350
400
450
JPL Part: $K
Costs Status and Plan

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Costs Status and Plan
Funding Plan 346,984 346,984 346,984 346,984 346,984 346,984 346,984 346,984 346,984 346,984 346,984 346,984 603,149 603,149 603,149 603,149 603,149 603,149 603,149 603,149 603,149 603,149 603,149 603,149 648,838 648,838 648,838
Cumulative Cost Actual 0 0 2498 9993 28880 56770 66100 74279 107932 126371 151600 181645 206108 218265 255809 323578 404836 434623 442374 461156 478063 482765 524561 578369 594214 628852 648838
Cumulative Cost Plan - - 2498 9993 28880 56770 66100 74279 107932 126371 151600 181645 208693 254642 301097 365107 411031 455801 471465 493314 508058 540992 561021 595815 616823 632186 648838
Variance 0 -2499 2499 -2499 2499 -2499 2499 -2499 2499 -2499 5083 31293 13995 27535 -21340 42518 -13426 45585 -15589 73817 -37356 54802 -32193 35527 -35528
• Actual costs are below or above plan
– Due to the GMU payment cycle is different from end of month
• Impact: None

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Online Products
1. PO.DAAC Labs website https://podaac.jpl.nasa.gov/podaac_labs
2. PO.DAAC forum page
https://podaac.jpl.nasa.gov/forum/viewforum.php?f=88
3. Open Source with Github and Apache License 2.0:
https://github.com/mudrod/mudrod
4. Demo Site: http://mudrod.jpl.nasa.gov/
5. PD Leverage: http://pd.cloud.gmu.edu/

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Demonstration
MUDROD deployed in PO.DAAC Labs
• https://podaac.jpl.nasa.gov/podaac_labs

64. 64
Publications
Journal / Conference Papers (over 10 peer review publications)
1. Jiang, Y., Y. Li, C. Yang, E. M. Armstrong, T. Huang & D. Moroni (2016) Reconstructing Sessions from Data Discovery and Access
Logs to Build a Semantic Knowledge Base for Improving Data Discovery. ISPRS International Journal of Geo-Information, 5, 54.
2. Y. Li, Jiang, Y., C. Yang, K. Liu, E. M. Armstrong, T. Huang & D. Moroni (2016) Leverage cloud computing to improve data access
log mining. IEEE Oceans 2016.
3. Jiang, Y., Y. Li, C. Yang, K. Liu, E. M. Armstrong, T. Huang & D. Moroni (2017) A Comprehensive Approach to Determining the
Linkage Weights among Geospatial Vocabularies - An Example with Oceanographic Data Discovery. International Journal of
Geographical Information Science (minor revision)
4. Jiang, Y., Y. Li, C. Yang, K. Liu, E. M. Armstrong, T. Huang, D. Moroni & L. J. McGibbney (2017) Towards intelligent geospatial
discovery: a machine learning ranking framework. International Journal of Digital Earth (minor revision)
5. Y. Li, Jiang, Y., C. Yang, K. Liu, E. M. Armstrong, T. Huang, D. Moroni & L. J. McGibbney (2017) A Geospatial Data Recommender
System based on Metadata and User Behaviour (revision)
6. Jiang, Y., Y. Li, C. Yang, K. Liu, E. M. Armstrong, T. Huang, D. Moroni & L. J. McGibbney (2017) A smart web-based data discovery
system for ocean sciences. (ongoing)
7. Yang, C., et al., 2017. Big Data and cloud computing: innovation opportunities and challenges. International Journal of
Digital Earth, 10(1), pp.13-53. (the 2nd most read paper of IJDE in it’s decadal history)
8. Yang, C.P., Yu, M., Xu, M., Jiang, Y., Qin, H., Li, Y., Bambacus, M., Leung, R.Y., Barbee, B.W., Nuth, J.A. and Seery, B., 2017,
March. An architecture for mitigating near earth object's impact to the earth. In Aerospace Conference, 2017 IEEE (pp. 1-13). IEEE
9. Yu, M. and Yang, C., 2016. Improving the Non-Hydrostatic Numerical Dust Model by Integrating Soil Moisture and Greenness
Vegetation Fraction Data with Different Spatiotemporal Resolutions. PloS one, 11(12), p.e0165616.
10. Vance, T.C., Merati, N., Yang, C. and Yuan, M., 2016, September. Cloud computing for ocean and atmospheric science. In OCEANS
2016 MTS/IEEE Monterey (pp. 1-4). IEEE. (also as a book with the same name)
11. Liu, K., Yang, C., Li, W., Gui, Z., Xu, C. and Xia, J., 2016. Using semantic search and knowledge reasoning to improve the discovery
of earth science records: an example with the ESIP semantic testbed. In Mobile Computing and Wireless Networks: Concepts,
Methodologies, Tools, and Applications (pp. 1375-1389). IGI Global.
12. Xia, J., Yang, C., Liu, K., Gui, Z., Li, Z., Huang, Q. and Li, R., 2015. Adopting cloud computing to optimize spatial web portals for
better performance to support Digital Earth and other global geospatial initiatives. International Journal of Digital Earth, 8(6), pp.451-
475.

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Students Cultivated
• Ph.D. Dissertations focused on MUDROD: Yongyao Jiang and
Yun Li (GMU), will complete in phase 2 (OceanWorks)
• Graduate Students worked on tasks； Fei Hu, Kai Liu (graduate in
2017 summer), Manzhu Yu (graduate in 2017 summer), Han Qin
(graduated), Mengchao Xu, Ishan Shams (GMU)
• Undergraduate students: Joseph Michael/Univ. of Richmond
(HBCU)， Alena/Virginia Tech (Female)
• Other (Approximately 20 presentations at AGU, ESIP, AAG,
GeoInformatics, and other national and international conferences)

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Acronyms
List of Acronyms
• MUDROD Mining and Utilizing Dataset Relevancy from
Oceanographic Dataset
• HTTP Hypertext Transfer Protocol
• FTP File Transfer Protocol
• SWEET Semantic web for earth and environmental
terminology
• PO.DAAC Physical Oceanography Distributed Active
Archive Center
• LSA Latent Semantic Analysis
• SVM Support vector machine
• NDCG Normalized Discounted Cumulative Gain

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1. NASA AIST Program (NNX15AM85G)
2. PO.DAAC SWEET Ontology Team (Initially funded by
ESTO)
3. Hydrology DAAC Rahul Ramachandran (providing the
earlier version of NOESIS)
4. ESDIS for providing testing logs of CMR
5. All team members at JPL and GMU
Acknowledgements