Doug Lindholm
Laboratory for Atmospheric and Space Physics
University of Colorado Boulder
ESIP
– July 8, 2014

Disparate Data
Unified
Interface

Native Data
Descriptors Adapters
ASCII
TSML
Binary
TSML
TSML
JDBC
FITS
TSML
TSML
Web
Service
Custom
Filters
Subset
Constrain
(sst > 20)
Convert
Units
Missing
Values
Derived
Products
Custom
Writers
CSV
JSON
DAP2
Image code snippet
Custom
Client
Applications
Web
Browser
Excel
Analysis
Tools
Program s
Web
Service

• Any OPeNDAP client. Available for most programming languages (python, IDL, Matlab,...).
• Analysis/visualization tools with built in OPeNDAP support.
• Web browser: Directly enter http URL query.
• wget, curl: command line tools for making an HTTP request.
• Custom Web Applications (Open Source coming soon) that make AJAX requests to LaTiS to get
JSON output and make interactive plots.
• Custom programming APIs that wrap a LaTiS call.

•
OPeNDAP
– Both implement DAP2 protocol (standard service API)
– OPeNDAP servers tend to be file centric
– LaTiS presents “virtual” dataset via aggregation
– LaTiS aims to be easier to install, configure, and extend
•
NetCDF Common Data Model (CDM)
– Multidimensional array centric
– Coupled to NetCDF file format
– Climate and forecast model (simulation) emphasis
•
THREDDS Data Server
– Built around NetCDF CDM
– Provides OPeNDAP and other service interfaces
•
TSDS
– First generation of LaTiS built on NetCDF CDM
•
VisAD
– Essentially the same logical data model as LaTiS with a clunkier implementation based on old Java capabilities
– LaTiS is implemented around modern paradigms like Functional Programming

• NOT a simulation or forecast
(climate model)
• NOT a meta data model (ISO 19115)
• NOT a file format
(NetCDF)
• NOT how the data are stored
(RDBMS)
• NOT the representation in computer memory
(data structure)
• Logical model
• What the data represent, conceptually
• How the data are used

bits 10110101000001001111001100110011111110 bytes 00105e0 e6b0 343b 9c74 0804 e7bc 0804 e7d5 0804 int, long, float, double, scientific notation (Number)
1, -506376193, 13.52, 0.177483826523, 1.02e-14 array 1.2
3.6
2.4
1.7
3.2

Multi-dimensional Arrays
Key Features:
- Single data type
- Access by index

Relational Database
Table =
Row =
Relation
Tuple of Attributes e.g. (0, 3.5, B)
2
3
4
0
1 time flux clas s
3.5
4.6
B
A
4.7
4.1
3.2
A
A
B
Key Features:
- Supports different data types
- Well suited for access by value e.g. time>2, class=A
But the relation is limited to a sequence of tuples:

• Extends the Relational Model to add Functional relationships.
• Represents multi-dimensional domain of data grids.
• Access by value or index.
(domain)
Dependent
Variables
(range)
Example: time series of gridded surface winds
Time -> ((Lon, Lat) -> (U,V))

Only Three Variable Types:
Scalar : single Variable
Tuple: group of Variables
Function : mapping from one Variable to another
Extend to capture higher level, domain specific abstractions

Philosophy: Leave data in their native form
Expose via a common interface
Software:
• Reusable adapters (software modules) to read common formats, extension points for custom formats
• XML dataset descriptors, map native data model to the
LaTiS data model
• Open Source, community
Web services:
• Standard service interfaces, currently OPeNDAP
• Server side processing and output format options

• The LaTiS Data Model is an abstract representation
• Can be represented several ways
– UML
– VisAD grammar
– Java Interface (no implementation)
• Need an implementation in code
• Scientific data Domain Specific Language (DSL)
– Expose an API that fits the application domain
• Scala programming language
– http://www.scala-lang.org/

• Evolution of Java
– Use with existing Java code
– Runs on the Java Virtual Machine (JVM)
– Command line (REPL), script, or compiled
– Statically typed (safer than dynamic languages)
– Industrial strength (Twitter, LinkedIn, …)
• Object-Oriented
– Encapsulation, polymorphism, …
– Traits: interfaces with implementation, multiple inheritance, mix-ins
• Functional Programming
– Immutable data structures
– Functions with no side effects
– Provable, parallelizable
• Syntactic sugar for Domain Specific Languages
• Operator “overloading”, natural math language for Variables
• Parallel collections

• Dataset as a Scala collection
• Functional Programming Paradigms:
– Function composition over object manipulation
– Functions as first class citizens
• a LaTiS Function can be used like a programming function
– Immutable data structures
– No side-effects: parallelizable, provable
– Lazy evaluation: scalable
• Math and resampling mixed in
– e.g. dataset3 = (dataset1 + dataset2) / 2
• Metadata encapsulated
– enforce data consistency: unit conversions ...
– track provenance

• RESTful web service API (OPeNDAP +)
• Java Servlet, build and deploy war file
• XML dataset descriptor (TSML) for each dataset
– Specify Adapter to use
– Map native data source to LaTiS data model
– Define transformations as Processing Instructions
• Catalog to map dataset names to TSML
• Plugins: implement the Adapter, Filter or Writer interfaces or extend existing ones
• Properties file to map filter and writer names to implementing classes

Sunspot data for October 2003
2003 10 01 75
2003 10 02 72
2003 10 03 59
2003 10 04 60
2003 10 05 53
2003 10 06 51
2003 10 07 50
2003 10 08 56
2003 10 09 58
2003 10 10 50
2003 10 11 44
2003 10 12 22
2003 10 13 12
2003 10 14 4
2003 10 15 17
2003 10 16 24
2003 10 17 37
2003 10 18 43
2003 10 19 43
2003 10 20 64
2003 10 21 66
2003 10 22 72
2003 10 23 68
2003 10 24 81
2003 10 25 89
2003 10 26 102
2003 10 27 141
2003 10 28 161
2003 10 29 167
2003 10 30 171
2003 10 31 156
TSML Dataset descriptor
<?xml version="1.0" encoding="UTF-8"?>
<tsml>
<dataset name="Sunspot_Number" history="Read by LaTiS">
<adapter class="latis.reader.tsml.AsciiAdapter" url="file:/data/latis/ssn.txt" />
<time units="yyyy MM dd
” />
<integer name="ssn ” />
</dataset>
</tsml>

• LASP Interactive Solar Irradiance Data Center (LISIRD)
– Uses LaTiS to read, subset, reformat data, metadata
– http://lasp.colorado.edu/lisird/
• Time Series Data Server (TSDS)
– Common RESTful interface to NASA Heliophysics data
– http://tsds.net/
Other LASP projects: MMS, MAVEN, database statistics, log files
External users?

• Operational:
– ASCII (file, web service, system call), binary, NetCDF,
Relational database, data “generators”
– Time Series of scalars, vectors, and spectra
– Arbitrarily long time series
• Prototyped:
– HDF, CDF, FITS, GRIB, OPeNDAP (e.g. other LaTiS servers), NoSQL (MongoDB)
– Nested 2D (gridded) data structures
• Planned:
– Arbitrarily complex data structures

• Operational:
– OPeNDAP, ASCII (e.g. csv), binary, JSON, Image
(PNG), IDL code, HTML dataset landing page
• Prototyped:
– NetCDF, HDF, IDL save file, interactive plot
• Planned:
– GeoTIFF, …

• Operational:
– Subset, aggregate, stride, thin, replace, integrate, bin average
• Prototyped:
– FFT, min, max, unique, resampling, unit conversion
• Planned:
– Coordinate system transformations
– Make it easier to plug in custom computations
– Track provenance

• Operational:
– OPeNDAP
– Java Servlet, simply deploy war file (Tomcat, Glassfish)
• Prototyped:
– Authentication
– Single executable (jetty)
– THREDDS Data Server (TDS) integration
• Planned:
– Open Geospatial Consortium (OGC) standards
• Web Map Server
• Web Coverage Server

• Operational:
– THREDDS catalog, static XML, browse
• Prototyped:
– Semantic Web triple store (RDF, SPARQL)
– Text search (Solr)
– Modeling RDF triples (subject, predicate, object)
– Track provenance, record Dataset modifications
• Planned:
– Serve metadata in various schema (e.g. ISO 19115,
SPASE)
– Unique IDs, Digital Object Identifiers (DOI) for publishing

• Operational:
– Time API with formatting
– Time conversions with leap seconds
• Prototyped:
– Caching, improve performance
– Parallel processing, multi-core
• Planned:
– Big Data, Hadoop, Map Reduce
– Workflow integration

• Time Series Server (a.k.a. TSS1)
– Core of Time Series Data Server (TSDS, tsds.net)
– Built around Unidata Common Data Model
– SourceForge: https://sourceforge.net/projects/tsds/
• LaTiS (a.k.a. TSS2)
– New LaTiS data model, scala implementation
– GitHub: https://github.com/dlindhol/LaTiS
– LASP internal development branch
– Plug-ins as separate projects (e.g. data collections, math, custom readers/writers,…), keep core small

• Astrophysicist by degree, software engineer by profession
• Data user and provider
• Scientific data applications developer:
– astrophysics, atmospheric science, space science
• Holy Grail: common data model
• Favorite scientific data models:
– VisAD (http://www.ssec.wisc.edu/~billh/visad.html)
– Unidata Common Data Model
(http://www.unidata.ucar.edu/software/netcdf-java/CDM/)
– OPeNDAP (http://www.opendap.org/)