Metadata Ingestion and Processinng
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1 biomedical and healthcare Data Discovery Index Ecosystem Ingestion and Processinng Jeffrey S. Grethe, Ph.D BioCADDIE All Hands Meeting
2 prototype Ingestion Indexing Repositories Ingestion ElasticSearch s Online datasets User Interface Searching Funding Agencies Publishers Data producers Terminology server Supported by the NIH grant #1-U24-AI to the University of California, San Diego
3 Data Indexing Pipeline 1. Configuration file developed by curator 2. of metadata/data from data resource or dataset via ingestion module w Cache information for further processing 3. Mapping of metadata/data to metadata model(s) 4. Process metadata/data via a set of processing modules 5. to target endpoint(s)via export modules 6. Search via ElasticSearch APIs Supported by the NIH grant #1-U24-AI to the University of California, San Diego
4 Ingestion and Indexing Transport Scalable metadata document store Cloud based NoSQL Datastore Current implementation utilizes mongodb Multiple transport mechanisms developed E.g. Rsync, ASPERA, FTP, REST web services, Open Archives Initiative Protocol for Harvesting (OAI-PMH) Source configuration based on format All incoming metadata converted to JSON
5 Ingestion and Indexing Ingestor Type Sample biocaddie Sources Number of sources using this ingestor Web Service Ingestor Clinical Trials, Uniprot 42 Database Ingestor NeuroMorpho,PeptideAtlas,Cl inical Trials Network OAI-PMH Ingestor Dryad,CVRG 2 Two-stage Web Service Ingestor * ICPSR,Dataverse Native 2 Rsync ingestor PDB, dbgap 2 FTP Ingestor BioProject,Biological Magnetic Resonance Data Bank (BMRB) Aspera Ingestor GEO Datasets 1 CSV Ingestor Gemma XML Ingestor ArrayExpress 1
6 Ingestion and Indexing
7 Ingestion and Indexing Based on DATS metadata model developed by WG3 transform column = 'primary')].'pdbx:pdbx_database_id_pubmed'.'_$'" to primarypublication.id" apply {{ result = 'pmid:' + value }};
8 Data
9 Curation Process programmer creates Source descriptor JSON file is input of Ingestor generates generates Transform web servicevalidates uses finalized by Sample Data Script Ingestion pipeline provides transformation template and sample data for curators
10 Ingestion and Indexing Collaboration across biocaddie Development of the underlying index infrastructure driven by biocaddie community Refinement and reconciliation of DATS biocaddie WG2 Data Identifiers Recommendation biocaddie WG3 Descriptive DATS Model biocaddie WG9 End User Evaluation Criteria biocaddie WG5 Dataset Citation Metrics biocaddie WG7 Accessibility biocaddie WG10 Repository Collaboration
11 v Number of Sources: 58 Statistics v Total Number of Records: 77,085,123 v v w Approximately 3X size of PubMed Total Size of : GB Average Record Size w w w w Average: 0.89 KB Median: 2.89 KB Min: 0.57 KB Max: 3.4 MB
12 Ingestion and Indexing of Pipeline allows for addition of enhancement modules 1. NLP Entity Recognition module developed by biocaddie CDT (UT Health) 2. Primary Publication Information 3. Data Citation module (Pilot Project 3.2) 4. Addition of dataset analytics (e.g. Diploid)
13 Ingestion and Indexing Final Pipeline retains both originally extracted metadata and transformed metadata Provenance for metadata generation is available Scalable document processing pipeline processing can be scaled horizontally Large scale tests with full enhancement pipeline and latest DATS model ~20,000 documents per hour per light-weight node
14 Source Processing Total Ingestion Time: 23 Hours, 35 Minutes 77+ Million Records Did not include data download time Trembl Ingestion rate: 224 documents per second
15 Horizontal Scaling Process timings with different number of consumers Consumer containers running on Amazon t2.medium (4GB RAM with 2 virtual CPUs) EC2 nodes.
16 Ingestion and Indexing Scalable index services Pipelines primary export is to Elasticsearch Scalable cloud based indexing/search platform
17 Thank you
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