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Fostering Serendipity through Big Linked Data
Fostering Serendipity through Big Linked Data
Fostering Serendipity through Big Linked Data
Fostering Serendipity through Big Linked Data
Fostering Serendipity through Big Linked Data
Fostering Serendipity through Big Linked Data
Fostering Serendipity through Big Linked Data
Fostering Serendipity through Big Linked Data
Fostering Serendipity through Big Linked Data
Fostering Serendipity through Big Linked Data
Fostering Serendipity through Big Linked Data
Fostering Serendipity through Big Linked Data
Fostering Serendipity through Big Linked Data
Fostering Serendipity through Big Linked Data
Fostering Serendipity through Big Linked Data
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Fostering Serendipity through Big Linked Data

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Semantic Web Challenge - Big Data track winner at ISWC2013

Semantic Web Challenge - Big Data track winner at ISWC2013

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  • 1. Fostering Serendipity through Big Linked Data Muhammad Saleem , Maulik R. Kamdar , Aftab Iqbal , Shanmukha Sampath , Helena F. Deus , and Axel-Cyrille Ngonga Ngomo Semantic Web Challenge at ISWC2013, October 21-25 , 2013, Sydney, Australia
  • 2. Agenda • Motivation • Datasets • Architecture • Evaluation • Requirements • Demo • Conclusion and Future Work
  • 3. Motivation Fostering Serendipity through Big Data Triplification, Continuous Integration, and Visualization
  • 4. Triplification: Linked TCGA • TCGA is publicly accessible atlas of cancer related data from National Cancer Institute (NCI) – 9000 patients – 33 cancer types – 147,645 raw data files – 12.7 TB • Only 46% of the total expected data with new data being submitted every day • Goal is to enable cancer researchers to make and validate important discoveries • Total Linked TCGA > 30 billion triples (Largest Dataset of LOD)
  • 5. Triplification:PubMed • Collection of publications from the bio-medical domain • Large amount of metadata (MESH Terms) • 23+ million publications • 10,000 new publications/month
  • 6. Big Data Continuous Integration TopFed Parser Federator Optimizer Integrator Results SPARQL Query Results Sub-query PubMed Entrez Utilities RDFizer Auto Loader TCGA Data Portal SPARQL endpoint RDF SPARQL endpoint RDF SPARQL endpoint RDF Index
  • 7. Exon-Expression Methylation C-1 ∨ Category Colour = blue For each query triple t(s, p, o) ∈ T Highly Scalable b1 b2 p1 p2 p3 p4 p5 p6 g1 g2 g3 g4 g5 g6 g7 g8 g9 C = {CNV, SNP, E-Gene, E-Protein, miRNA, Clinical} M = {beta_value, position} F = {Expression-Exon} (CNV, SNP, E-Gene, miRNA, E-Protein, Clinical) D = {seg_mean, rpmmm, scaled_est, p_exp_val} B = {DNA-Methylation} C-1 = {{p ∈ {D ∪ A ∪ G} ∨ {p = rdf:type ∧ o ∈ C}} ∧ {{S-Join(p, D ∪ C) ∨ P-Join(p, D ∪ C) } ∨ {!S-Join(p, M ∪ B ∪ E ∪ F) ∧ !P-Join(p, M ∪ B ∪ E ∪ F) }}} C-2 = {{p ∈ {E ∪ A ∪ G} ∨ {p = rdf:type ∧ o ∈ F}} ∧ {{S-Join(p, E ∪ F) ∨ P-Join(p, E ∪ F)} ∨ {!S-Join(p, M ∪ B ∪ D ∪ C) ∧ !P-Join(p, M ∪ B ∪ D ∪ C) }}} C-3 = {{p ∈ {M∪ A} ∨ {p = rdf:type ∧ o ∈ B}} ∧ {{S-Join(p,M ∪ B) ∨ P-Join(p, M∪ B) } ∨ {!S-Join(p, E ∪ F ∪ D ∪ C) ∧ !P-Join(p, E ∪ F ∪ D ∪ C) }}} IF tumour lookup is successful forward to corresponding leaf Else broadcast to every one A = {chromosome, result, bcr_patient_barcode} G = {start, stop} E = {RPKM} Tumours SPARQL endpoints C-2 ∨ Category Colour = pink C-3 ∨ Category Colour = green 1-16 17-33 1-5 6-11 12-16 17-22 23-27 28-33 1-4 5-8 9-12 13-16 17-20 21-24 25-27 28-30 31-33
  • 8. Evaluation:Number of Sub-Query Submission 60 50 40 30 20 10 FedX number of Sub-Query Submission TopFedE number of Sub-Query Submission • TopFed number of sub-queries submission is 1/3 to FedX • Number of ASK requests – FedX 480 – TopFed 10 0 1 2 3 4 5 6 7 8 9 10 Avg
  • 9. Evaluation: Query Runtime 100000 10000 1000 100 10 1 1 2 3 4 5 6 7 8 9 10 Average Query Execution Time (msec) in log scale FedX TopFed • TopFed outperform FedX significantly on 90% of the queries • On average, the query run time of TopFed is about 1/3 to that of FedX • TopFed‘s best run-time (query 2, query 3) is more than 75 times smaller than that of FedX
  • 10. Big Data Track Requirements • Data Volume – 7.36 billion triples from Linked TCGA – 23 million publications from PubMed • Data Variety – The Linked TCGA data was extracted from raw text files of different structures – Processed the metadata associated with PubMed publications and transform them into RDF – Unstructured data (publication abstracts) is processed to extract mentions of gene names and cancers • Data Velocity – TCGA data doubles /2 months – PubMed publications 10k/month
  • 11. Big Data Visualization
  • 12. Tumor-wise Visualization
  • 13. PubMed Paper-wise Visualization
  • 14. Genome-wise Patients Results Visualization
  • 15. Everything is Public • Demo: http://srvgal78.deri.ie/tcga-pubmed/ • TopFed: https://code.google.com/p/topfed/ • TCGA Data Refiner, RDFizer: http://goo.gl/vSnBEJ • Utilities: http://goo.gl/kNrFdI • Linked TCGA : http://tcga.deri.ie/ saleem@informatik.uni-leipzig.de AKSW, University of Leipzig, Germany

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