parsing metamapfiles in hadoop · map reduce split input by record--> identify pairs in parallel...

Parsing MetaMap Files in Hadoop Amy L. Olex, M.S., Alberto Cano, Ph.D., Bridget McInnes, Ph.D. Department of Computer Science The deluge of data in today’s information-centric world requires bigger and better computing resources for processing. This can be a limiting factor in how much data labs with limited computing resources are able to handle. This project explores the pitfalls of a serial program that parses MetaMap files to identify UMLS CUI bigrams in a large set of scientific literature. The algorithm is re-implemented in Hadoop MapReduce to overcome resource bottlenecks. Hadoop for the Desktop MapReduce in 30 seconds Conclusion and Future Work References [1] Bodenreider O. “The unified medical language system (UMLS): integrating biomedical terminology.” Nucleic acids research, 2004, 32, D267-D270. [2] Aronson AR and Lang F. “An overview of MetaMap: historical perspective and recent advances.” JAMIA, 2010, 17:3, 229-236. Contact [email protected] or [email protected] for more information. Results UMLS, CUIs, and MetaMap….Oh My! • Unified Medical Language System • Repository of biomedical vocabularies • Facilitates automated information retrieval (e.g. linking health information and billing codes across systems). • Provides language normalization by linking similar terms to same concept/meaning. UMLS 1 • Concept Unique Identifier • ID assigned to each unique concept in the UMLS. • E.g. Headache and Cranial Pain are both assigned to the CUI C0018681 CUI • Tool that identifies UMLS concepts in biomedical texts. • Output mapped text to compressed MetaMap Machine Output (MMO) files. •Parsed 23,343,329 citations to create the 2015 MedLine/PubMed Baseline dataset--779 MMO files compressed to 132GB. MetaMap 2 UMLS::Association UMLS CUIs can be used to normalize biomedical and clinical text for use in natural language processing applications. By counting CUI Bigram frequency—the number of times two CUIs appear close to each other in text-- the UMLS::Association package can identify related concepts. E.g. head ache and aspirin. MetaMap File Anatomy Adjacent CUI Bigrams C1710187 - C0868928 C0868928 - C0018081 C0018081 - C0259800 C1710187 - C1533148 C1533148 - C0018081 Serial Limitations • Processes one file/one utterance at a time with nested for loops. • Regularly writes nested bigram hash table to MySQL database due to memory limitations, introducing DB communication latency. • Perl code is not paralellizable due to limitations in sharing nested hashes across threads. Utterance… CUI1…CUI2… EOU Utterance… CUI1…CUI2… EOU Utterance… CUI1…CUI2… CUI3…CUI4… EOU Utterance… CUI1…CUI2… EOU CUI1-CUI2, 4 CUI2-CUI3, 1 CUI3-CUI4, 1 <K,V> CUI1-CUI2, 1 <K,V> CUI1-CUI2, 1 <K,V> CUI1-CUI2, 1 CUI2-CUI3, 1 CUI3-CUI4, 1 <K,V> CUI1-CUI2, 1 Sum Input Output Map Reduce Split input by record --> identify <Key, Value> pairs in parallel --> sum values with same key CuiCollectorMapReduce Extracts CUI bigrams using Hadoop MapReduce framework. Desktop implementation (a single node Hadoop system). MapReduce Advantages • Inherent and scalable parallelization. • Writes results to disk--all intermediate and final. • No MySQL communication. MapReduce 8 hrs Serial (MySQL) 229 hrs Serial (no MySQL) 22 hrs MetaMap 2015 MEDLINE Baseline Dataset 28.6x Speedup! 2.8x Speedup! Parsing CUI Bigrams in Hadoop on a desktop computer resulted in significant speedup, and enables parsing larger datasets that were not previously feasible. Algorithm improvements include a window size to collect distant CUI bigrams and crossing utterances to process full PubMed citations. Future work includes testing the scalability on a Hadoop cluster, resolving an issue with the compressed input file format to improve mapping efficiency, identifying optimal Hadoop settings for a desktop implementation, and re-implementing in SPARK to take advantage of its in-memory storage of intermediate results. In conclusion, desktop implementations of Hadoop can resolve computing resource problems and process data faster, opening up more research areas in big data processing for smaller labs.

Upload: others

Post on 14-Jul-2020

7 views

Category:

Documents

0 download

Report

Download

Embed Size (px):

TRANSCRIPT

Page 1: Parsing MetaMapFiles in Hadoop · Map Reduce Split input by record--> identify pairs in parallel -->sum values with same key CuiCollectorMapReduce Extracts

ParsingMetaMap FilesinHadoopAmyL.Olex,M.S.,AlbertoCano,Ph.D.,BridgetMcInnes,Ph.D.

DepartmentofComputerScience

The deluge of data in today’s information-centric world requires bigger andbetter computing resources for processing. This can be a limiting factor in howmuch data labs with limited computing resources are able to handle.This project explores the pitfalls of a serial program that parses MetaMap files toidentify UMLS CUI bigrams in a large set of scientific literature. The algorithm isre-implemented in Hadoop MapReduce to overcome resourcebottlenecks.

HadoopfortheDesktop MapReduce in30seconds

ConclusionandFutureWork

References[1]Bodenreider O.“Theunifiedmedicallanguagesystem(UMLS):integratingbiomedicalterminology.”Nucleicacidsresearch,2004,32,D267-D270.[2]AronsonARandLangF.“AnoverviewofMetaMap:historicalperspectiveandrecentadvances.”JAMIA,2010,17:3,229-236.

[email protected] [email protected] formoreinformation.

Results

UMLS,CUIs,andMetaMap….OhMy!

• Unified Medical Language System• Repositoryofbiomedicalvocabularies• Facilitatesautomatedinformationretrieval(e.g.linkinghealthinformationandbillingcodesacrosssystems).• Provideslanguage normalization bylinkingsimilartermstosameconcept/meaning.

UMLS1

• Concept Unique Identifier• ID assigned toeachunique concept intheUMLS.• E.g. HeadacheandCranialPainarebothassignedtotheCUIC0018681

CUI

• Toolthatidentifies UMLS concepts inbiomedicaltexts.• OutputmappedtexttocompressedMetaMap MachineOutput(MMO)files.•Parsed23,343,329citationstocreatethe2015MedLine/PubMedBaselinedataset--779MMOfilescompressedto132GB.

MetaMap2

UMLS::AssociationUMLS CUIs can be used to normalize biomedical and clinical text for use innatural language processing applications. By counting CUI Bigramfrequency—the number of times two CUIs appear close to each other in text--the UMLS::Association package can identify related concepts. E.g. headache and aspirin.

MetaMap FileAnatomy

AdjacentCUIBigramsC1710187- C0868928C0868928 - C0018081C0018081 - C0259800C1710187- C1533148C1533148- C0018081

SerialLimitations• Processesone file/one utterance atatimewithnestedforloops.• RegularlywritesnestedbigramhashtabletoMySQLdatabasedueto

memory limitations,introducingDB communication latency.• Perlcodeisnot paralellizable duetolimitationsinsharingnestedhashes

acrossthreads.

Utterance…CUI1…CUI2…EOU

Utterance…CUI1…CUI2…CUI3…CUI4…EOU

Utterance…CUI1…CUI2…EOU

CUI1-CUI2,4

CUI2-CUI3,1

CUI3-CUI4,1

<K,V>CUI1-CUI2,1

<K,V>CUI1-CUI2,1CUI2-CUI3,1CUI3-CUI4,1

<K,V>CUI1-CUI2,1

Sum

Input Output

Map Reduce

Splitinputbyrecord -->identify<Key, Value> pairsinparallel--> sumvalueswithsamekey

CuiCollectorMapReduceExtracts CUI bigrams usingHadoopMapReduce framework.

Desktop implementation (asinglenodeHadoopsystem).

MapReduceAdvantages• Inherentandscalable parallelization.• Writes results to disk--allintermediateandfinal.• No MySQL communication.

MapReduce

8hrs

Serial(MySQL)

229hrs

Serial(noMySQL)

22hrs

MetaMap 2015MEDLINEBaseline

Dataset

28.6xSpeedup!

2.8xSpeedup!

Parsing CUI Bigrams in Hadoop on a desktop computer resulted in significantspeedup, and enables parsing larger datasets that were not previouslyfeasible. Algorithm improvements include a window size to collect distant CUIbigrams and crossing utterances to process full PubMed citations. Future workincludes testing the scalability on a Hadoop cluster, resolving an issue with thecompressed input file format to improve mapping efficiency, identifying optimalHadoop settings for a desktop implementation, and re-implementing in SPARKto take advantage of its in-memory storage of intermediate results.

Inconclusion,desktop implementations ofHadoopcanresolve computing resource problems andprocess data faster,openingup

more research areas inbigdataprocessingforsmallerlabs.

PARSING WITH CONTEXT-FREE GRAMMARS cc437. PARSING Parsing is the process of recognizing and assigning STRUCTURE Parsing a string with a CFG: – Finding

Bare-Bones Dependency Parsing - Uppsala Universitynivre/docs/BareBones.pdf · I Parsing methods for bare-bones dependency parsing I Chart parsing techniques ... [Kuhlmann and Satta

OCL parsing / type checking in the context of GF and KeYi12key/keysymposium04/... · OCL parsing / type checking in the context of GF and KeY Kristofer Johannisson 1. I. Introduction

BAB 6: Contex-Free Grammar & Parsing - ocw.upj.ac.id€¦ · Bab 6: Context-Free Grammar & Parsing Agenda. • Context-Free Grammar • Top-Down Parsing • Buttom-Up Parsing Contex-Free

Parsing Learn to Read NT Greek - · PDF filelearning process. Why, then, provide answer keys? Students will find that having the answer key ... Parsing Learn to Read NT Greek

Basic Parsing Algorithms: Earley Parser and Left Corner Parsing

Bare-Bones Dependency Parsing - Uppsala Universitystp.lingfil.uu.se/~nivre/docs/BareBones.pdf · I Parsing methods for bare-bones dependency parsing I Chart parsing ... Eisner 2000]:

Parsing. 2301373Chapter 4 Parsing2 Outline Top-down v.s. Bottom-up Top-down parsing Recursive-descent parsing LL(1) parsing LL(1) parsing algorithm First

Truth About the Cold War: Key Document Extracts

Incremental, Predictive Parsing with Psycholinguistically ... · On the technical side, our key contribution is a novel parsing algorithm for prob-abilistic PLTAG. Incremental fully