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Algorithms and Data Structures for Massive Datasets (Acube Lab)

Rossano Venturini Dipartimento di Informatica Università di Pisa. Algorithms and Data Structures for Massive Datasets (Acube Lab). Paolo Ferragina Giuseppe Prencipe Marco Cornolti Andrea Farruggia Giovanni Micale Francesco Piccinno Giorgio Audrito. A 3 Lab (acube.di.unipi.it).

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Algorithms and Data Structures for Massive Datasets (Acube Lab)

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  1. Rossano Venturini Dipartimento di Informatica Università di Pisa • Algorithms and Data Structures • for Massive Datasets • (Acube Lab) Paolo Ferragina Giuseppe Prencipe Marco Cornolti Andrea Farruggia Giovanni Micale Francesco Piccinno Giorgio Audrito

  2. A3 Lab (acube.di.unipi.it) Algorithms and data structures for massive dataset • Data Compression • Compressed Indexing • Web or arbitrary texts • Storage and analysis of massive graphs • Information Retrieval on news, tweet, … Submitted US patents: 3 with Yahoo, 1 with NYU Accepted US patents: 1 with U. Rutgers, 1 with AT&T-Lucent

  3. Social Networks and Social Data • Given an idea, you need the right platform to implement it: • HW + SW (IT Center) • Algorithms (our Lab) • Graph structure + Textual Content • Nodes  users (~ 1 bil) • Edges explicit = friend, follower, retweet, +1, … (~ 10bil) • Edges implicit = similarity, co-occurrence, click, … (» 100 bil)

  4. Data Compression: Theory & Engineering J. ACM ‘05 ACM-SIAM Soda ’09-’14 ACM WSDM ‘10 ESA ’11-’14 Algorithmica ‘12 SIAM J. Computing ‘13 Key issue: • Minimize space occupancy • Maximize decompression speed A new algorithmic concept: Multi-objective design of compressors Two interesting scenarios: - Energy-efficiency issues - Cloud computing Can we fix the space occupancy and minimize the decompression time ? Or, vice versa ?

  5. Compressed Indexing: Theory & Engineering J. ACM ‘05 ACM SIGIR ‘07 J. ACM ‘09 ACM Trans. Algo. ’10 ESA ’13 ACM-SIAM SODA ’13 … and many others Key issue: • Minimize space occupancy • Maximize substring-search throughput Suffix-array compressible «-» Bzip searchable December 2003 • Performance over hundreds of MBs and commodity PC • Count(P) takes 5 microsecs/char, taking about bzip’s space • Locate(P) outputs 100K occ/sec, taking +10% space • This may be 4x faster than IL, within <35% space occupancy

  6. Compressed Indexing: Theory & Engineering No SQL DB The <key,value> problem: • Trie:14x more spacethan input data. • Front-coding & two-levelindexing: • 110% ofinput data • 4 microsecs/char • OurCompressedPermuterm: • < 25% of input data, i.e. closeto bzip2 • 1060 microsecs/char • So, timecloseto FC butone-fourth of itsspace Under Y!-patenting

  7. We know how to “manage” everything…

  8. TF-IDF vector Vector Space model t3 v 2.2 5.1 9.1 1.0 0.1 w a t2 t1 Similarity(v,w) ≈ cos(a) Information Retrieval “Diego Maradona won against Mexico” Dictionary against Diego Maradona Mexico won

  9. Mexico soccer team The soccer player Topic Annotators • “Diego Maradona won against Mexico” Detect mentionsand annotate them with entity/topic extracted from a catalog Wikipedia! we serve about 170k requests/day

  10. Paper at ACM WSDM 2012 Paper at IEEE Software 2012 Details on...http://acube.di.unipi.it/tagme Paper at ECIR 2012

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