1. 7th May 20131 Primary Research Team & Capabilities Dept. of Parallel and Distributed Computing Research and Development Areas: –Large-scale HPCN, Grid and MapReduce applications –Intelligent and Knowledge oriented Technologies Experience from IST: –3 project in FP5: ANFAS, CrosGRID, Pellucid –6 project in FP6: EGEE II, K-Wf Grid, DEGREE (coordinator), EGEE, int.eu.grid, MEDIGRID –4 projects in FP7: Commius, Admire, Secricom, EGEE III Several National Projects (SPVV, VEGA, APVT) IKT Group Focus: –Information Processing (Large Scale) –Graph Processing –Information Extraction and Retrieval –Semantic Web –Knowledge oriented Technologies –Parallel and Distributed Information Processing Solutions: –SGDB: Simple Graph Database –gSemSearch: Graph based Semantic Search –Ontea: Pattern-based Semantic Annotation –ACoMA: KM tool in Email –EMBET: Recommendation System –Experts on MapReduce and IR (Nutch, Solr, Lucene) Director & leader of PDC: Dr. Ladislav Hluchý URL: http://ikt.ui.sav.skhttp://ikt.ui.sav.sk

2. Large scale Text and Graph data processing Core Technology Web crawling –Nutch + plugins Full text indexing and search –lucene, Sorl Information Extraction –Ontea, GATE All above large scale –Hadoop, S4 Graph processing and Querying –Simple Graph Database (SGDB) –gSemSearch –Neo4j –Blueprints 7th May 20132 Underlined are the technologies developed by IISAS

3. Relation to Business Intelligence Old BI approaches –Data Integration from RDBM –Data ware houses –OLAP –… New BI approaches –Other than RDBM data structures: Networks, Semantics Networks/Graphs in Telecom, Social Networks, Transactions, Linked Data … NoSQL: key value (Tokyo Cabinet), column stores (HBase), Graph databases, RDF(s) –In-Memory computing –Commodity PCs solutions for large data: MapReduce style - Hadoop, Pregel style – Giraph, Hama –Big unstructured data processing (on Hadoop): Sentiment analysis, topic detection, named entity detection 7th May 20133

4. Ontea: Information Extraction Tool  Regex patterns  Gazetteers  Resuls  Key-value pairs  Structured into trees  graphs  Transformers, Configuration  Automatic loading of extractors  Visual Annotation Tool  Integration with external tools  GATE, Stemers, Hadoop …  Multilingual tests English, Slovak, Spanish, Italian 7th May 20134 http://ontea.sf.net Text with annotations Tree of annotations Network /Graph of annotations

5. Named Entity Recognition (NER) Combination of Existing NER –ANNIE (GATE), Apache OpenNLP, –Illinois NER, Illinois Wikifier, –LingPipe, Open Calais –Stanford NER,WikiMiner, –Miscinator Machine Learning –Decision Trees models Our approach was evaluated in best 6 from 17 word wide on MSM 2013 http://oak.dcs.shef.ac.uk/msm2013/challenge.html http://oak.dcs.shef.ac.uk/msm2013/challenge.html 7th May 20135

6. gSemSearch: Graph based Semantic Search Entity relation search in semantic networks/graphs Search, Navigation, Data Interaction Aiming at data integration of –Structured data (Relational data, LinkedData) –Unstructured Data (text, documents, communication) Applications: –Email, Web, Text documents, LinkedData 17 April 20136 http://ikt.ui.sav.sk/esns/

7. SemSets: Sematnic Search Answering list type questions: astronauts who walked on the Moon Wikipedia as text and networks/graph Text: IR methods, Lucene based Graph/network: sprading activation and SemSets Winning solution on Semantic Search Challenge 2011 17 April 20137 1.Eugene_Cernan 2.Alan_Bean 3.David_Scott 4.John_Young_(astronaut) 5.Neil_Armstrong 6.Pete_Conrad 7.Harrison_Schmitt 8.Alan_Shepard 9.Charles_Duke 10.Buzz_Aldrin 11.James_Irwin 12.Edgar_Mitchell

8. SGDB: Simple Graph Database Storage for graphs Optimized for graph traversing and spread of activation Faster then Neo4j for graph traversing operations Supports Blueprints API https://simplegdb.svn.sourceforge.net/svnroot/simplegdb/Sgdb3 Graph Database Benchmarks –Graph Traversal Benchmark for Graph Databases –http://ups.savba.sk/~marek/gbench.htmlhttp://ups.savba.sk/~marek/gbench.html –Blueprints API - possibility to test compliant Graph databases 7th May 20138 Source: http://geza.kzoo.edu/bionet/html/scalefree.html

9. Future Direction: Relations Discovery in Large Graph Data Motivation –Graph/Network data are everywhere: social networks, web, LinkedData, transactions, communication (email, phone). –Also text can be converted to graph. –Interconnecting graph data and searching for relations is crucial. Approach –Forming semantic trees and graphs from text, web, communication, databases and LinkedData –User interaction with graph data in order to achieve integration and data cleansing –Users will do it, if user effort have immediate impact on search results 7th May 20139