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
7th May 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 inEMBET:
Recommendation System Experts on MapReduce and IR (Nutch, Solr,
Lucene) Director & leader of PDC: Dr. Ladislav Hluch URL: 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 Underlined are the technologies
developed by IISAS 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 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 Text with annotations Tree of
annotations Network /Graph of annotations 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 th
May 20135 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: , Web, Text documents, LinkedData 17
April 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 April 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 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 Source: Future Direction: Relations Discovery in Large Graph
Data Motivation Graph/Network data are everywhere: social networks,
web, LinkedData, transactions, communication ( , 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
