Institute of Computing UNICAMP Use of graphs and taxonomic classifications to analyze content relationships among courseware Márcio de Carvalho Saraiva and Claudia Bauzer Medeiros
Background and Motivation Videos Slides 2
Background and Motivation 3
Background and Motivation More than 1600 items about "databases" Changuel et al., 2015 4
5
Background and Motivation It should be easy to understand how different materials are related. Relationships: ● Authorship ? ● Date ● Location ? ● Visual ● Topics Ouyang and Zhu, 2007 ● etc. ? ? ? 6
Related Work Educational Recognition Analysis of relationships Integration of data relationships using graph databases of multimedia data Data Mining (Sathiyamurthy et al. 2012) (Cavoto et al. 2015) (Santanchè et al. 2014) (Pereira, 2014) Objects Architecture ● Analysis on a single level ● one kind of data metadata with hierarchies ● not related to education ● semantic annotations ● training sets 7
Goal Allow the integration of different types of educational material, highlighting relationships among content. 8
Proposal CIMAL: Courseware Integration under Multiple relations to Assist Learning I'm having trouble on "Big Data" in discipline "X" of teacher "Y" what other material could help me to understand this issue? CIMAL Student Sources 1 to N 9
Proposal Step B - Intermediate Step C - Intermediate Representation Analysis Step A - Extraction of elements of interest Representation Instantiation DDEx elements input Extractor of interest Java + Youtube API courseware Step D - Courseware access 10
Proposal - Step A - Extraction of elements of interest Classification Algorithms Introduction to Databases 11
Proposal - Step A - Extraction of elements of interest Commented slide, highlighted concepts , Slide titles, Descriptions from figures and tables .... 12
Proposal - Step A - Extraction of elements of interest Data Science Data Mining Classification 13
Proposal - Step A - Extraction of elements of interest 0:00- 0:30 “ ... Databases are important...” 0:31- 1:00 “...everybody need to know SQL ...” 1:01- 1:30 “... the DBMS is a computer software application.. .” 14
Proposal - Step B - Intermediate Representation Instantiation Step B - Intermediate Step C - Intermediate Representation Analysis Representation Instantiation Step A - Extraction of elements of interest Shadows as graphs Builder Intermediate input elements of interest Metadata and Graph Extractor Text Extractor Representation Builder courseware Shadows Graph-based as graphs Representation Step D - Courseware access 15
Proposal - Step B - Intermediate Representation Instantiation Advanced Databases Discipline Author Prof. Saraiva Introduction Set of to Coursewar relevant Databases SQL Text e (video) Lorem ipsum dolor Databases concepts sit amet, onsectetur adipiscing elit... DBMS Date 10/11/2015 Mota and Medeiros, 2013 16
Proposal - Step C - Intermediate Representation Analysis Step B - Intermediate Step C - Intermediate Representation Analysis Representation Instantiation Step A - Extraction of elements of interest Java + Lucene APIs Graph Database Intermediate Topics (Neo4J) input elements of interest Metadata and Graph Extractor Classifier Text Extractor Representation Builder Classification courseware Graph-based of Shadows Representation Enriched Graph-based Taxonomy Classification Representation of Representations Shadows Step D - Courseware access Combiner as graphs Relationships Analyzer Information about Relations external sources Taxonomy 17
Proposal - Step C - Intermediate Representation Analysis The ACM Computing Classification System (CCS) General and reference Hardware Theory of computation Information systems A B C D Data management Information retrieval World Wide Web systems 2 1 3 Middleware for Query languages Information integration databases 1 3 2 18
Proposal - Step C - Intermediate Representation Analysis The ACM Computing Classification System (CCS) General and reference Hardware Theory of computation Information systems A B C D Data management Information retrieval World Wide Web systems 2 1 3 Middleware for Query languages Information integration databases 1 3 2 19
Proposal - Step C - Intermediate Representation Analysis Step B - Intermediate Step C - Intermediate Representation Analysis Representation Instantiation Step A - Extraction of elements of interest Java + Lucene APIs Graph Database Intermediate Topics (Neo4J) input elements of interest Metadata and Graph Extractor Classifier Text Extractor Representation Builder Classification courseware Graph-based Representation Enriched of Shadows Graph-based Taxonomy Representation Classification of Shadows Step D - Courseware access Representations Combiner as graphs Relationships Analyzer Information about Relations external sources Taxonomy 20
Proposal - Step C - Intermediate Representation Analysis The ACM Computing Classification System (CCS) Advanced Theory of computation Databases General and reference Hardware Information systems A C D B Prof. Saraiva Introduction Data management Information retrieval to World Wide Web systems Databases SQL, (video) Lorem ipsum dolor Database , 1 3 2 sit amet, onsectetur adipiscing elit... DBMS ... Middleware for Query languages Information integration 10/11/2015 databases 1 2 3 Topics??? 21
Proposal - Step C - Intermediate Representation Analysis Introduction to Databases (video) N wikipages 22
Proposal - Step C - Intermediate Representation Analysis 80% SQL Introduction to ESA Databases (video) 20% Depth-first search Gabrilovich and Markovitch, 2007 ; Apache Lucene, 2014 23
Proposal - Step C - Intermediate Representation Analysis 80% SQL Courseware ESA Query languages 24
Proposal - Step C - Intermediate Representation Analysis Advanced Databases Prof. Saraiva Introduction to SQL, Databases (video) Lorem ipsum dolor Database, sit amet, onsectetur adipiscing elit... DBMS... 10/11/2015 Data Query Information management languages Systems systems Topics 25
Proposal - Step C - Intermediate Representation Analysis Step B - Intermediate Step C - Intermediate Representation Analysis Representation Instantiation Step A - Extraction of elements of interest Java + Lucene APIs Graph Database elements of Intermediate Topics interest (Neo4J) input Metadata and Graph Extractor Classifier Text Extractor Representation Builder Classification courseware Graph-based of Shadows Representation Enriched Graph-based Taxonomy Classification Representation of Representations Shadows Step D - Courseware access Combiner as graphs Relationships Analyzer Information about Relations external sources Taxonomy 26
Proposal - Step C - Intermediate Representation Analysis Information Systems Introduction Classificatio to n Algorithms Databases (slides) (video) Data management systems Query Information languages Integration 27
Proposal - Step C - Intermediate Representation Analysis Information Systems Introduction Classificatio to n Algorithms Databases (slides) (video) Data management systems Query Information languages Integration 28
Proposal - Step C - Intermediate Representation Analysis Information Systems Introduction Classificatio to n Algorithms Databases (slides) (video) Data management systems Query Information languages Integration 29
Proposal - Step C - Intermediate Representation Analysis Information Systems Introduction to Databases I Databases (video) (video) Data management systems Query languages 30
Proposal - Step C - Intermediate Representation Analysis Information Systems Introduction to Databases I Databases (video) (video) Data management systems Query languages 31
Proposal - Step C - Intermediate Representation Analysis Introduction Classificatio to n Algorithms Databases Databases I Data Mining 32
Proposal - Step C - Intermediate Representation Analysis Introduction Clique Classification to Algorithms Databases Databases I Data Mining 33
Proposal - Step C - Intermediate Representation Analysis Shortest Path Graph Introduction Classification 3 to Algorithms to “Data Mining” Databases 2 2 1 Databases I Data Mining 34
Proposal - Step C - Intermediate Representation Analysis Centrality Introduction Classification to Algorithms Databases Databases I Data Mining 35
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