- Instructor : Prof. Amit Sheth
- Coordinator : Shreyansh Bhatt
- Class Time : 2.00 PM - 3.20 PM on Tuesdays and Thursdays
- Location : 399, Joshi Research Center
- Recommended Text : Semantics Empowered Web 3.0: Managing Enterprise, Social, Sensor, and Cloud-based Data and Services for Advanced Applications
Class 1 - Introduction
Prof. Amit Sheth discusses the basic concepts in Semantic Web and Web 3.0. Class video.
Class 2: Twitris Overview and Tutorial
Jeremy presents Twitris tool. Various features have been demonstrated.
Class 3 : Role of metadata and semantic search
Prof. Amit Sheth discusses role of metadata in semantic search.
Class 4 : Semantic search engine
Prof. Amit Sheth explains semantic search engine and discusses first semantic search engine Talee.
Class 5 : RDF/RDFS
Vinh discusses RDF/RDFS. Following is the class video.
Class 6 : RDF/RDFS 2, Data modelling
RDF/RDFS continues. Data modelling using RDF is discussed in detail.
Class 7 : Schema.org
Sanjaya explains Schema.org and annotation using schema.org.
Class 8 : Relationship Web
Prof. Amit Sheth talks about relationship web and roles of relationships in semantic web.
Class 9: Introduction on Big Data with an Emphasis on the Velocity and Variety Dimensions
Prof. Emanuele Della Velle discusses velocity and veriety dimensions of Big Data.
Class 10 : Mastering veriety dimension of Big data with Semantic technologies
Prof. Amit Sheth discusses roles of semantic technologies in Big data to address veriety.
Class 11 : Walkthrough of Semantic Technologies: RDF, SPARQL, OWL, and R2ML
Prof. Emanuele Della Velle discusses semantic technologies.
Class 12 : Examples of Applied Semantic Technologies to Solve Variety: SSN Annotation
Pramod Ananthram discusses SSN Ontology to address veriety dimension of Big data in sensor data.
Class 13 : Examples of Applied Semantic Technologies to Solve Variety: Social Data Annotation
Prof. Amit Sheth discusses how to address veriety aspect of Big data for social data.
Class 14 : Mastering the Velocity of Big Data with Stream Processing Technologies
Prof. Emanuele Della Valle discusses velocity aspect in Big data and various stream processing technologies.
Class 15 : Walkthrough on Stream Processing Technologies
Riccardo continues discussion about stream processing technologies and also gives hands on on some of the stream processing processing technologies.
Class 16 : Examples of Applied Stream Processing Technologies to Solve Velocity
Prof. Amit Sheth discusses stream processing technologies for real world application to address velocity dimension of Big data.
Class 17 : Stream Reasoning: Mastering the Velocity & Variety Dimension of Big Data at the Same Time
Prof. Emanuele Della Valle discusses technology for reasoning over streaming data.
Class 18 : Hands-on Stream Reasoning Technologies
Riccardo continues discussion about stream reasoning and provides hands on for reasoning over streaming data.
Class 19 : Semantic Filtering as an Example of Semantic Technologies for Real-time Analysis
Pavan discusses continuous semantics for streaming Twitter data.
Class 20 : Event Correlation Social and IoT as an Example of Data Integration
Pramod Ananthram discusses role of social and sensor data in event correlation.
Class 21 : Listening to the Pulse of our Cities Fusing Social Media Streams & Call
Prof. Emanuele Della Valle discusses role of social media streams and call data records for event identification.
Links for final project presentation of each group:
- Real time analysis of tiwtter trolling
- Crossing Media Streams for Harassment Detection
- Social Media as a Measurement Tool of Gender-Based Violence
- Identifying Gang Members’ Twitter Profiles and Study their Tweets across US States
- Semantic Similarity Between Two Sentences
- Driving Kibi through Entities
- Twester: Tweet Semantic Filter
- Continuous Monitoring of Asthma Control Level