Search and Data Mining: Techniques. Introduction Anna Yarygina Boris Novikov

Transcription

1 Search and Data Mining: Techniques Introduction Anna Yarygina Boris Novikov

2 Data Analytics: Conference Sections Fundamentals for data analytics Mechanisms and features Big Data Huge data Target analytics Application-oriented analytics Sentiment/opinion analysis 2

3 Fundamentals for data analytics Tools, frameworks and mechanisms for data analytics Open API for data analytics In-database analytics Pre-built analytics (pattern, time-series, clustering, graph, statistical analysis, etc. Analytics visualization Multi-modal support for data analytics Google/FaceBook/Twitter/etc. analytics High-performance data analytics 3

4 Mechanisms and features Scalable data analytics Big data analytics Deep data analytics Mass data analytics Storing, dropping and filtering data Relevant/redundant/obsolete data analytics Volume vs. semantics analytics Nomad analytics Predictive analytics Trust in data analytics Legal issues analytics Failure on data analytics 4

5 Big Data Foundational models for Big Data Big Data Analytics and Metrics Big Data processing and management Big Data search and mining Big Data platforms Big Data persistence and preservation Big Data and social networks Big Data economics 5

6 Huge data Knowledge Discovery from Huge Data Computational Intelligence for Huge Data Linked Huge Data Security Intelligence with Huge Data 6

7 Target analytis Business analytics Malware analytics Cyber-threats analytics Mining user logs Reputation analytics User choice analytics Branding analytics Utility proximity-search analytics Survey-based online asset analytics Online employment analytics Geology analytics Global climate analytics Remote learning analytics Homecare analytics Population growth and migration analytics Food-borne illness outbreaks analytics 7

8 Application-oriented analytics Statistical applications Simulation applications Crawling web services Cross-database analytics Forecast analytics Financial risk management ROI analytics 8

9 Sentiment/opinion analysis Architectures for generic sentiment analysis systems Sentiment analysis techniques on social media Document-level analysis Sentence-level analysis Aspect-based analysis Comparative-sentiment analysis Sentiment lexicon acquisition Optimizing sentiment analysis algorithms Applications of sentiment analysis 9

10 Statistic-Centered Viewpoint Statistics Data Science Data Mining Machine Learning Artificial Intelligence Technologies Information Retrieval Database 10

11 IR-CenteredViewpoint Statistics Data Science Database OLTP OLAP Information Retrieval Machine Learning Artificial Intelligence Natural Language Processing Data, Text, Log, Stream Mining 11

12 Database-Centered Viewpoint (the RIGHT ONE) Statistics Data Science Database OLTP Text search, similarity, ranking Information Retrieval Indexing OLAP Data Mining Machine Learning Artificial Intelligence 12

13 BalancedViewpoint Statistics Database Information Retrieval Artificial Intelligence Data Scien ce OLTP Text search, similarity, ranking Indexing Natural Language Processing OLAP Text, log, stream mining Data Mining Machine Learning 13

14 Topics to Covered 1. Data Preprocessing 2. OLAP Modeling and Presentation 3. Mining Association rules 4. Information Retrieval: Text search 5. Text processing 6. Mining: Classification 7. Extractomg frp, Data streams 8. Mining: Cluster Analysis 9. Back to the Earth: Applications 14