The main objective of this course is (a) to acquaint the students with the latest trends in Big Data management, and (b) to study advanced data analytics techniques focusing on do-mains, such as text, recommendation, visualizations, and graphs. As expected results, the students will gain deep insight of solutions for real-world Big Data management problems, while obtaining strong skills in designing and implementing such scalable solutions. Moreo-ver, students are expected to apply analytical processing and data analysis techniques in practical problems related to modern data management.
Open data and Linked open data
The case for Open data. Open data repositories. Open governmental data. Open geospatial data. Linked data. Linked open data.
Big Spatial Data
The value of spatial data in modern applications. Geotagging. Plat-forms for scalable management of Big Spatial Data. SpatialHadoop. Spatial exten-sions for Spark. SpatialSpark.
Trends in Big Data management
Modern techniques in Big Data management. “One size does not fit all”. Data exploration. In-memory processing. In-situ process-ing. Novel platforms. Polystores.
The Industry’s view on Big Data
Novel architectures for Big Data management from the industrial sector. Google (incl. Pregel, Dremel, Giraph, F1). Facebook. Twitter. LinkedIn (Kafka). SAP (HANA).
The case of NoDB
Minimize data-to-query time. Database processing on raw data files. Avoiding the bottleneck of database design and data loading. Specific applica-tions, such as scientific data analysis.
Data warehouses. Cubes. Multidimensional access methods. Dimensionality reduction. Multidimensional analysis.
Management of unstructured data. Challenges related to text man-agement. Text indexing and search. Scoring. Term weighting. Vector space model. Computing scores in a complete search system. ElasticSearch.
Social media analytics
Social media monitoring. Collecting data from social media. Understanding social data. Analysing social data. The value of social data for today’s business. Trend detection. Business intelligence and social media.
Data visualization tools and techniques. Visual data analytics. The Tableau tool. Applications in business intelligence. New visual interfaces. Advanced visualization techniques. Research prototype systems.
Novel data management systems and techniques for processing large-scale graphs. Graph indexing. Parallel and distributed graph processing. Graph partitioning.
- Samet H. (2006): Foundations of Multidimensional and Metric Data Structures. The Morgan Kaufmann Series in Computer Graphics, ISBN-10: 0123694469.
- Leskovec J, Rajaraman A, Ullman J: Mining of Massive Datasets. Cambridge Univer-sity Press.
- Mamoulis N. (2011): Spatial Data Management. Morgan & Claypool Publishers, 2011 Synthesis Lectures on Data Management.
- White R.W, and Roth R.A: Exploratory Search Beyond the Query-Response Paradigm. Morgan Claypool. ISBN: 9781598297836.
- Selected research articles.