CMSC828E: Probabilistic and Graph Data Management; Large-scale Analytics

Prof. Amol Deshpande;    CSIC 2107;    Mon-Wed 3:30pm-4:45pm

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Approach:

Course Description:

• Graph-structured Data: The main focus of the class will be graph data management. Some of the key emerging application domains have to deal with very large volumes of dynamic, rapidly-changing graph-structured data. This includes the Web, social networks, sensor networks, biological networks, traffic networks to name a few. Database management systems are not very good at managing such data, or querying over them, especially when we consider node and edge attributes, time-varying characteristics (e.g., edges being valid for a duration of time), and uncertainty (see next). Further, the types of analysis and queries that we typically want to perform (e.g., relationship analytics, ranking, proximity searching) are quite different from standard database queries.
• Data Uncertainty: Real-world data also often exhibits large amounts of uncertainty of various types. There has been much work in the area of uncertain data management in the recent years, however many challenges remain. Most of the prior work has generally made simplifying assumptions about the types of uncertainty that can be modeled. There has been almost no work in managing uncertain or probabilistic graph data. We will focus on the approach developed by us at UMD, that aims to integrate probabilistic graphical models (e.g. Bayesian networks), and statistical relational models (e.g. Probabilistic Relational Models, Markov Logic Networks) into relational databases, with a focus on the data management, querying, and scalable inference.
• Large-scale Analytics: Technologies like MapReduce and Hadoop have made it possible to analyze very large volumes of data, using a large number of distributed machines. Here we will focus on declarative abstractions for large-scale statistical analysis, machine learning, and graph analytics on Hadoop. Graph analytics in particular are challenging in such a framework, because graph algorithms are naturally sequential in nature.

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