How could we harness the increasingly available big data to understand our dynamic ecosystem? For example, why people or animals move in the space in certain ways and how do their movements respond to surrounding environments? Why are crimes more frequent in certain regions and can we explain it using heterogeneous urban data? Is shale gas development contaminating our environment... Read More

Given a stream of heterogeneous graphs containing different types of nodes and edges, how can we spot anomalous ones in real-time while consuming bounded memory? This problem is motivated by and generalizes from its application in security to host-level advanced persistent threat (APT) detection. We propose StreamSpot, a clustering based anomaly detection approach that addresses challenges in two key fronts:... Read More

Learn more about CockroachDB from Google alum and Member of Technical Staff, Alex Robinson! The talk will focus on how CockroachDB ensures data integrity, no matter how broadly distributed. Read More

Distributed data processing platforms such as Pregel and GraphLab have substantially simplified the design and deployment of certain classes of distributed graph analytics algorithms. However, these platforms do not represent a good match for distributed graph mining problems, for example, finding frequent subgraphs in a graph. Given an input graph, these problems require exploring a very large number of subgraphs... Read More

Performing inference over large uncertain data sets is becoming a central data management problem. Recent large knowledge bases, such as Yago, Nell or DeepDive, have millions to billions of uncertain tuples. Because general reasoning under uncertainty is highly intractable, many state-of-the-art systems today perform approximate inference by reverting to sampling. This talk shows an alternative approach that allows approximate ranking... Read More

With mobile and web-based techniques to create highly interactive platforms, social media becomes prevalent  in our daily life. It sees the interaction among people in which they create, share, discuss, or exchange ideas in virtual communities and networks. In this talk, he will introduce a series of his previous research work related to social media. They range from understanding short text, opinions,... Read More

Multi-version concurrency control (MVCC) is currently the most popular scheme used in modern database management systems (DBMSs). Although the protocol was discovered in the late 1970s, it is used in almost every major relational DBMS released in the last decade. Maintaining multiple versions of data potentially increases parallelism without sacrificing serializability. But scaling MVCC schemes in a multi-core, in-memory DBMS... Read More

The CMU Database Group is holding an orientation meeting for students that are interested in getting involved in research and development of CMU's new flagship database management system (DBMS). Peloton is a high-performance, in-memory relational DBMS for hybrid workloads. The key aspect of Peloton that makes it different from other systems is that it is designed to be completely autonomous... Read More

This is the first meeting of the Database Group for Fall 2016. We will do the usual meet & greet, followed by two talks from PKU/CMU summer interns. Read More

Given a data stream with many attributes and high frequency of events, how to cluster similar events? Can it be done in real time? For example, how to cluster decades of frequent measurements of tens of climatic attributes to aid real time alert systems in forecasting extreme climatic events, such as floods and hurricanes? The task of clustering data with... Read More