Charlie Swanson (MongoDB)

Speaker:

Charlie Swanson

System:

MongoDB

Everyone knows distributed systems are hard. At MongoDB we want to make it easy to express complex queries and extract insights from your data, but we also need to be able to scale to enormous data sets. To help you scale, we support a deployment which partitions the data amongst multiple machines, but a distributed system complicates even simple queries.... Read More

[DB Seminar] Fall 2016: Round table discussion

We will have a round table discussion. Read More

Jessie Li (Penn State)

Speaker:

Jessie Li

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

[DB Seminar] Fall 2016: Emaad Manzoor

Speaker:

Emaad Manzoor

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

Alex Robinson (CockroachDB)

Speaker:

Alex Robinson

System:

CockroachDB

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

[DB Seminar] Fall 2016: Ashraf Aboulnaga (QCRI)

Speaker:

Ashraf Aboulnaga

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

[DB Seminar] Fall 2016: Wolfgang Gatterbauer (CMU)

Speaker:

Prof. Wolfgang Gatterbauer

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

[DB Seminar] Fall 2016: Prof. Shenghua Liu

Speaker:

Prof. Shenghua Liu

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

[DB Seminar] Fall 2016: Yingjun Wu

Speaker:

Yingjun Wu

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

Peloton Project – Info Meeting (Fall 2016)

Speaker:

Andy Pavlo

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