[DB Seminar] Spring 2017: Joy Arulraj

Speaker:

Joy Arulraj

Joy will give a talk on his work.The difference in the performance characteristics of volatile (DRAM) and non-volatile storage devices (HDD/SSDs) influences the design of database management systems. The key assumption has always been that the latter is much slower than the former. This affects all aspects of a DBMS's runtime architecture. But the arrival of new non-volatile memory (NVM)... Read More

[DB Seminar] Fall 2016: Kijung Shin

Speaker:

Kijung Shin

How do the k-core structures of real-world graphs look like? What are the common patterns and the anomalies?  How can we use them for algorithm design and applications? A k-core is the maximal subgraph where all vertices have degree at least k. This concept has been applied to such diverse areas as hierarchical structure analysis, graph visualization, and graph clustering.... Read More

[DB Seminar] Fall 2016: Michael Zhang

Speaker:

Michael Zhang

Current architectures for main-memory online transaction processing (OLTP) database management systems (DBMS) are based on one of two design choices. In the partition choice, the data is assumed to be well partitioned. Transactions run with little or no concurrency control inside a partition. In the non-partition choice, the data is not required to be partitioned and the system carefully controls... Read More

[DB Seminar] Fall 2016: Ziqi Wang

Speaker:

Ziqi Wang

As multicore architecture is becoming the new normal of today’s computers, many traditional programming paradigms for mutual exclusion has become a major source of scalability bottleneck. To counter such bottlenecks for our in-memory database prototype at Carnegie Mellon University [1], we implemented a lock-free B+Tree multimap index based on BwTree, which was originally proposed by Microsoft Research [2]. In this... Read More

Neil Shah (Thesis proposal dry-run)

Speaker:

Neil Shah

Given the ever-growing prevalence of online social services, usefully leveraging mas- sive datasets has become an increasingly important challenge for businesses and end-users alike. Online services capture a wealth of information about user behavior and platform in- teractions, such as who-follows-whom relationships in social networks and who-rates-what- and-when relationships in e-commerce networks. Since many of these services rely on data-... Read More

[DB Seminar] Fall 2016: Canceled (Nov 14)

This week's DB seminar is cancelled. Read More

[DB Seminar] Fall 2016: Prakhar Ojha

Speaker:

Prakhar Ojha

In this talk, I shall discuss two interesting problems pertinent to quality-control and budget-optimization in complex crowdsourcing. Crowdsourcing has evolved from solving simpler tasks, like image-classification, to more complex tasks such as document editing, language translation, product designing etc. Unlike micro-tasks performed by a single worker, these complex tasks require a group of workers and greater resources. If the task-requester... Read More

[DB Seminar] Fall 2016: Neil Shah

Speaker:

Neil Shah

Livestreaming platforms have become increasingly popular in recent years as a means of sharing and advertising creative content. Popular content streamers who attract large viewership to their live broadcasts can earn a living by means of ad revenue, donations and channel subscriptions. Unfortunately, this incentivized popularity has simultaneously resulted in incentive for fraudsters to provide services to astroturf, or artificially... Read More

[DB Seminar] Fall 2016: Matteo Riondato (Two Sigma)

Speaker:

Matteo Riondato

TRIÈST is a suite of one-pass streaming algorithms to compute unbiased, low-variance, high- quality approximations of the global and local (i.e., incident to each vertex) number of triangles in a fully-dynamic graph represented as an adversarial stream of edge insertions and deletions. The algorithms use reservoir sampling and its variants to exploit the user-specified memory space at all times. This... Read More

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