most citedFast Data in the Era of Big Data: Twitter's Real-Time Related Query Suggestion Architecture

8 citations · 25 across the 6 of their papers we have counts for

collaborators

6 papers

cs.DC20136 cited

Monoidify! Monoids as a Design Principle for Efficient MapReduce Algorithms

Jimmy Lin

It is well known that since the sort/shuffle stage in MapReduce is costly, local aggregation is one important principle to designing efficient algorithms. This short paper represen…

cs.IR20131 cited

Dynamic Memory Allocation Policies for Postings in Real-Time Twitter Search

Nima Asadi, Jimmy Lin, Michael Busch

We explore a real-time Twitter search application where tweets are arriving at a rate of several thousands per second. Real-time search demands that they be indexed and searchable…

cs.DB20125 cited

Runtime Optimizations for Prediction with Tree-Based Models

Nima Asadi, Jimmy Lin, Arjen P. de Vries

Tree-based models have proven to be an effective solution for web ranking as well as other problems in diverse domains. This paper focuses on optimizing the runtime performance of…

cs.IR20128 cited

Fast Data in the Era of Big Data: Twitter's Real-Time Related Query Suggestion Architecture

Gilad Mishne, Jeff Dalton, Zhenghua Li +2

We present the architecture behind Twitter's real-time related query suggestion and spelling correction service. Although these tasks have received much attention in the web search…

cs.DC20122 cited

MapReduce is Good Enough? If All You Have is a Hammer, Throw Away Everything That's Not a Nail!

Jimmy Lin

Hadoop is currently the large-scale data analysis "hammer" of choice, but there exist classes of algorithms that aren't "nails", in the sense that they are not particularly amenabl…

cs.DB20123 cited

The Unified Logging Infrastructure for Data Analytics at Twitter

George Lee, Jimmy Lin, Chuang Liu +2

In recent years, there has been a substantial amount of work on large-scale data analytics using Hadoop-based platforms running on large clusters of commodity machines. A less-expl…