H
Henrique Andrade
Researcher at IBM
Publications - 81
Citations - 3440
Henrique Andrade is an academic researcher from IBM. The author has contributed to research in topics: Stream processing & Middleware. The author has an hindex of 27, co-authored 81 publications receiving 3387 citations. Previous affiliations of Henrique Andrade include J.P. Morgan & Co. & University of Maryland, College Park.
Papers
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Proceedings ArticleDOI
SPADE: the system s declarative stream processing engine
TL;DR: Spade is the System S declarative stream processing engine that allows developers to construct their applications with fine granular stream operators without worrying about the performance implications that might exist, even in a distributed system.
Patent
Fault tolerance in distributed systems
Henrique Andrade,Kirsten W. Hildrum,Michael John Elvery Spicer,Chitra Venkatramani,Rohit Wagle +4 more
TL;DR: Fault tolerance is provided in a distributed system by storing state related to a requested operation on the component, persisting that stored state in a data store, asynchronously processing the operation request, and if a failure occurs, restarting the component using the stored state from the data store.
Proceedings ArticleDOI
Design, implementation, and evaluation of the linear road bnchmark on the stream processing core
Navendu Jain,Lisa Amini,Henrique Andrade,Richard Pervin King,Yoonho Park,Philippe Selo,Chitra Venkatramani +6 more
TL;DR: This paper examines the performance bottlenecks of streaming data applications, in particular the Linear Road stream data management benchmark, in achieving good performance in large-scale distributed environments, using the Stream Processing Core (SPC), a stream processing middleware developed.
SPC: a distributed, scalable platform for data mining
Lisa Amini,Henrique Andrade,Ranjita Bhagwan,Frank Eskesen,Richard Pervin King,Philippe Selo,Yoonho Park,Chitra Venkatramani +7 more
TL;DR: The SPC programming model is described, which is to the best of the authors' knowledge, the first to support stream-mining applications using a subscription-like model for specifying stream connections as well as to provide support for non-relational operators.
Proceedings ArticleDOI
Elastic scaling of data parallel operators in stream processing
TL;DR: An approach to elastically scale the performance of a data analytics operator that is part of a streaming application that focuses on dynamically adjusting the amount of computation an operator can carry out in response to changes in incoming workload and the availability of processing cycles is described.