Balan Sethu Raman
Microsoft
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Publication
Featured researches published by Balan Sethu Raman.
very large data bases | 2009
Mohamed H. Ali; C. Gerea; Balan Sethu Raman; Beysim Sezgin; T. Tarnavski; Tomer Verona; Ping Wang; Peter Zabback; Asvin Ananthanarayan; Anton Kirilov; M. Lu; Alex Raizman; R. Krishnan; Roman Schindlauer; Torsten Grabs; S. Bjeletich; Badrish Chandramouli; Jonathan Goldstein; S. Bhat; Ying Li; V. Di Nicola; Xiaoyang Sean Wang; David Maier; S. Grell; O. Nano; Ivo Santos
In this demo, we present the Microsoft Complex Event Processing (CEP) Server, Microsoft CEP for short. Microsoft CEP is an event stream processing system featured by its declarative query language and its multiple consistency levels of stream query processing. Query composability, query fusing, and operator sharing are key features in the Microsoft CEP query processor. Moreover, the debugging and supportability tools of Microsoft CEP provide visibility of system internals to users. Web click analysis has been crucial to behavior-based online marketing. Streams of web click events provide a typical workload for a CEP server. Meanwhile, a CEP server with its processing capabilities plays a key role in web click analysis. This demo highlights the features of Microsoft CEP under a workload of web click events.
IEEE Computer | 2010
Badrish Chandramouli; Mohamed H. Ali; Jonathan Goldstein; Beysim Sezgin; Balan Sethu Raman
Because financial applications rely on a continual stream of time-sensitive data, any data management system must be able to process complex queries on the fly. Although many organizations turn to custom solutions, data stream management systems can offer the same low-latency processing with the flexibility to handle a range of applications.
advances in geographic information systems | 2010
Mohamed H. Ali; Badrish Chandramouli; Balan Sethu Raman; Ed Katibah
Microsoft StreamInsight (StreamInsight, for brevity) is a platform for developing and deploying streaming applications that run continuous queries over high-rate streaming events. StreamInsight adopts a temporal stream model to handle imperfections in event delivery and define consistency guarantees on the output. This demo highlights the ability of StreamInsight to monitor, analyze and correlate spatio-temporal stream data that is generated by moving objects. The demo scenario is based on the Microsoft Shuttle Service where GPS readings are generated and streamed by shuttles as they move around the Microsoft main campus in Redmond, WA. The demo presents a set of relational continuous queries as well as various real-time analytics that help improve the efficiency of the shuttle service in terms of the average wait time per passenger and the average daily mileage per shuttle.
Archive | 2009
Mohammed Samji; David G. De Vorchik; Ram Ramasubramanian; Chris J. Guzak; Timothy P. McKee; Nathaniel H. Ballou; Balan Sethu Raman
Archive | 2005
Dave D. Straube; Aaron M. Contorer; Arnold S. Miller; Balan Sethu Raman; Pradyumna K. Misra; Michael R C Seaman
Archive | 2003
Shaun A. Kaasten; Jason F. Moore; Balan Sethu Raman; Chris J. Guzak; David A. Orbits; Sudarshan A. Chitre; Eric R. Flo; Jeffrey M. Saathoff
Archive | 1996
Balan Sethu Raman; Arnold S. Miller; Dave D. Straube; Shioupyn Shen
Archive | 2004
Prasanna V. Krishnan; Sambavi Muthukrishnan; Sameet H. Agarwal; Balan Sethu Raman; Michael Eric Deem
Archive | 2004
Shishir Pardikar; Joseph L. Linn; Balan Sethu Raman; Robert E. Corrington
Archive | 2006
Balan Sethu Raman; Kangrong Yan; Rajeev B. Rajan
