NDLI: A high-throughput, scalable solution for calculating frequent routes and profitability of New York taxis

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A high-throughput, scalable solution for calculating frequent routes and profitability of New York taxis

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A high-throughput, scalable solution for calculating frequent routes and profitability of New York taxis

Content Provider	ACM Digital Library
Author	Suriarachchi, Amila Pallickara, Shrideep
Abstract	Processing complex queries on unbounded event streams in real-time, is a challenge for many data processing systems. These systems are expected to process data with reduced latency to generate real-time events, and at high throughput to minimize the required hardware. In this regard, Grand Challenge 2015 [6] focuses on evaluating two queries (frequent routes and profitable cells) in real-time with low latency and high throughput. These queries involve processing windows of thousands of records. Firstly, such processing demands efficient data structures and algorithms to minimize the processing overhead. Secondly, the system should partition data to evaluate them in parallel to make it scalable. In this paper, we present a set of data structures that we designed to evaluate the aforementioned queries with O(log n) time complexity and a data partitioning technique to evaluate them in parallel. We then evaluate our solution on a single machine as well as in a distributed setting in a commodity cluster of machines over a 1Gbps LAN. We were able to process the frequent routes query with the 173 million trips dataset within 5 minutes with less than 4 millisecond latency and the profitable cells query with same dataset within 11 minutes with less than 5 millisecond latency.
Starting Page	301
Ending Page	308
Page Count	8
File Format	PDF
ISBN	9781450332866
DOI	10.1145/2675743.2772589
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-06-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Event stream processing Running median Distributed event processing Real-time analytics Time complexity
Content Type	Text
Resource Type	Article

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