Online Association Rule Mining over Fast Data

被引：17

作者：

Olmezogullari, Erdi ^{[1
]}

Ari, Ismail ^{[1
]}

机构：

[1] Ozyegin Univ, Dept Comp Engn, Istanbul, Turkey

来源：

2013 IEEE INTERNATIONAL CONGRESS ON BIG DATA | 2013年

关键词：

Fast data; big data; association rule mining; complex event processing; FP-Growth;

D O I：

10.1109/BigData.Congress.2013.77

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

To extract useful and actionable information in real-time, the information technology (IT) world is coping with big data problems today. In this paper, we present implementation details and performance results of ReCEPtor, our system for "online" Association Rule Mining (ARM) over big and fast data streams. Specifically, we added Apriori and two different FP-Growth algorithms inside Esper Complex Event Processing (CEP) engine and compared their performances using LastFM social music site data. Our most important findings show that online ARM can generate (1) more unique rules, (2) with higher throughput, and (3) much sooner (lower latency) than offline rule mining. In addition, we have found many interesting and realistic musical preference rules such as "George Harrison -> Beatles". We demonstrate a sustained rate of similar to 15K rows/sec per core. We hope that our findings can shed light on the design and

引用

页码：110 / 117

页数：8

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