GARNET: A holistic system approach for trending queries in microblogs

2016 IEEE 32nd International Conference on Data Engineering (ICDE) Pub Date : 2016-05-16 DOI:10.1109/ICDE.2016.7498329

C. Jonathan, A. Magdy, M. Mokbel, A. Jonathan

{"title":"GARNET: A holistic system approach for trending queries in microblogs","authors":"C. Jonathan, A. Magdy, M. Mokbel, A. Jonathan","doi":"10.1109/ICDE.2016.7498329","DOIUrl":null,"url":null,"abstract":"The recent wide popularity of microblogs (e.g., tweets, online comments) has empowered various important applications, including, news delivery, event detection, market analysis, and target advertising. A core module in all these applications is a frequent/trending query processor that aims to find out those topics that are highly frequent or trending in the social media through posted microblogs. Unfortunately current attempts for such core module suffer from several drawbacks. Most importantly, their narrow scope, as they focus only on solving trending queries for a very special case of localized and very recent microblogs. This paper presents GARNET; a holistic system equipped with one-stop efficient and scalable solution for supporting a generic form of context-aware frequent and trending queries on microblogs. GARNET supports both frequent and trending queries, any arbitrary time interval either current, recent, or past, of fixed granularity, and having a set of arbitrary filters over contextual attributes. From a system point of view, GARNET is very appealing and industry-friendly, as one needs to realize it once in the system. Then, a myriad of various forms of trending and frequent queries are immediately supported. Experimental evidence based on a real system prototype of GARNET and billions of real Twitter data show the scalability and efficiency of GARNET for various query types.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"50 1","pages":"1251-1262"},"PeriodicalIF":0.0000,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDE.2016.7498329","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 18

Abstract

The recent wide popularity of microblogs (e.g., tweets, online comments) has empowered various important applications, including, news delivery, event detection, market analysis, and target advertising. A core module in all these applications is a frequent/trending query processor that aims to find out those topics that are highly frequent or trending in the social media through posted microblogs. Unfortunately current attempts for such core module suffer from several drawbacks. Most importantly, their narrow scope, as they focus only on solving trending queries for a very special case of localized and very recent microblogs. This paper presents GARNET; a holistic system equipped with one-stop efficient and scalable solution for supporting a generic form of context-aware frequent and trending queries on microblogs. GARNET supports both frequent and trending queries, any arbitrary time interval either current, recent, or past, of fixed granularity, and having a set of arbitrary filters over contextual attributes. From a system point of view, GARNET is very appealing and industry-friendly, as one needs to realize it once in the system. Then, a myriad of various forms of trending and frequent queries are immediately supported. Experimental evidence based on a real system prototype of GARNET and billions of real Twitter data show the scalability and efficiency of GARNET for various query types.

查看原文本刊更多论文

微博趋势查询的整体系统方法

最近微博的广泛流行(例如，tweet，在线评论)为各种重要应用提供了支持，包括新闻传递，事件检测，市场分析和目标广告。所有这些应用程序的核心模块是一个频繁/趋势查询处理器，旨在通过发布的微博找出社交媒体中频繁或流行的话题。不幸的是，目前这种核心模块的尝试有几个缺点。最重要的是，他们的范围很窄，因为他们只专注于解决一个非常特殊的本地化和最近的微博的趋势查询。本文介绍了GARNET;一个整体系统，配备一站式高效和可扩展的解决方案，用于支持微博上上下文感知的通用形式的频繁查询和趋势查询。GARNET支持频繁查询和趋势查询，支持固定粒度的任意时间间隔(当前、最近或过去)，并对上下文属性具有一组任意过滤器。从系统的角度来看，GARNET非常吸引人，并且对行业友好，因为需要在系统中实现它。然后，立即支持无数不同形式的趋势和频繁查询。基于GARNET真实系统原型和数十亿Twitter真实数据的实验证据表明，GARNET对于各种查询类型具有可扩展性和高效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

2016 IEEE 32nd International Conference on Data Engineering (ICDE)

自引率

0.00%

发文量