SocialTrove: A Self-summarizing Storage Service for Social Sensing

Amin, Md Tanvir Al; Li, Shen; Rahman, Muntasir Raihan; Seetharamu, Panindra Tumkur; Wang, Shiguang; Abdelzaher, Tarek F.; Gupta, Indranil; Srivatsa, Mudhakar; Ganti, Raghu K.; Ahmed, Reaz; Le, Hieu

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https://hdl.handle.net/2142/73300 Copy

Description

Title

SocialTrove: A Self-summarizing Storage Service for Social Sensing

Author(s)

• Amin, Md Tanvir Al
• Li, Shen
• Rahman, Muntasir Raihan
• Seetharamu, Panindra Tumkur
• Wang, Shiguang
• Abdelzaher, Tarek F.
• Gupta, Indranil
• Srivatsa, Mudhakar
• Ganti, Raghu K.
• Ahmed, Reaz
• Le, Hieu

Issue Date

2015-02-03

Keyword(s)

• Summarization Service
• Social Sensing
• Cluster Hierarchy
• Tweet Stream Summarization
• Self-summarizing Storage
• Nearest Neighbor Query

Abstract

The increasing availability of smartphones, cameras, and wearables with instant data sharing capabilities, and the exploitation of social networks for information broadcast, heralds a future of real-time information overload. With the growing excess of worldwide streaming data, such as images, geotags, text annotations, and sensory measurements, an increasingly common service will become one of data summarization. The objective of such a service will be to obtain a representative sampling of large data streams at a configurable granularity, in real-time, for subsequent consumption by a range of data-centric applications. This paper describes a general-purpose self-summarizing storage service, called SocialTrove, for social sensing applications. The service summarizes data streams from human sources, or sensors in their possession, by hierarchically clustering received information in accordance with an application-specific distance metric. It then serves a sampling of produced clusters at a configurable granularity in response to application queries. While SocialTrove is a general service, we illustrate its functionality and evaluate it in the specific context of workloads collected from Twitter. Results show that SocialTrove supports a high query throughput, while maintaining a low access latency to the produced real-time application-specific data summaries. As a specific application case-study, we implement a fact-finding service on top of SocialTrove.

Type of Resource

• text
• other

Language

en

Permalink

http://hdl.handle.net/2142/73300

Sponsor(s)/Grant Number(s)

• Army Research Laboratory, Cooperative Agreement W911NF-09-2-0053
• DTRA grant HDTRA1-10-1-0120
• NSF grants CNS 13-29886, CNS 09-58314, CNS 10-35736

Owning Collections