@techreport{TD:101567,
	att_abstract={{As the world evolves around us, so does the digital coverage of it. Events of diverse types, associated with different actors and various locations, are continuously captured by multiple information sources such as news articles, blogs, social media etc. day by day. In the digital world, these events are represented through information snippets that contain information on the involved entities, a description of the event, when the event occurred etc. In our work, we observe that events and thus also their corresponding digital representation are often inter-connected, i.e., they form stories which represent evolving relationships between entities over time. Take as an example the plane crash in the Ukraine in July 2014 which involved multiple entities such as the Ukraine, Malaysia, and Russia and ranging in events from the actual crash to the incident investigation and the presentation of their findings. In this demonstration, we present a framework to detect evolving stories in event datasets over time. To resolve stories, we differentiate between story identification, the problem of connecting events over time within a source, and story alignment, the problem of integrating stories across sources. The goal of this demonstration is to show an interactive exploration of both these problems
and how events can be dynamically interpreted and put into context. Furthermore, we present large-scale results from extensive experiments with real-world datasets that show the scalability and applicability of our techniques.}},
	att_authors={ds8961},
	att_categories={C_NSS.2, C_IIS.5},
	att_copyright={{ACM}},
	att_copyright_notice={{(c) ACM, 2015. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in 2015 {{, 2015-05-31}}.
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={},
	att_techdoc={true},
	att_techdoc_key={TD:101567},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:101567_DS1_2015-02-07T15:27:39.812Z.pdf},
	author={Divesh Srivastava and Anja Gruenheid and Theodoros Rekatsinas and Donald Kossmann},
	institution={{ACM SIGMOD International Conference on Management of Data}},
	month={May},
	title={{StoryPivot: Comparing and Contrasting Story Evolution}},
	year=2015,
}