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A User-driven Annotation Framework for Scientific Data

Li, Qinglan (2013) A User-driven Annotation Framework for Scientific Data. Doctoral Dissertation, University of Pittsburgh.

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Abstract

Annotations play an increasingly crucial role in scientific exploration and discovery, as the amount of data and the level of collaboration among scientists increases. There are many systems today focusing on annotation management, querying, and propagation. Although all such systems are implemented to take user input (i.e., the annotations themselves), very few systems are user-driven, taking into account user preferences on how annotations should be propagated and applied over data. In this thesis, we propose to treat annotations as first-class citizens for scientific data by introducing a user-driven, view-based annotation framework. Under this framework, we try to resolve two critical questions: Firstly, how do we support annotations that are scalable both from a system point of view and also from a user point of view? Secondly, how do we support annotation queries both from an annotator point of view and a user point of view, in an efficient and accurate way?

To address these challenges, we propose the VIew-base annotation Propagation (ViP) framework to empower users to express their preferences over the time semantics of annotations and over the network semantics of annotations, and define three query types for annotations. To efficiently support such novel functionality, ViP utilizes database views and introduces new annotation caching techniques. The use of views also brings a more compact representation of annotations, making our system easier to scale. Through an extensive experimental study on a real system (with both synthetic and real data), we show that the ViP framework can seamlessly introduce user-driven annotation propagation semantics while at the same time significantly improving the performance (in terms of query execution time) over the current state of the art.


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Details

Item Type: University of Pittsburgh ETD
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Li, Qinglanliqinglan@gmail.com
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairLabrinidis, Alexandros labrinid@cs.pitt.eduLABRINID
Committee CoChairChrysanthis, Panos Kpanos@cs.pitt.eduPANOS
Committee MemberMarai, Elisabeta marai@cs.pitt.eduMARAI
Committee MemberFaloutsos, Christos christos@cs.cmu.edu
Date: 8 October 2013
Date Type: Publication
Defense Date: 19 June 2013
Approval Date: 8 October 2013
Submission Date: 16 August 2013
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 151
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Computer Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: User-driven, Annotation, Scientific Data, Scalable Data, Big Data, Annotation Queries, Caching, DBMS
Date Deposited: 08 Oct 2013 23:05
Last Modified: 19 Dec 2016 14:41
URI: http://d-scholarship.pitt.edu/id/eprint/19668

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