Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form


Guirguis, Shenoda (2012) SCALABLE PROCESSING OF MULTIPLE AGGREGATE CONTINUOUS QUERIES. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

PDF (reviewed and approved by dean's office)
Accepted Version

Download (2MB) | Preview


Data Stream Management Systems (DSMSs) were developed to be at the heart of every monitor- ing application. Monitoring applications typically register hundreds of Continuous Queries (CQs) in DSMSs in order to continuously process unbounded data streams to detect events of interest. DSMSs must be designed to efficiently handle unbounded streams with large volumes of data and large numbers of CQs, i.e., exhibit scalability. This need for scalability means that the underlying processing techniques a DSMS adopts should be optimized for high throughput (i.e., tuple output rate). Towards this, two main approaches were proposed in the literature: (1) Multiple Query Opti- mization (MQO) and (2) Scheduling. In this dissertation we focus on optimizing the processing of multiple Aggregate Continuous Queries (ACQs), given their high processing cost and popularity in all monitoring applications.
Specifically, in this dissertation, we explore shared processing of ACQs and introduce the con- cept of ’Weaveability’ as an indicator of the potential gains of sharing the processing of ACQs. We develop Weave Share, a multiple ACQs optimizer that considers the different uncorrelated factors of the processing cost, such as the input rate and ACQs’ specifications. In order to fully reap the benefits of the new weave-based optimization techniques, we conceptualize a new underlying ag- gregate operator implementation and realize it in the TriOps framework. TriOps enables adaptive sharing of multiple ACQs that have different window specification, predicates and group-by at- tributes. The properties of the proposed techniques are studied analytically and their performance advantages are experimentally evaluated using simulation and in the context of the AQSIOS DSMS prototype.


Social Networking:
Share |


Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Guirguis, Shenodashenoda@cs.pitt.eduSHG18
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairChrysanthis, Panos K.panos@cs.pitt.eduPANOS
Committee CoChairLabrinidis, Alexandroslabrinid@cs.pitt.eduLABRINID
Committee MemberPruhs, Kirkkirk@cs.pitt.eduKRP2
Committee MemberMokbel,
Committee MemberSharaf,
Date: 1 February 2012
Date Type: Publication
Defense Date: 23 August 2011
Approval Date: 1 February 2012
Submission Date: 6 November 2011
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 130
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: Data Streams Management Systems, Continuous Queries, Query Optimization, Scal- able Processing, Aggregation, AQSIOS, Weaveability.
Additional Information: secondary email:
Date Deposited: 01 Feb 2012 12:20
Last Modified: 19 Dec 2016 14:34


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item