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dc.contributor.authorKonana, Prabhudev Chennabasappa.
dc.creatorKonana, Prabhudev Chennabasappa.en_US
dc.date.accessioned2011-10-31T18:35:26Z
dc.date.available2011-10-31T18:35:26Z
dc.date.issued1995en_US
dc.identifier.urihttp://hdl.handle.net/10150/187289
dc.description.abstractMany emerging database applications, such as, automated financial trading, network management and manufacturing process control involve accessing and manipulating large amounts of data under time constraints. This has led to the emergence of active and real-time database as a research area, wherein transactions trigger other transactions and have time deadlines. In this dissertation, three important issues are investigated: the correctness criteria for various classes of transactions; real-time transaction scheduling algorithms for overload situation; and, a concurrency control policy that is sensitive to time deadline and transaction triggering. The first part of the dissertation deals with the issue of consistency of sensor reported data. We formally define sensor data consistency and a new notion of visibility called quasi immediate visibility (QIV) for concurrent execution of write-only and read-only transactions. We propose a protocol for maintaining sensor data consistency that has lower response time and higher throughput. The protocol is validated through simulation. Real-time schedulers must perform well both under underloaded and overloaded situations. In this dissertation, we propose a variation of weighted priority scheduling algorithm called Deadline Access Parameter Ratio (DAPR), that actively considers the I/O requirements and the amount of unprocessed work for "canned" transaction assumption. We show through simulation that DAPR performs significantly better than existing scheduling algorithms under overloaded situations. The limitation of the proposed algorithm is that in underloaded situations DAPR is not an option. The last part of this dissertation proposes a concurrency control (CC), called OCCWB, which is an extension of conventional optimistic CC. OCCWB takes advantage of the "canned" transaction assumption and includes pre-analysis stage, wherein, transactions are selectively blocked from executing if there is a high probability of restarting. The algorithm defines favorable serialization orders considering transaction semantics and tries to achieve such orders through appropriate priority adjustment. OCCWB is shown to perform significantly better than other CC policies under reasonable pre-analysis overhead for underloaded situation, and consistently better in overloaded situations, even with high pre-analysis overhead, for a wide range of workload and resource parameters.
dc.language.isoenen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.titleA transaction model for active and real-time databases.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.contributor.chairRam, Sudhaen_US
dc.contributor.chairDatta, Anindyaen_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.contributor.committeememberPingry, Daviden_US
dc.contributor.committeememberSlaten, Pamelaen_US
dc.identifier.proquest9604515en_US
thesis.degree.disciplineBusiness Administrationen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.namePh.D.en_US
dc.description.noteDigitization note: p. 61, 129, & 118 missing from paper original. p. 27 missing from paper original; appears to be pagination error rather than missing content.
refterms.dateFOA2018-09-03T12:07:59Z
html.description.abstractMany emerging database applications, such as, automated financial trading, network management and manufacturing process control involve accessing and manipulating large amounts of data under time constraints. This has led to the emergence of active and real-time database as a research area, wherein transactions trigger other transactions and have time deadlines. In this dissertation, three important issues are investigated: the correctness criteria for various classes of transactions; real-time transaction scheduling algorithms for overload situation; and, a concurrency control policy that is sensitive to time deadline and transaction triggering. The first part of the dissertation deals with the issue of consistency of sensor reported data. We formally define sensor data consistency and a new notion of visibility called quasi immediate visibility (QIV) for concurrent execution of write-only and read-only transactions. We propose a protocol for maintaining sensor data consistency that has lower response time and higher throughput. The protocol is validated through simulation. Real-time schedulers must perform well both under underloaded and overloaded situations. In this dissertation, we propose a variation of weighted priority scheduling algorithm called Deadline Access Parameter Ratio (DAPR), that actively considers the I/O requirements and the amount of unprocessed work for "canned" transaction assumption. We show through simulation that DAPR performs significantly better than existing scheduling algorithms under overloaded situations. The limitation of the proposed algorithm is that in underloaded situations DAPR is not an option. The last part of this dissertation proposes a concurrency control (CC), called OCCWB, which is an extension of conventional optimistic CC. OCCWB takes advantage of the "canned" transaction assumption and includes pre-analysis stage, wherein, transactions are selectively blocked from executing if there is a high probability of restarting. The algorithm defines favorable serialization orders considering transaction semantics and tries to achieve such orders through appropriate priority adjustment. OCCWB is shown to perform significantly better than other CC policies under reasonable pre-analysis overhead for underloaded situation, and consistently better in overloaded situations, even with high pre-analysis overhead, for a wide range of workload and resource parameters.


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