On Invoking Transitivity to Enhance the &lt;italic&gt;Pursuit&lt;/italic&gt;-Oriented Object Migration Automata

Shirvani, Abdolreza; Oommen, B. John

doi:10.1109/access.2018.2827305

Cited by 8 publications

(9 citation statements)

References 15 publications

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“…The pursuit concept has also been incorporated into the EOMA to enhance it to yield the Pursuit EOMA (PEOMA). This scheme was the best-known object-partitioning algorithm until the authors [56] introduced the TPEOMA. Again, the pursuit concept, just as in the case of the POMA, filters queries by rejecting divergent queries.…”

Section: Peomamentioning

confidence: 99%

“…A further enhancement to the PEOMA was proposed in the paper [56]. This enhancement is based on the observation that the Pursuit matrix can also be used to infer underlying relations in the Environment.…”

Section: Transitivity For Peoma -(Tpeoma)mentioning

confidence: 99%

“…The reader is referred to [56] for the details on how to incorporate transitivity and the Pursuit paradigm into the OMA. However, suffice it to say that the transitive implementation of the PEOMA has, in some cases, about a 100% increase in the convergence rate, as opposed to the non-transitive PEOMA implementations.…”

Section: Transitivity For Peoma -(Tpeoma)mentioning

confidence: 99%

“…In all experiments, the number of states of the EOMA is set to 10. PEOMA and with the original OMA/EOMA, the reader is referred to [56]. However for the case mentioned earlier when W = 18 and R = 3 with p = 0.7, the PEOMA required 472 iterations, while the TPEOMA required only 244 iterations.…”

Section: Transitivity For Peoma -(Tpeoma)mentioning

confidence: 99%

“…return {ξ p } 8: end Source: Excerpt from [56] Self-organization is the ability for a list, for example, to re-order its constituent elements in response to queries from the underlying query system, that serves as an Environment. The probability distribution of the query accesses is unknown to the list.…”

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On Designing Adaptive Data Structures with Adaptive Data "Sub"-Structures

Bisong¹

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Data structures are key pillars for optimizing computational efficiency, as they contribute in no small measure to enhancing the "speed" in the accessing and subsequent processing of data. The need for enhancing speed is critical for almost all applications and domains, and this is most relevant when real-time or near real-time efficiency is desired. This thesis proposes the use of "Adaptive" Data-Structures (ADSs) that invoke reinforcement learning schemes from the theory of Learning Automata (LA). These operate in conjunction with select reorganization rules to update themselves as they receive queries from the Environment of interaction. The result of such a process is the subsequent minimization of the cost associated with query accesses. The Environments under consideration are those that exhibit a so-called "locality of reference", and are referred to as Non-stationary Environments (NSEs). A hierarchy of data "sub"-structures is used to design Singly-Linked Lists (SLLs) on Singly-Linked Lists, which thus contain outer and sub-list contexts. The elements that are more likely to be accessed together are grouped within the same sub-context, while the sub-lists themselves are moved "en masse" towards the head of the listcontext by following a reorganization strategy. The Object Migration Automaton (OMA) family of reinforcement schemes are employed to capture the probabilistic dependence of the elements in the data structure as it receives query accesses from the Environment. The Enhanced Object Migration Automaton (EOMA), the Pursuit Enhanced Object Migration Automaton (PEOMA), and the Transitivity Pursuit Enhanced Object Migration Automaton (TPEOMA) have each been individually incorporated into the hierarchical SLLs. The consequent results are currently the state-of-the-art methods for adaptive SLLs operating in NSEs. iii Finally, I will want to especially thank my Uncle and Aunt, Achu and Blessing Bisong, for their love, care, support and encouragement throughout the time of this thesis, and indeed, my stay in Canada. They have been a rock in my life. I am grateful to the faculty, the staff and the friends I made at the School of Computer Science at Carleton. They made my stay here enjoyable and fulfilling. iv Bibliography Appendices A OMA-Augmented Hierarchical SLLs Results B EOMA-Augmented Hierarchical SLLs Results C PEOMA-Augmented Hierarchical SLLs Results D TPEOMA-Augmented Hierarchical SLLs Results viii

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Section: Peomamentioning

confidence: 99%

Section: Transitivity For Peoma -(Tpeoma)mentioning

confidence: 99%

Section: Transitivity For Peoma -(Tpeoma)mentioning

confidence: 99%

Section: Transitivity For Peoma -(Tpeoma)mentioning

confidence: 99%

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confidence: 99%

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On Designing Adaptive Data Structures with Adaptive Data "Sub"-Structures

Bisong¹

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Optimizing Self-organizing Lists-on-Lists Using Transitivity and Pursuit-Enhanced Object Partitioning

Bisong

Oommen

2020

IFIP Advances in Information and Communication Technology

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The study of Self-organizing lists deals with the problem of lowering the average-case asymptotic cost of a list data structure receiving query accesses in Non-stationary Environments (NSEs) with the so-called "locality of reference" property. The de facto schemes for Adaptive lists in such Environments are the Move To Front (MTF) and Transposition (TR) rules. However, significant drawbacks exist in the asymptotic accuracy and speed of list re-organization for the MTF and TR rules. This paper improves on these schemes using the design of an Adaptive list data structure as a hierarchical data "sub"-structure. In this framework, we employ a hierarchical Singly-Linked-Lists on Singly-Linked-Lists (SLLs-on-SLLs) design, which divides the list data structure into an outer and inner list context. The inner-list context is itself a SLLs containing sub-elements of the list, while the outer-list context contains these sublist partitions as its primitive elements. The elements belonging to a particular sublist partition are determined using reinforcement learning schemes from the theory of Learning Automata. In this paper, we show that the Transitivity Pursuit-Enhanced Object Migration Automata (TPEOMA) can be used in conjunction with the hierarchical SLLs-on-SLLs as the dependence capturing mechanism to learn the probabilistic distribution of the elements in the Environment. The idea of Transitivity builds on the Pursuit concept that injects a noise filter into the EOMA to filter divergent queries from the Environment, thereby increasing the likelihood of training the Automaton to approximate the "true" distribution of the Environment. By taking advantage of the Transitivity phenomenon based on the statistical distribution of the queried elements, we can infer "dependent" query pairs from non-accessed elements in the transitivity relation. The TPEOMA-enhanced hierarchical SLLs-on-SLLs schemes results in superior performances to the MTF and TR schemes as well as to the EOMA-enhanced hierarchical SLLs-on-SLLs schemes in NSEs. However, the results are observed to have superior performances to the PEOMA-enhanced hierarchical schemes in Environments with a Periodic non-stationary distribution but were inferior in Markovian Switching Environments.B. John Oommen-This author is also an Adjunct Professor with the University of Agder in Grimstad, Norway.

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