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You searched for subject:( causal ordering). Showing records 1 – 2 of 2 total matches.

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University of Waterloo

1. Pramanik, Sukanta. Online Monitoring of Distributed Systems Using Causal Event Patterns.

Degree: 2014, University of Waterloo

Event monitoring and logging, that is, recording the communication events between processes, is a critical component in many highly reliable distributed systems. The event logs enable the identification of certain safety-condition violations, such as race conditions and mutual-exclusion violations, as safety is generally contingent on a specific causally ordered pattern of process communication. Previous efforts at finding such patterns have often focused on offline techniques, which are unable to identify operational problems as they occur. Online monitoring tools exist but they are often restricted to identifying a specific violation condition, such as a deadlock or a race condition, using dedicated data structures. We address the more general problem of detecting causally related event patterns that can be used to identify various undesired behaviours in the system. The main challenge for online pattern matching is the need to store the partial matches to the pattern, as they may combine with future events to form a complete match. Unlike pattern matching in most other domains, causally ordered patterns can span a potentially unbounded number of events and efficiently searching through this large collection poses a significant challenge. We present an efficient online causal-event-pattern-matching framework that bounds the number of partial matches it stores by reporting only a representative subset of pattern matches. We define a subset of matches as representative if it has at least one occurrence of each event in the pattern on each process, which is applicable for a large class of distributed applications. Our first pattern-matching algorithm, OCEP introduces a backtracking algorithm to efficiently find a representative subset from the history of events. An evaluation of the framework shows that OCEP is capable of handling several frequently occurring violation patterns at the event rates of some representative distributed applications. Our second algorithm, Ananke, introduces causality-based rules in the search pattern that can be used to specify the removal of an event from the maintained history. We used some of the most frequently occurring types of concurrency bugs in real-world applications to show that the desired causal order of events can be utilized to specify such removal rules. More importantly, these rules are able to maintain a finite history and still report a representative set of matches within a millisecond in most cases.

Subjects/Keywords: Causal Ordering; Event-Based System; Distributed System; Distributed System Monitoring

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Pramanik, S. (2014). Online Monitoring of Distributed Systems Using Causal Event Patterns. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/8619

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Pramanik, Sukanta. “Online Monitoring of Distributed Systems Using Causal Event Patterns.” 2014. Thesis, University of Waterloo. Accessed August 12, 2020. http://hdl.handle.net/10012/8619.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Pramanik, Sukanta. “Online Monitoring of Distributed Systems Using Causal Event Patterns.” 2014. Web. 12 Aug 2020.

Vancouver:

Pramanik S. Online Monitoring of Distributed Systems Using Causal Event Patterns. [Internet] [Thesis]. University of Waterloo; 2014. [cited 2020 Aug 12]. Available from: http://hdl.handle.net/10012/8619.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Pramanik S. Online Monitoring of Distributed Systems Using Causal Event Patterns. [Thesis]. University of Waterloo; 2014. Available from: http://hdl.handle.net/10012/8619

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Arizona

2. Hahn-Powell, Gus. Machine Reading for Scientific Discovery .

Degree: 2018, University of Arizona

The aim of this work is to accelerate scientific discovery by advancing machine reading approaches designed to extract claims and assertions made in the literature, assemble these statements into cohesive models, and generate novel hypotheses that synthesize findings from isolated research communities. Over 1 million new publications are added to the biomedical literature each year. This poses a serious challenge to researchers needing to understand the state of the field. It is effectively impossible for an individual to summarize the larger body of work or even remain abreast of research findings directly relevant to a subtopic. As the boundaries between disciplines continue to blur, the question of what to read grows more complicated. Researchers must inevitably turn to machine reading techniques to summarize findings, detect contradictions, and illuminate the inner workings of complex systems. Machine reading is a research program in artificial intelligence centered on teaching computers to read and comprehend natural language text. Through large-scale machine reading of the scientific literature, we can greatly advance our understanding of the natural world. Despite remarkable progress (Gunning et al., 2010; Berant et al., 2014; Cohen, 2015a), current machine reading systems face two major obstacles which impede wider adoption: <i>Assembly</i> The majority of machine reading systems extract disconnected findings from the literature (Berant et al., 2014). In areas of study such as biology, which involve large mechanistic systems with many interdependent components, it is essential that the insights scattered across the literature be contextualized and carefully integrated. The single greatest challenge facing machine reading is in learning to piece together this intricate puzzle to form coherent models and mitigate information overload. In this work, I will demonstrate how disparate biomolecular statements mined from text can be causally ordered into chains of reactions (Hahn-Powell et al., 2016b) that extend our understanding of mechanistic biology. Then, moving beyond a single domain, we will see how machine-read fragments (influence relations) drawn from a multitude of disciplines can be assembled into models of children’s heath. <i>Hypothesis generation and “undiscovered public knowledge”</i> (Swanson, 1986a) Without a notion of research communities and their interaction, machine reading systems struggle to identify knowledge gaps and key ideas capable of bridging disciplines and fostering the kind of collaboration that accelerates scientific progress. With this aim in mind, I introduce a procedure for detecting research communities using a large citation network and derive semantic representations that encode a measure of the flow of information between these groups. Finally, I leverage these representations to uncover influence relation pathways which connect otherwise isolated communities. Advisors/Committee Members: Fong, Sandiway (advisor), Surdeanu, Mihai (advisor), Fong, Sandiway (committeemember), Surdeanu, Mihai (committeemember), Morrison, Clayton (committeemember).

Subjects/Keywords: assembly; causal ordering; hypothesis generation; literature-based discovery; machine reading; Swanson linking

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hahn-Powell, G. (2018). Machine Reading for Scientific Discovery . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/630562

Chicago Manual of Style (16th Edition):

Hahn-Powell, Gus. “Machine Reading for Scientific Discovery .” 2018. Doctoral Dissertation, University of Arizona. Accessed August 12, 2020. http://hdl.handle.net/10150/630562.

MLA Handbook (7th Edition):

Hahn-Powell, Gus. “Machine Reading for Scientific Discovery .” 2018. Web. 12 Aug 2020.

Vancouver:

Hahn-Powell G. Machine Reading for Scientific Discovery . [Internet] [Doctoral dissertation]. University of Arizona; 2018. [cited 2020 Aug 12]. Available from: http://hdl.handle.net/10150/630562.

Council of Science Editors:

Hahn-Powell G. Machine Reading for Scientific Discovery . [Doctoral Dissertation]. University of Arizona; 2018. Available from: http://hdl.handle.net/10150/630562

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