Matching Items (4)
Filtering by
- All Subjects: Game Theory
- All Subjects: Query languages (Computer science)
- Creators: Chen, Yi
Description
This dissertation presents the Temporal Event Query Language (TEQL), a new language for querying event streams. Event Stream Processing enables online querying of streams of events to extract relevant data in a timely manner. TEQL enables querying of interval-based event streams using temporal database operators. Temporal databases and temporal query languages have been a subject of research for more than 30 years and are a natural fit for expressing queries that involve a temporal dimension. However, operators developed in this context cannot be directly applied to event streams. The research extends a preexisting relational framework for event stream processing to support temporal queries. The language features and formal semantic extensions to extend the relational framework are identified. The extended framework supports continuous, step-wise evaluation of temporal queries. The incremental evaluation of TEQL operators is formalized to avoid re-computation of previous results. The research includes the development of a prototype that supports the integrated event and temporal query processing framework, with support for incremental evaluation and materialization of intermediate results. TEQL enables reporting temporal data in the output, direct specification of conditions over timestamps, and specification of temporal relational operators. Through the integration of temporal database operators with event languages, a new class of temporal queries is made possible for querying event streams. New features include semantic aggregation, extraction of temporal patterns using set operators, and a more accurate specification of event co-occurrence.
ContributorsShiva, Foruhar Ali (Author) / Urban, Susan D (Thesis advisor) / Chen, Yi (Thesis advisor) / Davulcu, Hasan (Committee member) / Sarjoughian, Hessam S. (Committee member) / Arizona State University (Publisher)
Created2012
Description
One of the salient challenges of sustainability is the Tragedy of the Commons, where individuals acting independently and rationally deplete a common resource despite their understanding that it is not in the group's long term best interest to do so. Hardin presents this dilemma as nearly intractable and solvable only by drastic, government-mandated social reforms, while Ostrom's empirical work demonstrates that community-scale collaboration can circumvent tragedy without any elaborate outside intervention. Though more optimistic, Ostrom's work provides scant insight into larger-scale dilemmas such as climate change. Consequently, it remains unclear if the sustainable management of global resources is possible without significant government mediation. To investigate, we conducted two game theoretic experiments that challenged students in different countries to collaborate digitally and manage a hypothetical common resource. One experiment involved students attending Arizona State University and the Rochester Institute of Technology in the US and Mountains of the Moon University in Uganda, while the other included students at Arizona State and the Management Development Institute in India. In both experiments, students were randomly assigned to one of three production roles: Luxury, Intermediate, and Subsistence. Students then made individual decisions about how many units of goods they wished to produce up to a set maximum per production class. Luxury players gain the most profit (i.e. grade points) per unit produced, but they also emit the most externalities, or social costs, which directly subtract from the profit of everybody else in the game; Intermediate players produce a medium amount of profit and externalities per unit, and Subsistence players produce a low amount of profit and externalities per unit. Variables influencing and/or inhibiting collaboration were studied using pre- and post-game surveys. This research sought to answer three questions: 1) Are international groups capable of self-organizing in a way that promotes sustainable resource management?, 2) What are the key factors that inhibit or foster collective action among international groups?, and 3) How well do Hardin's theories and Ostrom's empirical models predict the observed behavior of students in the game? The results of gameplay suggest that international cooperation is possible, though likely sub-optimal. Statistical analysis of survey data revealed that heterogeneity and levels of trust significantly influenced game behavior. Specific traits of heterogeneity among students found to be significant were income, education, assigned production role, number of people in one's household, college class, college major, and military service. Additionally, it was found that Ostrom's collective action framework was a better predictor of game outcome than Hardin's theories. Overall, this research lends credence to the plausibility of international cooperation in tragedy of the commons scenarios such as climate change, though much work remains to be done.
ContributorsStanton, Albert Grayson (Author) / Clark, Susan Spierre (Thesis director) / Seager, Thomas (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2014-12
Description
The Resource Description Framework (RDF) is a specification that aims to support the conceptual modeling of metadata or information about resources in the form of a directed graph composed of triples of knowledge (facts). RDF also provides mechanisms to encode meta-information (such as source, trust, and certainty) about facts already existing in a knowledge base through a process called reification. In this thesis, an extension to the current RDF specification is proposed in order to enhance RDF triples with an application specific weight (cost). Unlike reification, this extension treats these additional weights as first class knowledge attributes in the RDF model, which can be leveraged by the underlying query engine. Additionally, current RDF query languages, such as SPARQL, have a limited expressive power which limits the capabilities of applications that use them. Plus, even in the presence of language extensions, current RDF stores could not provide methods and tools to process extended queries in an efficient and effective way. To overcome these limitations, a set of novel primitives for the SPARQL language is proposed to express Top-k queries using traditional query patterns as well as novel predicates inspired by those from the XPath language. Plus, an extended query processor engine is developed to support efficient ranked path search, join, and indexing. In addition, several query optimization strategies are proposed, which employ heuristics, advanced indexing tools, and two graph metrics: proximity and sub-result inter-arrival time. These strategies aim to find join orders that reduce the total query execution time while avoiding worst-case pattern combinations. Finally, extensive experimental evaluation shows that using these two metrics in query optimization has a significant impact on the performance and efficiency of Top-k queries. Further experiments also show that proximity and inter-arrival have an even greater, although sometimes undesirable, impact when combined through aggregation functions. Based on these results, a hybrid algorithm is proposed which acknowledges that proximity is more important than inter-arrival time, due to its more complete nature, and performs a fine-grained combination of both metrics by analyzing the differences between their individual scores and performing the aggregation only if these differences are negligible.
ContributorsCedeno, Juan Pablo (Author) / Candan, Kasim S (Thesis advisor) / Chen, Yi (Committee member) / Sapino, Maria L (Committee member) / Arizona State University (Publisher)
Created2010
Description
Recent years, there has been many attempts with different approaches to the human-robot interaction (HRI) problems. In this paper, the multi-agent interaction is formulated as a differential game with incomplete information. To tackle this problem, the parameter estimation method is utilized to obtain the approximated solution in a real time basis. Previous studies in the parameter estimation made the assumption that the human parameters are known by the robot; but such may not be the case and there exists uncertainty in the modeling of the human rewards as well as human's modeling of the robot's rewards. The proposed method, empathetic estimation, is tested and compared with the ``non-empathetic'' estimation from the existing works. The case studies are conducted in an uncontrolled intersection with two agents attempting to pass efficiently. Results have shown that in the case of both agents having inconsistent belief of the other agent's parameters, the empathetic agent performs better at estimating the parameters and has higher reward values, which indicates the scenarios when empathy is essential: when agent's initial belief is mismatched from the true parameters/intent of the agents.
ContributorsChen, Yi (Author) / Ren, Yi (Thesis advisor) / Zhang, Wenlong (Committee member) / Yong, Sze Zheng (Committee member) / Arizona State University (Publisher)
Created2021