• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Of Human Memory and Databases: Ardemia* The Relational Data Model and Management System as a Set Theory Based Modeling Architecture for Human Long Term Memory.
【24h】

Of Human Memory and Databases: Ardemia* The Relational Data Model and Management System as a Set Theory Based Modeling Architecture for Human Long Term Memory.

机译：人类记忆和数据库：Ardemia *关系数据模型和管理系统，是基于集合理论的人类长期记忆建模架构。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This dissertation establishes the modelling and simulation architecture Ardemia*, which was designed for the experimental investigation of human memory and relational data. Ardemia supports modelling and simulation of human memory retrieval (remembering) and retrieval failure (forgetting) using a relational database management system (RDBMS). To this end Ardemia was designed to integrate essential properties of human long term memory (H-LTM) with Codd's relational model of data. Within this interdisciplinary framework, the primary research questions are: 1. Can the relational model of data be used to conceptualize the organization of HLTM? 2. Can a relational database management system be used to model human retrieval and forgetting? 3. Considering that the brain manages continuously streaming data with H-LTM capacities in the order of Petabytes over decades, can data management techniques be learned from H-LTM to address some of the challenges of big data?;This is the first study that brings together relational databases and H-LTM. To demonstrate a relationship between human Memory and databases, it would be a trivial task to a pick memory phenomenon of H-LTM and model it in a database environment. For example, one might hypothesize that cognitive schemas are a brain technique that can be useful for large databases to shrink their size or simplify searches. On the other hand, modelling H-LTM with a relational database system requires a deeper integration and the conceptual development of the commonalities between two inherently different systems. The gain from this extra effort is a controlled, empirical research environment and an experimental tool for (a) simulating memory phenomena and factors that affect human remembering and (b) the implications of these phenomena or manipulations for the storage and management of large data. So, instead of narrowly focusing on one memory phenomenon, we chose to investigate questions 1 and 2 and to lay the foundation for researching question 3. Following this introduction and the hypotheses overview, we proceed as follows:;In the foundation sections we present a review of the relational model of data and of its implementation. This is followed by a review of human memory and the identification of five dimensions and recurrent research themes that appear instrumental to our understanding and research of H-LTM.;In the conceptual integration section we begin by viewing H-LTM from a set theoretical perspective. This first level establishes the rationale of the project. This is followed by a high level mapping of the 5 H-LTM concepts to (a) the relational model of data and (b) current DBMS technologies.;In the implementation section the database begins with a visual overview of the task challenges; they are the development of Ardemia's ER model for H-LTM, the relational schema and SQL DDL code, and of the data generation and population based on a fictitious character, Mr. Polly.;In the experimentation section, the dimensions of H-LTM that Ardemia embodies are tested. The first set of simulations compares Ardemia's with Neural Network performance and establishes a set of benchmark queries. The second set of simulations compares the performance of humanized (heuristically degraded) databases to a control using the benchmark queries.;The results of the experiments indicate that Ardemia's associativity is at least as powerful as that of a neural network and that Ardemia's modeling and simulation approach is more parsimonious than the neural network implementation. Furthermore, Ardemia's sophisticated query capability supports the implementation of benchmark queries for use as experimental dependent variables as well as performance metrics. Last but not least, Ardemia is demonstrated as an experimental research environment to selectively model data (memory) impairments and generate data for comparison between models and existing empirical data.;In summary, Ardemia met the challenges posed by questions 1 and 2. We developed and tested a modelling and simulation architecture whose conceptual foundation integrates human long term memory and the relational model of data. Next is the investigation of question 3: As a simulation environment and modelling toolbox, Ardemia is ready for experimental computer and cognitive sciences to embark on a comparative investigation of artificial memory. It remains for us to improve our understanding of human long term memory, and to investigate its data reduction techniques and underlying storage model, which give rise to real time data decisions and action. This investigation lays the groundwork to use this knowledge to meet the challenges that are posed by managing and storing increasing volumes of data.;*The name "Ardemia" is pronounced ('Ar - Dee - Me - Ah') and the phonetic spelling of the acronym RDMA (Relational Data Memory Architecture).
机译：本文建立了Ardemia *建模与仿真体系结构，该体系结构设计用于人类记忆和相关数据的实验研究。 Ardemia使用关系数据库管理系统（RDBMS）支持对人类记忆的检索（记忆）和检索失败（遗忘）进行建模和仿真。为此，Ardemia旨在将人类长期记忆（H-LTM）的基本属性与Codd的数据关系模型相集成。在这个跨学科的框架内，主要的研究问题是：1.可以使用数据的关系模型来概念化HLTM的组织吗？ 2.可以使用关系数据库管理系统来建模人类的取回和遗忘吗？ 3.考虑到几十年来大脑以Ht-LTM容量连续管理流数据，达到PB级，能否从H-LTM中学习数据管理技术来应对大数据的某些挑战？；这是第一项研究将关系数据库和H-LTM整合在一起。为了演示人类内存与数据库之间的关系，对H-LTM的内存选择现象进行建模并在数据库环境中进行建模将是一项艰巨的任务。例如，可能假设认知模式是一种大脑技术，对于大型数据库缩小其大小或简化搜索很有用。另一方面，使用关系数据库系统对H-LTM进行建模需要更深入的集成，并且需要在概念上发展两个固有的不同系统之间的共性。这种额外工作的收益是可控的，经验的研究环境和实验工具，用于（a）模拟记忆现象和影响人类记忆的因素，以及（b）这些现象或操作对大数据的存储和管理的影响。因此，我们不是只专注于一个记忆现象，而是选择研究问题1和2，并为研究问题3奠定基础。在介绍和假设概述之后，我们进行如下操作：在基础部分中，我们提出一个审查数据的关系模型及其实现。接下来是对人类记忆的回顾，以及对我们理解和研究H-LTM有用的五个维度和经常性研究主题的确定。在概念整合部分，我们将从固定的理论角度看待H-LTM。 。这是建立项目基础的第一层。接下来是5个H-LTM概念到（a）数据的关系模型和（b）当前的DBMS技术的高层映射。；在实现部分，数据库以可视化的方式概述任务挑战；它们是Ardemia针对H-LTM的ER模型，关系模式和SQL DDL代码以及基于虚构人物Polly先生的数据生成和填充的开发；在实验部分，H-LTM的维度对Ardemia的体现进行了测试。第一组模拟将Ardemia的性能与神经网络性能进行了比较，并建立了一组基准查询。第二组模拟使用基准查询将人性化（启发式降级）数据库与控件的性能进行了比较;实验结果表明Ardemia的关联性至少与神经网络的关联性一样强，并且Ardemia的建模和模拟这种方法比神经网络的实现更为简约。此外，Ardemia先进的查询功能支持基准查询的实施，以用作实验因变量以及性能指标。最后但并非最不重要的一点是，Ardemia被证明是一个实验研究环境，可以选择性地对数据（内存）损伤建模，并生成数据以用于模型与现有经验数据之间的比较。；总而言之，Ardemia解决了问题1和2带来的挑战。并测试了建模和仿真架构，该架构的概念基础整合了人类长期记忆和数据关系模型。接下来是问题3的研究：作为仿真环境和建模工具箱，Ardemia准备让实验计算机和认知科学着手进行人工记忆的比较研究。我们仍然需要提高对人类长期记忆的理解，并研究其数据缩减技术和底层存储模型，这些技术会引起实时数据决策和行动。这项调查奠定了基础，以利用这些知识来应对管理和存储不断增加的数据量所带来的挑战。* *“ Ardemia”的名称发音为（'Ar-Dee-Me-Ah'），首字母缩写词RDMA（关系数据存储体系结构）。

著录项

  • 作者

    Bahr, Gisela Susanne.;

  • 作者单位

    Florida Institute of Technology.;

  • 授予单位 Florida Institute of Technology.;
  • 学科 Computer science.;Artificial intelligence.;Experimental psychology.;Cognitive psychology.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 475 p.
  • 总页数 475
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 农学（农艺学）;
  • 关键词

  • 入库时间 2022-08-17 11:53:33

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号