Mining informative patterns from the Web, object-oriented and multiple data sources

从 Web、面向对象和多个数据源挖掘信息模式

• 批准号: 194134-2011
• 负责人: Ezeife, Christie
• 金额: $ 1.46万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=569735
• 关键词: Mining; informative; patterns; Web; object

Ability to mine multiple diverse datasets will provide answers to more complex pattern queries, discover hidden, derived, multimedia and accurate knowledge from historical data, in a more efficient fashion, as well as provide better recommendations to end users. Some example queries that can be solved are: (i) given a set of web pages, get the list of products with lowest price ranges in them in the past five years, (ii) given a set of University web pages, present faculty by gender, ranks and area of specialization, (iii) find all frequently purchased products from a set of databases including their image, customer comments, number sold over a period of time. Thus, the objectives of the proposed research are to (1) build a set of techniques for multiple database mining of diverse datasets using object-oriented data modeling of such diverse data as web data, (2) integrate pattern mining techniques in end user tools for web recommendations, personal organization and system monitoring. Scientific methods for accomplishing research goals include using non-deterministic finite state automata (NFA) to generate web site schemas, and the relationships between them as object and class hierarchies. Then, use association rule and pattern tree mining techniques to develop object-oriented frequent pattern mining techniques among many other mining techniques for extracting derived, rare, frequent, relevant and other patterns. The proposed research contributes to overall quality of life of Canadians as well as their productivity by cutting down drastically, the amount of time spent on the web searching and sifting through hidden and incorrect answers to queries manually. It increases organizations' and users' productivity by making pattern mining process less manual, more available, more applicable and more effective.

挖掘多个不同数据集的能力将为更复杂的模式查询提供答案，以更有效的方式从历史数据中发现隐藏的，派生的，多媒体和准确的知识，并为最终用户提供更好的建议。可以解决的一些示例查询是：（i）给定一组网页，获得在过去五年中具有最低价格范围的产品的列表，（ii）给定一组大学网页，按性别、等级和专业领域呈现教师，（iii）从一组数据库中找到所有经常购买的产品，包括它们的图像、客户评论、在一段时间内销售的数量。 因此，建议的研究的目标是（1）建立一套技术，用于多个数据库挖掘的不同的数据集，使用面向对象的数据建模，如Web数据，（2）集成模式挖掘技术的最终用户工具的Web推荐，个人组织和系统监控。实现研究目标的科学方法包括使用非确定性有限状态自动机（NFA）来生成网站模式，以及它们之间的关系作为对象和类层次结构。然后，利用关联规则和模式树挖掘技术，开发了面向对象的频繁模式挖掘技术，用于提取派生模式、稀有模式、频繁模式、相关模式等。拟议中的研究有助于加拿大人的整体生活质量以及他们的生产力大幅减少，花在网上搜索和筛选隐藏和不正确的答案手动查询的时间量。它通过使模式挖掘过程更少的人工操作，更可用，更适用和更有效，提高了组织和用户的生产力。