ITR: Information Extraction from Massive Data Sets

信息技术的进步导致各行各业产生了大量数据。需要有效的技术来处理这些数据。特别是，需要工具来从大量数据集中提取有用的信息。ITR计划的目标之一是“在全球范围内扩大处理、管理和交流信息的能力，这超出了今天的想象”。整个社会可以从这一竞技场的进步中受益匪浅。例如，从生物数据中提取的信息可以用于基因鉴定、疾病诊断、药物设计等。市场数据信息可以用于为客户定制商品目录、超市货架等。通过分析大气数据，天气预报和保护环境免受污染成为可能。据估计，超过40%的在线交易是欺诈性的。分析日志数据可以揭示可用于检测欺诈尝试的信息。信息提取的最新技术是使用不同的特定应用程序技术。 例如，关联规则算法用于处理市场数据，序列分析技术用于处理生物数据等。这种统一的信息提取技术可以从处理大量数据的各种社区中受益。从历史上看，这些社区之间的交流非常稀少。拟议的研究将仔细研究这些社区所采用的技术，并开发适用于各种数据的新技术。 预计新技术也将大大改善目前采用的个别技术。

Advances in information technology have resulted in the generation of voluminous data in every walk of life. Efficient techniques are needed to process these data. In particular, tools are needed to extract useful information from massive data sets. One of the objectives of the ITR Program is "extending the capability to process, manage, and communicate information on a global scale beyond what can be imagined today". Society at large can benefit immensely from advances in this arena. For example, information extracted from biological data can result in gene identification, diagnosis for diseases, drug design, etc. Market-data information can be used for custom-designed catalogues for customers, supermarket shelving, and so on. Weather prediction and protecting the environment from pollution are possible with the analysis of atmospheric data. Estimates indicate that more than 40% of online transactions are fraudulent. Analyzing the log data can reveal information that can be used to detect fraudulent attempts.The state of the art in information extraction is the use of disparate ad-hoc application-specific techniques. For example, association-rule algorithms are used for processing market data, Sequence-analysis techniques are employed in handling biological data, etc.Unifying techniques are needed for processing data. Such unifying information extraction techniques could benefit from and be of benefit to the various communities that deal with massive data. Historically, communication among these communities has been very sparse. The proposed research will examine closely the techniques employed by these communities and to develop novel techniques that will be applicable to all kinds of data. It is anticipated that the new techniques will also vastly improve the individual techniques currently employed.