Searching and Analyzing Big Data: Context-sensitive and Task-aware Approaches

使用谷歌很容易。使用谷歌查找信息是另一回事。在过去的几十年里，信息检索（Information Retrieval， IR）取得了显著的进步。然而，国际关系问题远未解决，仍然存在许多挑战。首先，大多数Web搜索引擎将一个简短的文本查询作为输入，并输出一个文档排序列表。检索决策主要基于当前查询和文档集合。给定查询的结果通常是相同的，独立于用户或用户发出请求的上下文。第二，国际关系是一个互动的过程。在当前以文档为中心的检索范式中，交互式检索被视为一系列独立的简单检索决策步骤。但是，需要注意的是，对任务感知的用户会话（其中包含用户为满足信息需求而提交的请求序列）的分析可以提供对用户查询行为的有用洞察。第三，现有的IR系统大多使用关键字来查询和索引文档。然而，这种传统的基于关键字的IR模型为理解用户信息需求提供了很少的语义上下文，这可能导致搜索中的查询和文档不匹配。理想情况下，如果查询和文档是主题相关的，人们希望看到它们相互匹配。因此，需要根据用户的信息需求和用户对文档的理解将语义上下文集成到IR系统中，以提高IR性能。第四，组织现在越来越多地处理pb级的数据集合。在处理大数据时，上下文敏感和任务感知方法变得更具挑战性。因此，提出新的算法和模型，能够有效和高效地处理大数据，并在大数据场景中实现上下文敏感和任务感知方法是很重要的。****在数据以惊人的速度增长的世界中，对更准确有效地搜索和分析大数据以发现有用信息的需求巨大。这个研究项目解决了从大文本数据中搜索和发现有用信息的问题。本研究的长期目标是克服现有IR方法的局限性，并正式开发一种新的检索范式，称为上下文敏感和任务感知的大数据信息搜索。特别是，(1)我们将开发一个新的理论检索框架，用于捕获丰富的用户信息并提供个性化的搜索结果；(2)我们将开发新的基于任务的上下文敏感信息检索方法，以优化整个检索会话的长期检索效用；(3)开发新的大数据自动分析和搜索模型，高效提取知识并进行语义匹配。**

Using Google is easy. Finding information using Google is another matter. Over the decades, significant progress has been made in Information Retrieval (IR). However, IR is far from a solved problem and many challenges remain. First, most Web search engines take a short text query as input and output a ranked list of documents. The retrieval decision is made primarily based on the current query and document collection. The results for a given query are usually identical, independent of the user or the context in which the user made the request. Second, IR is an interactive process. With the current document-centered retrieval paradigm, interactive retrieval is treated as a sequence of independent simple retrieval decision making steps. However, it has been brought into attention that analysis of task-aware user sessions, which contain a sequence of requests submitted by a user to fulfill an information need, provides useful insight into the query behavior of the user. Third, most of present IR systems use keywords to query and index documents. However, this traditional keyword-based IR model provides little semantic context for the understanding of user information needs, which can lead to mismatch between query and document in search. Ideally, one would like to see the query and document match with each other, if they are topically relevant. Thus, the integration of semantic context according to the user's information need and the user's understanding of the documents into IR systems is needed to improve the IR performance. Fourth, organizations are now increasingly dealing with petabyte-scale collections of data. Context-sensitive and task-aware approaches become more challenging when dealing with big data. Hence, it is important to propose new algorithms and models that can effectively and efficiently process big data and implement the context-sensitive and task-aware approaches in big-data scenarios.****In a world where data are growing at extraordinary rates, there is a huge demand for searching and analyzing big data more accurately and effectively to discover useful information. This research program tackles the problem of searching and discovering useful information from big text data. The long-term objective of the proposed research is to overcome the limitations of the existing IR methods and formally develop a new retrieval paradigm called context-sensitive and task-aware information search for big data. In particular, (1) we will develop a new theoretical retrieval framework for capturing rich user information and providing personalized search results; (2) we will develop novel task-based retrieval methods for context-sensitive information retrieval to optimize the long-term retrieval utility over an entire retrieval session; (3) we will develop new models for automatically analyzing and searching big data to efficiently extract knowledge and perform semantic matching. **