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

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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.