When you write papers, how many times do you want to make some citations at a place but you are not sure which papers to cite? Do you wish to have a recommendation system which can recommend a small number of good candidates for every place that you want to make some citations? In this paper, we present our initiative of building a context-aware citation recommendation system. High quality citation recommendation is challenging: not only should the citations recommended be relevant to the paper under composition, but also should match the local contexts of the places citations are made. Moreover, it is far from trivial to model how the topic of the whole paper and the contexts of the citation places should affect the selection and ranking of citations. To tackle the problem, we develop a contextaware approach. The core idea is toeffectively. Moreover, it can recommend a set of citations for a paper with high quality. We implement a prototype system in CiteSeerX. An extensive empirical evaluation in the CiteSeerX digital library against many baselines demonstrates the effectiveness and the scalability of our approach.

Geographically-grounded situational awareness (SA) is critical to crisis management and is essential in many other decision making domains that range from infectious disease monitoring, through regional planning, to political campaigning. Social media are becoming an important information input to support situational assessment (to produce awareness) in all domains. Here, we present a geovisual analytics approach to supporting SA for crisis events using one source of social media, Twitter. Specifically, we focus on leveraging explicit and implicit geographic information for tweets, on developing place-time-theme indexing schemes that support overview+detail methods and that scale analytical capabilities to relatively large tweet volumes, and on providing visual interface methods to enable understanding of place, time, and theme components of evolving situations. Our approach is user-centered, using scenario-based design methods that include formal scenarios to guide design and validate implementation as well as a systematic claims analysis to justify design choices and provide a framework for future testing. The work is informed by a structured survey of practitioners and the end product of Phase-I development is demonstrated / validated through implementation in SensePlace2, a map-based, web application initially focused on tweets but extensible to other media.

After an end-user has partially input a query, intelligent search engines can suggest possible completions of the partial query to help end-users quickly express their information needs. All major websearch engines and most proposed methods that suggest queries rely on search engine query logs to determine possible query suggestions. However, for customized search systems in the enterprise domain, intranet search, or personalized search such as email or desktop search or for infrequent queries, query logs are either not available or the user base and the number of past user queries is too small to learn appropriate models. We propose a probabilistic mechanism for generating query suggestions from the corpus without using query logs. We utilize the document corpus to extract a set of candidate phrases. As soon as a user starts typing a query, phrases that are highly correlated with the partial user query are selected as completions of the partial query and are offered as query suggestions. Our proposed approach is tested on a variety of datasets and is compared with state-of-the-art approaches. The experimental results clearly demonstrate the effectiveness of our approach in suggesting queries with higher quality.

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