We present in this paper a novel approach for as-you-type top- @math keyword search over social media. We adopt a natural "network-aware" interpretation for information relevance, by which information produced by users who are closer to the seeker is considered more relevant. In practice, this query model poses new challenges for effectiveness and efficiency in online search, even when a complete query is given as input in one keystroke. This is mainly because it requires a joint exploration of the social space and classic IR indexes such as inverted lists. We describe a memory-efficient and incremental prefix-based retrieval algorithm, which also exhibits an anytime behavior, allowing to output the most likely answer within any chosen running-time limit. We evaluate it through extensive experiments for several applications and search scenarios, including searching for posts in micro-blogging (Twitter and Tumblr), as well as searching for businesses based on reviews in Yelp. They show that our solution is effective in answering real-time as-you-type searches over social media.
Query auto-completion is the second main direction for instant response to queries in the typing, by which some top query completions are presented to the user (see for example @cite_15 @cite_6 @cite_3 and the references therein). This is done either by following a predictive approach, or by pre-computing completion candidates and storing them in trie structures. Probably the best known example today is the one of Google's instant search, which provides both query predictions (in the search box) and results for the top prediction. Query suggestion goes one step further by proposing alternative queries, which are not necessarily completions of the input one (see for instance @cite_12 @cite_29 ). In comparison, our work does not focus on queries as first-class citizens, but on instant results to incomplete queries.
@cite_29 @cite_3 @cite_6 @cite_15 @cite_12
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Query auto-completion (QAC) is the first step of information retrieval, which helps users formulate the entire query after inputting only a few prefixes. Regarding the models of QAC, the traditional method ignores the contribution from the semantic relevance between queries. However, similar queries always express extremely similar search intention. In this paper, we propose a hybrid model FS-QAC based on query semantic similarity as well as the query frequency. We choose word2vec method to measure the semantic similarity between intended queries and pre-submitted queries. By combining both features, our experiments show that FS-QAC model improves the performance when predicting the user's query intention and helping formulate the right query. Our experimental results show that the optimal hybrid model contributes to a 7.54 improvement in terms of MRR against a state-of-the-art baseline using the public AOL query logs. Query auto-completion (QAC) is one of the most prominent features of modern search engines. The list of query candidates is generated according to the prefix entered by the user in the search box and is updated on each new key stroke. Query prefixes tend to be short and ambiguous, and existing models mostly rely on the past popularity of matching candidates for ranking. However, the popularity of certain queries may vary drastically across different demographics and users. For instance, while instagram and imdb have comparable popularities overall and are both legitimate candidates to show for prefix i, the former is noticeably more popular among young female users, and the latter is more likely to be issued by men. In this paper, we present a supervised framework for personalizing auto-completion ranking. We introduce a novel labelling strategy for generating offline training labels that can be used for learning personalized rankers. We compare the effectiveness of several user-specific and demographic-based features and show that among them, the user's long-term search history and location are the most effective for personalizing auto-completion rankers. We perform our experiments on the publicly available AOL query logs, and also on the larger-scale logs of Bing. The results suggest that supervised rankers enhanced by personalization features can significantly outperform the existing popularity-based base-lines, in terms of mean reciprocal rank (MRR) by up to 9 . We consider the problem of answering queries from databases that may be incomplete. A database is incomplete if some tuples may be missing from some relations, and only a part of each relation is known to be complete. This problem arises in several contexts. For example, systems that provide access to multiple heterogeneous information sources often encounter incomplete sources. The question we address is to determine whether the answer to a specific given query is complete even when the database is incomplete. We present a novel sound and complete algorithm for the answer-completeness problem by relating it to the problem of independence of queries from updates. We also show an important case of the independence problem (and therefore ofthe answer-completeness problem) that can be decided in polynomial time, whereas the best known algorithm for this case is exponential. This case involves updates that are described using a conjunction of comparison predicates. We also describe an algorithm that determines whether the answer to the query is complete in the current state of the database. Finally, we show that our ‘treatment extends naturally to partiallyincorrect databases. Permission to copy without fee all or part of this material is granted provided that the copies aTe not made OT distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, OT to republish, requirea a fee and or special permission from the Endowment. Proceedings of the 22nd VLDB Conference Mumbai(Bombay), India, 1996 Structured Query Language (SQL) has emerged as an industry standard for querying relational database management systems, largely because a user need only specify what data are wanted, not the details of how to access those data. A query optimizer uses a mathematical model of query execution to determine automatically the best way to access and process any given SQL query. This model is heavily dependent upon the optimizer's estimates for the number of rows that will result at each step of the query execution plan (QEP), especially for complex queries involving many predicates and or operations. These estimates rely upon statistics on the database and modeling assumptions that may or may not be true for a given database. In this paper, we discuss an autonomic query optimizer that automatically self-validates its model without requiring any user interaction to repair incorrect statistics or cardinality estimates. By monitoring queries as they execute, the autonomic optimizer compares the optimizer's estimates with actual cardinalities at each step in a QEP, and computes adjustments to its estimates that may be used during future optimizations of similar queries. Moreover, the detection of estimation errors can also trigger reoptimization of a query in mid-execution. The autonomic refinement of the optimizer's model can result in a reduction of query execution time by orders of magnitude at negligible additional run-time cost. We discuss various research issues and practical considerations that were addressed during our implementation of a first prototype of LEO, a LEarning Optimizer for DB2Â® (Database 2TM) that learns table access cardinalities and for future queries corrects the estimation error for simple predicates by adjusting the database statistics of DB2.