We present the architecture behind Twitter's real-time related query suggestion and spelling correction service. Although these tasks have received much attention in the web search literature, the Twitter context introduces a real-time "twist": after significant breaking news events, we aim to provide relevant results within minutes. This paper provides a case study illustrating the challenges of real-time data processing in the era of "big data". We tell the story of how our system was built twice: our first implementation was built on a typical Hadoop-based analytics stack, but was later replaced because it did not meet the latency requirements necessary to generate meaningful real-time results. The second implementation, which is the system deployed in production, is a custom in-memory processing engine specifically designed for the task. This experience taught us that the current typical usage of Hadoop as a "big data" platform, while great for experimentation, is not well suited to low-latency processing, and points the way to future work on data analytics platforms that can handle "big" as well as "fast" data.
Prior to the web, most query expansion work focused on capturing term correlations across global and local contexts in the document collection @cite_2 . The advent of web search engines, however, provided a new and much richer resource to mine: query, clickthrough, and other behavioral interaction logs. One of the earliest use of logs for query expansion is the work of @cite_11 , who used clickthrough data to establish correlations between query terms and document terms, which were then extracted for query expansion. Related, a family of query suggestion techniques involves constructing a bipartite graph of query and clicked URLs, on which random walks @cite_12 or clustering can be performed @cite_15 ; cf. @cite_49 . Another use of query logs is to extract query substitutions from search sessions by mining statistical associations from users' successive queries @cite_27 ---this is the general approach we adopt. Similar techniques are also effective for spelling correction @cite_0 .
@cite_0 @cite_27 @cite_49 @cite_2 @cite_15 @cite_12 @cite_11
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Queries to search engines on the Web are usually short. They do not provide sufficient information for an effective selection of relevant documents. Previous research has proposed the utilization of query expansion to deal with this problem. However, expansion terms are usually determined on term co-occurrences within documents. In this study, we propose a new method for query expansion based on user interactions recorded in user logs. The central idea is to extract correlations between query terms and document terms by analyzing user logs. These correlations are then used to select high-quality expansion terms for new queries. Compared to previous query expansion methods, ours takes advantage of the user judgments implied in user logs. The experimental results show that the log-based query expansion method can produce much better results than both the classical search method and the other query expansion methods. Automatic query expansion may be used in document retrieval to improve search effectiveness. Traditional query expansion methods are based on the document collection itself. For example, pseudo-relevance feedback (PRF) assumes that the top retrieved documents are relevant, and uses the terms extracted from those documents for query expansion. However, there are other sources of evidence that can be used for expansion, some of which may give better search results with greater efficiency at query time. In this paper, we use the external evidence, especially the hints obtained from external web search engines to expand the original query. We explore 6 different methods using search engine query log, snippets and search result documents. We conduct extensive experiments, with state of the art PRF baselines and careful parameter tuning, on three TREC collections: AP, WT10g, GOV2. Log-based methods do not show consistent significant gains, despite being very efficient at query-time. Snippet-based expansion, using the summaries provided by an external search engine, provides significant effectiveness gains with good efficiency at query-time. We present a study that developed and tested three query expansion methods for the retrieval of clinical documents. Finding relevant documents in a large clinical data warehouse is a challenging task. To address this issue, first, we implemented a synonym expansion strategy that used a few selected vocabularies. Second, we trained a topic model on a large set of clinical documents, which was then used to identify related terms for query expansion. Third, we obtained related terms from a large predicate database derived from Medline abstracts for query expansion. The three expansion methods were tested on a set of clinical notes. All three methods successfully achieved higher average recalls and average F-measures when compared with the baseline method. The average precisions and precision at 10, however, decreased with all expansions. Amongst the three expansion methods, the topic model-based method performed the best in terms of recall and F-measure. Automatic query expansion has long been suggested as a technique for dealing with the fundamental issue of word mismatch in information retrieval. A number of approaches to expansion have been studied and, more recently, attention has focused on techniques that analyze the corpus to discover word relationship (global techniques) and those that analyze documents retrieved by the initial query ( local feedback). In this paper, we compare the effectiveness of these approaches and show that, although global analysis haa some advantages, local analysia is generally more effective. We also show that using global analysis techniques.