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Type-ahead search can on-the-fly find answers as a user types in a keyword query. A main challenge in this search paradigm is the high-efficiency requirement that queries must be answered within milliseconds. In this paper we study how to answer top-k queries in this paradigm, i.e., as a user types in a query letter by letter, we want to efficiently find the k best answers. Instead of inventing completely new algorithms from scratch, we study challenges when adopting existing top-k algorithms in the literature that heavily rely on two basic list-access methods: random access and sorted access. We present two algorithms to support random access efficiently. We develop novel techniques to support efficient sorted access using list pruning and materialization. We extend our techniques to support fuzzy type-ahead search which allows minor errors between query keywords and answers. We report our experimental results on several real large data sets to show that the proposed techniques can answer top-k queries efficiently in type-ahead search.