Time-travel queries that couple temporal constraints with keyword queries are useful in searching large-scale archives of time-evolving content such as the Web, document collections, wikis, and so on. Typical approaches for efficient evaluation of these queries involve \emph{slicing} along the time-axis either the entire collection~\cite{253349}, or individual index lists~\cite{kberberi:sigir2007}. Both these methods are not satisfactory since they sacrifice compactness of index for processing efficiency making them either too big or, otherwise, too slow. We present a novel index organization scheme that \emph{shards} the index with \emph{zero increase in index size}, still minimizing the cost of reading index index entries during query processing. Based on the optimal sharding thus obtained, we develop practically efficient sharding that takes into account the different costs of random and sequential accesses. Our algorithm merges shards from the optimal solution carefully to allow for few extra sequential accesses while gaining significantly by reducing the random accesses. Finally, we empirically establish the effectiveness of our novel sharding scheme via detailed experiments over the edit history of the English version of Wikipedia between 2001-2005 ($\approx$ 700 GB) and an archive of the UK governmental web sites ($\approx$ 400 GB). Our results demonstrate the feasibility of faster time-travel query processing with no space overhead.
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