The Keyword-Set Search System (KSS) is a Peer-to-Peer (P2P) keyword search system that uses a distributed inverted index. The main challenge in a distributed index and search system is finding the right scheme to partition the index across the nodes in the network. The most obvious scheme would be to partition the index by keyword. A keyword partitioned index requires that the list of index entries for each keyword in a search be retrieved, so all the lists can be joined; only a few nodes need to be contacted, but each sends a potentially large amount of data. In KSS, the index is partitioned by sets of keywords. KSS builds an inverted index that maps each set of keywords to a list of all the documents that contain the words in the keyword-set. When a user issues a query, the keywords in the query are divided into sets of keywords. The document list for each set of keywords is then fetched from the network. The lists are intersected to compute the list of matching documents. The list of index entries for each set of words is smaller than the list of entries for each word. Thus search using KSS results in a smaller query time overhead. Preliminary experiments using traces of real user queries show that the keywordset approach is more efficient than a standard inverted index in terms of communication costs for query. Insert overhead for KSS grows exponentially as the size of the keyword-set used to generate the keys for index entries. The query overhead for the target application (metadata search in a music file sharing system) is reduced to the result of the query as no intermediate lists are transferred across the network for the join operation. Given our assumption that free disk space is plenty, and queries are more frequent than insertions in P2P systems, we believe this is a good tradeoff.
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