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Our system applies authority-based ranking to keyword search in databases modeled as labeled graphs. Three ranking factors are used: the relevance to the query, the specificity and the importance of the result. All factors are handled using authority-flow techniques that exploit the link-structure of the data graph, in contrast to traditional Information Retrieval. We address the performance challenges in computing the authority flows in databases by using precomputation and exploiting the database schema if present. We conducted user surveys and performance experiments on multiple real and synthetic datasets, to assess the semantic meaningfulness and performance of our system.

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Finding the k-nearest neighbours of every node in a dataset is one of the most important data operations with wide application in various areas such as recommendation and information retrieval. However, a major challenge is that the execution time of existing approaches grows rapidly as the number of nodes or dimensions increases. In this paper, we present greedy filtering, an efficient and scalable algorithm for finding an approximate k-nearest neighbour graph. It selects a fixed number of nodes as candidates for every node by filtering out node pairs that do not have any matching dimensions with large values. Greedy filtering achieves consistent approximation accuracy across nodes in linear execution time. We also present a faster version of greedy filtering that uses inverted indices on the node prefixes. Through theoretical analysis, we show that greedy filtering is effective for datasets whose features have Zipfian distribution, a characteristic observed in majority of large datasets. We also conduct extensive comparative experiments against (a) three state-of-the-art algorithms, and (b) three algorithms in related research domains. Our experimental results show that greedy filtering consistently outperforms other algorithms in various types of high-dimensional datasets.

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