Let D be a collection of D documents, which are strings over an alphabet of size σ, of total length n. We describe a data structure that uses linear space and and reports k most relevant documents that contain a query pattern P , which is a string of length p, in time O(p/ log σ n+k), which is optimal in the RAM model in the general case where lgD = Θ(logn), and involves a novel RAM-optimal suf...

We consider the problem of efficiently finding the top-k answers for join queries over web-accessible databases. Classical algorithms for finding top-k answers use branch-and-bound techniques to avoid computing scores of all candidates in identifying the top-k answers. To be able to apply such techniques, it is critical to efficiently compute (lower and upper) bounds and expected scores of cand...

Systems that produce ranked lists of results are abundant. For instance, Web search engines return ranked lists of Web pages. There has been work on distance measure for list permutations, like Kendall tau and Spearman’s Footrule, as well as extensions to handle top-k lists, which are more common in practice. In addition to ranking whole objects (e.g., Web pages), there is an increasing number ...

Supervised descriptive rule discovery techniques like subgroup discovery are quite popular in applications like fraud detection or clinical studies. Compared with other descriptive techniques, like classical support/confidence association rules, subgroup discovery has the advantage that it comes up with only the top-k patterns, and that it makes use of a quality function that avoids patterns un...

Correlation mining has been widely studied due to its ability for discovering the underlying occurrence dependency between objects. However, correlation mining in graph databases is expensive due to the complexity of graph data. In this paper, we study the problem of mining top-k correlative subgraphs in the database, which share similar occurrence distributions with a given query graph. The se...

The top-k skyline groups query (k-SGQ) returns k skyline groups that dominate the maximum number of points in a given data set. It combines the advantages of skyline groups and top-k queries. The k-SGQ is an important tool for queries that need to analyze not only individual points but also groups of points, and can be widely used in areas such as decision support applications, market analysis ...

We consider the recently introduced monochromatic reverse top-k queries which asks for, given a new tuple q and a dataset D, all possible top-k queries on D ∪ {q} for which q is in the result. Towards this problem, we focus on designing indexes in two dimensions for repeated (or batch) querying, a novel but practical consideration. We present the novel insight that by representing the dataset a...

Association mining has been one of the most intensively researched areas of data mining. However, direct uptake of the resulting technologies has been relatively low. This paper examines some of the reasons why the dominant paradigms in association mining have not lived up to their promise, and argues that a powerful alternative is provided by top-k techniques coupled with appropriate statistic...

Mining association rules is a fundamental data mining task. However, depending on the choice of the parameters (the minimum confidence and minimum support), current algorithms can become very slow and generate an extremely large amount of results or generate too few results, omitting valuable information. This is a serious problem because in practice users have limited resources for analyzing t...

We propose an algorithm that obtains the top-k list of items out of a larger itemset, using human workers (e.g., through crowdsourcing) to perform comparisons among items. An example application is finding the best photographs in a large collection by asking humans to evaluate different photos. Our algorithm has to address several challenges: obtaining worker input has high latency; workers may...