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...

Several business applications such as marketing basket analysis, clickstream analysis, fraud detection and churning migration analysis demand gradient data analysis. By employing gradient data analysis one is able to identify trends, outliers and answering “what-if” questions over large databases. Gradient queries were first introduced by Imielinski et al [1] as the cubegrade problem. The main ...

Mining sequential rules requires specifying parameters that are often difficult to set (the minimal confidence and minimal support). Depending on the choice of these parameters, 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 reso...

Exploring a knowledge graph through keyword queries to discover meaningful patterns has been studied in many scenarios recently. From the perspective of query understanding, it aims to find a number of specific interpretations for ambiguous keyword queries. With the assistance of interpretation, the users can actively reduce the search space and get more relevant results. In this paper, we prop...

Martin Theobald, Gerhard Weikum, Ralf Schenkel Max-Planck Institute of Computer Science D-66123 Saarbruecken, Germany {mtb, weikum, schenkel}@mpi-sb.mpg.de Abstract Top-k queries based on ranking elements of multidimensional datasets are a fundamental building block for many kinds of information discovery. The best known general-purpose algorithm for evaluating top-k queries is Fagin’s thresho...

In many applications, users specify target values for certain attributes, without requiring exact matches to these values in return. Instead, the result to such queries is typically a rank of the “top k” tuples that best match the given attribute values. In this paper, we study the advantages and limitations of processing a top-k query by translating it into a single range query that traditiona...