The top-k retrieval problem aims to find the optimal set of k documents from a number of relevant documents given the user’s query. The key issue is to balance the relevance and diversity of the top-k search results. In this paper, we address this problem using Facility Location Analysis taken from Operations Research, where the locations of facilities are optimally chosen according to some cri...

We focus on the problem of obtaining top-k lists of items from larger itemsets, using human workers for doing comparisons among items. An example application is short-listing a large set of college applications using advanced students as workers. We describe novel efficient techniques and explore their tolerance to adversarial behavior and the tradeoffs among different measures of performance (...

—Frequent itemset mining has emerged as a fundamental problem in data mining and plays an important role in many data mining tasks, such as association analysis, classification, etc. In the framework of frequent itemset mining, the results are itemsets that are frequent in the whole database. However, in some applications, such recommendation systems and social networks, people are more interes...

We consider various encodings that support range Top-k queries on a two-dimensional array containing elements from a total order. For an m × n array, with m ≤ n, we first propose an almost optimal encoding for answering one-sided Top-k queries, whose query range is restricted to [1 . . .m][1 . . . a], for 1 ≤ a ≤ n. Next, we propose an encoding for the general Top-k queries that takes m2 lg ((k...

Top-k is the most important query type in OLAP, the most common data warehouses model. Recently, OLAP was extended to represent data ambiguity, specifically imprecision and uncertainty. However, how to deal with top-k queries in uncertain OLAP still remain an unanswered question. This project introduces a complete solution for top-k query in uncertain OLAP including query semantic definition, q...

Top-k processing is a well-studied problem with numerous applications that is becoming increasingly relevant with the growing availability of recommendation systems and decision making software. The objective of this tutorial is twofold. First, we will delve into the geometric aspects of top-k processing. Second, we will cover complementary features to top-k queries, with strong practical relev...

We consider a distributed system where each node has a local count for each item (similar to elections where nodes are ballot boxes and items are candidates). A top-k query in such a system asks which are the k items whose sum of counts, across all nodes in the system, is the largest. In this paper we present a Monte-Carlo algorithm that outputs, with high probability, a set of k candidates whi...

In this work, we introduce the average top-k (ATk) loss as a new ensemble loss for supervised learning, which is the average over the k largest individual losses over a training dataset. We show that the ATk loss is a natural generalization of the two widely used ensemble losses, namely the average loss and the maximum loss, but can combines their advantages and mitigate their drawbacks to bett...

SimRank is a well-studied similarity measure between two nodes in a network. However, evaluating SimRank of all nodes in a network is not only time-consuming but also not pragmatic, since users are only interested in the most similar pairs in many real-world applications. This paper focuses on topk similarity join based on SimRank. In this work, we first present an incremental algorithm for com...

Association rule mining is a fundamental data mining task. However, depending on the choice of the thresholds, current algorithms can become very slow and generate an extremely large amount of results or generate too few results, omitting valuable information. Furthermore, it is well-known that a large proportion of association rules generated are redundant. In previous works, these two problem...