Efficient processing of top-k queries has become a classical research area recently since it has lots of application fields. Fagin et al. proposed the “middleware cost” for a top-k query algorithm. In some databases there is no way to perform a random access, Fagin et al. proposed NRA (No Random Access) algorithm for this case. In this paper, we provided some key observations of NRA. Based on t...

Crowdsourced top-k computation has attracted significant attention recently, thanks to emerging crowdsourcing platforms, e.g., Amazon Mechanical Turk and CrowdFlower. Crowdsourced top-k algorithms ask the crowd to compare the objects and infer the top-k objects based on the crowdsourced comparison results. The crowd may return incorrect answers, but traditional top-k algorithms cannot tolerate ...

In the era of huge datasets, the topsearch becomes an effective way to decrease the search time of topobjects. Since we suppose locally accessible data only, the multidimensional indexes containing all attributes together seem to be more effective than a distribution of each attribute to a separate index. Therefore we introduce the topsearch algorithm over grid file – the multidimensional index...

Supporting top-k document retrieval queries on general text databases, that is, finding the k documents where a given pattern occurs most frequently, has become a topic of interest with practical applications. While the problem has been solved in optimal time and linear space, the actual space usage is a serious concern. In this paper we study various reduced-space structures that support top-k...

Modern recommendation systems leverage some forms of collaborative user or crowd sourced collection of information. For instance, services like TripAdvisor, Airbnb and HungyGoWhere rely on user-generated content to describe and classify hotels, vacation rentals and restaurants. By nature of such independent collection of information, the multiplicity, diversity and varying quality of the inform...

This paper addresses a problem of searching the best K objects in more attributes, which is indicated as a top-K problem. Last few years, research of top-K problem solving is in progress. The aim of this paper was to provide an introduction into top-K problem and present various interesting top-K processing methods. Firstly, this paper focuses on the family of threshold algorithm, which uses mo...

Emerging technologies have made available very large data repositories, which may be unreliable for a given preference criteria. In order to be able to process these repositories, users may need to discard useless information based on some preference conditions. Different preference-based query languages have been defined to support the bases for discriminating poor quality data and to express ...

Crowdsourced top-k computation aims to utilize the human ability to identify top-k objects from a given set of objects. Most of existing studies employ a pairwise comparison based method, which first asks workers to compare each pair of objects and then infers the top-k results based on the pairwise comparison results. Obviously, it is quadratic to compare every object pair and these methods in...

Top-k queries on large multi-attribute data sets are fundamental operations in information retrieval and ranking applications. In this paper, we initiate research on the anytime behavior of top-k algorithms. In particular, given specific top-k algorithms (TA and TASorted) we are interested in studying their progress toward identification of the correct result at any point during the algorithms’...

We consider the Top-k Approximate Subtree Matching (TASM) problem: finding the k best matches of a small query tree, e.g., a DBLP article with 15 nodes, in a large document tree, e.g., DBLP with 26M nodes, using the canonical tree edit distance as a similarity measure between subtrees. Evaluating the tree edit distance for large XML trees is difficult: the best known algorithms have cubic runti...