We propose a learning algorithm that utilizes an innovative set of features to rerank a list of top-K schema matches and improves upon the ranking of the best match. We provide a bound on the size of an initial match list, tying the number of matches in a desired level of confidence with finding the best match. We also propose the use of matching predictors as features in a learning task, and t...

Let D be a given set of (string) documents of total length n. The top-k document retrieval problem is to index D such that when a pattern P of length p, and a parameter k come as a query, the index returns those k documents which are most relevant to P . Hon et al. [22] proposed a linear space framework to solve this problem in O(p+k log k) time. This query time was improved to O(p+k) by Navarr...

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...

We consider the evaluation of approximate top-k queries from relations with a-priori unknown values. Such relations can arise for example in the context of expensive predicates, or cloud-based data sources. The task is to find an approximate top-k set that is close to the exact one while keeping the total processing cost low. The cost of a query is the sum of the costs of the entries that are r...

Consider two sets of spatial objects R and S, where each object is assigned a score (e.g., ranking). Given a spatial distance threshold and an integer k, the top-k spatial distance join (k-SDJ) returns the k pairs of objects, which have the highest combined score (based on an aggregate function γ) among all object pairs in R×S which have spatial distance at most . Despite the practical applicat...

The data sources of information systems running on various hardware and software platforms are independent to each other and mutually closed, which makes data exchange difficult. With the evolvement of the information application technology, data sharing between internal departments or external enterprises is necessarily required. Finally, data integration has been developed. The data integrati...

We tackle the problem of maintaining materialized topk views in this paper. Top-k queries, including MIN and MAX as important special cases, occur frequently in common database workloads. A top-k view can be materialized to improve query performance, but in general it is not selfmaintainable unless it contains all tuples in the base table. Deletions and updates on the base table may cause tuple...

ADiT is an adaptive approach for processing distributed topk queries over peer-to-peer networks optimizing both system load and query response time. This approach considers the size of the peer to peer network, the amount k of searched objects, the network capabilities of a connected peer, i.e. the transmission rate, the amount of objects stored on each peer, and the speed of a peer in processi...

Article history: Received 21 September 2009 Received in revised form 23 August 2010 Accepted 23 August 2010 Available online 8 September 2010 Recent years have witnessed an increased interest in computing cosine similarity in many application domains. Most previous studies require the specification of a minimum similarity threshold to perform the cosine similarity computation. However, it is us...

In this paper, we apply an efficient top-k shortest distance routing algorithm to the link prediction problem and test its efficacy. We compare the results with other base line and state-of-the-art methods as well as with the shortest path. Our results show that using top-k distances as a similarity measure outperforms classical similarity measures such as Jaccard and Adamic/Adar.