We propose a learning algorithm that utilizes an innovative set of features to re-rank a list of top-K 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 for finding the best match. We also propose the use of schema matching predictors as features in the learning task, and...

Erasable itemset mining first introduced in 2009 is an interesting variation of pattern mining. The managers can use the erasable itemsets for planning production plan of the factory. Besides the problem of mining erasable itemsets, the problem of mining top-rank-k erasable itemsets is an interesting and practical problem. In this paper, we first propose a new structure, call dPID_List and two ...

Information streams are mainly produced today by social networks, but current methods for continuous top-k processing of such streams are still limited to contentbased similarity. We present the SANTA algorithm, able to handle also social network criteria and events, and report a preliminary comparison with an extension of a state-of-the-art algorithm.

1. APPROXIMATE TOP-K Let {e1, e2, . . . , el} be a set of distinct records in a database, with unique IDs {id1, id2, . . . , idl}. Let A1, A2, . . . , Ap be a set of distinct attributes for each record. For every record ei, the attribute Aj is zero or some positive value. We denote the value of the attribute Aj of record ei by Aj(ei). The sum of the attributes of ei is denoted by Ni = ∑ j Aj(ei...

Researchers spent lots of time in searching published articles relevant to their project. Though having similar interest in projects researches perform individual and time overwhelming searches. But researchers are unable to control the results obtained from earlier search process, whereas they can share the results afterwards. We propose a research paper recommender system by enhancing existin...

Ci,hi(at)+ = si(at) • Query: Given a ∈ Σ, return f̂a := mediani=1..u{Ci,hi(a)si(a)} As usual, let fa = |{i : ai = a}| (the frequency of symbol a). For simplicity, you can assume that the stream is determined and fixed ahead of time, then you initialize your algorithm (by randomly choosing the hash functions), ond only then you view the stream. Let TOPk ⊆ Σ denote the set of top-k heavy hitters i...

The probabilistic top-k queries based on the interplay of score and probability, under the possible worlds semantic, become an important research issue that considers both score and uncertainty on the same basis. In the literature, many different probabilistic top-k queries are proposed. Almost all of them need to compute the probability of a tuple ti to be ranked at the j-th position across th...

This article is a survey into clustering applications and algorithms. A number of important well-known clustering methods are discussed. The authors present a brief history of the development of the field of clustering, discuss various types of clustering, and mention some of the current research directions in the field of clustering. More specifically, top-down and bottom-up hierarchical clust...

Let K be an n-dimensional local field, L/K a finite abelian extension. Define a filtration on Gal(L/K) (cf. [H], [F, sect. 4]) by Gal(L/K) = Υ L/K(UiK top n (K) +NL/KK top n (L)/NL/KK top n (L)), i ∈ Z n + , where UiK top n (K) = {Ui} ·K top n−1(K), Ui = 1 + PK(i), Υ L/K :K top n (K)/NL/KK top n (L) →̃ Gal(L/K) is the reciprocity map. Then for a subextension M/K of L/K Gal(M/K) = Gal(L/K) Gal(L/...

Top-k voting is a common form of preference elicitation due to its conceptual simplicity both on the voters’ side and on the decision maker’s side. In a typical setting, given a set of candidates, the voters are required to submit only the k-length prefixes of their intrinsic rankings of the candidates. The decision maker then tries to correctly predict the winning candidate with respect to the...