In this paper, we propose a 2D based partition method for solving the problem of Ranking under Team Context(RTC) on datasets without a priori. We first map the data into 2D space using its minimum and maximum value among all dimensions. Then we construct window queries with consideration of current team context. Besides, during the query mapping procedure, we can pre-prune some tuples which are not top ranked ones. This pre-classified step will defer processing those tuples and can save cost while providing solutions for the problem. Experiments show that our algorithm performs well especially on large datasets with correctness.
Besides concentrating on accuracy of resultset, researches in @cite_5 @cite_15 customize NN query into different contexts for providing higher quality result. In this paper, we plan to design a method in solving the NN problem under team context defined in @cite_6 .
@cite_5 @cite_15 @cite_6
2061458158 1925368782 2135857164 2745166287
In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. In this paper, a web mining problem, i.e. web index recommendation, is investigated from a multi-instance view. In detail, each web index page is regarded as a bag, while each of its linked pages is regarded as an instance. A user favoring an index page means that he or she is interested in at least one page linked by the index. Based on the browsing history of the user, recommendation could be provided for unseen index pages. An algorithm named Fretcit-kNN, which employs the Minimal Hausdorff distance between frequent term sets and utilizes both the references and citers of an unseen bag in determining its label, is proposed to solve the problem. Experiments show that in average the recommendation accuracy of Fretcit-kNN is 81.0 with 71.7 recall and 70.9 precision, which is significantly better than the best algorithm that does not consider the specific characteristics of multi-instance learning, whose performance is 76.3 accuracy with 63.4 recall and 66.1 precision. Context-aware database has drawn increasing attention from both industry and academia recently by taking users' current situation and environment into consideration. However, most of the literature focus on individual context, overlooking the team users. In this paper, we investigate how to integrate team context into database query process to help the users' get top-ranked database tuples and make the team more competitive. We introduce naive and optimized query algorithm to select the suitable records and show that they output the same results while the latter is more computational efficient. Extensive empirical studies are conducted to evaluate the query approaches and demonstrate their effectiveness and efficiency. Searching for similar objects (in terms of near and nearest neighbors) of a given query object from a large set is an essential task in many applications. Recent years have seen great progress towards efficient algorithms for this task. This paper takes a query language perspective, equipping SQL with the near and nearest search capability by adding a user-defined-predicate, called NN-UDP. The predicate indicates, among a set of objects, if an object is a near or nearest-neighbor of a given query object. The use of the NN-UDP makes the queries involving similarity searches intuitive to express. Unfortunately, traditional cost-based optimization methods that deal with traditional UDPs do not work well for such SQL queries. Better execution plans are possible with the introduction of a new operator, called NN-OP, which finds the near or nearest neighbors from a set of objects for a given query object. An optimization algorithm proposed in this paper can produce these plans that take advantage of the efficient search algorithms developed in recent years. To assess the proposed optimization algorithm, this paper focuses on applications that deal with streaming time series. Experimental results show that the optimization strategy is effective. Given a textual description of an image, phrase grounding localizes objects in the image referred by query phrases in the description. State-of-the-art methods address the problem by ranking a set of proposals based on the relevance to each query, which are limited by the performance of independent proposal generation systems and ignore useful cues from context in the description. In this paper, we adopt a spatial regression method to break the performance limit, and introduce reinforcement learning techniques to further leverage semantic context information. We propose a novel Query-guided Regression network with Context policy (QRC Net) which jointly learns a Proposal Generation Network (PGN), a Query-guided Regression Network (QRN) and a Context Policy Network (CPN). Experiments show QRC Net provides a significant improvement in accuracy on two popular datasets: Flickr30K Entities and Referit Game, with 14.25 and 17.14 increase over the state-of-the-arts respectively.