In contrast to XML query languages as e.g. XPath which require knowledge on the query language as well as on the document structure, keyword search is open to anybody. As the size of XML sources grows rapidly, the need for efficient search indices on XML data that support keyword search increases. In this paper, we present an approach of XML keyword search which is based on the DAG of the XML data, where repeated substructures are considered only once, and therefore, have to be searched only once. As our performance evaluation shows, this DAG-based extension of the set intersection search algorithm[1], [2], can lead to search times that are on large documents more than twice as fast as the search times of the XML-based approach. Additionally, we utilize a smaller index, i.e., we consume less main memory to compute the results.
JDeweyJoin @cite_6 returns the top-k most relevant results. They compute the results bottom-up by computing a kind of join on the list of DeweyIDs of the nodes in the inverted element list. Whenever they find a prefix that is contained in all relevant element lists, the node with this prefix as ID is a result candidate. In addition, they use a weight function to sort the list entries in such a way, that they can stop the computation after k results, returning the top-k most relevant results.
@cite_6
1991897642
We present a number of optimization techniques to compute prefix sums on linked lists and implement them on multithreaded GPUs using CUDA. Prefix computations on linked structures involve in general highly irregular fine grain memory accesses that are typical of many computations on linked lists, trees, and graphs. While the current generation of GPUs provides substantial computational power and extremely high bandwidth memory accesses, they may appear at first to be primarily geared toward streamed, highly data parallel computations. In this paper, we introduce an optimized multithreaded GPU algorithm for prefix computations through a randomization process that reduces the problem to a large number of fine-grain computations. We map these fine-grain computations onto multithreaded GPUs in such a way that the processing cost per element is shown to be close to the best possible. Our experimental results show scalability for list sizes ranging from 1M nodes to 256M nodes, and significantly improve on the recently published parallel implementations of list ranking, including implementations on the Cell Processor, the MTA-8, and the NVIDIA GeForce 200 series. They also compare favorably to the performance of the best known CUDA algorithm for the scan operation on the Tesla C1060. Diversified ranking on graphs is a fundamental mining task and has a variety of high-impact applications. There are two important open questions here. The first challenge is the measure - how to quantify the goodness of a given top-k ranking list that captures both the relevance and the diversity? The second challenge lies in the algorithmic aspect - how to find an optimal, or near-optimal, top-k ranking list that maximizes the measure we defined in a scalable way? In this paper, we address these challenges from an optimization point of view. Firstly, we propose a goodness measure for a given top-k ranking list. The proposed goodness measure intuitively captures both (a) the relevance between each individual node in the ranking list and the query; and (b) the diversity among different nodes in the ranking list. Moreover, we propose a scalable algorithm (linear wrt the size of the graph) that generates a provably near-optimal solution. The experimental evaluations on real graphs demonstrate its effectiveness and efficiency. Spearman's footrule and Kendall's tau are two well established distances between rankings. They, however, fail to take into account concepts crucial to evaluating a result set in information retrieval: element relevance and positional information. That is, changing the rank of a highly-relevant document should result in a higher penalty than changing the rank of an irrelevant document; a similar logic holds for the top versus the bottom of the result ordering. In this work, we extend both of these metrics to those with position and element weights, and show that a variant of the Diaconis-Graham inequality still holds - the generalized two measures remain within a constant factor of each other for all permutations. We continue by extending the element weights into a distance metric between elements. For example, in search evaluation, swapping the order of two nearly duplicate results should result in little penalty, even if these two are highly relevant and appear at the top of the list. We extend the distance measures to this more general case and show that they remain within a constant factor of each other. We conclude by conducting simple experiments on web search data with the proposed measures. Our experiments show that the weighted generalizations are more robust and consistent with each other than their unweighted counter-parts. We show how to store a compressed two-dimensional array such that, if we are asked for the elements with high relative frequency in a range, we can quickly return a short list of candidates that includes them. More specifically, given an m × n array A and a fraction α > 0, we can store A in O(mn(H + 1)log2(1 α) bits, where H is the entropy of the elements' distribution in A, such that later, given a rectangular range in A and a fraction β ≥ α, in O(1 β) time we can return a list of O(1 β) distinct array elements that includes all the elements that have relative frequency at least β in that range. We do not verify that the elements in the list have relative frequency at least β, so the list may contain false positives. In the case when m = 1, i.e., A is a string, we improve this space bound by a factor of log(1 α), and explore a spacetime trade off for verifying the frequency of the elements in the list. This leads to an O(n min(log(1 α), H +1) log n) bit data structure for strings that, in O(1 β) time, can return the O(1 β) elements that have relative frequency at least β in a given range, without false positives, for β ≥ α.