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. We propose a new family of distance measures on rankings, derived through an axiomatic approach, that consider the nonuniform relevance of the top and bottom of ordered lists and similarities between candidates. The proposed distance functions include specialized weighted versions of the Kendall τ distance and the Cayley distance, and are suitable for comparing rankings in a number of applications, including information retrieval and rank aggregation. In addition to proposing the distance measures and providing the theoretical underpinnings for their applications, we also analyze algorithmic and computational aspects of weighted distance-based rank aggregation. We present an aggregation method based on approximating weighted distance measures by a generalized version of Spearman's footrule distance as well as a Markov chain method inspired by PageRank, where transition probabilities of the Markov chain reflect the chosen weighted distances. Search effectiveness metrics quantify the relevance of the ranked document lists returned by retrieval systems. In this paper we characterize metrics according to seven numeric properties – boundedness, monotonicity, convergence, top-weightedness, localization, completeness, and realizability. We demonstrate that these properties partition the commonly-used evaluation metrics, and hence provide a framework in which the relationships between effectiveness metrics can be better understood, including their relative merits for different applications. Discovering frequent patterns from data is a popular exploratory technique in datamining. However, if the data are sensitive (e.g., patient health records, user behavior records) releasing information about significant patterns or trends carries significant risk to privacy. This paper shows how one can accurately discover and release the most significant patterns along with their frequencies in a data set containing sensitive information, while providing rigorous guarantees of privacy for the individuals whose information is stored there. We present two efficient algorithms for discovering the k most frequent patterns in a data set of sensitive records. Our algorithms satisfy differential privacy, a recently introduced definition that provides meaningful privacy guarantees in the presence of arbitrary external information. Differentially private algorithms require a degree of uncertainty in their output to preserve privacy. Our algorithms handle this by returning 'noisy' lists of patterns that are close to the actual list of k most frequent patterns in the data. We define a new notion of utility that quantifies the output accuracy of private top-k pattern mining algorithms. In typical data sets, our utility criterion implies low false positive and false negative rates in the reported lists. We prove that our methods meet the new utility criterion; we also demonstrate the performance of our algorithms through extensive experiments on the transaction data sets from the FIMI repository. While the paper focuses on frequent pattern mining, the techniques developed here are relevant whenever the data mining output is a list of elements ordered according to an appropriately 'robust' measure of interest. Diversified ranking is a fundamental task in machine learning. It is broadly applicable in many real world problems, e.g., information retrieval, team assembling, product search, etc. In this paper, we consider a generic setting where we aim to diversify the top-k ranking list based on an arbitrary relevance function and an arbitrary similarity function among all the examples. We formulate it as an optimization problem and show that in general it is NP-hard. Then, we show that for a large volume of the parameter space, the proposed objective function enjoys the diminishing returns property, which enables us to design a scalable, greedy algorithm to find the (1 - 1 e) near-optimal solution. Experimental results on real data sets demonstrate the effectiveness of the proposed algorithm. Abstract An efficient multiprocessor algorithm to merge m, m ⩾ 2, sorted lists containing N elements is described. In a hierarchical-memory multiprocessor, the proposed method substantially reduces the data access costs in comparison with traditional schemes that successively merge the lists two at a time. The proposed method uses a novel partitioning algorithm to split up the m lists into range-disjoint parts of equal size. Each of the parts can then be merged concurrently and independently by a different processor using an m-way merge. The time complexity to find a partition is O(m log ( fN m ) , where f, 0 Graph pattern matching has been widely used in e.g., social data analysis. A number of matching algorithms have been developed that, given a graph pattern Q and a graph G, compute the set M(Q,G) of matches of Q in G. However, these algorithms often return an excessive number of matches, and are expensive on large real-life social graphs. Moreover, in practice many social queries are to find matches of a specific pattern node, rather than the entire M(Q,G). This paper studies top-k graph pattern matching. (1) We revise graph pattern matching defined in terms of simulation, by supporting a designated output node uo. Given G and Q, it is to find those nodes in M(Q,G) that match uo, instead of the large set M(Q,G). (2) We study two classes of functions for ranking the matches: relevance functions δr() based on, e.g., social impact, and distance functions δd() to cover diverse elements. (3) We develop two algorithms for computing top-k matches of uo based on δr(), with the early termination property, i.e., they find top-k matches without computing the entire M(Q,G). (4) We also study diversified top-k matching, a bi-criteria optimization problem based on both δr() and δd(). We show that its decision problem is NP-complete. Nonetheless, we provide an approximation algorithm with performance guarantees and a heuristic one with the early termination property. (5) Using real-life and synthetic data, we experimentally verify that our (diversified) top-k matching algorithms are effective, and outperform traditional matching algorithms in efficiency. The invention relates to a computer device for managing documents, in particular software projects developed in co-operation. A memory stores contents of documents having time references. An extractor separates the document contents into document elements. A signature generator returns signatures of element contents. An imprint generator associates for each document the time reference thereof and the signatures of its elements. A time clock module calculates the results of a time election function. A supervisor operates the generator on the document contents. For each signature value, it calls the time clock module with a list of imprint time references containing the signature value. A unified time reference is obtained for each signature value. Each signature value and its unified time reference are stored. In this work, we define and solve the Fair Top-k Ranking problem, in which we want to determine a subset of k candidates from a large pool of n » k candidates, maximizing utility (i.e., select the "best" candidates) subject to group fairness criteria. Our ranked group fairness definition extends group fairness using the standard notion of protected groups and is based on ensuring that the proportion of protected candidates in every prefix of the top-k ranking remains statistically above or indistinguishable from a given minimum. Utility is operationalized in two ways: (i) every candidate included in the top-k should be more qualified than every candidate not included; and (ii) for every pair of candidates in the top-k, the more qualified candidate should be ranked above. An efficient algorithm is presented for producing the Fair Top-k Ranking, and tested experimentally on existing datasets as well as new datasets released with this paper, showing that our approach yields small distortions with respect to rankings that maximize utility without considering fairness criteria. To the best of our knowledge, this is the first algorithm grounded in statistical tests that can mitigate biases in the representation of an under-represented group along a ranked list. Search engines return ranked lists of Web pages in response to queries. These pages are starting points for post-query navigation, but may be insufficient for search tasks involving multiple steps. Search trails mined from toolbar logs start with a query and contain pages visited by one user during post-query navigation. Implicit endorsements from many trails can enhance result ranking. Rather than using trails solely to improve ranking, it may also be worth providing trail information directly to users. In this paper, we quantify the benefit that users currently obtain from trail-following and compare different methods for finding the best trail for a given query and each top-ranked result. We compare the relevance, topic coverage, topic diversity, and utility of trails selected using different methods, and break out findings by factors such as query type and origin relevance. Our findings demonstrate value in trails, highlight interesting differences in the performance of trailfinding algorithms, and show we can find best-trails for a query that outperform the trails most users follow. Findings have implications for enhancing Web information seeking using trails. This paper presents an algorithm for fast sorting of large lists using modern GPUs. The method achieves high speed by efficiently utilizing the parallelism of the GPU throughout the whole algorithm. Initially, GPU-based bucketsort or quicksort splits the list into enough sublists then to be sorted in parallel using merge-sort. The algorithm is of complexity nlogn, and for lists of 8 M elements and using a single Geforce 8800 GTS-512, it is 2.5 times as fast as the bitonic sort algorithms, with standard complexity of n(logn)^2, which for a long time was considered to be the fastest for GPU sorting. It is 6 times faster than single CPU quicksort, and 10 faster than the recent GPU-based radix sort. Finally, the algorithm is further parallelized to utilize two graphics cards, resulting in yet another 1.8 times speedup. Active Internet measurement studies rely on a list of targets to be scanned. While probing the entire IPv4 address space is feasible for scans of limited complexity, more complex scans do not scale to measuring the full Internet. Thus, a sample of the Internet can be used instead, often in form of a "top list". The most widely used list is the Alexa Global Top1M list. Despite their prevalence, use of top lists is seldomly questioned. Little is known about their creation, representativity, potential biases, stability, or overlap between lists. As a result, potential consequences of applying top lists in research are not known. In this study, we aim to open the discussion on top lists by investigating the aptness of frequently used top lists for empirical Internet scans, including stability, correlation, and potential biases of such lists. Assume that each object in a database has m grades, or scores, one for each of m attributes. For example, an object can have a color grade, that tells how red it is, and a shape grade, that tells how round it is. For each attribute, there is a sorted list, which lists each object and its grade under that attribute, sorted by grade (highest grade first). Each object is assigned an overall grade, that is obtained by combining the attribute grades using a fixed monotone aggregation function, or combining rule, such as min or average. To determine the top k objects, that is, k objects with the highest overall grades, the naive algorithm must access every object in the database, to find its grade under each attribute. Fagin has given an algorithm ("Fagin's Algorithm", or FA) that is much more efficient. For some monotone aggregation functions, FA is optimal with high probability in the worst case. We analyze an elegant and remarkably simple algorithm ("the threshold algorithm", or TA) that is optimal in a much stronger sense than FA. We show that TA is essentially optimal, not just for some monotone aggregation functions, but for all of them, and not just in a high-probability worst-case sense, but over every database. Unlike FA, which requires large buffers (whose size may grow unboundedly as the database size grows), TA requires only a small, constant-size buffer. TA allows early stopping, which yields, in a precise sense, an approximate version of the top k answers. We distinguish two types of access: sorted access (where the middleware system obtains the grade of an object in some sorted list by proceeding through the list sequentially from the top), and random access (where the middleware system requests the grade of object in a list, and obtains it in one step). We consider the scenarios where random access is either impossible, or expensive relative to sorted access, and provide algorithms that are essentially optimal for these cases as well. In this paper, we propose a decision-aided scheme for parallel SC-List decoding of polar codes. At the parallel SC-List decoder, each survival path is extended based on multiple information bits, therefore the number of split paths becomes very large and the sorting to find the top L paths becomes very complex. We propose a decision-aided scheme to reduce the number of split paths and thus reduce the sorting complexity. Inverted index data structures are the key to fast text search engines. We first investigate one of the predominant operation on inverted indexes, which asks for intersecting two sorted lists of document IDs of different lengths. We explore compression and performance of different inverted list data structures. In particular, we present Lookup, a new data structure that allows intersection in expected time linear in the smaller list. Based on this result, we present the algorithmic core of a full text data base that allows fast Boolean queries, phrase queries, and document reporting using less space than the input text. The system uses a carefully choreographed combination of classical data compression techniques and inverted-index-based search data structures. Our experiments show that inverted indexes are preferable over purely suffix-array-based techniques for in-memory (English) text search engines. A similar system is now running in practice in each core of the distributed data base engine TREX of SAP. Similarity join is a useful primitive operation underlying many applications, such as near duplicate Web page detection, data integration, and pattern recognition. Traditional similarity joins require a user to specify a similarity threshold. In this paper, we study a variant of the similarity join, termed top-k set similarity join. It returns the top-k pairs of records ranked by their similarities, thus eliminating the guess work users have to perform when the similarity threshold is unknown before hand. An algorithm, topk-join, is proposed to answer top-k similarity join efficiently. It is based on the prefix filtering principle and employs tight upper bounding of similarity values of unseen pairs. Experimental results demonstrate the efficiency of the proposed algorithm on large-scale real datasets. Existential second-order logic (ESO) and monadic second-order logic(MSO) have attracted much interest in logic and computer science. ESO is a much expressive logic over successor structures than MSO. However, little was known about the relationship between MSOand syntatic fragments of ESO. We shed light on this issue by completely characterizing this relationship for the prefix classes of ESO over strings, (i.e., finite successor structures). Moreover, we determine the complexity of model checking over strings, for all ESO-prefix classes. Let ESO( Q ) denote the prefix class containing all sentences of the shape ∃R Q 4 , where R is a list of predicate variables, Q is a first-order predicate qualifier from the prefix set Q and 4 is quantifier-free. We show that ESO( ∃*∀∃* ) and ESO( ∃*∀∀ ) are the maximal standard ESO-prefix classes contained in MSO, thus expressing only regular languages. We further prove the following dichotomy theorem: An ESO prefix-class either expresses only regular languages (and is thus in MSO), or it expresses some NP-complete languages. We also give a precise characterization of those ESO-prefix classes that are equivalent to MSO over strings, and of the ESO-prefix classes which are closed under complementation on strings.