Query auto completion is known to provide poor predictions of the user's query when her input prefix is very short (e.g., one or two characters). In this paper we show that context, such as the user's recent queries, can be used to improve the prediction quality considerably even for such short prefixes. We propose a context-sensitive query auto completion algorithm, NearestCompletion, which outputs the completions of the user's input that are most similar to the context queries. To measure similarity, we represent queries and contexts as high-dimensional term-weighted vectors and resort to cosine similarity. The mapping from queries to vectors is done through a new query expansion technique that we introduce, which expands a query by traversing the query recommendation tree rooted at the query.

Motivated by a problem of transmitting supplemental data over broadcast channels (Birk and Kol, INFOCOM 1998), we study the following coding problem: a sender communicates with n receivers R1,..., Rn. He holds an input x ∈ {0,01l}nand wishes to broadcast a single message so that each receiver Ri can recover the bit xi. Each Rihas prior side information about x, induced by a directed graph Grain nodes; Ri knows the bits of a; in the positions {j | (i,j) is an edge of G}.G is known to the sender and to the receivers. We call encoding schemes that achieve this goal INDEXcodes for {0,1}nwith side information graph G. In this paper we identify a measure on graphs, the minrank, which exactly characterizes the minimum length of linear and certain types of nonlinear INDEX codes. We show that for natural classes of side information graphs, including directed acyclic graphs, perfect graphs, odd holes, and odd anti-holes, minrank is the optimal length of arbitrary INDEX codes. For arbitrary INDEX codes and arbitrary graphs, we obtain a lower bound in terms of the size of the maximum acyclic induced subgraph. This bound holds even for randomized codes, but has been shown not to be tight.

We address the problem of externally measuring aggregate functions over documents indexed by search engines, like corpus size, index freshness, and density of duplicates in the corpus. State of the art estimators for such quantities [Bar-Yossef and Gurevich 2008b; Broder et al. 2006] are biased due to inaccurate approximation of the so called “document degrees”. In addition, the estimators in Bar-Yossef and Gurevich [2008b] are quite costly, due to their reliance on rejection sampling. We present new estimators that are able to overcome the bias introduced by approximate degrees. Our estimators are based on a careful implementation of an approximate importance sampling procedure. Comprehensive theoretical and empirical analysis of the estimators demonstrates that they have essentially no bias even in situations where document degrees are poorly approximated. By avoiding the costly rejection sampling approach, our new importance sampling estimators are significantly more efficient than the estimators proposed in Bar-Yossef and Gurevich [2008b]. Furthermore, building on an idea from Broder et al. [2006], we discuss Rao-Blackwellization as a generic method for reducing variance in search engine estimators. We show that Rao-Blackwellizing our estimators results in performance improvements, without compromising accuracy.

The ImpressionRank of a web page (or, more generally, of a web site) is the number of times users viewed the page while browsing search results. ImpressionRank captures the visibility of pages and sites in search engines and is thus an important measure, which is of interest to web site owners, competitors, market analysts, and end users.

We consider the problem of DUST: Different URLs with Similar Text. Such duplicate URLs are prevalent in Web sites, as Web server software often uses aliases and redirections, and dynamically generates the same page from various different URL requests. We present a novel algorithm, DustBuster , for uncovering DUST; that is, for discovering rules that transform a given URL to others that are likely to have similar content. DustBuster mines DUST effectively from previous crawl logs or Web server logs, without /examining page contents. Verifying these rules via sampling requires fetching few actual Web pages. Search engines can benefit from information about DUST to increase the effectiveness of crawling, reduce indexing overhead, and improve the quality of popularity statistics such as PageRank.

We consider the problem of approximating the PageRank of a target node using only local information provided by a link server. This problem was originally studied by Chen, Gan, and Suel (CIKM 2004), who presented an algorithm for tackling it. We prove that local approximation of PageRank, even to within modest approximation factors, is infeasible in the worst-case, as it requires probing the link server for Ω(n) nodes, where n is the size of the graph. The difficulty emanates from nodes of high in-degree and/or from slow convergence of the PageRank random walk.

Current twig join algorithms incur high memory costs on queries that involve child-axis nodes. In this paper we provide an analytical explanation for this phenomenon. In a first large-scale study of the space complexity of evaluating XPath queries over indexed XML documents we show the space to depend on three factors: (1) whether the query is a path or a tree; (2) the types of axes occurring in the query and their occurrence pattern; and (3) the mode of query evaluation (filtering, full- fledged, or "pattern matching"). Our lower bounds imply that evaluation of a large class of queries that have child-axis nodes indeed requires large space. Our study also reveals that on some queries there is a large gap between the space needed for pattern matching and the space needed for full-fledged evaluation or filtering. This implies that many existing twig join algorithms, which work in the pattern matching mode, incur significant space overhead. We present a new twig join algorithm that avoids this overhead. On certain queries our algorithm is exceedingly more space-efficient than existing algorithms, sometimes bringing the space down from linear in the document size to constant.

We consider the problem of approximating the PageRank of a target node using only local information provided by a link server. We prove that local approximation of PageRank is feasible if and only if the graph has low in-degree and admits fast PageRank convergence. While natural graphs, such as the web graph, are abundant with high in-degree nodes, making local PageRank approximation too costly, we show that reverse natural graphs tend to have low indegree while maintaining fast PageRank convergence. It follows that calculating Reverse PageRank locally is frequently more feasible than computing PageRank locally. Finally, we demonstrate the usefulness of Reverse PageRank in five different applications.

We revisit a problem introduced by Bharat and Broder almost a decade ago: How to sample random pages from the corpus of documents indexed by a search engine, using only the search engine's public interface? Such a primitive is particularly useful in creating objective benchmarks for search engines. The technique of Bharat and Broder suffers from a well-recorded bias: it favors long documents. In this article we introduce two novel sampling algorithms: a lexicon-based algorithm and a random walk algorithm. Our algorithms produce biased samples, but each sample is accompanied by a weight , which represents its bias. The samples, in conjunction with the weights, are then used to simulate near-uniform samples. To this end, we resort to four well-known Monte Carlo simulation methods: rejection sampling , importance sampling , the Metropolis--Hastings algorithm, and the Maximum Degree method. The limited access to search engines force our algorithms to use bias weights that are only “approximate”. We characterize analytically the effect of approximate bias weights on Monte Carlo methods and conclude that our algorithms are guaranteed to produce near-uniform samples from the search engine's corpus. Our study of approximate Monte Carlo methods could be of independent interest. Experiments on a corpus of 2.4 million documents substantiate our analytical findings and show that our algorithms do not have significant bias towards long documents. We use our algorithms to collect comparative statistics about the corpora of the Google, MSN Search, and Yahoo! search engines.

Many search engines and other web applications suggest auto-completions as the user types in a query. The suggestions are generated from hidden underlying databases, such as query logs, directories, and lexicons. These databases consist of interesting and useful information, but they are typically not directly accessible. In this paper we describe two algorithms for sampling suggestions using only the public suggestion interface. One of the algorithms samples suggestions uniformly at random and the other samples suggestions proportionally to their popularity. These algorithms can be used to mine the hidden suggestion databases. Example applications include comparison of popularity of given keywords within a search engine's query log, estimation of the volume of commercially-oriented queries in a query log, and evaluation of the extent to which a search engine exposes its users to negative content. Our algorithms employ Monte Carlo methods in order to obtain unbiased samples from the suggestion database. Empirical analysis using a publicly available query log demonstrates that our algorithms are efficient and accurate. Results of experiments on two major suggestion services are also provided.

We give the first exponential separation between quantum and bounded-error randomized one-way communication complexity. Specifically, we define the Hidden Matching Problem HM$_n$: Alice gets as input a string ${\bf x}\in\{0, 1\}^n$, and Bob gets a perfect matching M on the n coordinates. Bob's goal is to output a tuple $\langle i,j,b \rangle$ such that the edge $(i,j)$ belongs to the matching M and $b=x_i\oplus x_j$. We prove that the quantum one-way communication complexity of HM$_n$ is $O(\log n)$, yet any randomized one-way protocol with bounded error must use $\Omega({\sqrt{n}})$ bits of communication. No asymptotic gap for one-way communication was previously known. Our bounds also hold in the model of Simultaneous Messages (SM), and hence we provide the first exponential separation between quantum SM and randomized SM with public coins. For a Boolean decision version of HM$_n$, we show that the quantum one-way communication complexity remains $O(\log n)$ and that the 0-error randomized one-way communication complexity is $\Omega(n)$. We prove that any randomized linear one-way protocol with bounded error for this problem requires $\Omega(\sqrt[3]{n \log n})$ bits of communication.

This article presents RaWMS, a novel lightweight random membership service for ad hoc networks. The service provides each node with a partial uniformly chosen view of network nodes. Such a membership service is useful, for example, in data dissemination algorithms, lookup and discovery services, peer sampling services, and complete membership construction. The design of RaWMS is based on a novel reverse random walk (RW) sampling technique. The article includes a formal analysis of both the reverse RW sampling technique and RaWMS and verifies it through a detailed simulation study. In addition, RaWMS is compared both analytically and by simulations with a number of other known methods such as flooding and gossip-based techniques.

We address the problem of measuring global quality met-rics of search engines, like corpus size, index freshness, anddensity of duplicates in the corpus. The recently proposedestimators for such metrics [2, 6] suffer from significant biasand/or poor performance, due to inaccurate approximationof the so called .document degrees..We present two new estimators that are able to overcomethe bias introduced by approximate degrees. Our estimatorsare based on a careful implementation of an approximateimportance sampling procedure. Comprehensive theoreti-cal and empirical analysis of the estimators demonstratesthat they have essentially no bias even in situations wheredocument degrees are poorly approximated.Building on an idea from [6], we discuss Rao Blackwelliza-tion as a generic method for reducing variance in searchengine estimators. We show that Rao-Blackwellizing ourestimators results in significant performance improvements,while not compromising accuracy.

We consider the problem of DUST: Different URLs with Similar Text. Such duplicate URLs are prevalent in web sites, as web server software often uses aliases and redirections, and dynamically generates the same page from various different URLrequests. We present a novel algorithm, DustBuster, for uncovering DUST; that is, for discovering rules that transform a given URL to others that are likely to have similar content. DustBuster mines DUST effectively from previous crawl logs or web server logs, without examining page contents. Verifying these rules via sampling requires fetching few actual web pages. Search engines can benefit from information about DUST to increase the effectiveness of crawling, reduce indexing overhead, and improve the quality of popularity statistics such as PageRank.

Motivated by a problem of transmitting data over broadcast channels (BirkandKol, INFOCOM1998), we study the following coding problem: a sender communicates with n receivers Rl,.., Rn. He holds an input x isin {0, 1}nand wishes to broadcast a single message so that each receiver Rican recover the bit xi. Each Rihas prior side information about x, induced by a directed graph G on n nodes; Riknows the bits of x in the positions {j | (i, j) is anedge of G}. We call encoding schemes that achieve this goal INDEX codes for {0, 1}nwith side information graph G. In this paper we identify a measure on graphs, the minrank, which we conjecture to exactly characterize the minimum length of INDEX codes. We resolve the conjecture for certain natural classes of graphs. For arbitrary graphs, we show that the minrank bound is tight for both linear codes and certain classes of non-linear codes. For the general problem, we obtain a (weaker) lower bound that the length of an INDEX code for any graph G is at least the size of the maximum acyclic induced subgraph of G

We initiate the study of a new clustering framework, called cluster ranking. Rather than simply partitioning a network into clusters, a cluster ranking algorithm also orders the clusters by their strength. To this end, we introduce a novel strength measure for clusters - the integrated cohesion - which is applicable to arbitrary weighted networks. We then present C-Rank: a new cluster ranking algorithm. Given a network with arbitrary pairwise similarity weights, C-Rank creates a list of overlapping clusters and ranks them by their integrated cohesion. We provide extensive theoretical and empirical analysis of C-Rank and show that it is likely to have high precision and recall. Our experiments focus on mining mailbox networks. A mailbox network is an egocentric social network, consisting of contacts with whom an individual exchanges email. Ties among contacts are represented by the frequency of their co-occurrence on message headers. C-Rank is well suited to mine such networks, since they are abundant with overlapping communities of highly variable strengths. We demonstrate the effectiveness of C-Rank on the Enron data set, consisting of 130 mailbox networks.

RaWMS is a novel lightweight random membership service for ad hoc networks. The service provides each node with a partial uniformly chosen view of network nodes. Such a membership service is useful, e.g., in data dissemination algorithms, lookup and discovery services, peer sampling services, and complete member-ship construction. The design of RaWMS is based on a novel re-verse random walk (RW) sampling technique. The paper includes a formal analysis of both the reverse RW sampling technique and RaWMS and verifies it through a detailed simulation study. In addition, RaWMS is compared with a number of other known methods such as flooding and gossip-based techniques.

We revisit a problem introduced by Bharat and Broder almost a decade ago: how to sample random pages from a search engine's index using only the search engine's public interface? Such a primitive is particularly useful in creating objective benchmarks for search engines.The technique of Bharat and Broder suffers from two well recorded biases: it favors long documents and highly ranked documents. In this paper we introduce two novel sampling techniques: a lexicon-based technique and a random walk technique. Our methods produce biased sample documents, but each sample is accompanied by a corresponding "weight", which represents the probability of this document to be selected in the sample. The samples, in conjunction with the weights, are then used to simulate near-uniform samples. To this end, we resort to three well known Monte Carlo simulation methods: rejection sampling, importance sampling and the Metropolis-Hastings algorithm.We analyze our methods rigorously and prove that under plausible assumptions, our techniques are guaranteed to produce near-uniform samples from the search engine's index. Experiments on a corpus of 2.4 million documents substantiate our analytical findings and show that our algorithms do not have significant bias towards long or highly ranked documents. We use our algorithms to collect fresh data about the relative sizes of Google, MSN Search, and Yahoo!.

We consider the problem of dust: Different URLs with Similar Text. Such duplicate URLs are prevalent in web sites, as web server software often uses aliases and redirections, translates URLs to some canonical form, and dynamically generates the same page from various different URL requests. We present a novel algorithm, DustBuster, for uncovering dust; that is, for discovering rules for transforming a given URL to others that are likely to have similar content. DustBuster is able to detect dust effectively from previous crawl logs or web server logs, without examining page contents. Verifying these rules via sampling requires fetching few actual web pages. Search engines can benefit from this information to increase the effectiveness of crawling, reduce indexing overhead as well as improve the quality of popularity statistics such as PageRank.

All known algorithms for evaluating advanced XPath queries (e.g., ones with predicates or with closure axes) on XML streams employ buffers to temporarily store fragments of the document stream. In many cases, these buffers grow very large and constitute a major memory bottleneck. In this paper, we identify two broad classes of evaluation problems that independently necessitate the use of large memory buffers in evaluation of queries over XML streams: (1) full-fledged evaluation (as opposed to just filtering) of queries with predicates; (2) evaluation (whether full-fledged or filtering) of queries with "multi-variate" predicates.We prove quantitative lower bounds on the amount of memory required in each of these scenarios. The bounds are stated in terms of novel document properties that we define. We show that these scenarios, in combination with query evaluation over recursive documents, cover the cases in which large buffers are required. Finally, we present algorithms that match the lower bounds for an important fragment of XPath.

Edit distance has been extensively studied for the past several years. Nevertheless, no linear-time algorithm is known to compute the edit distance between two strings, or even to approximate it to within a modest factor. Furthermore, for various natural algorithmic problems such as low-distortion embeddings into normed spaces, approximate nearest-neighbor schemes, and sketching algorithms, known results for the edit distance are rather weak. We develop algorithms that solve gap versions of the edit distance problem: given two strings of length n with the promise that their edit distance is either at most k or greater than /spl lscr/, decide which of the two holds. We present two sketching algorithms for gap versions of edit distance. Our first algorithm solves the k vs. (kn)/sup 2/3/ gap problem, using a constant size sketch. A more involved algorithm solves the stronger k vs. /spl lscr/ gap problem, where /spl lscr/ can be as small as O(k/sup 2/) - still with a constant sketch - but works only for strings that are mildly "nonrepetitive". Finally, we develop an n/sup 3/7/-approximation quasilinear time algorithm for edit distance, improving the previous best factor of n/sup 3/4/ (Cole and Hariharan, 2002); if the input strings are assumed to be nonrepetitive, then the approximation factor can be strengthened to n/sup 1/3/.

The important challenge of evaluating XPath queries over XML streams has sparked much interest in the past two years, A number of algorithms have been proposed, supporting wider fragments of the query language, and exhibiting better performance and memory utilization. Nevertheless, all the algorithms known to date use a prohibitively large amount of memory for certain types of queries. A natural question then is whether this memory bottleneck is inherent or just an artifact of the proposed algorithms.In this paper we initiate the first systematic and theoretical study of lower bounds on the amount of memory required to evaluate XPath queries over XML streams. We present a general lower bound technique, which given a query, specifies the minimum amount of memory that any algorithm evaluating the query on a stream would need to incur. The lower bounds are stated in terms of new graph-theoretic properties of queries. The proof is based on tools from communication complexity.We then exploit insights learned from the lower bounds to obtain a new algorithm for XPath evaluation on streams. The algorithm uses space close to the optimum. Our algorithm deviates from the standard paradigm of using automata or transducers, thereby avoiding the need to store large transition tables.

We give the first exponential separation between quantum and bounded-error randomized one-way communication complexity. Specifically, we define the Hidden Matching Problem HMn: Alice gets as input a string x ∈ (0, 1)n and Bob gets a perfect matching M on the n coordinates. Bob's goal is to output a tuple [i,j,b] such that the edge (i,j) belongs to the matching M and b = xi ⊕ xj. We prove that the quantum one-way communication complexity of HMn is O(log n), yet any randomized one-way protocol with bounded error must use Ω(√n) bits of communication. No asymptotic gap for one-way communication was previously known. Our bounds also hold in the model of Simultaneous Messages (SM) and hence we provide the first exponential separation between quantum SM and randomized SM with public coins.For a Boolean decision version of HMn, we show that the quantum one-way communication complexity remains O(log n) and that the 0-error randomized one-way communication complexity is Ω(n). We prove that any randomized linear one-way protocol with bounded error for this problem requires Ω(√[3] n log n) bits of communication.

The rapid growth of the web has been noted and tracked extensively. Recent studies have however documented the dual phenomenon: web pages have small half lives, and thus the web exhibits rapid death as well. Consequently, page creators are faced with an increasingly burdensome task of keeping links up-to-date, and many are falling behind. In addition to just individual pages, collections of pages or even entire neighborhoods of the web exhibit significant decay, rendering them less effective as information resources. Such neighborhoods are identified only by frustrated searchers, seeking a way out of these stale neighborhoods, back to more up-to-date sections of the web; measuring the decay of a page purely on the basis of dead links on the page is too naive to reflect this frustration. In this paper we formalize a strong notion of a decay measure and present algorithms for computing it efficiently. We explore this measure by presenting a number of validations, and use it to identify interesting artifacts on today's web. We then describe a number of applications of such a measure to search engines, web page maintainers, ontologists, and individual users.

We present a novel technique, based on the Jensen-Shannon divergence from information theory, to prove lower bounds on the query complexity of sampling algorithms that approximate functions over arbitrary domain and range. Unlike previous methods, our technique does not use a reduction from a decision promise problem. As a result, it gives stronger bounds for functions that possess a large set of inputs, each two of which exhibit a gap in the function value.We demonstrate the technique with new query complexity lower bounds for three fundamental problems: (1) the "election problem", for which we obtain a quadratic improvement over previous bounds, (2) low rank matrix approximation, for which we prove the first lower bounds, showing that the algorithms given for this problem are almost optimal, and (3) matrix reconstruction.In addition, we introduce a new method for proving lower bounds on the expected query complexity of functions, using the Kullback-Leibler divergence. We demonstrate its use by a simple query complexity lower bound for the mean.

We use tools and techniques from information theory to study communication complexity problems in the one-way and simultaneous communication models. Our results include: (1) a tight characterization of multi-party one-way communication complexity for product distributions in terms of VC-dimension and shatter coefficients; (2) an equivalence of multi-party one-way and simultaneous communication models for product distributions; (3) a suite of lower bounds for specific functions in the simultaneous communication model, most notably an optimal lower bound for the multi-party set disjointness problem of Alon et al. (1999) and for the generalized addressing function problem of Babai et al. (1996) for arbitrary groups. Methodologically, our main contribution is rendering communication complexity problems in the framework of information theory. This allows us access to the powerful calculus of information theory and the use of fundamental principles such as Fano's inequality and the maximum likelihood estimate principle.

We prove (mostly tight) space lower bounds for "streaming" (or "on-line") computations of four fundamental combinatorial objects: error-correcting codes, universal hash functions, extractors, and dispersers. Streaming computations for these objects are motivated algorithmically by massive data set applications and complexity-theoretically by pseudorandomness and derandomization for space-bounded probabilistic algorithms. Our results reveal a surprising separation of extractors and dispersers in terms of the space required to compute them in the streaming model. While online extractors require space linear in their output length, we construct dispersers that are computable online with exponentially less space. We also present several explicit constructions of online extractors that match the lower bound. We show that online universal and almost-universal hash functions require space linear in their output length (this bound was known previously only for "pure" universal hash functions). Finally, we show that both online encoding and online decoding of error-correcting codes require space proportional to the product of the length of the encoded message and the code's relative minimum distance. Block encoding trivially matches the lower bounds for constant rate codes.

We present a new method for proving strong lower bounds in communication complexity. This method is based on the notion of the conditional information complexity of a function which is the minimum amount of information about the inputs that has to be revealed by a communication protocol for the function. While conditional information complexity is a lower bound on the communication complexity, we show that it also admits a direct sum theorem. Direct sum decomposition reduces our task to that of proving (conditional) information complexity lower bounds for simple problems (such as the AND of two bits). For the latter, we develop novel techniques based on Hellinger distance and its generalizations.

We formulate and propose the template detection problem, and suggest a practical solution for it based on counting frequent item sets. We show that the use of templates is pervasive on the web. We describe three principles, which characterize the assumptions made by hypertext information retrieval (IR) and data mining (DM) systems, and show that templates are a major source of violation of these principles. As a consequence, basic "pure" implementations of simple search algorithms coupled with template detection and elimination show surprising increases in precision at all levels of recall.

We develop a framework to study probabilistic sampling algorithms that approximate general functions of the form \genfunc, where \domain and \range are arbitrary sets. Our goal is to obtain lower bounds on the query complexity of functions, namely the number of input variables x_i that any sampling algorithm needs to query to approximate f(x_1,\ldots,x_n).We define two quantitative properties of functions --- the it block sensitivity and the minimum Hellinger distance --- that give us techniques to prove lower bounds on the query complexity. These techniques are quite general, easy to use, yet powerful enough to yield tight results. Our applications include the mean and higher statistical moments, the median and other selection functions, and the frequency moments, where we obtain lower bounds that are close to the corresponding upper bounds.We also point out some connections between sampling and streaming algorithms and lossy compression schemes.

This paper initiates the study of deterministic amplification of space-bounded probabilistic algorithms. The straightforward implementations of known amplification methods cannot be used for such algorithms, since they consume too much space. We present a new implementation of the Ajtai-Komlos-Szemeredi method, that enables to amplify an S-space algorithm that uses r random bits and errs with probability /spl epsiv/ to an O(kS)-space algorithm that uses r+O(k) random bits and errs with probability /spl epsiv//sup /spl Omega/(k)/. This method can be used to reduce the error probability of BPL algorithms below any constant, with only a constant addition of new random bits. This is weaker than the exponential reduction that can be achieved for BPP algorithms by methods that use only O(r) random bits. However we prove that any black-box amplification method that uses O(r) random bits and makes at most p parallel simulations reduces the error to at most /spl epsiv//sup O(p)/. Hence, in BPL, where p should be a constant, the error cannot be reduced to less than a constant. This means that our method is optimal with respect to black-box amplification methods, that use O(r) random bits. The new implementation of the AKS method is based on explicit constructions of constant-space online extractors and online expanders. These are extractors and expanders, for which neighborhoods can be computed in a constant space by a Turing machine with a one-way input tape.

Article Free Access Share on Pointer jumping requires concurrent read Authors: Noam Nisan Department of Computer Science, The Hebrew University of Jerusalem, Jerusalem 91904, Israel Department of Computer Science, The Hebrew University of Jerusalem, Jerusalem 91904, IsraelView Profile , Ziv Bar-Yossef Department of Computer Science, The Hebrew University of Jerusalem, Jerusalem 91904, Israel Department of Computer Science, The Hebrew University of Jerusalem, Jerusalem 91904, IsraelView Profile Authors Info & Claims STOC '97: Proceedings of the twenty-ninth annual ACM symposium on Theory of computingMay 1997 Pages 549–558https://doi.org/10.1145/258533.258648Online:04 May 1997Publication History 3citation256DownloadsMetricsTotal Citations3Total Downloads256Last 12 Months6Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF