A large fraction of online display advertising is sold via guaranteed contracts: a publisher guarantees to the advertiser a certain number of user visits satisfying the targeting predicates of the contract. The publisher is then tasked with solving the ad serving problem ---given a user visit, which of the thousands of matching contracts should be displayed, so that by the expiration time every contract has obtained the requisite number of user visits. The challenges of the problem come from (1) the sheer size of the problem being solved, with tens of thousands of contracts and billions of user visits, (2) the unpredictability of user behavior, since these contracts are sold months ahead of time, when only a forecast of user visits is available and (3) the minute amount of resources available online, as an ad server must respond with a matching contract in a fraction of a second.

The problem of efficiently evaluating a large collection of complex Boolean expressions - beyond simple conjunctions and Disjunctive/Conjunctive Normal Forms (DNF/CNF) - occurs in many emerging online advertising applications such as advertising exchanges and automatic targeting. The simple solution of normalizing complex Boolean expressions to DNF or CNF form, and then using existing methods for evaluating such expressions is not always effective because of the exponential blow-up in the size of expressions due to normalization. We thus propose a novel method for evaluating complex expressions, which leverages existing techniques for evaluating leaf-level conjunctions, and then uses a bottom-up evaluation technique to only process the relevant parts of the complex expressions that contain the matching conjunctions. We develop two such bottom-up evaluation techniques, one based on Dewey IDs and another based on mapping Boolean expressions to one-dimensional intervals. Our experimental evaluation based on data obtained from an online advertising exchange shows that the proposed techniques are efficient and scalable, both with respect to space usage as well as evaluation time.

When searching large hypertext document collections, it is often possible that there are too many results available for ambiguous queries. Query refinement is an interactive process of query modification that can be used to narrow down the scope of search results. We propose a new method for automatically generating refinements or related terms to queries by mining anchor text for a large hypertext document collection. We show that the usage of anchor text as a basis for query refinement produces high quality refinement suggestions that are significantly better in terms of perceived usefulness compared to refinements that are derived using the document content. Furthermore, our study suggests that anchor text refinements can also be used to augment traditional query refinement algorithms based on query logs, since they typically differ in coverage and produce different refinements. Our results are based on experiments on an anchor text collection of a large corporate intranet.

We present an efficient query evaluation method based on a two level approach: at the first level, our method iterates in parallel over query term postings and identifies candidate documents using an approximate evaluation taking into account only partial information on term occurrences and no query independent factors; at the second level, promising candidates are fully evaluated and their exact scores are computed. The efficiency of the evaluation process can be improved significantly using dynamic pruning techniques with very little cost in effectiveness. The amount of pruning can be controlled by the user as a function of time allocated for query evaluation. Experimentally, using the TREC Web Track data, we have determined that our algorithm significantly reduces the total number of full evaluations by more than 90%, almost without any loss in precision or recall. At the heart of our approach there is an efficient implementation of a new Boolean construct called WAND or Weak AND that might be of independent interest.

This paper describes Seeker, a platform for large-scale text analytics, and SemTag, an application written on the platform to perform automated semantic tagging of large corpora. We apply SemTag to a collection of approximately 264 million web pages, and generate approximately 434 million automatically disambiguated semantic tags, published to the web as a label bureau providing metadata regarding the 434 million annotations. To our knowledge, this is the largest scale semantic tagging effort to date.We describe the Seeker platform, discuss the architecture of the SemTag application, describe a new disambiguation algorithm specialized to support ontological disambiguation of large-scale data, evaluate the algorithm, and present our final results with information about acquiring and making use of the semantic tags. We argue that automated large scale semantic tagging of ambiguous content can bootstrap and accelerate the creation of the semantic web.

This paper presents a new spectral partitioning formulation which directly incorporates vertex size information by modifying the Laplacian of the graph. Modifying the Laplacian produces a generalized eigenvalue problem, which is reduced to the standard eigenvalue problem. Experiments show that the scaled ratio-cut costs of results on benchmarks with arbitrary vertex size improve by 22% when the eigenvectors of the Laplacian in the spectral partitioner KP are replaced by the eigenvectors of our modified Laplacian. The inability to handle vertex sizes in the spectral partitioning formulation has been a limitation in applying spectral partitioning in a multilevel setting. We investigate whether our new formulation effectively removes this limitation by combining it with a simple multilevel bottom-up clustering algorithm and an iterative improvement algorithm for partition refinement. Experiments show that in a multilevel setting where the spectral partitioner KP provides the initial partitions of the most contracted graph, using the modified Laplacian in place of the standard Laplacian is more efficient and more effective in the partitioning of graphs with arbitrary-size and unit-size vertices; average improvements of 17% and 18% are observed for graphs with arbitrary-size and unit-size vertices, respectively. Comparisons with other ratio-cut based partitioners on hypergraphs with unit-size as well as arbitrary-size vertices, show that the multilevel spectral partitioner produces either better results or almost identical results more efficiently.

We develop a new multi-way, hybrid spectral/iterative hypergraph partitioning algorithm that combines the strengths of spectral partitioners and iterative improvement algorithms to create a new class of partitioners. We use spectral information (the eigenvectors of a graph) to generate initial partitions, influence the selection of iterative improvement moves, and break out of local minima. Our 3-way and 4-way partitioning results exhibit significant improvement over current published results, demonstrating the effectiveness of our new method. Our hybrid algorithm produces an improvement of 25% over GFM for 3-way partitions, 41% improvement over GFM for 4-way partitions, and 58% improvement over ML/sub F/ for 4-way partitions.

This paper presents a new spectral partitioning formation which directly incorporates vertex size information. The new formulation results in a generalized eigenvalue problem, and this problem is reduced to the standard eigenvalue problem. Experimental results show that incorporating vertex sizes into the eigenvalue calculation produces results that are 50% better than the standard formation in terms of scaled ratio-cut cost, even when a Kernighan-Lin style iterative improvement algorithm taking into account vertex sizes is applied as a post-processing step. To evaluate the new method for use in multi-level partitioning, we combine the partitioner with a multi-level bottom-up clustering algorithm and an iterative improvement algorithm for partition refinement. Experimental results show that our new spectral algorithm is more effective than the standard spectral formulation and other partitioners in the multi-level partitioning of hypergraphs.

Recent research on FPGA partitioning has focused on finding minimum cuts between partitions without regard to the routability of the partitioned subcircuits. In this paper, we develop a spectral approach to multiway partitioning in which the primary goal is to produce routable subcircuits while maximizing FPGA device utilization. To assist the partitioner in assessing the routability of the partitioned subcircuits, we have developed a theory to predict the routability of the partitioned subcircuits prior to partitioning. Advancement over previous work is evidenced by results of experiments on standard MCNC benchmarks.

Recent research on FPGA partitioning has focussed on finding minimum cuts between partitions without regard to the routability of the partitioned subcircuits. In this paper we develop a spectral approach to multi-way partitioning in which the primary goal is to produce routable subcircuits while maximizing FPGA device utilization. To assist the partitioner in assessing the routability of the partitioned subcircuits, we have developed a theory to predict the routability of the partitioned subcircuits prior to partitioning. Advancement over the current work is evidenced by results of experiments on the standard MCNC benchmarks.

Recent research on partitioning has focused on the ratio-cut cost metric, which maintains a balance between the cost of the edges cut and the sizes of the partitions without fixing the size of the partitions a priori. Iterative approaches and spectral approaches to two-way ratio-cut partitioning have yielded higher quality partitioning results. In this paper, we develop a spectral approach to multi-way ratio-cut partitioning that provides a generalization of the ratio-cut cost metric to L-way partitioning and a lower bound on this cost metric. Our approach involves finding the k smallest eigenvalue/eigenvector pairs of the Laplacian of the graph. The eigenvectors provide an embedding of the graph's n vertices into a k-dimensional subspace. We devise a time and space efficient clustering heuristic to coerce the points in the embedding into k partitions. Advancement over the current work is evidenced by the results of experiments on the standard benchmarks.>

Efficient utilization of Field Programmable Gate Arrays (FPGAs) depends on the ability to determine whether designs will exceed the logic or routing capacities of the devices.Here, we focus on the problem of assessing the routability of designs for FPGAs before place-and-rpute.Specifically, we identify the relevant wmeabilit y t heorles, modify and adapt the theories for FPGAs, and conduct experiments to validate the theories.

Article Free Access Share on Spectral K-way ratio-cut partitioning and clustering Authors: Pak K. Chan View Profile , Martine D. F. Schlag View Profile , Jason Y. Zien View Profile Authors Info & Claims DAC '93: Proceedings of the 30th international Design Automation ConferenceJuly 1993 Pages 749–754https://doi.org/10.1145/157485.165117Published:01 July 1993Publication History 93citation903DownloadsMetricsTotal Citations93Total Downloads903Last 12 Months83Last 6 weeks9 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