Abstract Weakly supervised learning aims to reduce the cost of labeling data by using expert-designed labeling rules. However, existing methods require experts to design effective rules in a single shot, which is difficult in the absence of proper guidance and tooling. Therefore, it is still an open question whether experts should spend their limited time writing rules or instead providing instance labels via active learning. In this paper, we investigate how to exploit an expert’s limited time to create effective supervision. First, to develop practical guidelines for rule creation, we conduct an exploratory analysis of diverse collections of existing expert-designed rules and find that rule precision is more important than coverage across datasets. Second, we compare rule creation to individual instance labeling via active learning and demonstrate the importance of both across 6 datasets. Third, we propose an interactive learning framework, INTERVAL, that achieves efficiency by automatically extracting candidate rules based on rich patterns (e.g., by prompting a language model), and effectiveness by soliciting expert feedback on both candidate rules and individual instances. Across 6 datasets, INTERVAL outperforms state-of-the-art weakly supervised approaches by 7% in F1. Furthermore, it requires as few as 10 queries for expert feedback to reach F1 values that existing active learning methods cannot match even with 100 queries.

Giannis Karamanolakis, Daniel Hsu, Luis Gravano. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Objective: We developed a system for the discovery of foodborne illness mentioned in online Yelp restaurant reviews using text classification. The system is used by the New York City Department of Health and Mental Hygiene (DOHMH) to monitor Yelp for foodborne illness complaints. Materials and Methods: We built classifiers for 2 tasks: (1) determining if a review indicated a person experiencing foodborne illness and (2) determining if a review indicated multiple people experiencing foodborne illness. We first developed a prototype classifier in 2012 for both tasks using a small labeled dataset. Over years of system deployment, DOHMH epidemiologists labeled 13 526 reviews selected by this classifier. We used these biased data and a sample of complementary reviews in a principled bias-adjusted training scheme to develop significantly improved classifiers. Finally, we performed an error analysis of the best resulting classifiers. Results: We found that logistic regression trained with bias-adjusted augmented data performed best for both classification tasks, with F1-scores of 87% and 66% for tasks 1 and 2, respectively. Discussion: Our error analysis revealed that the inability of our models to account for long phrases caused the most errors. Our bias-adjusted training scheme illustrates how to improve a classification system iteratively by exploiting available biased labeled data. Conclusions: Our system has been instrumental in the identification of 10 outbreaks and 8523 complaints of foodborne illness associated with New York City restaurants since July 2012. Our evaluation has identified strong classifiers for both tasks, whose deployment will allow DOHMH epidemiologists to more effectively monitor Yelp for foodborne illness investigations.

The proliferation and ubiquity of temporal data across many disciplines has generated substantial interest in the analysis and mining of time series. Clustering is one of the most popular data-mining methods, not only due to its exploratory power but also because it is often a preprocessing step or subroutine for other techniques. In this article, we presentk-Shape andk-MultiShapes (k-MS), two novel algorithms for time-series clustering.k-Shape andk-MS rely on a scalable iterative refinement procedure. As their distance measure,k-Shape andk-MS use shape-based distance (SBD), a normalized version of the cross-correlation measure, to consider the shapes of time series while comparing them. Based on the properties of SBD, we develop two new methods, namely ShapeExtraction (SE) and MultiShapesExtraction (MSE), to compute cluster centroids that are used in every iteration to update the assignment of time series to clusters.k-Shape relies on SE to compute a single centroid per cluster based on all time series in each cluster. In contrast,k-MS relies on MSE to compute multiple centroids per cluster to account for the proximity and spatial distribution of time series in each cluster. To demonstrate the robustness of SBD,k-Shape, andk-MS, we perform an extensive experimental evaluation on 85 datasets against state-of-the-art distance measures and clustering methods for time series using rigorous statistical analysis. SBD, our efficient and parameter-free distance measure, achieves similar accuracy to Dynamic Time Warping (DTW), a highly accurate but computationally expensive distance measure that requires parameter tuning. For clustering, we comparek-Shape andk-MS against scalable and non-scalable partitional, hierarchical, spectral, density-based, and shapelet-based methods, with combinations of the most competitive distance measures.k-Shape outperforms all scalable methods in terms of accuracy. Furthermore,k-Shape also outperforms all non-scalable approaches, with one exception, namelyk-medoids with DTW, which achieves similar accuracy. However, unlikek-Shape, this approach requires tuning of its distance measure and is significantly slower thank-Shape.k-MS performs similarly tok-Shape in comparison to rival methods, butk-MS is significantly more accurate thank-Shape. Beyond clustering, we demonstrate the effectiveness ofk-Shape to reduce the search space of one-nearest-neighbor classifiers for time series. Overall, SBD,k-Shape, andk-MS emerge as domain-independent, highly accurate, and efficient methods for time-series comparison and clustering with broad applications.

New scientific concepts, interpreted broadly, are continuously introduced in the literature, but relatively few concepts have a long‐term impact on society. The identification of such concepts is a challenging prediction task that would help multiple parties—including researchers and the general public—focus their attention within the vast scientific literature. In this paper we present a system that predicts the future impact of a scientific concept, represented as a technical term, based on the information available from recently published research articles. We analyze the usefulness of rich features derived from the full text of the articles through a variety of approaches, including rhetorical sentence analysis, information extraction, and time‐series analysis. The results from two large‐scale experiments with 3.8 million full‐text articles and 48 million metadata records support the conclusion that full‐text features are significantly more useful for prediction than metadata‐only features and that the most accurate predictions result from combining the metadata and full‐text features. Surprisingly, these results hold even when the metadata features are available for a much larger number of documents than are available for the full‐text features.

Information extraction (IE) systems discover structured information from natural language text, to enable much richer querying and data mining than possible directly over the unstructured text. Unfortunately, IE is generally a computationally expensive process, and hence improving its efficiency, so that it scales over large volumes of text, is of critical importance. State-of-the-art approaches for scaling the IE process focus on one text collection at a time. These approaches prioritize the extraction effort by learning keyword queries to identify the "useful" documents for the IE task at hand, namely, those that lead to the extraction of structured "tuples." These approaches, however, do not attempt to predict which text collections are useful for the IE task---and hence merit further processing---and which ones will not contribute any useful output---and hence should be ignored altogether, for efficiency. In this paper, we focus on an especially valuable family of text sources, the so-called deep web collections, whose (remote) contents are only accessible via querying. Specifically, we introduce and study techniques for ranking deep web collections for an IE task, to prioritize the extraction effort by focusing on collections with substantial numbers of useful documents for the task. We study both (adaptations of) state-of-the-art resource selection strategies for distributed information retrieval, and IE-specific approaches. Our extensive experimental evaluation over realistic deep web collections, and for several different IE tasks, shows the merits and limitations of the alternative families of approaches, and provides a roadmap for addressing this critically important building block for efficient, scalable information extraction.

Information extraction systems extract structured data from natural language text, to support richer querying and anal-ysis of the data than would be possible over the unstruc-tured text. Unfortunately, information extraction is a com-putationally expensive task, so exhaustively processing all documents of a large collection might be prohibitive. Such exhaustive processing is generally unnecessary, though, be-cause many times only a small set of documents in a collec-tion is useful for a given information extraction task. There-fore, by identifying these useful documents, and not process-ing the rest, we could substantially improve the efficiency and scalability of an extraction task. Existing approaches for identifying such documents often miss useful documents and also lead to the processing of useless documents unnec-essarily, which in turn negatively impacts the quality and efficiency of the extraction process. To address these limita-tions of the state-of-the-art techniques, we propose a prin-cipled, learning-based approach for ranking documents ac-cording to their potential usefulness for an extraction task. Our low-overhead, online learning-to-rank methods exploit the information collected during extraction, as we process new documents and the fine-grained characteristics of the useful documents are revealed. Then, these methods decide when the ranking model should be updated, hence signifi-cantly improving the document ranking quality over time. Our experiments show that our approach achieves higher ac-curacy than the state-of-the-art alternatives. Importantly, our approach is lightweight and efficient, and hence is a sub-stantial step towards scalable information extraction. 1.

The proliferation and ubiquity of temporal data across many disciplines has generated substantial interest in the analysis and mining of time series. Clustering is one of the most popular data mining methods, not only due to its exploratory power, but also as a preprocessing step or subroutine for other techniques. In this paper, we present k-Shape, a novel algorithm for time-series clustering. k-Shape relies on a scalable iterative refinement procedure, which creates homogeneous and well-separated clusters. As its distance measure, k-Shape uses a normalized version of the cross-correlation measure in order to consider the shapes of time series while comparing them. Based on the properties of that distance measure, we develop a method to compute cluster centroids, which are used in every iteration to update the assignment of time series to clusters. To demonstrate the robustness of k-Shape, we perform an extensive experimental evaluation of our approach against partitional, hierarchical, and spectral clustering methods, with combinations of the most competitive distance measures. k-Shape outperforms all scalable approaches in terms of accuracy. Furthermore, k-Shape also outperforms all non-scalable (and hence impractical) combinations, with one exception that achieves similar accuracy results. However, unlike k-Shape, this combination requires tuning of its distance measure and is two orders of magnitude slower than k-Shape. Overall, k-Shape emerges as a domain-independent, highly accurate, and highly efficient clustering approach for time series with broad applications.

A wealth of information produced by individuals and organizations is expressed in natural language text. This is a problem since text lacks the explicit structure that is necessary to support rich querying and analysis. Information extraction systems are sophisticated software tools to discover structured information in natural language text. Unfortunately, information extraction is a challenging and time-consuming task. In this paper, we address the limitations of state-of-the-art systems for the optimization of information extraction programs, with the objective of producing efficient extraction executions. Our solution relies on exploiting a wide range of optimization opportunities. For efficiency, we consider a wide spectrum of execution plans, including approximate plans whose results differ in their precision and recall. Our optimizer accounts for these characteristics of the competing execution plans, and uses accurate predictors of their extraction time, recall, and precision. We demonstrate the efficiency and effectiveness of our optimizer through a large-scale experimental evaluation over real-world datasets and multiple extraction tasks and approaches.

User-contributed Web data contains rich and diverse information about a variety of events in the physical world, such as shows, festivals, conferences and more. This information ranges from known event features (e.g., title, time, location) posted on event aggregation platforms (e.g., Last.fm events, EventBrite, Facebook events) to discussions and reactions related to events shared on different social media sites (e.g., Twitter, YouTube, Flickr). In this paper, we focus on the challenge of automatically identifying user-contributed content for events that are planned and, therefore, known in advance, across different social media sites. We mine event aggregation platforms to extract event features, which are often noisy or missing. We use these features to develop query formulation strategies for retrieving content associated with an event on different social media sites. Further, we explore ways in which event content identified on one social media site can be used to retrieve additional relevant event content on other social media sites. We apply our strategies to a large set of user-contributed events, and analyze their effectiveness in retrieving relevant event content from Twitter, YouTube, and Flickr.

Time is an important dimension of relevance for a large number of searches, such as over blogs and news archives. So far, research on searching over such collections has largely focused on locating topically similar documents for a query. Unfortunately, topic similarity alone is not always sufficient for document ranking. In this paper, we observe that, for an important class of queries that we call time-sensitive queries, the publication time of the documents in a news archive is important and should be considered in conjunction with the topic similarity to derive the final document ranking. Earlier work has focused on improving retrieval for “recency” queries that target recent documents. We propose a more general framework for handling time-sensitive queries and we automatically identify the important time intervals that are likely to be of interest for a query. Then, we build scoring techniques that seamlessly integrate the temporal aspect into the overall ranking mechanism. We present an extensive experimental evaluation using a variety of news article data sets, including TREC data as well as real web data analyzed using the Amazon Mechanical Turk. We examine several techniques for detecting the important time intervals for a query over a news archive and for incorporating this information in the retrieval process. We show that our techniques are robust and significantly improve result quality for time-sensitive queries compared to state-of-the-art retrieval techniques.

Twitter, Facebook, and other related systems that we call social awareness streams are rapidly changing the information and communication dynamics of our society. These systems, where hundreds of millions of users share short messages in real time, expose the aggregate interests and attention of global and local communities. In particular, emerging temporal trends in these systems, especially those related to a single geographic area, are a significant and revealing source of information for, and about, a local community. This study makes two essential contributions for interpreting emerging temporal trends in these information systems. First, based on a large dataset of Twitter messages from one geographic area, we develop a taxonomy of the trends present in the data. Second, we identify important dimensions according to which trends can be categorized, as well as the key distinguishing features of trends that can be derived from their associated messages. We quantitatively examine the computed features for different categories of trends, and establish that significant differences can be detected across categories. Our study advances the understanding of trends on Twitter and other social awareness streams, which will enable powerful applications and activities, including user-driven real-time information services for local communities.

Recent progress in information extraction technology has enabled a vast array of applications that rely on structured data that is embedded in natural-language text. In particular, the extraction of concepts from the Web---with their desired attributes---is important to provide applications with rich, structured access to information. In this paper, we focus on an important family of concepts, namely, entities (e.g., people or organizations) and their attributes, and study how to efficiently and effectively extract them from Web-accessible text documents. Unfortunately, information extraction over the Web is challenging for both quality and efficiency reasons. Regarding quality, many sources on the Web contain misleading or invalid information; furthermore, extraction systems often return incorrect data. Regarding efficiency, information extraction is a time-consuming process, often involving expensive text-processing steps. We present a top-k extraction processing approach that addresses both the quality and efficiency challenges: for each entity and attribute of interest, we return the top-k values of the attribute for the entity according to a scoring function for extracted attribute values. This scoring function weighs the extraction confidence from individual documents, as well as the "importance" of the documents where the information originates. We define the document importance in terms of entity-specific document "popularity" statistics from a major search engine. Overall, our top-k extraction processing approach manages to identify the top attribute values for the entities of interest efficiently, as we demonstrate with a large-scale experimental evaluation over real-life data.

Social media sites (e.g., Flickr, YouTube, and Facebook) are a popular distribution outlet for users looking to share their experiences and interests on the Web. These sites host substantial amounts of user-contributed materials (e.g., photographs, videos, and textual content) for a wide variety of real-world events of different type and scale. By automatically identifying these events and their associated user-contributed social media documents, which is the focus of this paper, we can enable event browsing and search in state-of-the-art search engines. To address this problem, we exploit the rich "context" associated with social media content, including user-provided annotations (e.g., title, tags) and automatically generated information (e.g., content creation time). Using this rich context, which includes both textual and non-textual features, we can define appropriate document similarity metrics to enable online clustering of media to events. As a key contribution of this paper, we explore a variety of techniques for learning multi-feature similarity metrics for social media documents in a principled manner. We evaluate our techniques on large-scale, real-world datasets of event images from Flickr. Our evaluation results suggest that our approach identifies events, and their associated social media documents, more effectively than the state-of-the-art strategies on which we build.

Information extraction (IE) systems are trained to extract specific relations from text databases. Real-world applications often require that the output of multiple IE systems be joined to produce the data of interest. To optimize the execution of a join of multiple extracted relations, it is not sufficient to consider only execution time. In fact, the quality of the join output is of critical importance: unlike in the relational world, different join execution plans can produce join results of widely different quality whenever IE systems are involved. In this paper, we develop a principled approach to understand, estimate, and incorporate output quality into the join optimization process over extracted relations. We argue that the output quality is affected by (a) the configuration of the IE systems used to process documents, (b) the document retrieval strategies used to retrieve documents, and (c) the actual join algorithm used. Our analysis considers several alternatives for these factors, and predicts the output quality - and, of course, the execution time - of the alternate execution plans. We establish the accuracy of our analytical models, as well as study the effectiveness of a quality-aware join optimizer, with a large-scale experimental evaluation over real-world text collections and state-of-the-art IE systems.

Time is an important dimension of relevance for a large number of searches, such as over blogs and news archives. So far, research on searching over such collections has largely focused on locating topically similar documents for a query. Unfortunately, topic similarity alone is not always sufficient for document ranking. In this paper, we observe that, for an important class of queries that we call time-sensitive queries, the publication time of the documents in a news archive is important and should be considered in conjunction with the topic similarity to derive the final document ranking. Earlier work has focused on improving retrieval for "recency" queries that target recent documents. We propose a more general framework for handling time-sensitive queries and we automatically identify the important time intervals that are likely to be of interest for a query. Then, we build scoring techniques that seamlessly integrate the temporal aspect into the overall ranking mechanism. We extensively evaluated our techniques using a variety of news article data sets, including TREC data as well as real web data analyzed using the Amazon Mechanical Turk. We examined several alternatives for detecting the important time intervals for a query over a news archive and for incorporating this information in the retrieval process. Our techniques are robust and significantly improve result quality for time-sensitive queries compared to state-of-the-art retrieval techniques.

Text documents often embed data that is structured in nature, and we can expose this structured data using information extraction technology. By processing a text database with information extraction systems, we can materialize a variety of structured "relations," over which we can then issue regular SQL queries. A key challenge to process SQL queries in this text-based scenario is efficiency: information extraction is time-consuming, so query processing strategies should minimize the number of documents that they process. Another key challenge is result quality: in the traditional relational world, all correct execution strategies for a SQL query produce the same (correct) result; in contrast, a SQL query execution over a text database might produce answers that are not fully accurate or complete, for a number of reasons. To address these challenges, we study a family of select-project-join SQL queries over text databases, and characterize query processing strategies on their efficiency and - critically - on their result quality as well. We optimize the execution of SQL queries over text databases in a principled, cost-based manner, incorporating this tradeoff between efficiency and result quality in a user-specific fashion. Our large-scale experiments- over real data sets and multiple information extraction systems - show that our SQL query processing approach consistently picks appropriate execution strategies for the desired balance between efficiency and result quality.

Many valuable text databases on the web have noncrawlable contents that are “hidden” behind search interfaces. Metasearchers are helpful tools for searching over multiple such “hidden-web” text databases at once through a unified query interface. An important step in the metasearching process is database selection, or determining which databases are the most relevant for a given user query. The state-of-the-art database selection techniques rely on statistical summaries of the database contents, generally including the database vocabulary and associated word frequencies. Unfortunately, hidden-web text databases typically do not export such summaries, so previous research has developed algorithms for constructing approximate content summaries from document samples extracted from the databases via querying. We present a novel “focused-probing” sampling algorithm that detects the topics covered in a database and adaptively extracts documents that are representative of the topic coverage of the database. Our algorithm is the first to construct content summaries that include the frequencies of the words in the database. Unfortunately, Zipf's law practically guarantees that for any relatively large database, content summaries built from moderately sized document samples will fail to cover many low-frequency words; in turn, incomplete content summaries might negatively affect the database selection process, especially for short queries with infrequent words. To enhance the sparse document samples and improve the database selection decisions, we exploit the fact that topically similar databases tend to have similar vocabularies, so samples extracted from databases with a similar topical focus can complement each other. We have developed two database selection algorithms that exploit this observation. The first algorithm proceeds hierarchically and selects the best categories for a query, and then sends the query to the appropriate databases in the chosen categories. The second algorithm uses “shrinkage,” a statistical technique for improving parameter estimation in the face of sparse data, to enhance the database content summaries with category-specific words. We describe how to modify existing database selection algorithms to adaptively decide (at runtime) whether shrinkage is beneficial for a query. A thorough evaluation over a variety of databases, including 315 real web databases as well as TREC data, suggests that the proposed sampling methods generate high-quality content summaries and that the database selection algorithms produce significantly more relevant database selection decisions and overall search results than existing algorithms.

Text documents often embed data that is structured in nature. By processing a text database with information extraction systems, we can define a variety of structured "relations" over which we can then issue SQL queries. Processing SQL queries in this text-based scenario presents multiple challenges. One key challenge is efficiency: information extraction is a time-consuming process, so query processing strategies should pick efficient extraction systems whenever possible, and also minimize the number of documents that they process. Another key challenge is result quality: extraction systems might output erroneous information or miss information that they should capture; also, efficiency-related query processing decisions (e.g., to avoid processing large numbers of useless documents) may compromise result completeness. To address these challenges, we characterize SQL query processing strategies in terms of their efficiency and result quality, and discuss the (user-specific) tradeoff between these two properties.

Keyword search is a familiar and potentially effective way to find information of interest that is "locked" inside relational databases. Current work has generally assumed that answers for a keyword query reside within a single database. Many practical settings, however, require that we combine tuples from multiple databases to obtain the desired answers. Such databases are often autonomous and heterogeneous in their schemas and data. This paper describes Kite, a solution to the keyword-search problem over heterogeneous relational databases. Kite combines schema matching and structure discovery techniques to find approximate foreign-key joins across heterogeneous databases. Such joins are critical for producing query results that span multiple databases and relations. Kite then exploits the joins - discovered automatically across the databases - to enable fast and effective querying over the distributed data. Our extensive experiments over real-world data sets show that (1) our query processing algorithms are efficient and (2) our approach manages to produce high-quality query results spanning multiple heterogeneous databases, with no need for human reconciliation of the different databases.

Text is ubiquitous and, not surprisingly, many important applications rely on textual data for a variety of tasks. As a notable example, information extraction applications derive structured relations from unstructured text; as another example, focused crawlers explore the Web to locate pages about specific topics. Execution plans for text-centric tasks follow two general paradigms for processing a text database: either we can scan, or “crawl,” the text database or, alternatively, we can exploit search engine indexes and retrieve the documents of interest via carefully crafted queries constructed in task-specific ways. The choice between crawl- and query-based execution plans can have a substantial impact on both execution time and output “completeness” (e.g., in terms of recall). Nevertheless, this choice is typically ad hoc and based on heuristics or plain intuition. In this article, we present fundamental building blocks to make the choice of execution plans for text-centric tasks in an informed, cost-based way. Towards this goal, we show how to analyze query- and crawl-based plans in terms of both execution time and output completeness. We adapt results from random-graph theory and statistics to develop a rigorous cost model for the execution plans. Our cost model reflects the fact that the performance of the plans depends on fundamental task-specific properties of the underlying text databases. We identify these properties and present efficient techniques for estimating the associated parameters of the cost model. We also present two optimization approaches for text-centric tasks that rely on the cost-model parameters and select efficient execution plans. Overall, our optimization approaches help build efficient execution plans for a task, resulting in significant efficiency and output completeness benefits. We complement our results with a large-scale experimental evaluation for three important text-centric tasks and over multiple real-life data sets.

Large amounts of (often valuable) information are stored in web-accessible text databases. “Metasearchers” provide unified interfaces to query multiple such databases at once. For efficiency, metasearchers rely on succinct statistical summaries of the database contents to select the best databases for each query. So far, database selection research has largely assumed that databases are static, so the associated statistical summaries do not evolve over time. However, databases are rarely static and the statistical summaries that describe their contents need to be updated periodically to reflect content changes. In this article, we first report the results of a study showing how the content summaries of 152 real web databases evolved over a period of 52 weeks. Then, we show how to use “survival analysis” techniques in general, and Cox's proportional hazards regression in particular, to model database changes over time and predict when we should update each content summary. Finally, we exploit our change model to devise update schedules that keep the summaries up to date by contacting databases only when needed, and then we evaluate the quality of our schedules experimentally over real web databases.

Text is ubiquitous and, not surprisingly, many important applications rely on textual data for a variety of tasks. As a notable example, information extraction applications derive structured relations from unstructured text; as another example, focused crawlers explore the web to locate pages about specific topics. Execution plans for text-centric tasks follow two general paradigms for processing a text database: either we can scan, or 'crawl," the text database or, alternatively, we can exploit search engine indexes and retrieve the documents of interest via carefully crafted queries constructed in task-specific ways. The choice between crawl- and query-based execution plans can have a substantial impact on both execution time and output "completeness" (e.g., in terms of recall). Nevertheless, this choice is typically ad-hoc and based on heuristics or plain intuition. In this paper, we present fundamental building blocks to make the choice of execution plans for text-centric tasks in an informed, cost-based way. Towards this goal, we show how to analyze query- and crawl-based plans in terms of both execution time and output completeness. We adapt results from random-graph theory and statistics to develop a rigorous cost model for the execution plans. Our cost model reflects the fact that the performance of the plans depends on fundamental task-specific properties of the underlying text databases. We identify these properties and present efficient techniques for estimating the associated cost-model parameters. Overall, our approach helps predict the most appropriate execution plans for a task, resulting in significant efficiency and output completeness benefits. We complement our results with a large-scale experimental evaluation for three important text-centric tasks and over multiple real-life data sets.

Large amounts of (often valuable) information are stored in Web-accessible text databases. "Metasearchers" provide unified interfaces to query multiple such databases at once. For efficiency, metasearchers rely on succinct statistical summaries of the database contents to select the best databases for each query. So far, database selection research has largely assumed that databases are static, so the associated statistical summaries do not need to change over time. However, databases are rarely static and the statistical summaries that describe their contents need to be updated periodically to reflect content changes. In this paper, we first report the results of a study showing how the content summaries of 152 real Web databases evolved over a period of 52 weeks. Then, we show how to use "survival analysis" techniques in general, and Cox's proportional hazards regression in particular, to model database changes over time and predict when we should update each content summary. Finally, we exploit our change model to devise update schedules that keep the summaries up to date by contacting databases only when needed, and then we evaluate the quality of our schedules experimentally over real Web databases.

Queries with (equality or LIKE) selection predicates over string attributes are widely used in relational databases. However, state-of-the-art techniques for estimating selectivities of string predicates are often biased towards severely underestimating selectivities. We develop accurate selectivity estimators for string predicates that adapt to data and query characteristics, and which can exploit and build on a variety of existing estimators. A thorough experimental evaluation over real data sets demonstrates the resilience of our estimators to variations in both data and query characteristics.

Database selection is an important step when searching over large numbers of distributed text databases. The database selection task relies on statistical summaries of the database contents, which are not typically exported by databases. Previous research has developed algorithms for constructing an approximate content summary of a text database from a small document sample extracted via querying. Unfortunately, Zipf's law practically guarantees that content summaries built this way for any relatively large database will fail to cover many low-frequency words. Incomplete content summaries might negatively affect the database selection process, especially for short queries with infrequent words. To improve the coverage of approximate content summaries, we build on the observation that topically similar databases tend to have related vocabularies. Therefore, the approximate content summaries of topically related databases can complement each other and increase their coverage. Specifically, we exploit a (given or derived) hierarchical categorization of the databases and adapt the notion of "shrinkage" -a form of smoothing that has been used successfully for document classification-to the content summary construction task. A thorough evaluation over 315 real web databases as well as over TREC data suggests that the shrinkage-based content summaries are substantially more complete than their "unshrunk" counterparts. We also describe how to modify existing database selection algorithms to adaptively decide -at run-time-whether to apply shrinkage for a query. Our experiments, which rely on TREC data sets, queries, and the associated "relevance judgments," show that our shrinkage-based approach significantly improves state-of-the-art database selection algorithms, and also outperforms a recently proposed hierarchical strategy that exploits database classification as well.

Repositories of multimedia objects having multiple types of attributes (e.g., image, text) are becoming increasingly common. A query on these attributes will typically, request not just a set of objects, as in the traditional relational query model (filtering), but also a grade of match associated with each object, which indicates how well the object matches the selection condition (ranking). Furthermore, unlike in the relational model, users may just want the k top-ranked objects for their selection queries for a relatively small k. In addition to the differences in the query model, another peculiarity of multimedia repositories is that they may allow access to the attributes of each object only through indexes. We investigate how to optimize the processing of top-k selection queries over multimedia repositories. The access characteristics of the repositories and the above query model lead to novel issues in query optimization. In particular, the choice of the indexes used to search the repository strongly influences the cost of processing the filtering condition. We define an execution space that is search-minimal, i.e., the set of indexes searched is minimal. Although the general problem of picking an optimal plan in the search-minimal execution space is NP-hard, we present an efficient algorithm that solves the problem optimally with respect to our cost model and execution space when the predicates in the query are independent. We also show that the problem of optimizing top-k selection queries can be viewed, in many cases, as that of evaluating more traditional selection conditions. Thus, both problems can be viewed together as an extended filtering problem to which techniques of query processing and optimization may be adapted.

A query to a web search engine usually consists of a list of keywords, to which the search engine responds with the best or "top" k pages for the query. This top- k query model is prevalent over multimedia collections in general, but also over plain relational data for certain applications. For example, consider a relation with information on available restaurants, including their location, price range for one diner, and overall food rating. A user who queries such a relation might simply specify the user's location and target price range, and expect in return the best 10 restaurants in terms of some combination of proximity to the user, closeness of match to the target price range, and overall food rating. Processing top- k queries efficiently is challenging for a number of reasons. One critical such reason is that, in many web applications, the relation attributes might not be available other than through external web-accessible form interfaces, which we will have to query repeatedly for a potentially large set of candidate objects. In this article, we study how to process top- k queries efficiently in this setting, where the attributes for which users specify target values might be handled by external, autonomous sources with a variety of access interfaces. We present a sequential algorithm for processing such queries, but observe that any sequential top- k query processing strategy is bound to require unnecessarily long query processing times, since web accesses exhibit high and variable latency. Fortunately, web sources can be probed in parallel, and each source can typically process concurrent requests, although sources may impose some restrictions on the type and number of probes that they are willing to accept. We adapt our sequential query processing technique and introduce an efficient algorithm that maximizes source-access parallelism to minimize query response time, while satisfying source-access constraints. We evaluate our techniques experimentally using both synthetic and real web-accessible data and show that parallel algorithms can be significantly more efficient than their sequential counterparts.

We introduce a method for learning to find documents on the Web that contain answers to a given natural language question. In our approach, questions are transformed into new queries aimed at maximizing the probability of retrieving answers from existing information retrieval systems. The method involves automatically learning phrase features for classifying questions into different types, automatically generating candidate query transformations from a training set of question/answer pairs, and automatically evaluating the candidate transformations on target information retrieval systems such as real-world general purpose search engines. At run-time, questions are transformed into a set of queries, and reranking is performed on the documents retrieved. We present a prototype search engine,Tritus, that applies the method to Web search engines. Blind evaluation on a set of real queries from a Web search engine log shows that the method significantly outperforms the underlying search engines, and outperforms a commercial search engine specializing in question answering. Our methodology cleanly supports combining documents retrieved from different search engines, resulting in additional improvement with a system that combines search results from multiple Web search engines.

Web pages (and resources, in general) can be characterized according to their geographical locality. For example, a web page with general information about wildflowers could be considered a global page, likely to be of interest to a geographically broad audience. In contrast, a web page with listings on houses for sale in a specific city could be regarded as a local page, likely to be of interest only to an audience in a relatively narrow region. Similarly, some search engine queries (implicitly) target global pages, while other queries are after local pages. For example, the best results for query [wildflowers] are probably global pages about wildflowers such as the one discussed above. However, local pages that are relevant to, say, San Francisco are likely to be good matches for a query [houses for sale] that was issued by a San Francisco resident or by somebody moving to that city. Unfortunately, search engines do not analyze the geographical locality of queries and users, and hence often produce sub-optimal results. Thus query [wildflowers] might return pages that discuss wildflowers in specific U.S. states (and not general information about wildflowers), while query [houses for sale] might return pages with real estate listings for locations other than that of interest to the person who issued the query. Deciding whether an unseen query should produce mostly local or global pages---without placing this burden on the search engine users---is an important and challenging problem, because queries are often ambiguous or underspecify the information they are after. In this paper, we address this problem by first defining how to categorize queries according to their (often implicit) geographical locality. We then introduce several alternatives for automatically and efficiently categorizing queries in our scheme, using a variety of state-of-the-art machine learning tools. We report a thorough evaluation of our classifiers using a large sample of queries from a real web search engine, and conclude by discussing how our query categorization approach can help improve query result quality.

A wealth of information is hidden within unstructured text. This information is often best exploited in structured or relational form, which is suited for sophisticated query processing, for integration with relational databases, and for data mining. Current information extraction techniques extract relations from a text database by examining every document in the database, or use filters to select promising documents for extraction. The exhaustive scanning approach is not practical or even feasible for large databases, and the current filtering techniques require human involvement to maintain and to adapt to new databases and domains. We develop an automatic query-based technique to retrieve documents useful for the extraction of user-defined relations from large text databases, which can be adapted to new domains, databases, or target relations with minimal human effort. We report a thorough experimental evaluation over a large newspaper archive that shows that we significantly improve the efficiency of the extraction process by focusing only on promising documents.

XML path queries form the basis of complex filtering of XML data. Most current XML path query processing techniques can be divided in two groups. Navigation-based algorithms compute results by analyzing an input document one tag at a time. In contrast, index-based algorithms take advantage of precomputed numbering schemes over the input XML document. We introduce a new index-based technique, index-filter, to answer multiple XML path queries. Index-filter uses indexes built over the document tags to avoid processing large portions of the input document that are guaranteed not to be part of any match. We analyze index-filter and compare it against Y-filter, a state-of-the-art navigation-based technique. We show that both techniques have their advantages, and we discuss the scenarios under which each technique is superior to the other one. In particular, we show that while most XML path query processing techniques work off SAX events, in some cases it pays off to preprocess the input document, augmenting it with auxiliary information that can be used to evaluate the queries faster. We present experimental results over real and synthetic XML documents that validate our claims.

An organization's data records are often noisy because of transcription errors, incomplete information, lack of standard formats for textual data or combinations thereof. A fundamental task in a data cleaning system is matching textual attributes that refer to the same entity (e.g., organization name or address). This matching is effectively performed via the cosine similarity metric from the information retrieval field. For robustness and scalability, these "text joins" are best done inside an RDBMS, which is where the data is likely to reside. Unfortunately, computing an exact answer to a text join can be expensive. We propose an approximate, sampling-based text join execution strategy that can be robustly executed in a standard, unmodified RDBMS.

No abstract available.

The integration of data produced and collected across autonomous, heterogeneous web services is an increasingly important and challenging problem. Due to the lack of global identifiers, the same entity (e.g., a product) might have different textual representations across databases. Textual data is also often noisy because of transcription errors, incomplete information, and lack of standard formats. A fundamental task during data integration is matching of strings that refer to the same entity. In this paper, we adopt the widely used and established cosine similarity metric from the information retrieval field in order to identify potential string matches across web sources. We then use this similarity metric to characterize this key aspect of data integration as a join between relations on textual attributes, where the similarity of matches exceeds a specified threshold. Computing an exact answer to the text join can be expensive. For query processing efficiency, we propose a sampling-based join approximation strategy for execution in a standard, unmodified relational database management system (RDBMS), since more and more web sites are powered by RDBMSs with a web-based front end. We implement the join inside an RDBMS, using SQL queries, for scalability and robustness reasons. Finally, we present a detailed performance evaluation of an implementation of our algorithm within a commercial RDBMS, using real-life data sets. Our experimental results demonstrate the efficiency and accuracy of our techniques.

The contents of many valuable Web-accessible databases are only available through search interfaces and are hence invisible to traditional Web "crawlers." Recently, commercial Web sites have started to manually organize Web-accessible databases into Yahoo!-like hierarchical classification schemes. Here we introduce QProber, a modular system that automates this classification process by using a small number of query probes, generated by document classifiers. QProber can use a variety of types of classifiers to generate the probes. To classify a database, QProber does not retrieve or inspect any documents or pages from the database, but rather just exploits the number of matches that each query probe generates at the database in question. We have conducted an extensive experimental evaluation of QProber over collections of real documents, experimenting with different types of document classifiers and retrieval models. We have also tested our system with over one hundred Web-accessible databases. Our experiments show that our system has low overhead and achieves high classification accuracy across a variety of databases.

A query to a Web search engine usually consists of a list of keywords, to which the search engine responds with the best or "top" k pages for the query. This top-k query model is prevalent over multimedia collections in general, but also over plain relational data for certain applications. For example, consider a relation with information on available restaurants, including their location, price range for one diner, and overall food rating. A user who queries such a relation might simply specify the user's location and target price range, and expect in return the best 10 restaurants in terms of some combination-of proximity to the user, closeness of match to the target price range, and overall food rating. Processing such top-k queries efficiently is challenging for a number of reasons. One critical such reason is that, in many Web applications, the relation attributes might not be available other than through external Web-accessible form interfaces, which we will have to query repeatedly for a potentially large set of candidate objects. In this paper, we study how to process top-k queries efficiently in this setting, where the attributes for which users specify target values might be handled by external, autonomous sources with a variety of access interfaces. We present several algorithms for processing such queries, and evaluate them thoroughly using both synthetic and real Web-accessible data.

In many applications, users specify target values for certain attributes, without requiring exact matches to these values in return. Instead, the result to such queries is typically a rank of the "top k " tuples that best match the given attribute values. In this paper, we study the advantages and limitations of processing a top- k query by translating it into a single range query that a traditional relational database management system (RDBMS) can process efficiently. In particular, we study how to determine a range query to evaluate a top- k query by exploiting the statistics available to an RDBMS, and the impact of the quality of these statistics on the retrieval efficiency of the resulting scheme. We also report the first experimental evaluation of the mapping strategies over a real RDBMS, namely over Microsoft's SQL Server 7.0. The experiments show that our new techniques are robust and significantly more efficient than previously known strategies requiring at least one sequential scan of the data sets.

No abstract available.

Attributes of a relation are not typically independent. Multidimensional histograms can be an effective tool for accurate multiattribute query selectivity estimation. In this paper, we introduce STHoles, a “workload-aware” histogram that allows bucket nesting to capture data regions with reasonably uniform tuple density. STHoles histograms are built without examining the data sets, but rather by just analyzing query results. Buckets are allocated where needed the most as indicated by the workload, which leads to accurate query selectivity estimations. Our extensive experiments demonstrate that STHoles histograms consistently produce good selectivity estimates across synthetic and real-world data sets and across query workloads, and, in many cases, outperform the best multidimensional histogram techniques that require access to and processing of the full data sets during histogram construction.

The contents of many valuable web-accessible databases are only accessible through search interfaces and are hence invisible to traditional web “crawlers.” Recent studies have estimated the size of this “hidden web” to be 500 billion pages, while the size of the “crawlable” web is only an estimated two billion pages. Recently, commercial web sites have started to manually organize web-accessible databases into Yahoo!-like hierarchical classification schemes. In this paper, we introduce a method for automating this classification process by using a small number of query probes. To classify a database, our algorithm does not retrieve or inspect any documents or pages from the database, but rather just exploits the number of matches that each query probe generates at the database in question. We have conducted an extensive experimental evaluation of our technique over collections of real documents, including over one hundred web-accessible databases. Our experiments show that our system has low overhead and achieves high classification accuracy across a variety of databases.

We introduce a method for learning query transformations that improves the ability to retrieve answers to questions from an information retrieval system. During the training stage the method involves automatically learning phrase features for classifying questions into different types, automatically generating candidate query transformations from a training set of question/answer pairs, and automatically evaluating the candidate transforms on target information retrieval systems such as real-world general purpose search engines. At run time, questions are transformed into a set of queries, and re-ranking is performed on the documents retrieved. We present a prototype search engine, Tritus, that applies the method to web search engines. Blind evaluation on a set of real queries from a web search engine log shows that the method significantly outperforms the underlying web search engines as well as a commercial search engine specializing in question answering. Keywords Web search, quer...

We investigate four hierarchical clustering methods (single-link, complete-link, groupwise-average, and single-pass) and two linguistically motivated text features (noun phrase heads and proper names) in the context of document clustering. A statistical model for combining similarity information from multiple sources is described and applied to DARPA's Topic Detection and Tracking phase 2 (TDT2) data. This model, based on log-linear regression, alleviates the need for extensive search in order to determine optimal weights for combining input features. Through an extensive series of experiments with more than 40,000 documents from multiple news sources and modalities, we establish that both the choice of clustering algorithm and the introduction of the additional features have an impact on clustering performance. We apply our optimal combination of features to the TDT2 test data, obtaining partitions of the documents that compare favorably with the results obtained by participants in the official TDT2 competition.

The dramatic growth of the Internet has created a new problem for users: location of the relevant sources of documents. This article presents a framework for (and experimentally analyzes a solution to) this problem, which we call the text-source discovery problem . Our approach consists of two phases. First, each text source exports its contents to a centralized service. Second, users present queries to the service, which returns an ordered list of promising text sources. This article describes GlOSS , Glossary of Servers Server, with two versions: bGlOSS , which provides a Boolean query retrieval model, and vGlOSS , which provides a vector-space retrieval model. We also present hGlOSS , which provides a decentralized version of the system. We extensively describe the methodology for measuring the retrieval effectiveness of these systems and provide experimental evidence, based on actual data, that all three systems are highly effective in determining promising text sources for a given query.

Document sources are available everywhere, both within the internal networks of organizations and on the Internet. Even individual organizations use search engines from different vendors to index their internal document collections. These search engines are typically incompatible in that they support different query models and interfaces, they do not return enough information with the query results for adequate merging of the results, and finally, in that they do not export metadata about the collections that they index (e.g., to assist in resource discovery). This paper describes STARTS, an emerging protocol for Internet retrieval and search that facilitates the task of querying multiple document sources. STARTS has been developed in a unique way. It is not a standard, but a group effort coordinated by Stanford's Digital Library project, and involving over 11 companies and organizations. The objective of this paper is not only to give an overview of the STARTS protocol proposal, but also to discuss the process that led to its definition.

With the profusion of text databases on the Internet, it is becoming increasingly hard to find the most useful databases for a given query. To attack this problem, several existing and proposed systems employ brokers to direct user queries, using a local database of summary information about the available databases. This summary information must effectively distinguish relevant databases and must be compact while allowing efficient access. We offer evidence that one broker, GlOSS , can be effective at locating databases of interest even in a system of hundreds of databased and can examine the performance of accessing the GlOSS summeries for two promising storage methods: the grid file and partitioned hashing. We show that both methods can be tuned to provide good performance for a particular workload (within a broad range of workloads), and we discuss the tradeoffs between the two data structures. As a side effect of our work, we show that grid files are more broadly applicable than previously thought; inparticular, we show that by varying the policies used to construct the grid file we can provide good performance for a wide range of workloads even when storing highly skewed data.

Repositories of multimedia objects having multiple types of attributes (e.g., image, text) are becoming increasingly common. A selection on these attributes will typically produce not just a set of objects, as in the traditional relational query model (filtering), but also a grade of match associated with each object, indicating how well the object matches the selection condition (ranking). Also, multimedia repositories may allow access to the attributes of each object only through indexes. We investigate how to optimize the processing of queries over multimedia repositories. A key issue is the choice of the indexes used to search the repository. We define an execution space that is search-minimal, i.e., the set of indexes searched is minimal. Although the general problem of picking an optimal plan in the search-minimal execution space is NP-hard, we solve the problem efficiently when the predicates in the query are independent. We also show that the problem of optimizing queries that ask for a few top-ranked objects can be viewed, in many cases, as that of evaluating selection conditions. Thus, both problems can be viewed together as an extended filtering problem.

The popularity of on-line document databases has led to a new problem: finding which text databases (out of many candidate choices) are the most relevant to a user. Identifying the relevant databases for a given query is the text database discovery problem. The first part of this paper presents a practical solution based on estimating the result size of a query and a database. The method is termed GlOSS—Glossary of Servers Server. The second part of this paper evaluates the effectiveness of GlOSS based on a trace of real user queries. In addition, we analyze the storage cost of our approach.

This paper studies the problem of deadlock-free packet routing in parallel and distributed architectures. Three main results are presented. First, it is shown that the standard technique of ordering the buffers so that every packet always has the possibility of moving to a higher-ordered buffer is not necessary for deadlock freedom. Second, it is shown that every deadlock-free, adaptive packet routing algorithm can be restricted, by limiting the adaptivity available, to obtain an oblivious algorithm which is also deadlock-free. Third, it is shown that any packet routing algorithm for a cycle or torus network which is free of deadlock and which uses only minimal length paths must require at least three buffers in some node. This matches the known upper bound of three buffers per node for deadlock-free, minimal packet routing on cycle and torus networks.

We present two new packet routing algorithms for parallel computers with torus interconnection networks of arbitrary size and dimension. Both algorithms use only minimal length paths, are fully adaptive in the sense that all minimal length paths may be used to avoid congestion, and are free of deadlock, livelock and starvation. Algorithm 1 requires only three central queues per routing node. It is the first known minimal length packet routing algorithm for torus networks which requires a constant number of queues per node, regardless of the size and dimension of the torus. In fact, the requirement of three queues per node is optimal, as no such algorithm is possible when all nodes have two or fewer queues. Algorithm 2 requires only that each node have two input buffers per edge. It is the first known minimal-fully-adaptive packet routing algorithm for torus networks which does not require central queues and which does not require any node to have more than two input or two output buffers per edge. Both algorithms are simple and appear to be well-suited to VLSI implementation. They can be used with either store-and-forward or virtual cut-through routing.>

This paper consists of two parts. In the first part, two new algorithms for deadlock- and livelock-free wormhole routing in the torus network are presented. The first algorithm, called Channels, is for the n-dimensional torus network. This technique is fully-adaptive minimal, that is, all paths with a minimal number of hops from source to destination are available for routing, and needs only five virtual channels per bidirectional link, the lowest channel requirement known in the literature for fully-adaptive minimal worm-hole routing. In addition, this result also yields the lowest buffer requirement known in the literature for packet-switched fully-adaptive minimal routing. The second algorithm, called 4-Classes, is for the bidimensional torus network. This technique is fully-adaptive minimal and requires only eight virtual channels per bidirectional link. Also, it allows for a highly parallel implementation of its associated routing node. In the second part of this paper, four worm-hole routing techniques for the two-dimensional torus are experimentally evaluated using a dynamic message injection model and different traffic patterns and message lengths.>

This paper consists of two parts. In the first one, two new algorithms for wormhole routing on the hypercube network are presented. These techniques are adaptive and are ensured to be deadlock- and livelock-free. These properties are guaranteed by using a small number of resources in the routing node. The first algorithm is adaptive and nonminimal and will be referred to as Nonminimal. In this technique, some moderate derouting is allowed in order to alleviate the potential congestion arising from highly structured communication patterns. The second algorithm, dubbed Subcubes, is adaptive and minimal, and is based on partitioning the hypercube into subcubes of smaller dimension; This technique requires only two virtual channels per physical link of the node. In the second part of the paper, a wide variety of techniques for wormhole routing in the hypercube are evaluated from an algorithmic point of view. Five partially adaptive algorithms are considered: the Hanging algorithm, the Zenith algorithm, the Hanging-Order algorithm, the Nonminimal algorithm; and the Subcubes algorithm. One oblivious algorithm, the Dimension-Order, or E-Cube routing algorithm, is also used. Finally, a Fully Adaptive Minimal algorithm is tried. A simple node model was designed and adapted to all the algorithms.>

This paper deals with the problem of packet-switched routing in parallel machines. Several new routing algorithms for different interconnection networks are presented. While the new techniques apply to a wide variety of networks, routing algorithms will be shown for the hypercube, the two-dimensional mesh, and the shuffle-exchange. Although the new techniques are designed for packet routing, they can be used alternatively for virtual cut-through routing models. The techniques presented for hypercubes and meshes are fully-adaptive and minimal. A fully-adaptive and minimal routing is one in which all possible minimal paths between a source and a destination are of potential use at the time a message is injected into the network. Minimal paths followed by messages ultimately depend on the local congestion encountered in each node of the network. All of the new techniques are completely free of deadlock situations.>

This paper studies the problem of deadlock-free packet routing in parallel and distributed architectures. We present three main results. First, we show that the standard technique of ordering the queues so that every packet always has the possibility of moving to a higher ordered queue is not necessary for deadlock-freedom. Second, we show that every deadlock-free, adaptive packet routing algorithm can be restricted, by limiting the adaptivity available, to obtain an oblivious algorithm which is also deadlock-free. Third, we show that any packet routing algorithm for a cycle or torus network which is free of deadlock and which uses only minimal length paths must require at least three queues in some node. This matches the known upper bound of three queues per node for deadlock-free, minimal packet routing on cycle and torus networks.

This paper consists of two parts. In the first part, a new algorithm for deadlock- and livelock-free routing for the n-dimensional torus network is presented. This algo- rithm, called *-Channels, is fully-adaptive minimal, i.e. all paths with a minimal number of hops from source to destination are available for routing. *-Channels works for messages of unknown size, thus yielding new rout- ing techniques for both packet-switched and worm-hole models. *.Channels differs radically from the packetswitched fully-adaptive minimal methods presented in SPAA '91 by Pifarr6, Gravano, Felperin, and Sanz [PGFS91]. In particular, the packet-based techniques in [PGFS91] do not work for worm-hole routing as dead- lock situations can be constructed.

Article Free Access Share on Fully-adaptive routing: packet switching performance and wormhole algorithms Authors: S. A. Felperin CRAAG IBM Argentina, ESLAI CRAAG IBM Argentina, ESLAIView Profile , L. Gravano CRAAG IBM Argentina, ESLAI CRAAG IBM Argentina, ESLAIView Profile , G. D. Pifarré CRAAG IBM Argentina, ESLAI CRAAG IBM Argentina, ESLAIView Profile , J. L. C. Sanz IBM Almaden Research Center, CRAAG IBM Argentina IBM Almaden Research Center, CRAAG IBM ArgentinaView Profile Authors Info & Claims Supercomputing '91: Proceedings of the 1991 ACM/IEEE conference on SupercomputingAugust 1991 Pages 654–663https://doi.org/10.1145/125826.126141Published:01 August 1991Publication History 22citation327DownloadsMetricsTotal Citations22Total Downloads327Last 12 Months8Last 6 weeks0 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

Article Free Access Share on Fully-adaptive minimal deadlock-free packet routing in hypercubes, meshes, and other networks Authors: Gustavo D. Pifarré ESLAI, Escuela Superior Latino Americana de Informática, CC 3193, (1000) Buenos Aires, Argentina. and Computer Research and Advanced Applications Group, IBM, Argentina, Ing. E. Butti 275, (1300) Buenos Aires, Argentina. and Computer Science Dept., IBM Almadeu Research Center, San, José, California ESLAI, Escuela Superior Latino Americana de Informática, CC 3193, (1000) Buenos Aires, Argentina. and Computer Research and Advanced Applications Group, IBM, Argentina, Ing. E. Butti 275, (1300) Buenos Aires, Argentina. and Computer Science Dept., IBM Almadeu Research Center, San, José, CaliforniaView Profile , Luis Gravano ESLAI, Escuela Superior Latino Americana de Informática, CC 3193, (1000) Buenos Aires, Argentina. and Computer Research and Advanced Applications Group, IBM, Argentina, Ing. E. Butti 275, (1300) Buenos Aires, Argentina. and Computer Science Dept., IBM Almadeu Research Center, San, José, California ESLAI, Escuela Superior Latino Americana de Informática, CC 3193, (1000) Buenos Aires, Argentina. and Computer Research and Advanced Applications Group, IBM, Argentina, Ing. E. Butti 275, (1300) Buenos Aires, Argentina. and Computer Science Dept., IBM Almadeu Research Center, San, José, CaliforniaView Profile , Sergio A. Felperin ESLAI, Escuela Superior Latino Americana de Informática, CC 3193, (1000) Buenos Aires, Argentina. and Computer Research and Advanced Applications Group, IBM, Argentina, Ing. E. Butti 275, (1300) Buenos Aires, Argentina. and Computer Science Dept., IBM Almadeu Research Center, San, José, California ESLAI, Escuela Superior Latino Americana de Informática, CC 3193, (1000) Buenos Aires, Argentina. and Computer Research and Advanced Applications Group, IBM, Argentina, Ing. E. Butti 275, (1300) Buenos Aires, Argentina. and Computer Science Dept., IBM Almadeu Research Center, San, José, CaliforniaView Profile , Jorge L. C. Sanz Computer Science Dept., IBM Almaden Research Center, San, José, California., Computer Research and Advanced Applications Group, IBM, Argentina, Ing. E. Butti 275, (1300) Buenos Aires, Argentina Computer Science Dept., IBM Almaden Research Center, San, José, California., Computer Research and Advanced Applications Group, IBM, Argentina, Ing. E. Butti 275, (1300) Buenos Aires, ArgentinaView Profile Authors Info & Claims SPAA '91: Proceedings of the third annual ACM symposium on Parallel algorithms and architecturesJune 1991 Pages 278–290https://doi.org/10.1145/113379.113405Published:01 June 1991Publication History 24citation298DownloadsMetricsTotal Citations24Total Downloads298Last 12 Months5Last 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

A survey of some packet-switched routing methods for massively parallel computers is presented. Some of the techniques are applicable to both shared-memory and message-passing architectures. These routing methods are compared in terms of their efficiency in supporting programming models, efficiency in mapping to parallel machines, and practicality. Among the outlined methods, three nonadaptive techniques and some adaptive routing algorithms are discussed.>