Results 1 - 10 of 131,401

Relational Databases for Querying XML Documents: Limitations and Opportunities

by Jayavel Shanmugasundaram, Kristin Tufte, Gang He, Chun Zhang, David DeWitt, Jeffrey Naughton , 1999
"... XML is fast emerging as the dominant standard for representing data in the World Wide Web. Sophisticated query engines that allow users to effectively tap the data stored in XML documents will be crucial to exploiting the full power of XML. While there has been a great deal of activity recently prop ..."
Abstract - Cited by 478 (9 self) - Add to MetaCart
XML is fast emerging as the dominant standard for representing data in the World Wide Web. Sophisticated query engines that allow users to effectively tap the data stored in XML documents will be crucial to exploiting the full power of XML. While there has been a great deal of activity recently

A block-sorting lossless data compression algorithm

by M Burrows , D J Wheeler , 1994
"... We describe a block-sorting, lossless data compression algorithm, and our implementation of that algorithm. We compare the performance of our implementation with widely available data compressors running on the same hardware. The algorithm works by applying a reversible transformation to a block o ..."
Abstract - Cited by 809 (5 self) - Add to MetaCart
We describe a block-sorting, lossless data compression algorithm, and our implementation of that algorithm. We compare the performance of our implementation with widely available data compressors running on the same hardware. The algorithm works by applying a reversible transformation to a block

DBpedia: A Nucleus for a Web of Open Data

by Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Zachary Ives, et al. - PROC. 6TH INT’L SEMANTIC WEB CONF , 2007
"... DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe the extractio ..."
Abstract - Cited by 651 (37 self) - Add to MetaCart
DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe

Data Mining: An Overview from Database Perspective

by Ming-syan Chen, Jiawei Hun, Philip S. Yu - IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 1996
"... Mining information and knowledge from large databases has been recognized by many researchers as a key research topic in database systems and machine learning, and by many industrial companies as an important area with an opportunity of major revenues. Researchers in many different fields have sh ..."
Abstract - Cited by 532 (26 self) - Add to MetaCart
the business opportunities. In response to such a demand, this article is to provide a survey, from a database researcher's point of view, on the data mining techniques developed recently. A classification of the available data mining techniques is provided and a comparative study of such techniques

Optimizing Search Engines using Clickthrough Data

by Thorsten Joachims , 2002
"... This paper presents an approach to automatically optimizing the retrieval quality of search engines using clickthrough data. Intuitively, a good information retrieval system should present relevant documents high in the ranking, with less relevant documents following below. While previous approaches ..."
Abstract - Cited by 1314 (23 self) - Add to MetaCart
-log of the search engine in connection with the log of links the users clicked on in the presented ranking. Such clickthrough data is available in abundance and can be recorded at very low cost. Taking a Support Vector Machine (SVM) approach, this paper presents a method for learning retrieval functions. From a

Generic Schema Matching with Cupid

by Jayant Madhavan, Philip Bernstein, Erhard Rahm - In The VLDB Journal , 2001
"... Schema matching is a critical step in many applications, such as XML message mapping, data warehouse loading, and schema integration. In this paper, we investigate algorithms for generic schema matching, outside of any particular data model or application. We first present a taxonomy for past s ..."
Abstract - Cited by 604 (17 self) - Add to MetaCart
Schema matching is a critical step in many applications, such as XML message mapping, data warehouse loading, and schema integration. In this paper, we investigate algorithms for generic schema matching, outside of any particular data model or application. We first present a taxonomy for past

Combining labeled and unlabeled data with co-training

by Avrim Blum, Tom Mitchell , 1998
"... We consider the problem of using a large unlabeled sample to boost performance of a learning algorithm when only a small set of labeled examples is available. In particular, we consider a setting in which the description of each example can be partitioned into two distinct views, motivated by the ta ..."
Abstract - Cited by 1633 (28 self) - Add to MetaCart
We consider the problem of using a large unlabeled sample to boost performance of a learning algorithm when only a small set of labeled examples is available. In particular, we consider a setting in which the description of each example can be partitioned into two distinct views, motivated

BIRCH: an efficient data clustering method for very large databases

by Tian Zhang, Raghu Ramakrishnan, Miron Livny - In Proc. of the ACM SIGMOD Intl. Conference on Management of Data (SIGMOD , 1996
"... Finding useful patterns in large datasets has attracted considerable interest recently, and one of the most widely st,udied problems in this area is the identification of clusters, or deusel y populated regions, in a multi-dir nensional clataset. Prior work does not adequately address the problem of ..."
Abstract - Cited by 576 (2 self) - Add to MetaCart
multi-dimensional metric data points to try to produce the best quality clustering with the available resources (i. e., available memory and time constraints). BIRCH can typically find a goocl clustering with a single scan of the data, and improve the quality further with a few aclditioual scans. BIRCH

Dryad: Distributed Data-Parallel Programs from Sequential Building Blocks

by Michael Isard, Mihai Budiu, Yuan Yu, Andrew Birrell, Dennis Fetterly - In EuroSys , 2007
"... Dryad is a general-purpose distributed execution engine for coarse-grain data-parallel applications. A Dryad applica-tion combines computational “vertices ” with communica-tion “channels ” to form a dataflow graph. Dryad runs the application by executing the vertices of this graph on a set of availa ..."
Abstract - Cited by 762 (27 self) - Add to MetaCart
simultaneously on multi-ple computers, or on multiple CPU cores within a computer. The application can discover the size and placement of data at run time, and modify the graph as the computation pro-gresses to make efficient use of the available resources. Dryad is designed to scale from powerful multi-core sin

A comparison of bayesian methods for haplotype reconstruction from population genotype data.

by Matthew Stephens , Peter Donnelly , Dr Matthew Stephens - Am J Hum Genet , 2003
"... In this report, we compare and contrast three previously published Bayesian methods for inferring haplotypes from genotype data in a population sample. We review the methods, emphasizing the differences between them in terms of both the models ("priors") they use and the computational str ..."
Abstract - Cited by 557 (7 self) - Add to MetaCart
operates through the transmission of chromosomal segments. Experimental methods for haplotype determination exist, but they are currently timeconsuming and expensive. Statistical methods for inferring haplotypes are therefore of considerable interest. In some studies, data may be available on related
Results 1 - 10 of 131,401