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4,688,637
massive data sets
, 2015
"... Order statistics and estimating cardinalities of massive data sets ..."
Clustering in Massive Data Sets
- Handbook of massive data sets
, 1999
"... We review the time and storage costs of search and clustering algorithms. We exemplify these, based on case-studies in astronomy, information retrieval, visual user interfaces, chemical databases, and other areas. Sections 2 to 6 relate to nearest neighbor searching, an elemental form of clustering, ..."
Abstract
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Cited by 17 (0 self)
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, and a basis for clustering algorithms to follow. Sections 7 to 11 review a number of families of clustering algorithm. Sections 12 to 14 relate to visual or image representations of data sets, from which a number of interesting algorithmic developments arise.
Order Statistics and Estimating Cardinalities of massive Data Sets
, 2011
"... statistics and estimating cardinalities of massive data sets ..."
Synopsis Data Structures for Massive Data Sets
"... Abstract. Massive data sets with terabytes of data are becoming commonplace. There is an increasing demand for algorithms and data structures that provide fast response times to queries on such data sets. In this paper, we describe a context for algorithmic work relevant to massive data sets and a f ..."
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Cited by 116 (13 self)
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Abstract. Massive data sets with terabytes of data are becoming commonplace. There is an increasing demand for algorithms and data structures that provide fast response times to queries on such data sets. In this paper, we describe a context for algorithmic work relevant to massive data sets and a
Data Mining: The Massive Data Set
"... Abstract-Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. It uses machine learning, statistical and visualization techniques to disc ..."
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Abstract-Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. It uses machine learning, statistical and visualization techniques
Graph Algorithms for Massive Data-Sets
"... In this dissertation we face a number of fundamental graph problems which, on the one hand, share applications to the state explosion problem in model checking, and, on the other hand, pose interesting algorithmic questions when very large graphs are dealt with. In more detail, this thesis is divide ..."
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is divided into three main parts. Part I (Symbolic Graph Algorithms) deals with the algorithmic solution of fundamental graph problems (especially graph connectivity problems) assuming a symbolic OBDD-based graph representation. Working on symbolically represented data has potential: the standards achieved
Anytime Exploratory Data Analysis for Massive Data Sets
"... smythQics.uci.edu Exploratory data analysis is inherently an iterative, interactive endeavor. In the context of massive data sets, however, many current data analysis algorithms will not scale appropriately to permit interaction on a human time-scale. In this paper “anytime data anal-ysis ” is propo ..."
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smythQics.uci.edu Exploratory data analysis is inherently an iterative, interactive endeavor. In the context of massive data sets, however, many current data analysis algorithms will not scale appropriately to permit interaction on a human time-scale. In this paper “anytime data anal
SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets
"... Companies providing cloud-scale services have an increasing need to store and analyze massive data sets such as search logs and click streams. For cost and performance reasons, processing is typically done on large clusters of shared-nothing commodity machines. It is imperative to develop a programm ..."
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Cited by 192 (9 self)
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Companies providing cloud-scale services have an increasing need to store and analyze massive data sets such as search logs and click streams. For cost and performance reasons, processing is typically done on large clusters of shared-nothing commodity machines. It is imperative to develop a
Streaming Algorithms for Distributed, Massive Data Sets
- In Proc. IEEE Symposium on Foundations of Computer Science
, 1999
"... Massive data sets are increasingly important in a wide range of applications, including observational sciences, product marketing, and monitoring and operations of large systems. In network operations, raw data typically arrive in streams, and decisions must be made by algorithms that make one pass ..."
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Cited by 5 (0 self)
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Massive data sets are increasingly important in a wide range of applications, including observational sciences, product marketing, and monitoring and operations of large systems. In network operations, raw data typically arrive in streams, and decisions must be made by algorithms that make one pass
Algorithms and Arrays for Computing on Massive Data Sets
, 2001
"... In this work we investigate three approaches to computing on massive data sets. In the first approach, we give a sampling algorithm to estimate the maximum of a large data set. It gives estimates strictly better than the largest sample for an infinite family of data sets. In addition, the algorithm ..."
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In this work we investigate three approaches to computing on massive data sets. In the first approach, we give a sampling algorithm to estimate the maximum of a large data set. It gives estimates strictly better than the largest sample for an infinite family of data sets. In addition, the algorithm
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