Results 1 - 10 of 21,779

Data Streams: Algorithms and Applications

by S. Muthukrishnan , 2005
"... In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has emerg ..."
Abstract - Cited by 533 (22 self) - Add to MetaCart
In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has

Models and issues in data stream systems

by Brian Babcock, Shivnath Babu, Mayur Datar, Rajeev Motwani, Jennifer Widom - IN PODS , 2002
"... In this overview paper we motivate the need for and research issues arising from a new model of data processing. In this model, data does not take the form of persistent relations, but rather arrives in multiple, continuous, rapid, time-varying data streams. In addition to reviewing past work releva ..."
Abstract - Cited by 786 (19 self) - Add to MetaCart
In this overview paper we motivate the need for and research issues arising from a new model of data processing. In this model, data does not take the form of persistent relations, but rather arrives in multiple, continuous, rapid, time-varying data streams. In addition to reviewing past work

Mining high-speed data streams

by Pedro Domingos , 2000
"... Categories and Subject ���������� � �¨�������������������������¦���¦����������¡¤�� ¡ � ¡����������������¦¡¤����§�£���� ..."
Abstract - Cited by 402 (10 self) - Add to MetaCart
Categories and Subject ���������� � �¨�������������������������¦���¦����������¡¤�� ¡ � ¡����������������¦¡¤����§�£����

Approximate Frequency Counts over Data Streams

by Gurmeet Singh Manku, Rajeev Motwani - VLDB , 2002
"... We present algorithms for computing frequency counts exceeding a user-specified threshold over data streams. Our algorithms are simple and have provably small memory footprints. Although the output is approximate, the error is guaranteed not to exceed a user-specified parameter. Our algorithms can e ..."
Abstract - Cited by 418 (1 self) - Add to MetaCart
We present algorithms for computing frequency counts exceeding a user-specified threshold over data streams. Our algorithms are simple and have provably small memory footprints. Although the output is approximate, the error is guaranteed not to exceed a user-specified parameter. Our algorithms can

Finding frequent items in data streams

by Moses Charikar, Kevin Chen, Martin Farach-colton , 2002
"... Abstract. We present a 1-pass algorithm for estimating the most frequent items in a data stream using very limited storage space. Our method relies on a novel data structure called a count sketch, which allows us to estimate the frequencies of all the items in the stream. Our algorithm achieves bett ..."
Abstract - Cited by 341 (0 self) - Add to MetaCart
Abstract. We present a 1-pass algorithm for estimating the most frequent items in a data stream using very limited storage space. Our method relies on a novel data structure called a count sketch, which allows us to estimate the frequencies of all the items in the stream. Our algorithm achieves

Continuous Queries over Data Streams

by Shivnath Babu, Jennifer Widom , 2001
"... In many recent applications, data may take the form of continuous data streams, rather than finite stored data sets. Several aspects of data management need to be re-considered in the presence of data streams, offering a new research direction for the database community. In this pa-per we focus prim ..."
Abstract - Cited by 308 (10 self) - Add to MetaCart
In many recent applications, data may take the form of continuous data streams, rather than finite stored data sets. Several aspects of data management need to be re-considered in the presence of data streams, offering a new research direction for the database community. In this pa-per we focus

Mining time-changing data streams

by Geoff Hulten, Laurie Spencer, Pedro Domingos - IN PROC. OF THE 2001 ACM SIGKDD INTL. CONF. ON KNOWLEDGE DISCOVERY AND DATA MINING , 2001
"... Most statistical and machine-learning algorithms assume that the data is a random sample drawn from a station-ary distribution. Unfortunately, most of the large databases available for mining today violate this assumption. They were gathered over months or years, and the underlying pro-cesses genera ..."
Abstract - Cited by 338 (5 self) - Add to MetaCart
-changing data streams, based on the ultra-fast VFDT decision tree learner. This algorithm, called CVFDT, stays current while making the most of old data by growing an alternative subtree whenever an old one becomes ques-tionable, and replacing the old with the new when the new becomes more accurate. CVFDT

Clustering Data Streams

by Sudipto Guha, et al. , 2000
"... ..."
Abstract - Cited by 295 (12 self) - Add to MetaCart
Abstract not found

A framework for clustering evolving data streams. In:

by Charu C Aggarwal , T J Watson , Resch Jiawei Ctr , Jianyong Han , Wang , Philip Yu , T J Watson , Resch Ctr - Proc of VLDB’03, , 2003
"... Abstract The clustering problem is a difficult problem for the data stream domain. This is because the large volumes of data arriving in a stream renders most traditional algorithms too inefficient. In recent years, a few one-pass clustering algorithms have been developed for the data stream proble ..."
Abstract - Cited by 359 (36 self) - Add to MetaCart
Abstract The clustering problem is a difficult problem for the data stream domain. This is because the large volumes of data arriving in a stream renders most traditional algorithms too inefficient. In recent years, a few one-pass clustering algorithms have been developed for the data stream

An improved data stream summary: The Count-Min sketch and its applications

by Graham Cormode, S. Muthukrishnan - J. Algorithms , 2004
"... Abstract. We introduce a new sublinear space data structure—the Count-Min Sketch — for summarizing data streams. Our sketch allows fundamental queries in data stream summarization such as point, range, and inner product queries to be approximately answered very quickly; in addition, it can be applie ..."
Abstract - Cited by 413 (43 self) - Add to MetaCart
Abstract. We introduce a new sublinear space data structure—the Count-Min Sketch — for summarizing data streams. Our sketch allows fundamental queries in data stream summarization such as point, range, and inner product queries to be approximately answered very quickly; in addition, it can
Results 1 - 10 of 21,779