Article

Incremental stock time series data delivery and visualization

Authors:

Chak-man NgAuthors Info & Claims

CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management

Pages 279 - 280

https://doi.org/10.1145/1099554.1099626

Published: 31 October 2005 Publication History

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Abstract

SB-Tree is a binary tree data structure proposed to represent time series according to the importance of data points. Its use in stock data management is distinguished by preserving the critical data points' attribute values, retrieving time series data according to the importance of data points and facilitating multi-resolution time series retrieval. As new stock data are available continuously, an effective updating mechanism for SB-Tree is needed. In this paper, a study of different updating approaches is reported. Three families of updating methods are proposed. They are periodic rebuild, batch update and point-by-point update. Their efficiency, effectiveness and characteristics are compared and reported.

References

[1]

T. C. Fu, F. L. Chung, R. Luk and C. M. Ng, "A specialized binary tree for financial time series representation," In 10th ACM SIGKDD Workshop on Temporal Data Mining, pp.96--104, 2004.

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[2]

F. L. Chung, T. C. Fu, R. Luk and V. Ng, "Flexible time series pattern matching based on perceptually important points," IJCAI Workshop on Learning from Temporal and Spatial Data, pp.1--7, 2001.

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[3]

T. C. Fu, F. L. Chung, R. Luk and C. M. Ng, "Progressive time series visualization in a mobile environment," In Proc. of the 9th IEEE ISCC, Vol. 2, pp. 662--667, 2004.

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Fu T(2018)A review on time series data miningEngineering Applications of Artificial Intelligence10.1016/j.engappai.2010.09.00724:1(164-181)Online publication date: 27-Dec-2018
https://dl.acm.org/doi/10.1016/j.engappai.2010.09.007
Fu TChung FLuk RNg C(2018)Representing financial time series based on data point importanceEngineering Applications of Artificial Intelligence10.1016/j.engappai.2007.04.00921:2(277-300)Online publication date: 27-Dec-2018
https://dl.acm.org/doi/10.1016/j.engappai.2007.04.009
Gensler ASick B(2018)Performing event detection in time series with SwiftEventPattern Analysis & Applications10.1007/s10044-017-0657-021:2(543-562)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s10044-017-0657-0
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Index Terms

Incremental stock time series data delivery and visualization
1. Information systems
  1. Information storage systems
    1. Record storage systems
      1. Directory structures
        B-trees
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Trees

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Published In

CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management

October 2005

854 pages

ISBN:1595931406

DOI:10.1145/1099554

General Chair:
Otthein Herzog
University of Bremen, Germany
,
Program Chairs:
Hans-Jörg Schek
University for Health Sciences, Medical Informatics and Technology, Austria
,
Norbert Fuhr
University of Duisburg-Essen, Germany
,
Abdur Chowdhury
America Online, USA
,
Wilfried Teiken
IBM T.J. Watson Research Center, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 October 2005

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CIKM05: Conference on Information and Knowledge Management

October 31 - November 5, 2005

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CIKM '05 Paper Acceptance Rate 77 of 425 submissions, 18%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Fu T(2018)A review on time series data miningEngineering Applications of Artificial Intelligence10.1016/j.engappai.2010.09.00724:1(164-181)Online publication date: 27-Dec-2018
https://dl.acm.org/doi/10.1016/j.engappai.2010.09.007
Fu TChung FLuk RNg C(2018)Representing financial time series based on data point importanceEngineering Applications of Artificial Intelligence10.1016/j.engappai.2007.04.00921:2(277-300)Online publication date: 27-Dec-2018
https://dl.acm.org/doi/10.1016/j.engappai.2007.04.009
Gensler ASick B(2018)Performing event detection in time series with SwiftEventPattern Analysis & Applications10.1007/s10044-017-0657-021:2(543-562)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s10044-017-0657-0
Lee SPark JKwon DPark BCho HLee DKim DKim SLee SHanzo LIsmail R(2015)A new visualization method for pairwise time-series data with random walk plotProceedings of the 9th International Conference on Ubiquitous Information Management and Communication10.1145/2701126.2701144(1-8)Online publication date: 8-Jan-2015
https://dl.acm.org/doi/10.1145/2701126.2701144
Bose I(2009)Financial Time Series Data MiningEncyclopedia of Data Warehousing and Mining, Second Edition10.4018/978-1-60566-010-3.ch136(883-889)Online publication date: 2009
https://doi.org/10.4018/978-1-60566-010-3.ch136
Fu TChung FKwok KNg C(2008)Stock time series visualization based on data point importanceEngineering Applications of Artificial Intelligence10.1016/j.engappai.2008.01.00521:8(1217-1232)Online publication date: 1-Dec-2008
https://dl.acm.org/doi/10.1016/j.engappai.2008.01.005
Fu TChan HChung FNg C(2006)Time series subsequence searching in specialized binary treeProceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery10.1007/11881599_67(568-577)Online publication date: 24-Sep-2006
https://dl.acm.org/doi/10.1007/11881599_67

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Representing financial time series based on data point importance

IMBT--A Binary Tree for Efficient Support Counting of Incremental Data Mining

Incremental maintenance of object-oriented data warehouses

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