NDLI: Analyzing very large time series using suffix arrays

Content Provider	Springer Nature Link
Author	Xylogianpoulos, Konstantis F. Karampelas, Panagiotis Alhajj, Reda
Copyright Year	2014
Abstract	Suffix arrays form a powerful data structure for pattern detection and matching. In a previous work, we presented a novel algorithm (COV) which is the only algorithm that allows the detection of all repeated patterns in a time series by using the actual suffix array. However, the requirements for storing the actual suffix strings even on external media makes the use of suffix arrays impossible for very large time series. We have already proved that using the concept of Longest Expected Repeated Pattern (LERP) allows the actual suffices to be stored in linear capacity O(n) on external media. The repeated pattern detection using LERP has analogous time complexity, and thus makes the analysis of large time series feasible and limited only to the size of the external media and not memory. Yet, there are cases when hardware limitations might be an obstacle for the analysis of very larger time series of size comparable to hard disk capacity. With the Moving LERP (MLERP) method introduced in this paper, it is possible to analyze very large time series (of size tens or hundreds thousands times larger than what the LERP can analyze) by maximal utilization of the available hardware. Further, when empirical knowledge related to the distribution of repeated pattern’s length is available, the proposed method (MLERP) can achieve better time performance compared to the standard LERP method and definitely much better than using any other pattern matching algorithm and applying brute force techniques which are unfeasible in logical (human) time frame. Thus, we may argue that MLERP is a very useful tool for detecting all repeated patterns in a time series regardless of its size and hardware limitations.
Starting Page	941
Ending Page	955
Page Count	15
File Format	PDF
ISSN	0924669X
Journal	Applied Intelligence
Volume Number	41
Issue Number	3
e-ISSN	15737497
Language	English
Publisher	Springer US
Publisher Date	2014-08-23
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Suffix arrays Repeated patterns detection Data mining Time series DNA analysis Artificial Intelligence (incl. Robotics) Mechanical Engineering Manufacturing, Machines, Tools
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Analyzing very large time series using suffix arrays

Repeated patterns detection in big data using classification and parallelism on LERP Reduced Suffix Arrays

STNR: A suffix tree based noise resilient algorithm for periodicity detection in time series databases

Mining non-redundant time-gap sequential patterns

Nearest-Neighbours for Time Series

An efficient method for mining frequent sequential patterns using multi-Core processors

Stream mining on univariate uncertain data

A Data Mining Approach for Retailing Bank Customer Attrition Analysis

Progress in Intelligent Data Analysis

Analyzing very large time series using suffix arrays

Similar Documents

Analyzing very large time series using suffix arrays

Repeated patterns detection in big data using classification and parallelism on LERP Reduced Suffix Arrays

STNR: A suffix tree based noise resilient algorithm for periodicity detection in time series databases

Mining non-redundant time-gap sequential patterns

Nearest-Neighbours for Time Series

An efficient method for mining frequent sequential patterns using multi-Core processors

Stream mining on univariate uncertain data

A Data Mining Approach for Retailing Bank Customer Attrition Analysis

Progress in Intelligent Data Analysis

Analyzing very large time series using suffix arrays