The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
스마트폰과 IoT 기기가 보편화되면서 센싱 데이터를 지속적으로 분석한 결과인 확률적 이벤트 스트림(Probabilistic Event Stream)이 많은 주목을 받고 있다. 확률적 이벤트 스트림의 응용 프로그램 중 하나는 정규식을 기반으로 시계열 이벤트를 모니터링하는 것입니다. 즉, "와 같은 모니터링 쿼리를 설명합니다.추적된 물체가 다음 위치에서 이동했습니까? RoomA 에 RoomB 지난 30분 동안?”라는 정규식을 이용하여 확률적 이벤트 스트림에서 해당 이벤트가 발생하는지 슬라이딩 윈도우를 통해 확인합니다. 이전 연구에서 시계열 이벤트의 기본적인 모니터링 방법을 제안했지만 세 가지 문제가 남아 있습니다. 1) 슬라이딩 윈도우의 슬라이드 크기에 대한 비정상적인 가정을 기반으로 하고 있습니다. 2) 패턴 쿼리의 문법에 "부정"이 포함되지 않았습니다. , 3) 다중 모니터링 쿼리에 최적화되지 않았습니다. 본 논문에서는 위와 같은 문제를 해결하기 위한 몇 가지 기법을 제안한다. 첫째, 슬라이드 크기에 대한 가정을 제거하고 효율적인 확률 계산을 위해 슬라이딩 윈도우의 적응형 슬라이싱을 제안합니다. 둘째, 역DFA를 이용하여 부정 패턴의 발생 확률을 계산한다. 마지막으로, 우리는 다중 쿼리를 효율적으로 처리하기 위해 분리 기반 다중 DFA 병합을 제안합니다. 실제 및 합성 데이터 세트를 사용한 실험 결과는 우리 접근 방식의 효율성을 보여줍니다.
Kento SUGIURA
Nagoya University
Yoshiharu ISHIKAWA
Nagoya University
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부
Kento SUGIURA, Yoshiharu ISHIKAWA, "Multiple Regular Expression Pattern Monitoring over Probabilistic Event Streams" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 5, pp. 982-991, May 2020, doi: 10.1587/transinf.2019DAP0009.
Abstract: As smartphones and IoT devices become widespread, probabilistic event streams, which are continuous analysis results of sensing data, have received a lot of attention. One of the applications of probabilistic event streams is monitoring of time series events based on regular expressions. That is, we describe a monitoring query such as “Has the tracked object moved from RoomA to RoomB in the past 30 minutes?” by using a regular expression, and then check whether corresponding events occur in a probabilistic event stream with a sliding window. Although we proposed the fundamental monitoring method of time series events in our previous work, three problems remain: 1) it is based on an unusual assumption about slide size of a sliding window, 2) the grammar of pattern queries did not include “negation”, and 3) it was not optimized for multiple monitoring queries. In this paper, we propose several techniques to solve the above problems. First, we remove the assumption about slide size, and propose adaptive slicing of sliding windows for efficient probability calculation. Second, we calculate the occurrence probability of a negation pattern by using an inverted DFA. Finally, we propose the merge of multiple DFAs based on disjunction to process multiple queries efficiently. Experimental results using real and synthetic datasets demonstrate effectiveness of our approach.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019DAP0009/_p
부
@ARTICLE{e103-d_5_982,
author={Kento SUGIURA, Yoshiharu ISHIKAWA, },
journal={IEICE TRANSACTIONS on Information},
title={Multiple Regular Expression Pattern Monitoring over Probabilistic Event Streams},
year={2020},
volume={E103-D},
number={5},
pages={982-991},
abstract={As smartphones and IoT devices become widespread, probabilistic event streams, which are continuous analysis results of sensing data, have received a lot of attention. One of the applications of probabilistic event streams is monitoring of time series events based on regular expressions. That is, we describe a monitoring query such as “Has the tracked object moved from RoomA to RoomB in the past 30 minutes?” by using a regular expression, and then check whether corresponding events occur in a probabilistic event stream with a sliding window. Although we proposed the fundamental monitoring method of time series events in our previous work, three problems remain: 1) it is based on an unusual assumption about slide size of a sliding window, 2) the grammar of pattern queries did not include “negation”, and 3) it was not optimized for multiple monitoring queries. In this paper, we propose several techniques to solve the above problems. First, we remove the assumption about slide size, and propose adaptive slicing of sliding windows for efficient probability calculation. Second, we calculate the occurrence probability of a negation pattern by using an inverted DFA. Finally, we propose the merge of multiple DFAs based on disjunction to process multiple queries efficiently. Experimental results using real and synthetic datasets demonstrate effectiveness of our approach.},
keywords={},
doi={10.1587/transinf.2019DAP0009},
ISSN={1745-1361},
month={May},}
부
TY - JOUR
TI - Multiple Regular Expression Pattern Monitoring over Probabilistic Event Streams
T2 - IEICE TRANSACTIONS on Information
SP - 982
EP - 991
AU - Kento SUGIURA
AU - Yoshiharu ISHIKAWA
PY - 2020
DO - 10.1587/transinf.2019DAP0009
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2020
AB - As smartphones and IoT devices become widespread, probabilistic event streams, which are continuous analysis results of sensing data, have received a lot of attention. One of the applications of probabilistic event streams is monitoring of time series events based on regular expressions. That is, we describe a monitoring query such as “Has the tracked object moved from RoomA to RoomB in the past 30 minutes?” by using a regular expression, and then check whether corresponding events occur in a probabilistic event stream with a sliding window. Although we proposed the fundamental monitoring method of time series events in our previous work, three problems remain: 1) it is based on an unusual assumption about slide size of a sliding window, 2) the grammar of pattern queries did not include “negation”, and 3) it was not optimized for multiple monitoring queries. In this paper, we propose several techniques to solve the above problems. First, we remove the assumption about slide size, and propose adaptive slicing of sliding windows for efficient probability calculation. Second, we calculate the occurrence probability of a negation pattern by using an inverted DFA. Finally, we propose the merge of multiple DFAs based on disjunction to process multiple queries efficiently. Experimental results using real and synthetic datasets demonstrate effectiveness of our approach.
ER -