Distributed recognition of patterns in time series data

被引：18
|
作者：
Morrill, J [1 ]
机构：
[1] Point & Click Solut Inc, Woburn, MA 01801 USA
来源：
COMMUNICATIONS OF THE ACM | 1998年 / 41卷 / 05期
关键词：
D O I：
10.1145/274946.274955
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Pattern recognition provides a needed layer of abstraction between raw data and decision making, offering a powerful diagnostic and troubleshooting capability that is relatively easy to develop and apply. The software automates the work of monitoring equipment and processes. When anomalies occur, the system offers a full suite of statistical capabilities to analyze the data in real time or off line. Once the experts have defined the patterns of events and conditions to scan for, that valuable expertise is captured in the system and can be applied by all operators. Enabling the system to dynamically link in new patterns greatly increases the utility of the system to the experts, providing an interactive system that is much easier to use. Two examples of real problems that have been solved with this technology are presented.
引用
收藏
页码：45 / 51
页数：7
相关论文
共 50 条
  • [1] Online Recognition of Fuzzy Time Series Patterns
    Herbst, Gernot
    Bocklisch, Steffen F.
    [J]. PROCEEDINGS OF THE JOINT 2009 INTERNATIONAL FUZZY SYSTEMS ASSOCIATION WORLD CONGRESS AND 2009 EUROPEAN SOCIETY OF FUZZY LOGIC AND TECHNOLOGY CONFERENCE, 2009, : 974 - 979
  • [2] Time series patterns recognition with genetic algorithms
    Borkowski, M
    [J]. Computer Recognition Systems, Proceedings, 2005, : 113 - 120
  • [3] Clustering Distributed Short Time Series with Dense Patterns
    da Silva, Josenildo C.
    Oliveira, Gustavo H. B. S.
    Lodi, Stefano
    Klusch, Matthias
    [J]. 2017 16TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), 2017, : 34 - 41
  • [4] Context-Aware Collaborative Object Recognition For Distributed Multi Camera Time Series Data
    Shin, Philip W.
    Sampson, Jack
    Narayanan, Vijaykrishnan
    [J]. SOICT 2019: PROCEEDINGS OF THE TENTH INTERNATIONAL SYMPOSIUM ON INFORMATION AND COMMUNICATION TECHNOLOGY, 2019, : 154 - 161
  • [5] Tracing Patterns in Electrophysiological Time Series Data
    Huewel, Jan David
    Gresch, Anne
    Berns, Fabian
    Koch, Ruben
    Duefer, Martina
    Beecks, Christian
    [J]. 2022 IEEE 9TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA), 2022, : 634 - 643
  • [6] Data mining on time series of sequential patterns
    Visa, A
    [J]. DATA MINING AND KNOWLEDGE DISCOVERY: THEORY, TOOLS AND TECHNOLOGY IV, 2002, 4730 : 166 - 171
  • [7] Observational data patterns for time series data quality assessment
    Pastorello, Gilberto
    Agarwal, Deb
    Samak, Taghrid
    Poindexter, Cristina
    Faybishenko, Boris
    Gunter, Dan
    Hollowgrass, Rachel
    Papale, Dario
    Trotta, Carlo
    Ribeca, Alessio
    Canfora, Eleonora
    [J]. 2014 IEEE 10TH INTERNATIONAL CONFERENCE ON E-SCIENCE (E-SCIENCE), VOL 1, 2014, : 271 - 278
  • [8] Partitioning Time Series Sensor Data for Activity Recognition
    Hong, Xin
    Nugent, Chris D.
    [J]. 2009 9TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND APPLICATIONS IN BIOMEDICINE, 2009, : 242 - +
  • [9] Time Series Data Visualized Using Three Patterns
    Ragavendra, K. Sri
    Beevi, L. Sherin
    Sathishkumar, S.
    [J]. 2016 INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, AND OPTIMIZATION TECHNIQUES (ICEEOT), 2016, : 4741 - 4743
  • [10] Mining asynchronous periodic patterns in time series data
    Yang, J
    Wang, W
    Yu, PS
    [J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2003, 15 (03) : 613 - 628
12345