What’s Different: Distributed, Continuous Monitoring of Duplicate-Resilient Aggregates on Data Streams

2006 ◽  
Author(s):  
G. Cormode ◽  
S. Muthukrishnan ◽  
Wei Zhuang
Keyword(s):  
Data Streams ◽  
Continuous Monitoring

Continuous monitoring of skylines over uncertain data streams

2012 ◽  
Vol 184 (1) ◽  
pp. 196-214 ◽  
Author(s):  
Xiaofeng Ding ◽  
Xiang Lian ◽  
Lei Chen ◽  
Hai Jin
Keyword(s):  
Data Streams ◽  
Continuous Monitoring ◽  
Uncertain Data ◽  
Uncertain Data Streams

scholarly journals Continuous Monitoring of Distributed Data Streams over a Time-Based Sliding Window

Algorithmica ◽  
2011 ◽  
Vol 62 (3-4) ◽  
pp. 1088-1111 ◽  
Author(s):  
Ho-Leung Chan ◽  
Tak-Wah Lam ◽  
Lap-Kei Lee ◽  
Hing-Fung Ting
Keyword(s):  
Data Streams ◽  
Continuous Monitoring ◽  
Sliding Window ◽  
Distributed Data ◽  
Distributed Data Streams

Continuous monitoring of distance-based outliers over data streams

2011 ◽  
Author(s):  
Maria Kontaki ◽  
Anastasios Gounaris ◽  
Apostolos N. Papadopoulos ◽  
Kostas Tsichlas ◽  
Yannis Manolopoulos
Keyword(s):  
Data Streams ◽  
Continuous Monitoring

scholarly journals DEN-DIS: “May Get Life in Future” - Hybridized Data Stream Clustering Framework in Market Research Arena

2020 ◽  
Vol 9 (9) ◽  
pp. 101-107
Keyword(s):  
Data Streams ◽  
Continuous Monitoring ◽  
Massive Data ◽  
Stream Data ◽  
Dissimilarity Matrix ◽  
Density Based Clustering ◽  
Optimistic Solution ◽  
Data Stream Clustering ◽  
Exploratory Data ◽  
Almost All

Data streams pose several computational challenges due to their large volume of massive data arriving at a very fast rate. Data streams are gaining the attention of today’s research community for their utility in almost all fields. In turn, organizing the data into groups enables the researchers to derive with many useful and valuable information and conclusions based on the categories that were discovered. Clustering makes this organization or grouping easier and plays an important role in exploratory data analysis. This paper focuses on the amalgamation of two very important algorithms namely Density Based clustering used to group the data and the dissimilarity matrix algorithm used to find the outlier among the data. Before feeding the data, the algorithm filters out the sparse data and a continuous monitoring system provides the frequent outlier and inlier checks on the live stream data using buffer timer. This approach provides an optimistic solution in recognizing the outlier data which may later get reverted as inlier based on certain criteria. The concept of DenDis approach will pave a new innovation world of considering every data which “May Get Life in Future”.

scholarly journals The CMS Electromagnetic Calorimeter workflow

2020 ◽  
Vol 245 ◽  
pp. 01024
Author(s):  
Chiara Rovelli
Keyword(s):  
Data Streams ◽  
Relational Databases ◽  
Continuous Monitoring ◽  
Electromagnetic Calorimeter ◽  
Radiation Environment ◽  
Data Handling ◽  
Energy Response ◽  
Lead Tungstate ◽  
Cms Experiment ◽  
Set Up

The CMS experiment at the LHC features an electromagnetic calorimeter (ECAL) made of lead tungstate scintillating crystals. The ECAL energy response is fundamental for both triggering purposes and offline analysis. Due to the challenging LHC radiation environment, the response of both crystals and photodetectors to particles evolves with time. Therefore continuous monitoring and correction of the ageing effects are crucial. Fast, reliable and efficient workflows are set up to have a first set of corrections computed within 48 hours from data-taking, making use of dedicated data streams and processing. Such corrections, stored in relational databases, are then accessed during the prompt offline reconstruction of the CMS data. Twice a week, the calibrations used in the trigger are also updated in the database and accessed during the data-taking. In this note, the design of the CMS ECAL data handling and processing is reviewed.

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