Anomaly management using complex event processing

Hoßbach, Bastian; Seeger, Bernhard

doi:10.1145/2452376.2452395

Cited by 23 publications

(17 citation statements)

References 14 publications

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“…One of the common applications of CEP is Anomaly Management [13], which can be applicable in many systems such as in fraud detection to provide safety to user in case on stolen credit card or mobile, in health care to monitor different vital signs such as heartbeat, blood pressure, sugar level. of the severe ill patients, Just-in-line (JIT)-logistics, Stock market Analysis and many more areas.…”

Section: Further Application Of Complex Event Processing (Cep)mentioning

confidence: 99%

“…of the severe ill patients, Just-in-line (JIT)-logistics, Stock market Analysis and many more areas. In [13] the anomaly management using CEP have been developed for the novel security monitoring tool for computer system. The authors of [14] have proposed another application of CEP with Graphical Processing Unit (GPU).…”

Section: Further Application Of Complex Event Processing (Cep)mentioning

confidence: 99%

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Survey of Applications of Complex Event Processing (CEP) in Health Domain

Mahmood

Pasha

2017

SJCMS

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Abstract:It is always difficult to manipulate the production of huge amount of data which comes from multiple sources and to extract meaningful information to make appropriate decisions. When data comes from various input resources, to get required streams of events form this complex input network, the one of the strong functionality of Business Intelligence (BI) the Complex Event Processing (CEP) is the appropriate solution for the abovementioned problems. Real time processing, pattern matching, stream processing, big data management, sensor data processing and many more are the application areas of CEP. Health domain itself is a multidimension domain such as hospital supply chain, OPD management, disease diagnostic, Inpatient, out-patient management, and emergency care etc. In this paper, the main focus is to discuss the application areas of Complex Event Processing (CEP) in health domain by using sensor device, such that how CEP manipulate health data set events coming from sensor devices such as blood pressure, heartbeat, fall detection, sugar level, temperature or any other vital signs and how these systems respond to these events as quickly as possible. Different existing models and application using CEP are discussed and summarized according to different characteristics.

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Section: Further Application Of Complex Event Processing (Cep)mentioning

confidence: 99%

Section: Further Application Of Complex Event Processing (Cep)mentioning

confidence: 99%

Survey of Applications of Complex Event Processing (CEP) in Health Domain

Mahmood

Pasha

2017

SJCMS

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“…Recently, versioning has also played a key role in developing robust transactional file systems [24]. Event processing system can take benefit from storing temporal data in event stores [12,15].…”

Section: Related Workmentioning

confidence: 99%

“…Index entries of new live nodes are inserted in the parent node (lines [8][9][10][11][12][13][14][15][16][17][18]. If the live root is reorganized, a new live root is created (lines 10 -15) and a corresponding index entry is inserted in root * .…”

Section: Mvbtmentioning

confidence: 99%

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Efficient bulk updates on multiversion B-trees

Achakeev

Seeger

2013

Proc. VLDB Endow.

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Partial persistent index structures support efficient access to current and past versions of objects, while updates are allowed on the current version. The Multiversion B-Tree (MVBT) represents a partially persistent index-structure with both, asymptotic worst-case performance and excellent performance in real life applications. Updates are performed tuple-by-tuple with the same asymptotic performance as for standard B + trees. To the best of our knowledge, there is no efficient algorithm for bulk loading and bulk update of MVBT and other partially persistent index structures. In this paper, we propose the first loading algorithm for MVBT that meets the lower-bound of external sorting. In addition, our approach is also applicable to bulk updates. This is achieved by combining two basic technologies, weight balancing and buffer tree. Our extensive set of experiments confirm the theoretical findings: Our loading algorithm runs considerably faster than performing updates tuple-by-tuple.

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