Similarity Analysis of Time Interval Data Sets—A Graph Theory Approach

Haßler, Marc; Kohlschein, Christian; Meisen, Tobias

doi:10.1007/978-3-319-96944-2_11

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Legacy distance/similarity measures (e.g., the Euclidean distance) cannot cope with incorporating cases where w can be completely contained in f i . In [29], the interested reader can find a study for calculating the distance over interval data. Based on these metrics, we propose the use of the overlapping metric ψ k , thus, r i is defined as follows: r i (w, f i ) = h(ψ ik ), ∀k ∈ {1, 2, .…”

Section: Processors Characteristics and Local Datasetsmentioning

confidence: 99%

Proactive, uncertainty-driven queries management at the edge

Kolomvatsos

Anagnostopoulos

2021

Future Generation Computer Systems

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Section: Processors Characteristics and Local Datasetsmentioning

confidence: 99%

Proactive, uncertainty-driven queries management at the edge

Kolomvatsos

Anagnostopoulos

2021

Future Generation Computer Systems

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“…Typical distance/similarity measures (e.g., the Euclidean distance) cannot efficiently manage cases where w can be completely contained in f i . We rely on the research performed in [29], where a study on calculating the distance over interval data is provided. Based on the discussed metrics, we propose the use of the overlapping metric ψ k to finally deliver r i as follows:…”

Section: Distance Between Queries and Datasetsmentioning

confidence: 99%

An Intelligent Edge-centric Queries Allocation Scheme based on Ensemble Models

Kolomvatsos

Anagnostopoulos

2020

ACM Trans. Internet Technol.

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The combination of Internet of Things (IoT) and Edge Computing (EC) can assist in the delivery of novel applications that will facilitate end-users' activities. Data collected by numerous devices present in the IoT infrastructure can be hosted into a set of EC nodes becoming the subject of processing tasks for the provision of analytics. Analytics are derived as the result of various queries defined by end-users or applications. Such queries can be executed in the available EC nodes to limit the latency in the provision of responses. In this article, we propose a meta-ensemble learning scheme that supports the decision making for the allocation of queries to the appropriate EC nodes. Our learning model decides over queries' and nodes' characteristics. We provide the description of a matching process between queries and nodes after concluding the contextual information for each envisioned characteristic adopted in our meta-ensemble scheme. We rely on widely known ensemble models, combine them, and offer an additional processing layer to increase the performance. The aim is to result a subset of EC nodes that will host each incoming query. Apart from the description of the proposed model, we report on its evaluation and the corresponding results. Through a large set of experiments and a numerical analysis, we aim at revealing the pros and cons of the proposed scheme. CCS Concepts: • Computing methodologies → Ensemble methods;

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Section: Distance Between Queries and Datasetsmentioning

confidence: 99%

An Intelligent Edge-Centric Queries Allocation Scheme based on Ensemble Models

Kolomvatsos¹,

Anagnostopoulos²

2020

Preprint

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The combination of Internet of Things (IoT) and Edge Computing (EC) can assist in the delivery of novel applications that will facilitate end users activities. Data collected by numerous devices present in the IoT infrastructure can be hosted into a set of EC nodes becoming the subject of processing tasks for the provision of analytics. Analytics are derived as the result of various queries defined by end users or applications. Such queries can be executed in the available EC nodes to limit the latency in the provision of responses. In this paper, we propose a meta-ensemble learning scheme that supports the decision making for the allocation of queries to the appropriate EC nodes. Our learning model decides over queries' and nodes' characteristics. We provide the description of a matching process between queries and nodes after concluding the contextual information for each envisioned characteristic adopted in our meta-ensemble scheme. We rely on widely known ensemble models, combine them and offer an additional processing layer to increase the performance. The aim is to result a subset of EC nodes that will host each incoming query. Apart from the description of the proposed model, we report on its evaluation and the corresponding results. Through a large set of experiments and a numerical analysis, we aim at revealing the pros and cons of the proposed scheme.

show abstract

Similarity Analysis of Time Interval Data Sets—A Graph Theory Approach

Cited by 4 publications

References 11 publications

Proactive, uncertainty-driven queries management at the edge

Proactive, uncertainty-driven queries management at the edge

An Intelligent Edge-centric Queries Allocation Scheme based on Ensemble Models

An Intelligent Edge-Centric Queries Allocation Scheme based on Ensemble Models

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