Event Clustering & Event Series Characterization on Expected Frequency
| Autor: | Hendrik F. Hamann, Marcus Freitag, Siyuan Lu, Conrad M. Albrecht, Theodore G. Van Kessel |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Networking and Internet Architecture (cs.NI)
FOS: Computer and information sciences Series (mathematics) Computer science 010501 environmental sciences Characterization (mathematics) Missing data 01 natural sciences Computer Science - Networking and Internet Architecture Computer Science - Distributed Parallel and Cluster Computing Computer Science - Data Structures and Algorithms Data Structures and Algorithms (cs.DS) Distributed Parallel and Cluster Computing (cs.DC) Time series Cluster analysis Algorithm 0105 earth and related environmental sciences Event (probability theory) |
| Zdroj: | IEEE BigData |
| Popis: | We present an efficient clustering algorithm applicable to one-dimensional data such as e.g. a series of timestamps. Given an expected frequency $\Delta T^{-1}$, we introduce an $\mathcal{O}(N)$-efficient method of characterizing $N$ events represented by an ordered series of timestamps $t_1,t_2,\dots,t_N$. In practice, the method proves useful to e.g. identify time intervals of "missing" data or to locate "isolated events". Moreover, we define measures to quantify a series of events by varying $\Delta T$ to e.g. determine the quality of an Internet of Things service. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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