English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Data mining and knowledge discovery in huge data streams have recently involved in more applications used for decision making. Currently in wireless sensor networks, various mining techniques are used to discover knowledge on sensor data. Applying mining algorithm in wireless sensor data faces many challenges such as continuous arrival of sensor data, fast and huge data arrival, changes of mining results over time, online mining, data transformation, changing network topology, resource constraints and have emerged into various research problems. In Wireless Sensor Database, this paper presents a review on various approaches of association rule mining algorithms using various techniques forming sensor association rules generating frequent patterns to find upcoming sensor events or sensor fault detection or to estimate the missing sensor readings.

Discovery of Knowledge Using Association Rules in Wireless Sensor Epocs-a Survey