Search results for " data stream"
showing 4 items of 4 documents
Managing sensor data streams in a smart home application
2020
A challenge in developing an ambient activity recognition system for use in elder care is finding a balance between the sophistication of the system and a cost structure that fits within the budgets of public and private sector healthcare organisations. Much activity recognition research in the context of elder care is based on dense networks of sensors and advanced methods, such as supervised machine learning algorithms. This paper presents the data processing aspects of an activity recognition system based on a simpler, knowledge-based unsupervised approach, designed for a sparse network of sensors. By structuring sensor data management as a streaming system, we provide a simple programmi…
New results for finding common neighborhoods in massive graphs in the data stream model
2008
AbstractWe consider the problem of finding pairs of vertices that share large common neighborhoods in massive graphs. We give lower bounds for randomized, two-sided error algorithms that solve this problem in the data-stream model of computation. Our results correct and improve those of Buchsbaum, Giancarlo, and Westbrook [On finding common neighborhoods in massive graphs, Theoretical Computer Science, 299 (1–3) 707–718 (2004)]
DeCyMo: Decentralized Cyber-physical System for Monitoring and Controlling Industries and Homes
2018
The recent revolution of the Internet of Things has given the birth of a series of new technologies and cyber-physical systems to be used in industrial and home scenarios. Cyber- physical systems include physical and software components for providing smart monitoring and control with flexibility and adaptability to the operating context. The IoT paradigm enables the intertwined use of physical and software components through the interconnection of devices that exchange data with each other without direct human interaction in several fields, especially in industrial and home environments. We propose DeCyMo, a decentralized architecture that aims at solving common IoT issues and vulnerabiliti…
Mining Maximal Frequent Patterns in Transactional Databases and Dynamic Data Streams: A Spark-based Approach
2018
Mining maximal frequent patterns (MFPs) in transactional databases (TDBs) and dynamic data streams (DDSs) is substantially important for business intelligence. MFPs, as the smallest set of patterns, help to reveal customers’ purchase rules and market basket analysis (MBA). Although, numerous studies have been carried out in this area, most of them extend the main-memory based Apriori or FP-growth algorithms. Therefore, these approaches are not only unscalable but also lack parallelism. Consequently, ever increasing big data sources requirements cannot be met. In addition, mining performance in some existing approaches degrade drastically due to the presence of null transactions. We, therefo…