BDCaM: Big Data for Context-Aware Monitoring—A Personalized Knowledge Discovery Framework for Assisted Healthcare

Abstract

Context-aware monitoring is an emerging technology that provides real-time personalised health-care services and a rich area of big data application. In this paper, we propose a knowledge discovery-based approach that allows the context-aware system to adapt its behaviour in runtime by analysing large amounts of data generated in ambient assisted living (AAL) systems and stored in cloud repositories. The proposed BDCaM model facilitates analysis of big data inside a cloud environment. It first mines the trends and patterns in the data of an individual patient with associated probabilities and utilizes that knowledge to learn proper abnormal conditions. The outcomes of this learning method are then applied in context-aware decision-making processes for the patient. A use case is implemented to illustrate the applicability of the framework that discovers the knowledge of classification to identify the true abnormal conditions of patients having variations in blood pressure (BP) and heart rate (HR). The evaluation shows a much better estimate of detecting proper anomalous situations for different types of patients. The accuracy and efficiency obtained for the implemented case study demonstrate the effectiveness of the proposed model.

SecEDMO: Enabling Efficient Data Mining with Strong Privacy Protection in Cloud Computing

“…For example, the classical association rules mining algorithm (i.e., Apriori [1]) is identified as a top 3 algorithm in data mining [2]. It is widely utilized in many fields including market supervision and management [3], web browsing preference prediction [4], intrusion detection [5], health-care services [6], to just name a few. The traditional algorithms for frequent itemsets mining and association rules mining mainly consider how to mine on plaintext domain, so they lack security and privacy concerns.…”

“…Various countermeasures (including secret-key dividing, ciphertext characterizing and mining share) are adopted to resist different kinds of attacks from the data owners, the cloud, and the outside adversary. Schemes [8], [11], [12] Schemes [6]- [9] Classic Solutions…”

Data Mining Usage and Applications in Health Services

“…b) Discovery: DM requires to construct the observed data model or to identify the patterns in the observed data. The model fitting assumes the role [29] of information extraction: The model is a part of the interactive KDD process which the model typically refers to as a subjective human judgment, whether it points to useful information or not. Two basic mathematical structures are used statistically and logically in constructing model [30].…”

Ambient intelligence middleware architecture based on awareness-cognition framework

“…A variety of context awareness solutions exist on AmI system where the research focus is restricted to specific services such as extracting context information, context reasoning (Jianping and Yu 2010), activity monitoring (Kozina et al 2011;Forkan et al 2015), and different application settings (Gravier et al 2015;Sain et al 2010). Most of these systems have proposed different context modeling approaches depending on the problem domains.…”