CAS-Mine: providing personalized services in context-aware applications by means of generalized rules

Baralis, Elena; Cagliero, Luca; Cerquitelli, Tania; Garza, Paolo; Marchetti, M.

doi:10.1007/s10115-010-0359-z

Cited by 30 publications

(18 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The works are differentiated by: context-aware platforms for supporting continuous care [24], activity monitoring [50], [30], cloud-based healthcare [21], [51] and personalized care [17]. The context-aware systems that are developed using rule-mining and data mining only solve some specific diseases [33], [52].…”

Section: Related Workmentioning

confidence: 99%

“…Such systems can generate many false alarms to the monitoring centres; this is not desirable. The flexibility of using low-cost cloud platforms such as Amazon web service (AWS) [14], Windows Azure [15] and Google cloud [16] has added greater advantage to learn of true abnormal situations by storing and analysing every past and current event of a patient, and generate personalized rules [17]. Such an individualized rule-mining process also promotes the inference of more generalized medical rules for a particular patient category.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Forkan

Khalil

Ibaida

et al. 2017

IEEE Trans. Cloud Comput.

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Forkan

Khalil

Ibaida

et al. 2017

IEEE Trans. Cloud Comput.

View full text Add to dashboard Cite

show abstract

“…According to the above formula, when the numerator and the denominator have similar values (i.e., lift ≥1.0), it means events A and B are associated with each other. As a matter of fact, previous studies have frequently used lift as a reference for screening association rules (Baralis et al 2011). When lift is ≥1.0, events A and B are correlated; when lift is <1.0, events A and B are not correlated (i.e., the probability of the numerator is smaller than that of the denominator).…”

Section: Association Rulesmentioning

confidence: 99%

Examining Association Between Construction Inspection Grades and Critical Defects Using Data Mining and Fuzzy Logic

Lin

Fan²

2018

Journal of Civil Engineering and Management

View full text Add to dashboard Cite

This paper explores the relations between defect types and quality inspection grades of public construction projects in Taiwan. Altogether, 499 defect types (classified from 17,648 defects) were found after analyzing 990 construction projects from the Public Construction Management Information System of the public construction commission which is a government unit that administers all the public construction. The core of this research includes the following steps. (1) Data mining (DM) was used to derive 57 association rules which altogether contain 30 of the 499 defect types. (2) K-means clustering was used to regroup the 990 projects of two attributes (defect frequency and original grading score of each project) into four new quality classes, so the 990 projects can be more evenly distributed in the four new classes and the correctness and reliability of the following analyses can be ensured. (3) Finally analysis of variance (ANOVA), fuzzy logic, and correlation analysis were used to verify that the aforementioned 30 defect types are the important ones determining inspection grades. Results of this research can help stakeholders of construction projects paying more attention on the root causes of the critical defect types so to dramatically raise their management effectiveness.

show abstract

“…A system is context-aware if it uses context to provide relevant information and/or services to the user, where relevancy depends on the user's task [1,7]. Contextawareness frameworks typically support acquisition, representation, delivery and reaction [19].…”

Section: Context-awarenessmentioning

confidence: 99%

The MASSIF platform: a modular and semantic platform for the development of flexible IoT services

et al. 2016

View full text Add to dashboard Cite

In the Internet of Things (IoT), data-producing entities sense their environment and transmit these observations to a data-processing platform for further analysis. Applications can have a notion of context-awareness by combining this sensed data, or by processing the combined data. The processes of combining data can consist both of merging the dynamic sensed data, as well as fusing the sensed data with background and historical data. Semantics can aid in this task, as they have proven their use in data integration, knowledge exchange and reasoning. Semantic services performing reasoning on the integrated sensed data, combined with background knowledge, such as profile data, allow extracting useful information and support intelligent decision making. However, advanced reasoning on the combination of this sensed data and background knowledge is still hard to achieve. Furthermore, the collaboration between semantic services allows to reach complex decisions. The dynamic composition of such collaborative workflows that can adapt to the current context, has not received much attention yet.In this paper, we present MASSIF, a data-driven platform for the semantic annotation of and reasoning on IoT data. It allows the integration of multiple modular reasoning services, that can collaborate in a flexible manner to facilitate complex decision making processes. Data-driven workflows are enabled by letting services specify the data they would like to consume. After thorough processing, these services can decide to share their decisions with other consumers. By defining the data these services would like to consume, they can operate on a subset of data, improving reasoning efficiency. Furthermore, each of these services can integrate the consumed data with background knowledge in its own context model, for rapid intelligent decision making. To show the strengths of the platform, two use cases are detailed and thoroughly evaluated.

show abstract

CAS-Mine: providing personalized services in context-aware applications by means of generalized rules

Abstract: Providing personalized services in context-aware applications by means of generalized rules.

Cited by 30 publications

References 36 publications

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

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

Examining Association Between Construction Inspection Grades and Critical Defects Using Data Mining and Fuzzy Logic

The MASSIF platform: a modular and semantic platform for the development of flexible IoT services

Contact Info

Product

Resources

About