Designing parallel data processing for enabling large-scale sensor applications

Kabáč, Milan; Consel, Charles; Volanschi, Nic

doi:10.1007/s00779-017-1009-1

Cited by 8 publications

(4 citation statements)

References 26 publications

(24 reference statements)

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“…The HA Platform HA services are apps developed using the DiaSuiteBox technology [25], except for some services for social participation where existing apps are used (see further sections). HA is an AAL platform based on pervasive computing leveraged from the Internet-of-Things (IoT).…”

Section: Description Of the Ha Interventionmentioning

confidence: 99%

Effectiveness of an Ambient Assisted Living (HomeAssist) Platform for Supporting Aging in Place of Older Adults With Frailty: Protocol for a Quasi-Experimental Study

Sauzéon¹,

Edjolo²,

Amieva³

et al. 2022

JMIR Res Protoc

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Background Ambient assisted living (AAL) technologies are viewed as a promising way to prolong aging in place, particularly when they are designed as closely as possible to the needs of the end users. However, very few evidence-based results have been provided to support its real value, notably for frail older adults who have a high risk of autonomy loss as well as entering a nursing home. Objective We hypothesized that the benefit from an AAL with a user-centered design is effective for aging in place for frail older adults in terms of everyday functioning (instrumental activities of daily-life scale). In addition, our secondary hypotheses are that such an AAL decreases or neutralizes the frailty process and reduces the rates of institutionalization and hospitalization and that it improves the psychosocial health of participants and their caregivers when compared with the control condition. We also assume that a large proportion of equipped participants will have a satisfactory experience and will accept a subscription to an internet connection to prolong their participation. Methods HomeAssist (HA) is an AAL platform offering a large set of apps for 3 main age-related need domains (activities of daily-living, safety, and social participation), relying on a basic set of entities (sensors, actuators, tablets, etc). The HA intervention involves monitoring based on assistive services to support activities related to independent living at home. The study design is quasi-experimental with a duration of 12 months, optionally extensible to 24 months. Follow-up assessments occurred at 0, 12, and 24 months. The primary outcome measures are related to everyday functioning. Secondary outcome measures include indices of frailty, cognitive functioning, and psychosocial health of the participants and their caregivers. Every 6 months, user experience and attitudes toward HA are also collected from equipped participants. Concomitantly, data on HA use will be collected. All measures of the study will be tested based on an intention-to-treat approach using a 2-tailed level of significance set at α=.05, concerning our primary and secondary efficacy outcomes. Results Descriptive analyses were conducted to characterize the recruited equipped participants compared with the others (excluded and refusals) on the data available at the eligibility visit, to describe the characteristics of the recruited sample at baseline, as well as those of the dropouts. Finally, recruitment at 12 months included equipped participants (n=73), matched with control participants (n=474, from pre-existing cohorts). The results of this study will be disseminated through scientific publications and conferences. This will provide a solid basis for the creation of a start-up to market the technology. Conclusions This trial will inform the real-life efficacy of HA in prolonging aging in place for frail older adults and yield an informed analysis of AAL use and adoption in frail older individuals. International Registered Report Identifier (IRRID) DERR1-10.2196/33351

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Section: Description Of the Ha Interventionmentioning

confidence: 99%

Effectiveness of an Ambient Assisted Living (HomeAssist) Platform for Supporting Aging in Place of Older Adults With Frailty: Protocol for a Quasi-Experimental Study

Sauzéon¹,

Edjolo²,

Amieva³

et al. 2022

JMIR Res Protoc

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“…This detection was done by using visual anomaly detection tool which infuses the impact of air pollution caused due to vehicles in London City. Kabáč et al (2017) contributed a design-driven approach for emerging areas of smart city where numerous amount of sensors are integrated to parallely process the large amount of sensed data. The design-driven approach gathers the data from the declarative domain-specific design language, DiaSwarm has three data delivery models namely periodic, event-driven and query driven working specially under the wireless sensor networks.…”

Section: Smart Citymentioning

confidence: 99%

SMSS : Does Social,Mobile,Spatial and Sensor data have high impact on big data analytics

Govardanan

Gnanapandithan

2019

IJIE

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Big data refers to the huge torrent of large-scale datasets that are being generated at an exponential growth. Since we live in this digital world, the era of big data has emerged in part and parcel of our lives. The emergence of big data has reached in almost several domains like healthcare industry, telecom industry, molecular biology, biochemistry, physics, astronomy, computer science, business and others. In this paper, we have termed the types of big data by the form SMSS data which is simply meaning social, mobile, spatial and sensor data. This paper aims to provide the importance of big data analytics brought over the different types of big data extracted from heterogeneous data sources. To achieve this objective, we have made an intensive study of several literatures and considered a variety of big data applications which are being discussed to showcase its value. Also, a generic framework is proposed that can be applicable to any kind of big data types extracted from such a diverse heterogeneous data sources. Finally, a few open source tools that can be used for processing the big data are presented.

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“…[43]. JAQL facilitates parallel processing by translating high-level queries into low-level ones [44]. It is a dataflow language that manipulates semi-structured data using JSON values.…”

Section: Hive: a Data Warehousing Over Hadoopmentioning

confidence: 99%

Evaluation of high-level query languages based on MapReduce in Big Data

2018

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Since it was presented by Google in 2004, MapReduce (MR) [1] has been emerged as a popular framework for Big Data processing model in cluster environment and cloud computing [2]. It has become a key of success for processing, analyzing and managing large data sets with some number of implementations including the open-source Hadoop framework [3, 4]. MR has many interesting qualities, highly noticed in its design and plainness in writing programs. It has only two functions, known as Map and Reduce, written by developer to process key-value data pairs. Even though, MR is very simple to understand its principle and basic concepts but it is hard to develop, optimize, and maintain its functions especially in large-scale projects [5]. MR requires approaching any problem in terms of key-value pairs where each pair can be independently computed. Also, coding efficient MapReduce programs, mainly in Java, was non-trivial for those who interested to build large-scale projects even though their programming level. This meant that many operations need multiple inputs/outputs, both simple and complex, that were very hard to achieve without wasting programming efforts and time.

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Designing parallel data processing for enabling large-scale sensor applications

Cited by 8 publications

References 26 publications

Effectiveness of an Ambient Assisted Living (HomeAssist) Platform for Supporting Aging in Place of Older Adults With Frailty: Protocol for a Quasi-Experimental Study

Effectiveness of an Ambient Assisted Living (HomeAssist) Platform for Supporting Aging in Place of Older Adults With Frailty: Protocol for a Quasi-Experimental Study

SMSS : Does Social,Mobile,Spatial and Sensor data have high impact on big data analytics

Evaluation of high-level query languages based on MapReduce in Big Data

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