Privacy models for big data: a survey

Victor, Nancy; Lopez, Daphne; Abawajy, Jemal

doi:10.1504/ijbdi.2016.073904

Cited by 70 publications

(28 citation statements)

References 46 publications

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“…Whereas, their recommendation process is based on text semantics and query personalization to improve the relevant of the retrieved results. Victor and Lopez [12] also illustrated a big data processing framework for developing a privacy models for big data as big data cannot be processed using traditional systems.…”

Section: Review On Bi Frameworkmentioning

confidence: 99%

Research on Business Intelligence with Data Mining Applications

Chen¹

2017

IJBER

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Business Intelligence (BI) has become an important agenda for many top executives because they have become extremely aware of its value in providing a competitive differentiator at all levels of the organizations. This paper discusses the concepts and technologies of business intelligence, specially, data warehousing and data mining and how these can positively influence and benefit a business. Review on BI frameworks and research models for developing data warehousing and data mining are presented and analyzed. The paper also illustrates a business scenario in which the Rapidminer, a data mining tool can be used to extrapolate relevant data to a small startup ski shop.

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Section: Review On Bi Frameworkmentioning

confidence: 99%

Research on Business Intelligence with Data Mining Applications

Chen¹

2017

IJBER

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“…However in personal healthcare system where sensors are installed and is produced data continuously, the amount of data to be stored and processed becomes a significant problem in real life. Relational database management system (RDBMS) is generally used to store the traditional data, but day by day the volume, velocity and variety of sensor data is growing towards Exabyte (Thilagavathi et al, 2014;Victor et al, 2016). This requires advanced tools and techniques to store, process and display such large amount of sensor data to the end users.…”

Section: Introductionmentioning

confidence: 99%

Health data analytics using scalable logistic regression with stochastic gradient descent

Manogaran

Lopez

2018

IJAIP

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As wearable medical sensors continuously generate enormous data, it is difficult to process and analyse. This paper focuses on developing scalable sensor data processing architecture in cloud computing to store and process body sensor data for healthcare applications. Proposed architecture uses big data technologies such as Apache Flume, Apache Pig and Apache HBase to collect and store huge sensor data in the Amazon web service. Apache Mahout implementation of MapReduce-based online stochastic gradient descent algorithm is used in the logistic regression to develop the scalable diagnosis model. Cleveland heart disease database (CHDD) is used to train the logistic regression model. Wearable body sensors are used to get the blood pressure, blood sugar level and heart rate of the patient to predict the heart disease status. Proposed prediction model efficiently classifies the heart disease with the accuracy of training and validation sample is 81.99% and 81.52%, respectively.

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“…Recently, the data generation sources are augmented and it causes diversity of data such as text, video, audio and large size images. In order to process the enormous amount of data, our conventional data processing platforms and techniques has to be enhanced [2]. Velocity: The rate of the incoming data has increased dramatically this velocity indeed represents Big Data.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, people are not concerned in old post (a tweet, status updates etc.) and notice to most hot updates [2]. Variety: The variety of the data indeed represents Big Data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Big Data Knowledge System in Healthcare

Manogaran

Thota

Lopez

et al. 2017

Studies in Big Data

Self Cite

105

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The health care systems are rapidly adopting large amounts of data, driven by record keeping, compliance and regulatory requirements, and patient care. The advances in healthcare system will rapidly enlarge the size of the health records that are accessible electronically. Concurrently, fast progress has been made in clinical analytics. For example, new techniques for analyzing large size of data and gleaning new business insights from that analysis is part of what is known as big data. Big data also hold the promise of supporting a wide range of medical and healthcare functions, including among others disease surveillance, clinical decision support and population health management. Hence, effective big data based knowledge management system is needed for monitoring of patients and identify the clinical decisions to the doctor. The chapter proposes a big data based knowledge management system to develop the clinical decisions. The proposed knowledge system is developed based on variety of databases such as Electronic Health Record (EHR), Medical Imaging Data, Unstructured Clinical Notes and Genetic Data. The proposed methodology asynchronously communicates with different data sources and produces many alternative decisions to the doctor.

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Privacy models for big data: a survey

Cited by 70 publications

References 46 publications

Research on Business Intelligence with Data Mining Applications

Research on Business Intelligence with Data Mining Applications

Health data analytics using scalable logistic regression with stochastic gradient descent

Big Data Knowledge System in Healthcare

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