SpaGRID: A Spatial Grid Framework for High Dimensional Medical Databases

Kaur, Harleen; Chauhan, Ritu; Alam, M. Afshar; Aljunid, Syed Mohamed; Salleh, Mohd Najib Mohd

doi:10.1007/978-3-642-28942-2_62

Cited by 19 publications

(13 citation statements)

References 24 publications

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“…Eventually, to deal with complexity of big data researchers and scientists are laying down numerous efforts to ascertain new techniques for retrieval of hidden information. Although analysis of big data lay challenges and offers knowledge which can benefit several application domains which include marketing, political elections, clinical data, sports, media, micro array , imaging data, geographic information data, sensor data, social media, remote sensing and online transactional system [12,14,15,17,19,23,25,26]. The benefits of big data are abundant and far exceeding, so appropriate data analytics technology to discover hidden facts and knowledge from large volumes of data is required [4].…”

Section: Framework For Big Data Analyticsmentioning

confidence: 99%

“…As we know the large volume of data collected from online database system may require storage as well as fast computation technology to handle complexity of data and requires algorithms which can interfere in developing future decision making policy [7,9,11,13,20,25]. For this chapter, several features of clinical big data is reviewed and determine different methods used for manipulation and analysis of these datasets.…”

Section: Introductionmentioning

confidence: 99%

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A Spectrum of Big Data Applications for Data Analytics

Chauhan¹,

Kaur

2015

Adaptation, Learning, and Optimization

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Abstract. As technology is gaining its insights, vast amount of data is getting collected from various resources. Foremost complex nature of data is providing challenging task among the researchers to store, process and analyze big data. At present, big data analytics tends to be an emerging domain which potentially has limitless opportunities for possible future outcomes. However, big data mining provides application capabilities to extract hidden information from large volumes of data for knowledge discovery process. In fact big data mining is demonstration varied challenges and vast opportunity among researchers and scientist for another upcoming decade. This chapter provides broad view of big data in medical application domain. In addition, a framework which can handle big data by using several preprocessing and data mining technique to discover hidden knowledge from large scale databases is designed and implemented. The proposed chapter also discuss the challenges in big data to gain insight knowledge for future outcomes.

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Section: Framework For Big Data Analyticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Spectrum of Big Data Applications for Data Analytics

Chauhan¹,

Kaur

2015

Adaptation, Learning, and Optimization

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“…The lower layer of KD model which contains solid box is a composition of SpaGrid framework which consists of proposed spatial clustering algorithm which is determined by integrated approach of several spatial data mining techniques [9]. To measures the interestingness of cluster retrieved several apprehension are justified with domain expert knowledge.…”

Section: A Knowledge Driven (Kd) Model To Extract Interesting CLmentioning

confidence: 99%

“…Octree has being utilized as spatial data structure due its capability to handle data in three dimensional spaces [9]. Each node in octree divides itself recursively to each eight leaf nodes.…”

Section: Spatial Algorithm With Similarity (Spas)mentioning

confidence: 99%

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A Knowledge Driven Model: Extract Knowledge from High Dimensional Medical Databases

Chauhan

Kaur

2013

2013 International Conference on Machine Intelligence and Research Advancement

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The extensive amount of spatial databases accumulated from various computed technology requires automated data mining tools to discover hidden and novel information from large and complex databases. In other words, the major concern is high dimensionality and complexity of spatial data which has created serious concerns among the researchers to retrieve effective and efficient clusters from large and complex spatial features. In this paper we have proposed a spatial clustering algorithm (SPAS) and Knowledge driven framework to discover clusters of variant shapes and size with domain specific knowledge. The application of our proposed algorithm is tested on real world spatial medical databases collected from SEER datasets which has record of Lung cancer patients from the year 1975 -2008. The case includes information on patient's gender, ZIP code of a patient's residence, year of diagnosis, primary site, stage at diagnosis, and age group. Each record represents a diagnosed cancer case assigned to the patient's residence at time of diagnosis. The objective of study was to discover effective and efficient spatial clusters with domain specific knowledge for futuristic decision making.

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