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A survey of issues and solutions of health data management systems

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Abstract

In the recent era, data science plays an important role in the health-care domain to provide a cost-effective and better treatment procedure. To achieve this goal, the data management system has a huge contribution by controlling, arranging, storing and preprocessing a large volume of health dataset. Already there are a lot of investigation and designing of different approaches to support the big data applications in different domain. Still, management of big data is a challenging task for the data scientist due to the complex characteristics of data and demands of the application. In this survey paper, we discuss the occurring challenges and it’s possible solutions by considering the entities related to data services. It will help the data scientist to understand the supporting parameters of data storage system for designing big data management system.

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References

  1. (Last access 2018) Amazon s3. https://aws.amazon.com/s3/

  2. (Last access 2018) Cassandra. http://cassandra.apache.org/

  3. (Last access 2018) Couchdb. http://couchdb.apache.org/

  4. (Last access 2018) Disaster definitions. In: Public health guide for emergencies, pp 24–43

  5. (Last access 2018) Hbase. https://hbase.apache.org/

  6. (Last access 2018) Microsoft azure. https://docs.microsoft.com/en-us/azure/storage/storage-introduction

  7. (Last access 2018) Microsoft healthvault. https://international.healthvault.com/

  8. (Last access 2018) Mongodb. https://www.mongodb.org/

  9. (Last access 2018) Voltdb. https://voltdb.com/

  10. Abrahams J (2011) Disaster risk management for health: overview. In: Global platform, developed by the World Health Organization, United Kingdom Health Protection Agency and partners, 6 p

  11. Amazon R (2016) Amazon relational database service (Amazon RDS)

  12. Anderson JC, Lehnardt J, Slater N (2010) CouchDB: the definitive guide: time to relax. O’Reilly Media Inc, Sebastopol

    Google Scholar 

  13. Atzeni P, Bugiotti F, Rossi L (2012) Uniform access to non-relational database systems: The SOS platform. In: advanced information systems engineering, Springer, New York, pp 160–174

  14. Brown SJ (2012) Networked remote patient monitoring with handheld devices. US Patent 8,249,894

  15. Brumley R, Enguidanos S, Jamison P, Seitz R, Morgenstern N, Saito S, McIlwane J, Hillary K, Gonzalez J (2007) Increased satisfaction with care and lower costs: results of a randomized trial of in-home palliative care. J Am Geriatr Soc 55(7):993–1000

    Article  Google Scholar 

  16. Buffington J (2010) Microsoft SQL server, chapter 8. In: Data protection for virtual data centers. Wiley, pp 267–315

  17. Bugiotti F, Cabibbo L (2013) An object-datastore mapper supporting nosql database design. http://cabibbo.dia.uniroma3.it/pub/ondm.pdf

  18. Bugiotti F, Cabibbo L, Atzeni P, Torlone R (2013) A logical approach to nosql databases. http://cabibbo.dia.uniroma3.it/pub/noam.pdf

  19. Cabibbo L (2013) ONDM (Object-NoSQL Datastore Mapper). Faculty of Engineering, Roma TRE University Retrieved June 15th

  20. Chang F, Dean J, Ghemawat S, Hsieh W, Wallach D, Burrows M, Chandra T, Fikes A, Gruber R (2006) Bigtable: a distributed structured data storage system. In: 7th OSDI, pp 305–314

  21. Chen PM, Lee EK, Gibson GA, Katz RH, Patterson DA (1994) Raid: high-performance, reliable secondary storage. ACM Computing Surveys (CSUR) 26:145–185

    Article  Google Scholar 

  22. Cooper BF, Ramakrishnan R, Srivastava U, Silberstein A, Bohannon P, Jacobsen HA, Puz N, Weaver D, Yerneni R (2008) Pnuts: Yahoo!’s hosted data serving platform. Proc VLDB Endow 1(2):1277–1288

    Article  Google Scholar 

  23. Curé O, Kerdjoudj F, Duc CL, Lamolle M, Faye D (2012) On the potential integration of an ontology-based data access approach in NoSQL stores. In: 2012 Third international conference on emerging intelligent data and web technologies (EIDWT), pp 166–173

  24. Curé O, Lamolle M, Duc CL (2013) Ontology based data integration over document and column family oriented NoSQL. arXiv preprint arXiv:13072603

  25. Date CJ, White CJ (1989) A guide to DB2. Addison Wesley Publishing Company, Boston

    Google Scholar 

  26. Dean J, Ghemawat S (2008) Mapreduce: simplified data processing on large clusters. Commun ACM 51(1):107–113

    Article  Google Scholar 

  27. DeCandia G, Hastorun D, Jampani M, Kakulapati G, Lakshman A, Pilchin A, Sivasubramanian S, Vosshall P, Vogels W (2007) Dynamo: Amazon’s highly available key-value store. In: ACM SIGOPS operating systems review, ACM, vol 41

  28. Erling O (2012) Virtuoso, a hybrid RDBMS/graph column store. IEEE Data Eng Bull 35:3–8

    Google Scholar 

  29. Fan W, Huai JP (2014) Querying big data: bridging theory and practice. J Comput Sci Technol 29:849–869

    Article  MathSciNet  Google Scholar 

  30. Fernández-Alemán JL, Señor IC, Lozoya PÁO, Toval A (2013) Security and privacy in electronic health records: a systematic literature review. J Biomed Inform 46(3):541–562

    Article  Google Scholar 

  31. Fichman RG, Kohli R, Krishnan R (2011) The role of information systems in healthcare: current research and future trends. Inf Syst Res 22:419–428

    Article  Google Scholar 

  32. Gaonkar PE, Bojewar S, Das JA (2013) A survey: data storage technologies. Int J Eng Sci Innov Technol 2(2):547–554

    Google Scholar 

  33. Ghemawat S, Gobioff H, Leung ST (2003) The google file system. In: ACM SIGOPS operating systems review, ACM, vol 37

  34. Ginsberg J, Mohebbi MM, Patel RS, Brammer L, Smolinski MS, Brilliant L (2009) Detecting influenza epidemics using search engine query data. Nature 457(7232):1012–1014

    Article  Google Scholar 

  35. Greenspan J, Bulger B (2001) MySQL/PHP database applications. Wiley, New York

    MATH  Google Scholar 

  36. Groves P, Kayyali B, Knott D, Kuiken SV (2013) The ‘big data’ revolution in healthcare. McKinsey Quarterly, Seattle

    Google Scholar 

  37. Härder T (1984) Observations on optimistic concurrency control schemes. Inf Syst 9:111–120

    Article  Google Scholar 

  38. Harris S, Seaborne A, Prud’hommeaux E (2013) SPARQL 1.1 query language. W3C recommendation 21

  39. Hassanalieragh M, Page A, Soyata T, Sharma G, Aktas M, Mateos G, Kantarci B, Andreescu S (2015) Health monitoring and management using internet-of-things (IoT) sensing with cloud-based processing: Opportunities and challenges. In: 2015 IEEE international conference on services computing, IEEE, pp 285–292

  40. Hermon R, Williams PAH (2014) Big data in healthcare: what is it used for? In: Australian eHealth informatics and security conference, pp 40–49

  41. Huang T, Lan L, Fang X, An P, Min J, Wang F (2015) Promises and challenges of big data computing in health sciences. Big Data Res 2(1):2–11

    Article  Google Scholar 

  42. Jennings B (2008) disaster planning and public health. In: From Birth to Death and Bench to Clinic: the hastings center bioethics briefing book for journalists, policymakers, and Campaigns, The Hastings Center, Garrison, NY pp 41–44

  43. Ji Z, Ganchev I, O’Droma M, Zhang X, Zhang X (2014) A cloud-based x73 ubiquitous mobile healthcare system: design and implementation. Sci World J 2014:145803

  44. Kiran KV, Vijayakumar R (2014) Ontology based data integration of NoSQL datastores. In: 2014 9th international conference on industrial and information systems (ICIIS), IEEE, pp 1–6

  45. Kaur K, Rani R (2015) Managing data in healthcare information systems: many models, one solution. Computer 48(3):52–59

    Article  Google Scholar 

  46. Kulkarni G, Sutar R, Gambhir J (2012) Cloud computing-infrastructure as service-amazon ec2. Int J Eng Res Appl 2(1):117–125

  47. Kung HT, Robinson JT (1981) On optimistic methods for concurrency control. ACM Trans Database Syst (TODS) 6(2):213–226

    Article  Google Scholar 

  48. Kwong T, O’Brien A, Kwong Q, Hill K, Haswell J (2009) Medical communication skills and law made easy: the patient-centred approach. Elsevier, Amsterdam

    Google Scholar 

  49. Lin Y, Agrawal D, Chen C, Ooi BC, Wu S (2011) Llama: leveraging columnar storage for scalable join processing in the mapreduce framework. In: Proceedings of the 2011 ACM SIGMOD international conference on management of data, ACM, pp 961–972

  50. Lubbers C, Elkington S, Hess R, Sicola SJ, McCarty J, Korgaonkar A, Leveille J (2005) Flexible data replication mechanism. US Patent 6,947,981

  51. Madden S (2012) From databases to big data. IEEE Intern Comput 16(3):4–6

    Article  Google Scholar 

  52. Matthew N, Stones R (2005) Beginning databases with PostgreSQL: from novice to professional. Apress, 664 p

  53. Miller FP, Vandome AF, McBrewster J (2010) Amazon web services. Alpha Press

  54. Muro S, Kameda T, Minoura T (1984) Multi-version concurrency control scheme for a database system. J Comput Syst Sci 29:207–224

    Article  MATH  Google Scholar 

  55. Narayanam S, Wang S (2016) Oracle nosql database. https://scholarworks.bridgeport.edu/xmlui/bitstream/handle/123456789/1559/161.pdf

  56. Nicolae B (2010) Blobseer: Towards efficient data storage management for large-scale, distributed systems. PhD thesis, Université Rennes 1

  57. Paksula M (2010) Persisting objects in redis key-value database. University of Helsinki, Department of Computer Science, Helsinki

    Google Scholar 

  58. Palankar MR, Iamnitchi A, Ripeanu M, Garfinkel S (2008) Amazon s3 for science grids: a viable solution? In: Proceedings of the 2008 international workshop on data-aware distributed computing, New York, NY

  59. Paul M, Das A (2017) Health informatics as a service (HIAAS) for developing countries. In: Internet of things and big data technologies for next generation healthcare. Springer, New York, pp 251–279

  60. Proctor S (2013) Exploring the architecture of the NuoDB database, part 1. Dosegljivo Na

  61. Roijackers J, Fletcher GH (2013) On bridging relational and document-centric data stores. In: Big Data, Springer, New York, pp 135–148

  62. Rolison JJ, Hanoch Y, Wood S, Liu PJ (2014) Risk-taking differences across the adult life span: a question of age and domain. J Gerontol Ser B: Psychol Sci Soc Sci 69(6):870–880

    Article  Google Scholar 

  63. Rosenthal B (2006) Method and system for providing low cost, readily accessible healthcare. US Patent App. 11/105,220

  64. Rossi R, Hirama K (2015) Characterizing big data management. Issues Inform Sci Inf Technol 12:165–180

    Article  Google Scholar 

  65. Russom P (2013) Managing big data. TDWI Research TDWI Best Practices Report

  66. Sawarkar S (2013) Remote healthcare solution. https://www.who.int/ehealth/resources/compendium_ehealth2013_7.pdf

  67. Shih KY, Srinivasan U (2003) Method and system for data replication. US Patent 6,615,223

  68. Shvachko K, Kuang H, Radia S, Chansler R (2010) The hadoop distributed file system. In: 2010 IEEE 26th symposium on mass storage systems and technologies (MSST), pp 1–10

  69. Silva LAB, Costa C, Oliveira JL (2012) A pacs archive architecture supported on cloud services. Int J Comput Assist Radiol Surg 7(3):349–358

    Article  Google Scholar 

  70. Sivasubramanian S (2012) Amazon dynamodb: a seamlessly scalable non-relational database service. In: Proceedings of the 2012 ACM SIGMOD international conference on management of data, ACM, pp 729–730

  71. Skourletopoulos G, Mavromoustakis CX, Mastorakis G, Batalla JM, Dobre C, Panagiotakis S, Pallis E (2017) Big data and cloud computing: a survey of the state-of-the-art and research challenges. In: Advances in mobile cloud computing and big data in the 5G Era, Springer, New York, pp 23–41

  72. Tran VT, Narayanan D, Antoniu G, Bougé L (2012) Dstore: an in-memory document-oriented store. PhD thesis, INRIA

  73. Vernica R, Carey MJ, Li C (2010) Efficient parallel set-similarity joins using mapreduce. In: Proceedings of the 2010 ACM SIGMOD international conference on management of data, ACM, pp 495–506

  74. West KG, Moon JB, Colquitt NL, Weiner HS, Petersen EG, Howell WH (2003) Patient monitoring system. US Patent 6,544,174

  75. White T (2012) Hadoop: the definitive guide. O’Reilly Media Inc, Sebastopol

    Google Scholar 

  76. Wu L, Yuan L, You J (2015) Survey of large-scale data management systems for big data applications. J Comput Sci Technol 30(1):163–183

    Article  Google Scholar 

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Correspondence to Anindita Sarkar Mondal.

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Mondal, A.S., Neogy, S., Mukherjee, N. et al. A survey of issues and solutions of health data management systems. Innovations Syst Softw Eng 15, 155–166 (2019). https://doi.org/10.1007/s11334-019-00336-4

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