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DATABASE SYSTEM SUGGESTIONS FOR THE INTERNET OF THINGS (IOT) SYSTEMS

Yıl 2018, Cilt: 4 Sayı: 1, 46 - 52, 27.06.2018
https://doi.org/10.22531/muglajsci.418488

Öz

Internet of
Things (IoT) is an interconnection of different types of information assets in
which data is continuously generated and transmitted over the Internet.
Technologies of the sensor, RFID, GPS, mobile devices, and Internet-enabled
actuators play a significant role in IoT systems. IoT brings out new challenges
in terms of data and information management because it is not easy to collect
and manage a large amount of heterogeneous data that is aggregated at very high
velocity as well as to retrieve and manage the information that is hidden
within this large volume of data. In this
paper, I discuss the main factors affecting the efficiency of data management
in IoT systems, specifically query processing and transaction management. There
are many lessons learned from traditional database systems, distributed
systems, and sensor networks, however, traditional solutions are often
inadequate to meet the needs of applications in such a complex ecosystem,
namely IoT. In traditional database systems, for instance, query operations are
usually local, and execution costs depend on the current processor power and
other resource constraints (i.e. memory). On the other hand, transaction
management mechanisms guarantee the ACID properties in order to provide overall
data integrity. It is apparent that different types of IOT applications that
operate on heterogeneous, streaming, real-time, and geographically distributed
large data will significantly change the well-known aspects of querying and
transaction management. Context-aware querying, distributed querying, MapReduce
computing model and flexible transaction models such as web-based transaction
handling are some of the current issues discussed in this paper. With the succinct yet comprehensive information
presented in this work, I intend to provide a guide for researchers in the IoT
systems, especially in the context of database systems.

Kaynakça

  • [1] Welbourne E., Battle L., Cole G., Gould K., Rector K., Raymer S., Balazinska M., and Borriello G., “Building the internet of things using rfid: The rfid ecosystem experience,” Internet Computing, IEEE, vol. 13, no. 3, pp. 48–55, 2009.
  • [2] Leavitt N., “Will nosql databases live up to their promise?” Journal Computer Vol. 43, Issue 2 (2010), 12-14.
  • [3] Moniruzzaman A.B.M., Hossain S., (2013). “NoSQL Database: New Era of Databases for Big data Analytics - Classification, Characteristics and Comparison.” Int J Database Theory Appl. 6.
  • [4] Dean J., Ghemawat S., “MapReduce: A Flexible Data Processing Tool.” Commun. ACM. 53. 72-77. (2010)
  • [5] Pavlo A., Paulson E., Rasin A., Abadi D.J., DeWitt D.J., Madden S., and Stonebraker M., “A comparison of approaches to large-scale data analysis” In Proceedings of the 2009 ACM SIGMOD International Conference ACM Press, New York, 2009
  • [6] Dewitt D., and Stonebraker M., “MapReduce: A Major Step Backwards” Available: http://databasecolumn.vertica.com/database-innovation/mapreduce-a-majorstep-backwards/
  • [7] Phan T. A. M., Nurminen J. K. and Francesco M. Di, "Cloud Databases for Internet-of-Things Data," 2014 IEEE International Conference on Internet of Things (iThings), and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing(CPSCom),Taipei,117-124.
  • [8] Ramakrishnan R., and Gehrke J., Database Management Systems (Third Edition). McGraw-Hill, Boston, 2003.
  • [9] Dean J. and Ghemawat S., “Mapreduce: simplified data processing on large clusters.” Communications of the ACM 51, 1 (2008), 107-113.
  • [10] Laney D., “3-D Data Management: Controlling Data Volume, Velocity and Variety” Research Note, META Group, February 2001.
  • [11] Memishi B., Ibrahim S., Pérez M.S., Antoniu G. “Fault Tolerance in MapReduce: A Survey.” Resource Management for Big Data Platforms. Computer Communications and Networks. Springer, Cham(2016)
  • [12] Francesco C., Massimo D. S., Vincenzo M., Picariello A., Schreiber F. A., Tanca L. Data Management in Pervasive Systems, Data-Centric Systems and Applications book series (DCSA), (2015) DOI: https://doi.org/10.1007/978-3-319-20062-0
  • [13] Abu-Elkheir M., Hayajneh M., Ali NA.. “Data Management for the Internet of Things: Design Primitives and Solution”. Sensors (Basel, Switzerland). 2013;13(11):15582-15612. doi:10.3390/s131115582.
  • [14] Cooper J., and James A., “Challenges for database management in the internet of things.” IETE Technical Review, volume 26 (5):320-329 (2009) http://dx.doi.org/10.4103/0256-4602.55275
  • [15] Babu S. and Herodotou H.. “Massively Parallel Databases and MapReduce Systems” Foundations and Trends in Databases Vol. 5, No. 1 (2012) 1–104c 2013 DOI: 10.1561/1900000036
  • [16] Jonathan A., Ryden M., Oh K., Chandra A. and Weissman J., "Nebula: Distributed Edge Cloud for Data Intensive Computing," in IEEE Transactions on Parallel and Distributed Systems, vol. 28, no. 11, pp. 3229-3242, Nov. 1 2017.
  • [17] Arasu A., Babu S., Widom J.,“The cql continuous query language: semantic foundations and query execution.” J. Int. J. Very Large Data Bases 15(2), 121–142 (2006)
  • [18] Chandrasekaran S., Cooper O., Deshpande A., Franklin M.J., Hellerstein J.M., Hong W., Krishnamurthy S., Madden S.R., Reiss F., Shah M.A.,: “Telegraphcq: continuous dataflow processing.” In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, pp. 668–668. ACM, New York (2003)
  • [19] Chen J., DeWitt D.J., Tian F., Wang Y., “Niagaracq: a scalable continuous query system for internet databases.” In: ACM SIGMOD Record, vol. 29, pp. 379–390. ACM, New York (2000)
  • [20] Apache Kafka. Available: https://kafka.apache.org/intro
  • [21] Bartholomew D., “Sql vs. nosql”. Linux Journal 2010, 195 (2010), 4.
  • [22] Hecht R. and Jablonski S., “Nosql evaluation: A use case oriented survey” In Cloud and Service Computing (CSC), 2011 International Conference, IEEE, pp. 336-341.
  • [23] Liebig T., Vialard V., Opitz M., and Metzl S., “GraphScale: Adding Expressive Reasoning to Semantic Data Stores.” Demo Proceedings of the 14th International Semantic Web Conference (ISWC 2015)
  • [24] Satoh I., "MapReduce-Based Data Processing on IoT," International Conference on Internet of Things (iThings), and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom), Taipei, 2014, pp. 161-168.

IOT SİSTEMLERİ İÇİN VERİTABANI SİSTEM ÖNERİLERİ

Yıl 2018, Cilt: 4 Sayı: 1, 46 - 52, 27.06.2018
https://doi.org/10.22531/muglajsci.418488

Öz

Nesnelerin İnterneti (IoT), verilerin sürekli olarak
üretilip İnternet üzerinden iletildiği farklı tip bilgi kaynaklarından oluşan
bir ağdır. Sensörler, telsiz frekans tanıma (RFID) cihazları, küresel
konumlandırma sistemleri (GPS), mobil cihazlar ve Internet özellikli aktüatör
teknolojileri IoT sistemlerinde önemli bir rol oynamaktadır. IoT, veri ve bilgi
yönetimi açısından yeni zorluklar getiriyor, çünkü çok yüksek hızda üretilen
büyük miktarda heterojen veriyi toplamak ve işlemenin zorluğu yanında, bu büyük
veride gizlenen bilgileri almak ve yönetmek de kolay değildir. 
Bu makalede, IoT sistemlerinde veri işleme verimliliğini
etkileyen temel faktörleri, özellikle sorgulama ve hareket yönetimini ele
alıyorum. Geleneksel veri tabanı sistemlerinden, dağıtık sistemlerden ve sensör
ağlarından öğrenilen çok sayıda dersler vardır, ancak geleneksel çözümler, IoT
gibi karmaşık bir ekosistemdeki uygulamaların ihtiyaçlarını karşılamada
çoğunlukla yetersiz kalmaktadır. Geleneksel veri tabanı sistemlerinde, örneğin,
sorgulama işlemleri, genellikle yereldir ve yürütme maliyetleri mevcut işlemci
gücü ve bellek gibi kaynak kısıtlamalarına bağlıdır. Diğer taraftan geleneksel
hareket yönetimi mekanizmaları, genel veri bütünlüğünü sağlamak için ACID
özelliklerini garanti eder. Heterojen, sürekli, gerçek-zamanlı ve coğrafi
olarak dağınık büyük veri üzerinde çalışan farklı tip IoT uygulamalarının,
sorgulama işleminin ve hareket yönetiminin iyi bilinen yönlerini önemli ölçüde
değiştireceği açıktır. İçeriğe duyarlı sorgulama, dağıtılmış sorgulama,
MapReduce hesaplama modeli ve web tabanlı hareket yönetimi gibi esnek işlem
modelleri bu makalede ele alınan güncel konulardan bazılarıdır. 
Bu çalışmadaki kısa fakat kapsamlı bilgilerle,
IoT sistemlerinde, özellikle veri tabanı sistemleri üzerine, çalışan
araştırmacılar için bir kılavuz sağlamayı amaçladım.

Kaynakça

  • [1] Welbourne E., Battle L., Cole G., Gould K., Rector K., Raymer S., Balazinska M., and Borriello G., “Building the internet of things using rfid: The rfid ecosystem experience,” Internet Computing, IEEE, vol. 13, no. 3, pp. 48–55, 2009.
  • [2] Leavitt N., “Will nosql databases live up to their promise?” Journal Computer Vol. 43, Issue 2 (2010), 12-14.
  • [3] Moniruzzaman A.B.M., Hossain S., (2013). “NoSQL Database: New Era of Databases for Big data Analytics - Classification, Characteristics and Comparison.” Int J Database Theory Appl. 6.
  • [4] Dean J., Ghemawat S., “MapReduce: A Flexible Data Processing Tool.” Commun. ACM. 53. 72-77. (2010)
  • [5] Pavlo A., Paulson E., Rasin A., Abadi D.J., DeWitt D.J., Madden S., and Stonebraker M., “A comparison of approaches to large-scale data analysis” In Proceedings of the 2009 ACM SIGMOD International Conference ACM Press, New York, 2009
  • [6] Dewitt D., and Stonebraker M., “MapReduce: A Major Step Backwards” Available: http://databasecolumn.vertica.com/database-innovation/mapreduce-a-majorstep-backwards/
  • [7] Phan T. A. M., Nurminen J. K. and Francesco M. Di, "Cloud Databases for Internet-of-Things Data," 2014 IEEE International Conference on Internet of Things (iThings), and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing(CPSCom),Taipei,117-124.
  • [8] Ramakrishnan R., and Gehrke J., Database Management Systems (Third Edition). McGraw-Hill, Boston, 2003.
  • [9] Dean J. and Ghemawat S., “Mapreduce: simplified data processing on large clusters.” Communications of the ACM 51, 1 (2008), 107-113.
  • [10] Laney D., “3-D Data Management: Controlling Data Volume, Velocity and Variety” Research Note, META Group, February 2001.
  • [11] Memishi B., Ibrahim S., Pérez M.S., Antoniu G. “Fault Tolerance in MapReduce: A Survey.” Resource Management for Big Data Platforms. Computer Communications and Networks. Springer, Cham(2016)
  • [12] Francesco C., Massimo D. S., Vincenzo M., Picariello A., Schreiber F. A., Tanca L. Data Management in Pervasive Systems, Data-Centric Systems and Applications book series (DCSA), (2015) DOI: https://doi.org/10.1007/978-3-319-20062-0
  • [13] Abu-Elkheir M., Hayajneh M., Ali NA.. “Data Management for the Internet of Things: Design Primitives and Solution”. Sensors (Basel, Switzerland). 2013;13(11):15582-15612. doi:10.3390/s131115582.
  • [14] Cooper J., and James A., “Challenges for database management in the internet of things.” IETE Technical Review, volume 26 (5):320-329 (2009) http://dx.doi.org/10.4103/0256-4602.55275
  • [15] Babu S. and Herodotou H.. “Massively Parallel Databases and MapReduce Systems” Foundations and Trends in Databases Vol. 5, No. 1 (2012) 1–104c 2013 DOI: 10.1561/1900000036
  • [16] Jonathan A., Ryden M., Oh K., Chandra A. and Weissman J., "Nebula: Distributed Edge Cloud for Data Intensive Computing," in IEEE Transactions on Parallel and Distributed Systems, vol. 28, no. 11, pp. 3229-3242, Nov. 1 2017.
  • [17] Arasu A., Babu S., Widom J.,“The cql continuous query language: semantic foundations and query execution.” J. Int. J. Very Large Data Bases 15(2), 121–142 (2006)
  • [18] Chandrasekaran S., Cooper O., Deshpande A., Franklin M.J., Hellerstein J.M., Hong W., Krishnamurthy S., Madden S.R., Reiss F., Shah M.A.,: “Telegraphcq: continuous dataflow processing.” In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, pp. 668–668. ACM, New York (2003)
  • [19] Chen J., DeWitt D.J., Tian F., Wang Y., “Niagaracq: a scalable continuous query system for internet databases.” In: ACM SIGMOD Record, vol. 29, pp. 379–390. ACM, New York (2000)
  • [20] Apache Kafka. Available: https://kafka.apache.org/intro
  • [21] Bartholomew D., “Sql vs. nosql”. Linux Journal 2010, 195 (2010), 4.
  • [22] Hecht R. and Jablonski S., “Nosql evaluation: A use case oriented survey” In Cloud and Service Computing (CSC), 2011 International Conference, IEEE, pp. 336-341.
  • [23] Liebig T., Vialard V., Opitz M., and Metzl S., “GraphScale: Adding Expressive Reasoning to Semantic Data Stores.” Demo Proceedings of the 14th International Semantic Web Conference (ISWC 2015)
  • [24] Satoh I., "MapReduce-Based Data Processing on IoT," International Conference on Internet of Things (iThings), and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom), Taipei, 2014, pp. 161-168.
Toplam 24 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Mustafa Utku Kalay 0000-0002-8002-0268

Yayımlanma Tarihi 27 Haziran 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 4 Sayı: 1

Kaynak Göster

APA Kalay, M. U. (2018). DATABASE SYSTEM SUGGESTIONS FOR THE INTERNET OF THINGS (IOT) SYSTEMS. Mugla Journal of Science and Technology, 4(1), 46-52. https://doi.org/10.22531/muglajsci.418488
AMA Kalay MU. DATABASE SYSTEM SUGGESTIONS FOR THE INTERNET OF THINGS (IOT) SYSTEMS. Mugla Journal of Science and Technology. Haziran 2018;4(1):46-52. doi:10.22531/muglajsci.418488
Chicago Kalay, Mustafa Utku. “DATABASE SYSTEM SUGGESTIONS FOR THE INTERNET OF THINGS (IOT) SYSTEMS”. Mugla Journal of Science and Technology 4, sy. 1 (Haziran 2018): 46-52. https://doi.org/10.22531/muglajsci.418488.
EndNote Kalay MU (01 Haziran 2018) DATABASE SYSTEM SUGGESTIONS FOR THE INTERNET OF THINGS (IOT) SYSTEMS. Mugla Journal of Science and Technology 4 1 46–52.
IEEE M. U. Kalay, “DATABASE SYSTEM SUGGESTIONS FOR THE INTERNET OF THINGS (IOT) SYSTEMS”, Mugla Journal of Science and Technology, c. 4, sy. 1, ss. 46–52, 2018, doi: 10.22531/muglajsci.418488.
ISNAD Kalay, Mustafa Utku. “DATABASE SYSTEM SUGGESTIONS FOR THE INTERNET OF THINGS (IOT) SYSTEMS”. Mugla Journal of Science and Technology 4/1 (Haziran 2018), 46-52. https://doi.org/10.22531/muglajsci.418488.
JAMA Kalay MU. DATABASE SYSTEM SUGGESTIONS FOR THE INTERNET OF THINGS (IOT) SYSTEMS. Mugla Journal of Science and Technology. 2018;4:46–52.
MLA Kalay, Mustafa Utku. “DATABASE SYSTEM SUGGESTIONS FOR THE INTERNET OF THINGS (IOT) SYSTEMS”. Mugla Journal of Science and Technology, c. 4, sy. 1, 2018, ss. 46-52, doi:10.22531/muglajsci.418488.
Vancouver Kalay MU. DATABASE SYSTEM SUGGESTIONS FOR THE INTERNET OF THINGS (IOT) SYSTEMS. Mugla Journal of Science and Technology. 2018;4(1):46-52.

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