SavedAdd your journal

Journals / Doğuş Üniversitesi Dergisi / 2005 / Cilt: 6 - Sayı: 2

Bloom Filtreleri: Çeşitleri ve Analizi

Bloom's Filters: Their Types and Analysis

Ayşe SALMAN
Pages
268–278
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In this paper, we will examine and analyse the different types of this filter.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this paper we discuss Bloom filter in its original form and the varieties of its extensions.
📊
Key Findings & Data:Although it was devised in 1970 for the purpose of spell checking, it was seldom used except in database optimization.
💡
Conclusion & Impact:In this paper, we will examine and analyse the different types of this filter.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In this paper we discuss Bloom filter in its original form and the varieties of its extensions. A Bloom filter is a randomized data-structure for concisely representing a set in order to support approximate membership queries. Although it was devised in 1970 for the purpose of spell checking, it was seldom used except in database optimization. In recent years, it has been rediscovered by the networking community, and has become a key component in many networking systems applications. In this paper, we will examine and analyse the different types of this filter.

Özet

Bloom filtrelerini ve çeşitlerini inceleyen bir çalışmanın özetidir. Bloom filtresi sorgulama üyeliklerini desteklemek amacıyla setleri temsil eden rasgele bir veri yapısıdır. 1970'lerde daha çok veri tabanı optimizasyonlarında kullanılmıştır. Bu yakınlarda bilgisayar ağları ile ilgili çalışma yapanlar daha sık kullanmaya başlamıştır. Bu çalışmada filtrelerin çeşitleri analiz edilecektir.

🔖

Cite This Article

SALMAN, A. (2005). Bloom Filtreleri: Çeşitleri ve Analizi. Doğuş Üniversitesi Dergisi, Cilt: 6 - Sayı: 2, 268–278. https://ajindex.com/articles/129071

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (14)

  1. BLOOM, B. (1970). Space/time tradeoffs in hash coding with allowable errors. Communications of the ACM, 13(7). BRODERY, A. & MITZENMACHERZ, M. (2002). Network applications of Bloom Filters: a survey. Proceedings of 40th Annual Allerton Conference. Also available at _____http://www.eecs.harvard.edu/_michaelm>
  2. BYERS, J., CONSIDINE, J., MITZENMACHER, M., & ROST, S. (2002). Informed content delivery across adaptive overly networks. Proceedings of ACM SIGCOMM 2002, pp. 47-60.
  3. CARTER, L., & WEGMAN, M. (1979). Universal classes of hash functions. Journal of Computer and System Sciences, pp. 143-154. CS223 Final Project Report. CHANG, F., FENG, W. & LI, K. (2004). Approximate caches for packet classification NSF Grant EIA-0130344, IEEE INFOCOM 2004.
  4. CZERWINSKI, S., ZHAO, B.Y., HODES, T., JOSEPH, A.D., & KATZ, R. (1999). An architecture for a secure service discovery service. Proceedings of MobiCom- 99, pp. 24-35.
  5. DHARMAPURIKAR, S., KRISHNAMURTHY, P., & TAYLOR, D. (2003). Longest prefix matching using Bloom Filters. Proceedings of the ACM SIGCOMM, pp. 201-212.
  6. FAN, L., P. CAO, J. ALMEIDA, & BRODER, A. Z. (2000). Summary cache: a scalable wide-area web cache sharing protocol. IEEE/ACM Transactions on Networking, 8(3), pp. 281-293.
  7. FELDMANN, A., & MUTHUKRISHNAN, S. (2000).
  8. Tradeoffs for packet classification, IEEE INFOCOM, 2000.
  9. FENG, W., KANDLUR, D., SAHA D., SHIN, K., BLUE (1999). A new class of active queue management algorithms, U. Michigan CSE-TR-387-99.
  10. FENG, W. & KANDLUR, D.D. (2001). Stochastic fair blue: a queue management algorithm for enforcing fairness. Proceedings of IEEE INFOCOM 2001.
  11. HSIAO, P. & HUANG, J. (2001). Geographical region summary service for wireless routing. MobiHoc 2001,
  12. Long Beach, CA, USA, pp. 263-266. Also available at _____http://www.eecs.harvard.edu/~shawn/papers/courses/cs223_ final.pdf> as CS223 Final Project Report. HSIAO, P (2001). Geographical region summary service for geographical routing. Mobile Computing and Communications Review, 5(4), pp. 25-39.
  13. IAMNITCHI, A., RIPEANU, M. & FOSTER, I. (2002). Locating data in (Small-World?) peer-to-peer scientific collaborations. 1st International Workshop on Peer-to-Peer Systems IPTPS 2002. Also available at http://arxiv.org/abs/cs/0209031.
  14. KOLONIARI, G. & PITOURA, E. (2003). Bloom-based filters for hierarchical data. 5th Workshop on Distributed Data Structures and Algorithms (WDAS'03). -----. (2004). Filters for XML-based service discovery in pervasive computing. The Computer Journal,