Bringing efficient advanced queries to distributed hash tables

Bauer, Daniel; Hurley, Paul; Pletka, Roman; Waldvogel, Marcel

doi:10.1109/lcn.2004.32

Cited by 18 publications

(16 citation statements)

References 22 publications

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“…It is applied in networking literature [4]. [12]) and share Web cache information ( [13]). BFs have great potential for representing a set in main memory [14] in stand-alone applications.…”

Section: Fig 1: Bloom Filtermentioning

confidence: 99%

Artificial Immune System for Bloom filter Optimization

Natarajan¹,

P²,

Subramanian³

2012

IJCA

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Bloom filter is a probabilistic and space efficient data structure designed to check the membership of an element in a set. The trade-off to use Bloom filter may have configurable risk of false positives. The percentages of a false positive can be made low if the hash bit map is sufficiently massive. Spam is an unsolicited or irrelevant message sent on the internet to an outsized range of users or newsgroup. A spam word may be a list of well-known words that usually appear in spam mails. In the proposed system, Bin Bloom Filter (BBF) groups the words into number of bloom filters that have different false positive rates primarily based on the weights of the spam words. Clonal Selection Algorithm is one of the methods in Artificial Immune System (AIS) involved with computational methods inspired by the process of the biological immune system. This paper demonstrates the CSA algorithm for minimizing the total membership invalidation cost of the BBF which finds the optimal false positive rates and number of elements to be stored in bloom filters of Bin. The experimental results demonstrate the application of CSA in BBF and compare the results with Genetic Algorithm (GA).

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“…It is applied in networking literature [4]. [12]) and share Web cache information ( [13]). BFs have great potential for representing a set in main memory [14] in stand-alone applications.…”

Section: Fig 1: Bloom Filtermentioning

confidence: 99%

Artificial Immune System for Bloom filter Optimization

Natarajan¹,

P²,

Subramanian³

2012

IJCA

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“…Guo et al [8] proposed dynamic Bloom filters that can grow as needed by adding static sized Bloom filters to a list of Bloom filters as soon as the actual Bloom filter's false positive rate increases over a given threshold. Bauer et al [2] propose simple boolean set operations on Bloom filters to combine sub-queries for efficient queries on distributed hash tables. However, Bloom filters are by far not the only way to go.…”

Section: Related Workmentioning

confidence: 99%

“…We use Bloom filters to encode the social information of users as they allow efficient set operations according to [2]. As already stated, we enhance our system with this user specific information and utilise this information in the lookup procedure of potentially interesting contacts as well as in our two-way message protocol.…”

Section: Related Workmentioning

confidence: 99%

I seek for knowledge: Exploiting social properties in Mobile Ad Hoc Networks

Belle

Islam

Waldvogel

2008

2008 1st IFIP Wireless Days

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Abstract-New social networks are born each day, at a formal conference, at informal social gathering, at family reunions etc. Internet has already been playing an important role in modern socialising. But it is still not the optimal way of interaction as one has to be very active updating profiles. With the easy access to mobile devices, modern technologies have now started to adopt to new ways of socialising. mobile devices accompany their users almost all the time, they can record and observe their users behavior as well as gather information about their social circle. Therefore, they can help users to get information from contacts, that they potentially not even know. In this paper we put our efforts towards the initial design of an architecture that will sniff for information around the user's surrounding, leveraging useful answers on their demand.

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“…The first strategy requires d DHT lookups; one lookup per attribute. The intermediate result sets of these lookups are intersected to produce a final result set, either at the query initiator [8] or by pipelining the intermediate result sets through a number of nodes [9], [11], [12]. The second strategy requires only one lookup by taking only one intermediate result set to derive the query answers, but requires each key-value pair to include the complete attributes of its associated resource.…”

Section: Related Workmentioning

confidence: 99%

Multi-Attribute Range Queries on Read-Only DHT

March

Teo

2006

Proceedings of 15th International Conference on Computer Communications and Networks

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Abstract-R-DHT is a class of DHT whereby each node supports "read-only" accesses to its key-value pairs, but does not allow key-value pairs belonging to other nodes to be written on it. Recently, supporting efficient multi-attribute range queries on DHT has been an active area of research. This paper presents the design and evaluation of Midas, an approach to support multiattribute range queries on R-DHT. Midas indexes multi-attribute resources using a d-to-one mapping scheme, and optimizes a range query by searching only for available keys. Our simulation results show that Midas on R-DHT achieves a higher lookup resiliency than conventional DHT, and it has a lower cost of query processing when the query selectivity is much larger than the number of query results.

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Bringing efficient advanced queries to distributed hash tables

Cited by 18 publications

References 22 publications

Artificial Immune System for Bloom filter Optimization

Artificial Immune System for Bloom filter Optimization

I seek for knowledge: Exploiting social properties in Mobile Ad Hoc Networks

Multi-Attribute Range Queries on Read-Only DHT

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