In-memory hash tables for accumulating text vocabularies

Zobel, Justin; Heinz, Steffen; Williams, Hugh E.

doi:10.1016/s0020-0190(01)00239-3

Cited by 38 publications

(22 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The key goal of our experiments is to investigate how our algorithm improves the flow state lookup efficiency. The hash function we use is the bit-wise hash function from Zobel et al [7]. …”

Section: Discussionmentioning

confidence: 99%

“…The conventional approach only focuses on organizing the hash chain according to the first packet of each flow, and fails to exploit the locality within each burst. To address this shortcoming and inspired by Zobel's idea in accumulating text vocabularies [7], we propose three different algorithms focusing on exploiting the locality within a burst. All of these three schemes share the same feature that each matched flow will trigger a restructure of the hash chain, i.e., moving the matched flow to the beginning of the list.…”

Section: Proposed Schemes For Flow State Table Managementmentioning

confidence: 99%

See 1 more Smart Citation

Exploiting Traffic Localities for Efficient Flow State Lookup

Peng

Leckie

Ramamohanarao

2005

NETWORKING 2005. Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile A

View full text Add to dashboard Cite

Abstract. Flow state tables are an essential component for improving the performance of packet classification in network security and traffic management. Generally, a hash table is used to store the state of each flow due to its fast lookup speed. However, hash table collisions can severely reduce the effectiveness of packet classification using a flow state table. In this paper, we propose three schemes to reduce hash collisions by exploiting the locality in traffic. Our experiments show that all our proposed schemes perform better than the standard practice of hashing with overflow chains. More importantly, our move and insert to front scheme is insensitive to the hash table size.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Proposed Schemes For Flow State Table Managementmentioning

confidence: 99%

Exploiting Traffic Localities for Efficient Flow State Lookup

Peng

Leckie

Ramamohanarao

2005

NETWORKING 2005. Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile A

View full text Add to dashboard Cite

show abstract

“…We have recently investigated variations of hashing for accumulating vocabularies and reported these results elsewhere [26]. With other string hashing functions, all versions of trees were faster.…”

Section: Comparison Of Structures 11cmmentioning

confidence: 99%

Self‐adjusting trees in practice for large text collections

2001

Self Cite

View full text Add to dashboard Cite

Splay and randomised search trees are self-balancing binary tree structures with little or no space overhead compared to a standard binary search tree. Both trees are intended for use in applications where node accesses are skewed, for example in gathering the distinct words in a large text collection for index construction. We investigate the efficiency of these trees for such vocabulary accumulation. Surprisingly, unmodified splaying and randomised search trees are on average around 25% slower than using a standard binary tree. We investigate heuristics to limit splay tree reorganisation costs and show their effectiveness in practice. In particular, a periodic rotation scheme improves the speed of splaying by 27%, while other proposed heuristics are less effective. We also report the performance of efficient bit-wise hashing and red-black trees for comparison.

show abstract

“…Hashed Index, imposes a constraint of load factor which limits the performance of hashing beyond certain pick value. In addition, enormous amount of hashing in case of collision, to find the right bucket to map the key to be retrieved or to be placed, is also the limiting factor in hashing technique [7][8][9][10][11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…Though hash table can offer rapid insertion, deletion, and search of both strings and integers, it requires a form of collision resolution to resolve cases where two or more keys are hashed to the same bucket. To resolve this, various mechanisms have been proposed like, linked lists [7] -used when number of keys is not known in advance, array hash [8] -a cache conscious scheme for previous method, open addressing -stores homogenous keys directly within bucket & gives better usage of CPU & cache [9,10]. Open addressing schemes: Linear probing, where the interval between probes is fixed [18]; quadratic probing [12] where probe interval is increased by addition of successive outputs of a polynomial to the starting value; and double hashing [12] where probe interval is computed by second hash function.…”

Section: Introductionmentioning

confidence: 99%

Efficient Dynamic Index Structure for Natural Number Intensive Application

Patel¹,

Parmar²,

Patel³

et al. 2015

IJCA

View full text Add to dashboard Cite

Wide range of indexing techniques exists in the world of relational database. Speed of data insertion & retrieval depends on the type of query and available Indexing mechanism. Prevalent mechanisms lack in terms of spacetime efficiency and simple structure, for real time applications where the database system needs to handle queries like equality search & range search. Even for simple tasks like getting data by ID, a system imposes heavy resource utilization. For example, Applications such as, telephone directory, transaction information details in banking, status about railway reservation etc., backed with relational database system that employs complex structure like B-Tree or B + -Tree. Hence in such cases, instead of those complex structures, if some lighter technique can be used, which can greatly enhance the overall performance in terms of memory

show abstract

In-memory hash tables for accumulating text vocabularies

Cited by 38 publications

References 7 publications

Exploiting Traffic Localities for Efficient Flow State Lookup

Exploiting Traffic Localities for Efficient Flow State Lookup

Self‐adjusting trees in practice for large text collections

Efficient Dynamic Index Structure for Natural Number Intensive Application

Contact Info

Product

Resources

About