Trie memory

Fredkin, Edward

doi:10.1145/367390.367400

Cited by 866 publications

(421 citation statements)

References 4 publications

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“…These P suffixes are searched at the broker to determine which machine should handle each query (more precisely, we search for each of the two lexicographical extremes of the query). The CPU cost per pattern can be made O(m) by using, for example, a trie data structure for the P strings [26]. After determining the correct processor for each query, all the queries are routed to their corresponding processors in a single superstep.…”

Section: Global Suffix Arraysmentioning

confidence: 99%

Distributed text search using suffix arrays

Arroyuelo¹,

Bonacic²,

Gil-Costa³

et al. 2014

Parallel Computing

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Text search is a classical problem in Computer Science, with many data-intensive applications. For this problem, suffix arrays are among the most widely known and used data structures, enabling fast searches for phrases, terms, substrings and regular expressions in large texts. Potential application domains for these operations include large-scale search services, such as Web search engines, where it is necessary to efficiently process intensive-traffic streams of on-line queries. This paper proposes strategies to enable such services by means of suffix arrays. We introduce techniques for deploying suffix arrays on clusters of distributed-memory processors and then study the processing of multiple queries on the distributed data structure. Even though the cost of individual search operations in sequential (non-distributed) suffix arrays is low in practice, the problem of processing multiple queries on distributed-memory systems, so that hardware resources are used efficiently, is relevant to services aimed at achieving high query throughput at low operational costs. Our theoretical and experimental performance studies show that our proposals are suitable solutions for building efficient and scalable on-line search services based on suffix arrays. IntroductionIn the last decade, the design of efficient data structures and algorithms for textual databases and related applications has received a great deal of attention, due to the rapid growth of the amount of text data available from different sources. Typical applications support text searches over big text collections in a client-server fashion, where the user queries are answered by a dedicated server [15]. The server efficiency-in terms of running time-is of paramount importance in cases where the services demanded by clients generate a heavy work load. A feasible way to overcome the limitations of sequential computers is to resort to the use of several computers, or processors, which work together to serve the ever increasing client demands [19].One such approach to efficient parallelization is to distribute the data onto the processors, in such a way that it becomes feasible to exploit locality via parallel processing of user requests, each on a subset of the data. As opposed to shared-memory models, this distributed-memory model provides the benefit of better * Corresponding author. Address: Av. España 1680, Valparaíso, Chile. Phone: +56 2 432 6722. Fax: +56 2 432 6702. , in distributed memory systems, and describes strategies to reduce the inter-processor communication and to improve the load balance at search time. Indexed Text SearchingThe advent of powerful processors and cheap storage has enabled alternative models for information retrieval, other than the traditional one of a collection of documents indexed by a fixed set of keywords. One is the full text model, in which the user expresses its information need via words, phrases or patterns to be matched for, and the information system retrieves those documents containing the user-specified pa...

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Section: Global Suffix Arraysmentioning

confidence: 99%

Distributed text search using suffix arrays

Arroyuelo¹,

Bonacic²,

Gil-Costa³

et al. 2014

Parallel Computing

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“…We distinguish between the input row types of the input matrix M and the output row types of the output matrix M . Note that in the beginning we can compute the input row types of M in O(nm) time using a trie [11], but the output row types are unknown. By the definition of Pattern-Guided k-Anonymity, each output row type R has to match a pattern vector v ∈ P .…”

Section: Complexity and Algorithmsmentioning

confidence: 99%

Pattern-Guided k-Anonymity

Bredereck

Nichterlein

Niedermeier

2013

Frontiers in Algorithmics and Algorithmic Aspects in Information and Management

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Abstract. We suggest a user-oriented approach to combinatorial data anonymization. A data matrix is called k-anonymous if every row appears at least k times-the goal of the NP-hard k-Anonymity problem then is to make a given matrix k-anonymous by suppressing (blanking out) as few entries as possible. We describe an enhanced k-anonymization problem called Pattern-Guided k-Anonymity where the users can express the differing importance of various data features. We show that PatternGuided k-Anonymity remains NP-hard. We provide a fixed-parameter tractability result based on a data-driven parameterization and, based on this, develop an exact ILP-based solution method as well as a simple but very effective greedy heuristic. Experiments on several real-world datasets show that our heuristic easily matches up to the established "Mondrian" algorithm for k-Anonymity in terms of quality of the anonymization and outperforms it in terms of running time.

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“…When a user types a command, it usually depends on the previous typed commands and it is related to the following commands. According to this aspect, and as it was used in [14], in order to get the most representative set of subsequences from the acquired sequence, the use of a trie data structure [15] is proposed. This structure is also proposed in [16] to learn a team behavior and in [17] to classify the behavior patterns of a RoboCup soccer simulation team.…”

Section: Construction Of the User Behavior Profilesmentioning

confidence: 99%

Creating User Profiles from a Command-Line Interface: A Statistical Approach

Iglesias

Ledezma

Sanchís

2009

User Modeling, Adaptation, and Personalization

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Abstract. Knowledge about computer users is very beneficial for assisting them, predicting their future actions or detecting masqueraders. In this paper, an approach for creating and recognizing automatically the behavior profile of a user from the commands (s)he types in a commandline interface, is presented.Specifically, in this research, a computer user behavior is represented as a sequence of UNIX commands. This sequence is transformed into a distribution of relevant subsequences in order to find out a profile that defines its behavior. Then, statistical methods are used for recognizing a user from the commands (s)he types. The experiment results, using 2 different sources of UNIX command data, show that a system based on our approach can efficiently recognize a UNIX user. In addition, a comparison with a HMM-base method is done.Because a user profile usually changes constantly, we also propose a method to keep up to date the created profiles using an age-based mechanism.

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Trie memory

Cited by 866 publications

References 4 publications

Distributed text search using suffix arrays

Distributed text search using suffix arrays

Pattern-Guided k-Anonymity

Creating User Profiles from a Command-Line Interface: A Statistical Approach

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