Parallel leap: large-scale maximal pattern mining in a distributed environment

El-Hajj, Mohammad; Zaı̈ane, Osmar R.

doi:10.1109/icpads.2006.77

Cited by 28 publications

(12 citation statements)

References 14 publications

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“…parallel FP‐growth ), data locality becomes important to ensure that the latency for reads/writes are within application limits. Guaranteeing data locality requires policies that depend upon the application being modeled and the access patterns . In , the problem of ensuring locality is handled by providing APIs for co‐allocating tasks with corresponding table partitions and co‐allocating partitions of different tables.…”

Section: Discussion On Shared Data Structurementioning

confidence: 99%

Generate‐map‐reduce: An extension to map‐reduce to support shared data and recursive computations

Iyer

Sriram

2013

Concurrency and Computation

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SUMMARYIt is difficult to express the parallelism present in complex computations by using existing higher level abstractions such as MapReduce and Dryad. These computations include applications from wide variety of domains, like Artificial Intelligence, Decision Tree Algorithms, Association Rule Mining, Recommender Systems, Graph Algorithms, Clustering Algorithms, Compute Intensive Scientific Workflows, Optimization Algorithms, and so forth. Their execution graphs introduce new challenges in terms of programmer expressibility and runtime performance such as iterative and recursive computations, shared communication model, and so forth. We propose an extension to MapReduce, called Generate-Map-Reduce (GMR), targeted towards modeling these applications. GMR introduces a new Generate abstraction into the MapReduce framework that captures recursive computations. The runtime also supports iterative jobs and a distributed communication model by using shared data structures. We illustrate recursive computations with GMR by modeling complex applications such as simulated annealing, A* search, and adaptive quadrature computation that require recursive spawning of new tasks to handle variable degree of parallelism. GMR runtime supports caching of common data across iterations in memory and local disks. We illustrate how this caching helps in achieving significant speedup for iterative computations by modeling k-means clustering. Copyright

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Section: Discussion On Shared Data Structurementioning

confidence: 99%

Generate‐map‐reduce: An extension to map‐reduce to support shared data and recursive computations

Iyer

Sriram

2013

Concurrency and Computation

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“…FP-growth introduces a graphic structure to process the innovative algorithm, calculative framework and database structure. Improved algorithms based on FP-growth focus on active areas such as the distributed system [19] and the project database [20]. However, these algorithms rely too heavily on a "tree data structure".…”

Section: Association Rules Algorithmsmentioning

confidence: 99%

Towards Provenance Cloud Security Auditing Based on Association Rule Mining

Huang

2020

IJEM

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Cloud storage provides external data storage services by combining and coordinating different types of devices in a network to work collectively. However, there is always a trust relationship between users and service providers, therefore, an effective security auditing of cloud data and operational processes is necessary. We propose a trusted cloud framework based on a Cloud Accountability Life Cycle (CALC). We suggest that auditing provenance data in cloud servers is a practical and efficient method to log data, being relatively stable and easy to collect type of provenance data. Furthermore, we suggest a scheme based on user behaviour (UB) by analysing the log data from cloud servers. We present a description of rules for a UB operating system log, and put forward an association rule mining algorithm based on the Long Sequence Frequent Pattern (LSFP) to extract the UB. Finally, the results of our experiment prove that our solution can be implemented to track and forensically inspect the data leakage in an efficient manner for cloud security auditing. KEY WORDS:cloud security auditing; provenance data; log analysis; user behaviour; association rule mining algorithm.

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“…Existing algorithms such as the Count Distribution, Data Distribution, and Candidate Distribution algorithms [3], as well as the FDM [8] and Parallel-HFP-Leap [12] algorithms mine frequent patterns from distributed precise data. However, they do not handle constraints nor do they mine uncertain data.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Distributed Uncertain Data Mining for Frequent Patterns Satisfying Anti-monotonic Constraints

Leung

MacKinnon

Jiang

2014

2014 28th International Conference on Advanced Information Networking and Applications Workshops

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High volumes of uncertain data can be generated in distributed environments in many real-life biological, medical, and life science applications. As an important data mining task, frequent pattern mining helps discover frequently co-occurring items, objects, or events from these distributed databases. However, users may be interested in only some small portions of all the frequent patterns that can be mined from these databases. In this paper, we propose an intelligent computing system that (i) allows users to express their interests via the use of userspecified constraints and (ii) effectively exploits anti-monotonic properties of user-specified constraints and efficiently discovers frequent patterns satisfying these constraints from the distributed databases containing uncertain data.

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Parallel leap: large-scale maximal pattern mining in a distributed environment

Abstract: Abstract

Cited by 28 publications

References 14 publications

Generate‐map‐reduce: An extension to map‐reduce to support shared data and recursive computations

Generate‐map‐reduce: An extension to map‐reduce to support shared data and recursive computations

Towards Provenance Cloud Security Auditing Based on Association Rule Mining

Distributed Uncertain Data Mining for Frequent Patterns Satisfying Anti-monotonic Constraints

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