Computation Analysis for Finding Co-Location Patterns using Map-Reduce Framework

Sheshikala, M.; Rao, D. Rajeswara; Prakash, Ravi

doi:10.17485/ijst/2017/v10i8/106709

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…In [3], the problem studied was to find statistically significant co-location patterns based on hypothesis testing, where some models are assumed which limits its application scope. In [30], [23] and [29], [2], [21], Map Reduce based methods and parallel algorithms on GPU were developed for the co-location pattern mining problem, respectively. In [11], [10], it was studied to find co-location patterns where a set C of spatial labels corresponds to a pattern if the clusterings each based on the objects with a spatial label in C have at least a certain degree of overlap which is captured by the area intersected by the polygons formed based on the clusters.…”

Section: Conclusion On Resultsmentioning

confidence: 99%

Fraction-Score: A New Support Measure for Co-location Pattern Mining

Chan

Long

Yan

et al. 2019

2019 IEEE 35th International Conference on Data Engineering (ICDE)

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Co-location patterns are well-established on spatial objects with categorical labels, which capture the phenomenon that objects with certain labels are often located in close geographic proximity. Similar to frequent itemsets, co-location patterns are defined based on a support measure which quantifies the popularity (or prevalence) of a pattern candidate (a label set). Quite a few support measures exist for defining co-location patterns and they share an idea of counting the number of instances of a given label set C as its support, where an instance of C is an object set whose objects carry all the labels in C and are located close to one another. Unfortunately, these measures suffer from various weaknesses, e.g., some fail to capture all possible instances while some others overlook the cases when multiple instances overlap. In this paper, we propose a new measure called Fraction-Score whose idea is to count instances fractionally if they overlap. Compared to existing measures, Fraction-Score not only captures all possible instances, but also handles the cases where instances overlap appropriately (so that the supports defined are more meaningful and consistent with the desirable antimonotonicity property). To solve the co-location pattern mining problem based on Fraction-Score, we develop efficient algorithms which are significantly faster than a baseline that adapts the stateof-the-art. We conduct extensive experiments using both real and synthetic datasets, which verified the superiority of Fraction-Score and also the efficiency of our developed algorithms.

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Section: Conclusion On Resultsmentioning

confidence: 99%

Fraction-Score: A New Support Measure for Co-location Pattern Mining

Chan

Long

Yan

et al. 2019

2019 IEEE 35th International Conference on Data Engineering (ICDE)

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“…In [36], Yang et al studied the problem of finding the co-location patterns with or without rare features. In [41], [29] (resp. [40], [2], [27]), MapReduce…”

Section: Variants Of Co-location Pattern Miningmentioning

confidence: 99%

Fraction-Score: A Generalized Support Measure for Weighted and Maximal Co-Location Pattern Mining

Chan,

Long,

Yan

et al. 2024

IEEE Trans. Knowl. Data Eng.

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“…Table 2 condenses the primary created ways to deal with help remote access systems cutting and virtualization. The idea of system virtualization substrate (NVS) is presented in [6], as appeared in Figure 2. It proposes a cut scheduler, which progressively allots MAC layer assets to cuts with the goal that each cut (administrator) can accomplish its saved physical asset units or ensured throughput rate.…”

Section: Problem Statement and Related Workmentioning

confidence: 99%

An Enhanced on Bidirectional LI-FI Attocell Access Point Slicing and Virtualization using Das2 Conspire

2019

ijrte

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LiFi attocell get to systems will be conveyed wherever to help different applications and administration provisioning to different end-clients. The LiFi foundation suppliers should offer LiFi passages (APs) assets as an administration. This, be that as it may, requires an exploration test to be fathomed to progressively and adequately allot assets amonspecialist co-ops (SPs), while ensuring execution detachment among them and their separate clients. This paper presents an autonomic asset cutting (virtualization) conspire, which acknowledges autonomic administration and setup of virtual APs, in a LiFi Attocell get to arrange, in light of SP’s and their clients benefit necessities. The proposed asset cutting plan gathers and breaks down the movement insights of the distinctive applications upheld on the cuts characterized in each LiFi AP and appropriates the accessible assets reasonably and relatively among them. It utilizes a control calculation to change the base conflict window of client gadgets to accomplish the objective throughput and guarantee broadcast appointment decency among SP’s and their clients.

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Computation Analysis for Finding Co-Location Patterns using Map-Reduce Framework

Cited by 10 publications

References 11 publications

Fraction-Score: A New Support Measure for Co-location Pattern Mining

Fraction-Score: A New Support Measure for Co-location Pattern Mining

Fraction-Score: A Generalized Support Measure for Weighted and Maximal Co-Location Pattern Mining

An Enhanced on Bidirectional LI-FI Attocell Access Point Slicing and Virtualization using Das2 Conspire

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