Association rules extraction for the identification of functional dependencies in complex technical infrastructures

Antonello, Federico; Baraldi, Piero; Shokry, Ahmed; Zio, Enrico; Gentile, Ugo; Serio, L.

doi:10.1016/j.ress.2020.107305

Cited by 10 publications

(14 citation statements)

References 27 publications

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“…The above-described techniques for producing the suggestions are covering almost all cases. Recently, we have also analysed the usage of Functional Dependencies and their imprecise/ relaxed and/or precise approaches [4,12,13,27]. Those approaches are mainly focussed on identifying the complexity of relationships on data models.…”

Section: Considerations On Functional Dependenciesmentioning

confidence: 99%

Multi Clustering Recommendation System for Fashion Retail

Bellini

Palesi

Nesi

et al. 2022

Multimed Tools Appl

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Fashion retail has a large and ever-increasing popularity and relevance, allowing customers to buy anytime finding the best offers and providing satisfactory experiences in the shops. Consequently, Customer Relationship Management solutions have been enhanced by means of several technologies to better understand the behaviour and requirements of customers, engaging and influencing them to improve their shopping experience, as well as increasing the retailers’ profitability. Current solutions on marketing provide a too general approach, pushing and suggesting on most cases, the popular or most purchased items, losing the focus on the customer centricity and personality. In this paper, a recommendation system for fashion retail shops is proposed, based on a multi clustering approach of items and users’ profiles in online and on physical stores. The proposed solution relies on mining techniques, allowing to predict the purchase behaviour of newly acquired customers, thus solving the cold start problems which is typical of the systems at the state of the art. The presented work has been developed in the context of Feedback project partially founded by Regione Toscana, and it has been conducted on real retail company Tessilform, Patrizia Pepe mark. The recommendation system has been validated in store, as well as online.

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Section: Considerations On Functional Dependenciesmentioning

confidence: 99%

Multi Clustering Recommendation System for Fashion Retail

Bellini

Palesi

Nesi

et al. 2022

Multimed Tools Appl

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“…[7]. This evolution modifies the physical interconnections between the systems of the CTI, which consequently alters the functional dependencies among the components and, therefore, their criticalities [1,8,9].…”

Section: Introductionmentioning

confidence: 99%

Identification of Critical Components in the Complex Technical Infrastructure of the Large Hadron Collider Using Relief Feature Ranking and Support Vector Machines

et al. 2021

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This work proposes a data-driven methodology for identifying critical components in Complex Technical Infrastructures (CTIs), for which the functional logic and/or the system structure functions are not known due the CTI’s complexity and evolving nature. The methodology uses large amounts of CTI monitoring data acquired over long periods of time and under different operating conditions. The critical components are identified as those for which the condition monitoring signals permit the optimal classification of the CTI functioning or failed state. The methodology includes two stages: in the first stage, a feature selection filter method based on the Relief technique is used to rank the monitoring signals according to their importance with respect to the CTI functioning or failed state; the second stage identifies the subset of signals among those highlighted by the Relief technique that are most informative with respect to the CTI state. This identification is performed on the basis of evaluating the performance of a Cost-Sensitive Support Vector Machine (CS-SVM) classifier trained with several subsets of the candidate signals. The capabilities of the methodology proposed are assessed through its application to different benchmarks of highly imbalanced datasets, showing performances that are competitive to those obtained by other methods presented in the literature. The methodology is finally applied to the monitoring signals of the Large Hadron Collider (LHC) of the European Organization for Nuclear Research (CERN), a CTI for experiments of physics; the criticality of the identified components has been confirmed by CERN experts.

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“…General guidelines and conceptual definitions have been provided in Zio (2016). In this context, datadriven methods for the identification of FEDPs in CTIs using alarm data have been developed (Serio et al 2018;Antonello et al 2019;Antonello et al 2021a). They are based on the application of the Association Rule Mining (ARM) (Agrawal and Imieliński 1993;Srikant and Agrawal 1996;Witten and Frank 2016) algorithm for scanning the alarm databases and identifying associations among patterns of alarms in the form of "if (antecedent) then (consequent)" rules; from these, the FDEPs are derived.…”

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confidence: 99%

“…A main challenge in the application of the Apriori-based algorithms to alarm databases is the difficulty of identifying rare FDEPs, which are typically unknown and can be actually the most relevant for CTI vulnerability (Wang et al 2000;Kim and Yun 2016;Zio 2016;Antonello et al 2021a). Their identification typically requires the use of a small value for the minimum support threshold, which renders the search computationally unaffordable (Lin and Tseng 2006;Wulandari et al 2019) and leads to the generation of a very large set of rules, which are not strongly supported and hard to analyse for discovering vulnerabilities in the CTI (Marin et al 2008;Zhang et al 2013).…”

mentioning

confidence: 99%

“…Their identification typically requires the use of a small value for the minimum support threshold, which renders the search computationally unaffordable (Lin and Tseng 2006;Wulandari et al 2019) and leads to the generation of a very large set of rules, which are not strongly supported and hard to analyse for discovering vulnerabilities in the CTI (Marin et al 2008;Zhang et al 2013). As a consequence, a relatively large value of minimum support threshold is employed at the risk of (i) not identifying rare patterns of alarms and (ii) extracting somewhat trivial association rules, which are already known to the CTI operators (Antonello et al 2021a).…”

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confidence: 99%

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A novelty-based multi-objective evolutionary algorithm for identifying functional dependencies in complex technical infrastructures from alarm data

et al. 2022

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In this work, a Multi-Objective Evolutionary Algorithm (MOEA) is developed to identify Functional Dependencies (FDEPs) in Complex Technical Infrastructures (CTIs) from alarm data. The objectives of the search are the maximization of a measure of novelty, which drives the exploration of the solution space avoiding to get trapped in local optima, and of a measure of dependency among alarms, which drives the uncovering of functional dependencies. The main contribution of the work is the direct identification of patterns of dependent alarms; this avoids going through the preliminary step of mining association rules, as typically done by state-of-the-art methods which, however, fail to identify rare functional dependencies due to the need of setting a balanced minimum occurrence threshold. The proposed framework for FDEPs identification is applied to a synthetic alarm database generated by a simulated CTI model and to a real large-scale database of alarms collected at the CTI of CERN (European Organization for Nuclear Research). The obtained results show that the framework enables the thorough exploration of the solution space and captures also rare functional dependencies.

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Association rules extraction for the identification of functional dependencies in complex technical infrastructures

Cited by 10 publications

References 27 publications

Multi Clustering Recommendation System for Fashion Retail

Multi Clustering Recommendation System for Fashion Retail

Identification of Critical Components in the Complex Technical Infrastructure of the Large Hadron Collider Using Relief Feature Ranking and Support Vector Machines

A novelty-based multi-objective evolutionary algorithm for identifying functional dependencies in complex technical infrastructures from alarm data

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