Fault management preventive maintenance approach in mobile networks using sequential pattern mining

Mobile networks' fault management can take advantage of Machine Learning (ML) algorithms making its maintenance more proactive and preventive. Currently, Network Operations Centers (NOCs) still operate in reactive mode, where the troubleshoot is only performed after the problem identification. The network evolution to a preventive maintenance enables the problem prevention or quick resolution, leading to a greater network and services availability, a better operational efficiency and, above all, ensures customer satisfaction. In this paper, different algorithms for Sequential Pattern Mining (SPM) and Association Rule Learning (ARL) are explored, to identify alarm patterns in a live Long Term Evolution (LTE) network, using Fault Management (FM) data. A comparative performance analysis between all the algorithms was carried out, having observed, in the best case scenario, a decrease of 3.31% in the total number of alarms and 70.45% in the number of alarms of a certain type. There was also a considerable reduction in the number of alarms per network node in a considered area, having identified 39 nodes that no longer had any unresolved alarm. These results demonstrate that the recognition of sequential alarm patterns allows taking the first steps in the direction of preventive maintenance in mobile networks.info:eu-repo/semantics/publishedVersio

Utilizing Index‑Based Periodic High Utility Mining to Study Frequent Itemsets

The potential employability in diferent applications has garnered more signifcance for Periodic High-Utility Itemset Mining (PHUIM). It is to be noted that the conventional utility mining algorithms focus on an itemset’s utility value rather than that of its periodicity in the transaction. A MEAN periodicity measure is added to the minimum (MIN) and maximum (MAX) periodicity to incorporate the periodicity feature into PHUIM in this proposed work. The MEAN-periodicity measure brings a new dimension to the periodicity factor and is arrived at by dividing itemset’s period value by the total number of transactions in that dataset. Further, an algorithm to mine Index-Based Periodic High Utility Itemset Mining (IBPHUIM) from the database using an indexing approach is also proposed in this paper. The proposed IBPHUIM algorithm employs a projectionbased technique and indexing procedure to increase memory and execution speed efciency. The proposed model avoids redundant database scans by generating sub-databases using an indexing data structure. The proposed IBPHUIM model has experimented with test datasets, and the results drawn show that the proposed IBPHUIM model performs considerably better

RDD-Eclat: Approaches to Parallelize Eclat Algorithm on Spark RDD Framework

Initially, a number of frequent itemset mining (FIM) algorithms have been designed on the Hadoop MapReduce, a distributed big data processing framework. But, due to heavy disk I/O, MapReduce is found to be inefficient for such highly iterative algorithms. Therefore, Spark, a more efficient distributed data processing framework, has been developed with in-memory computation and resilient distributed dataset (RDD) features to support the iterative algorithms. On the Spark RDD framework, Apriori and FP-Growth based FIM algorithms have been designed, but Eclat-based algorithm has not been explored yet. In this paper, RDD-Eclat, a parallel Eclat algorithm on the Spark RDD framework is proposed with its five variants. The proposed algorithms are evaluated on the various benchmark datasets, which shows that RDD-Eclat outperforms the Spark-based Apriori by many times. Also, the experimental results show the scalability of the proposed algorithms on increasing the number of cores and size of the dataset.Comment: 16 pages, 6 figures, ICCNCT 201

Characterizing transactional databases for frequent itemset mining

This paper presents a study of the characteristics of transactional databases used in frequent itemset mining. Such characterizations have typically been used to benchmark and understand the data mining algorithms working on these databases. The aim of our study is to give a picture of how diverse and representative these benchmarking databases are, both in general but also in the context of particular empirical studies found in the literature. Our proposed list of metrics contains many of the existing metrics found in the literature, as well as new ones. Our study shows that our list of metrics is able to capture much of the datasets’ inner complexity and thus provides a good basis for the characterization of transactional datasets. Finally, we provide a set of representative datasets based on our characterization that may be used as a benchmark safely.Both authors have been partially supported by TIN2017-89244-R from MINECO (Spain’s Ministerio de Economia, Industria y Competitividad) and the recognition 2017SGR-856 (MACDA) from AGAUR (Generalitat de Catalunya). Christian Lezcano is supported by Paraguay’s Foreign Postgraduate Scholarship Programme Don Carlos Antonio López (BECAL).Peer ReviewedPostprint (published version

Security assessment framework for educational ERP systems

The educational ERP systems have vulnerabilities at the different layers such as version-specific vulnerabilities, configuration level vulnerabilities and vulnerabilities of the underlying infrastructure. This research has identified security vulnerabilities in an educational ERP system with the help of automated tools; penetration testing tool and public vulnerability repositories (CVE, CCE) at all layers. The identified vulnerabilities are analyzed for any false positives and then clustered with mitigation techniques, available publicly in security vulnerability solution repository like CCE and CWE. These mitigation techniques are mapped over reported vulnerabilities using mapping algorithms. Security vulnerabilities are then prioritized based on the Common Vulnerability Scoring System (CVSS). Finally, open standards-based vulnerability mitigation recommendations are discussed

Navigating Transparent Pork Production: Analyzing Visual Attention of The Maschhoffs Website

When it comes to food, consumers are increasingly concerned with the welfare of the livestock used to produce it. To display transparency and build trust with patrons, businesses such as The Maschhoffs, a pork production company, have created online tools to answer questions about production processes. This study implemented eye tracking and survey methodologies to attain insight regarding the visual attention of website visitors. While results revealed no discernable pattern of navigation among respondents, the findings illustrated a sector of the pork industry consumers expressed interest in was the breed-to-wean section of the farm. Researchers encourage agricultural communicators to develop messaging surrounding this specific process to continue building trust with consumer audiences as well as make recommendations for utilizing eye tracking to assess visual attention to websites