Multi-Objective Design of Profit Volumes and Closeness Ratings Using MBHS Optimizing Based on the PrefixSpan Mining Approach (PSMA) for Product Layout in Supermarkets

Product layout significantly impacts consumer demand for purchases in supermarkets. Product shelf renovation is a crucial process that can increase supermarket efficiency. The development of a sequential pattern mining algorithm for investigating the correlation patterns of product layouts, solving the numerous problems of shelf design, and the development of an algorithm that considers in-store purchase and shelf profit data with the goal of improving supermarket efficiency, and consequently profitability, were the goals of this research. The authors of this research developed two types of algorithms to enhance efficiency and reach the goals. The first was a PrefixSpan algorithm, which was used to optimize sequential pattern mining, known as the PrefixSpan mining approach. The second was a new multi-objective design that considered the objective functions of profit volumes and closeness rating using the mutation-based harmony search (MBHS) optimization algorithm, which was used to evaluate the performance of the first algorithm based on the PrefixSpan algorithm. The experimental results demonstrated that the PrefixSpan algorithm can determine correlation rules more efficiently and accurately ascertain correlation rules better than any other algorithms used in the study. Additionally, the authors found that MBHS with a new multi-objective design can effectively find the product layout in supermarket solutions. Finally, the proposed product layout algorithm was found to lead to higher profit volumes and closeness ratings than traditional shelf layouts, as well as to be more efficient than other algorithms.

CLAP: A Cross-Layer Analytic Platform for the Correlation of Cyber and Physical Security Events Affecting Water Critical Infrastructures

Water CIs are exposed to a wide number of IT challenges that go from the cooperation and alignment between physical and cyber security teams to the proliferation of new vulnerabilities and complex cyber-attacks with potential disastrous consequences. Although novel and powerful solutions are proposed in the literature, most of them lack appropriate mechanisms to detect cyber and physical attacks in real time. We propose a Cross-Layer Analytic Platform (denoted as CLAP) developed for the correlation of Cyber and Physical security events affecting water CIs. CLAP aims to improve the detection of complex attack scenarios in real time based on the correlation of cyber and physical security events. The platform assigns appropriate severity values to each correlated alarm that will guide security analysts in the decision-making process of prioritizing mitigation actions. A series of passive and active attack scenarios against the target infrastructure are presented at the end of the paper to show the mechanisms used for the detection and correlation of cyber–physical security events. Results show promising benefits in the improvement of response accuracy, false rates reduction and real-time detection of complex attacks based on cross-correlation rules.

Predicting the APT for Cyber Situation Comprehension in 5G-Enabled IoT Scenarios Based on Differentially Private Federated Learning

Driven by the advancements in 5G-enabled Internet of Things (IoT) technologies, the IoT devices have shown an explosive growth trend with massive data generated at the edge of the network. However, IoT systems exhibit inherent vulnerability for diverse attacks, and Advanced Persistent Threat (APT) is one of the most powerful attack models that could lead to a significant privacy leakage of systems. Moreover, recent detection technologies can hardly meet the demands of effective security defense against APTs. To address the above problems, we propose an APT Prediction Method based on Differentially Private Federated Learning (APTPMFL) to predict the probability of subsequent APT attacks occurring in IoT systems. It is the first time to apply a federated learning mechanism for aggregating suspicious activities in the IoT systems, where the APT prediction phase does not need any correlation rules. Moreover, to achieve privacy-preserving property, we further adopt a differentially private data perturbation mechanism to add the Laplacian random noises to the IoT device training data features, so as to achieve the maximum protection of privacy data. We also present a 5G-enabled edge computing-based framework to train and deploy the model, which can alleviate the computing and communication overhead of the typical IoT systems. Our evaluation results show that APTPMFL can efficiently predict subsequent APT behaviors in the IoT system accurately and efficiently.

Precision Marketing Method of E-Commerce Platform Based on Clustering Algorithm

In user cluster analysis, users with the same or similar behavior characteristics are divided into the same group by iterative update clustering, and the core and larger user groups are detected. In this paper, we present the formulation and data mining of the correlation rules based on the clustering algorithm through the definition and procedure of the algorithm. In addition, based on the idea of the K-mode clustering algorithm, this paper proposes a clustering method combining related rules with multivalued discrete features (MDF). In this paper, we construct a method to calculate the similarity between users using Jaccard distance and combine correlation rules with Jaccard distances to improve the similarity between users. Next, we propose a clustering method suitable for MDF. Finally, the basic K-mode algorithm is improved by the similarity measure method combining the correlation rule with the Jaccard distance and the cluster center update method which is the ARMDKM algorithm proposed in this paper. This method solves the problem that the MDF cannot be effectively processed in the traditional model and demonstrates its theoretical correctness. This experiment verifies the correctness of the new method by clustering purity, entropy, contour, and other indicators.

Creating Content and Use Cases for Testing Semantic Correlation Rules in Content Delivery

Information retrieval (IR) systems like content delivery portals (CDP) help users to complete processes like searching tasks. However, these systems are facing difficulties like a lack of context or an abundance of content within the required information. An approach to solve this problem is the creation of microDocs with semantic correlation rules (SCR). The objective of this paper is to examine the realization of SCR in CDP according to use cases by focusing on the impact SCR have on the effectiveness of CDP for IR. SCR can be implemented in various creation systems. Furthermore, the paper focuses on the exemplary development of SCR in a component content management system (CCMS). Therefore, a system-based evaluation was conducted. In addition a test collection, following an ontology and a corresponding metadata architecture was created to evaluate the impact of SCR on CDP. The evaluation includes three systems, representing the different types of CDP. It uses methodology precision and recall, as well-fitting methods for the intended purpose. To conclude and finalize, testing results will be summed up, interpreted and the findings will be provided with an outlook.

Linguistic Approach to Semantic Correlation Rules

As communication between humans and machines in natural language still seems essential, especially for end users, Natural Language Processing (NLP) methods are used to classify and interpret this. NLP, as a technology, combines grammatical, semantical, and pragmatical analyses with statistics or machine learning to make language logically understandable by machines and to allow new interpretations of data in contrast to predefined logical structures. Some NLP methods do not go far beyond a retrieving of the indexation of content. Therefore, indexation is considered as a very simple linguistic approach. Semantic correlation rules offer the possibility to retrieve easy semantic relations without a special tool by using a set of predefined rules. Therefore, this paper aims to examine, to which extend Semantic Correlation Rules (SCRs) will be able to retrieve linguistic semantic relations and to what extend a simple NLP method can be set up to allow further interpretation of data. In order to do so, an easy linguistic model was modelled by an indexation that is enriched with semantical relations to give data more context. These semantic relations were then queried by SCRs to set up an NLP method.

Semantic Correlation Rules as a Logic Layer between Content Management and Content Delivery

Semantic technologies have recently gained considerable influence and attention in the field of technical communication and information management. While metadata management is already a well-known field of content management technologies, its semantic extension addresses more recently, for example, problems of model-based product development and related content engineering processes. On the other hand, dynamic search technology and content delivery can benefit from semantic modelling by enhancing search functionalities or by integrating various data sources utilizing semantic mapping. In this evolving environment, we propose a logical layer of content correlations as so-called semantic correlation rules (SCR). This layer can be understood as an interface between content management systems, semantic modelling systems and content delivery portals. Semantic correlation rules serve as a light-weight ontology consisting primarily of untyped semantic relations between metadata classes. In doing so, class-to-class linking mechanisms can be implemented in content delivery and search environments while serving as a basis for the previously introduced microDoc concept

Prototyping of creation, implementation and visualization of correlation rules and microDocs

Semantic Correlation Rules (SCR) and microDocs are new concepts in the field of content delivery. SCR allow to define relationships between information units based on their metadata and, therefore, allow for the dynamic aggregation of microDocs. The creation of SCR heavily relies on the capabilities of modern content management systems (CMS) or ontology editors. The evaluation and visualization of the emerging microDocs, on the other hand, relies on the capabilities of content delivery portals (CDP). At this time, the support of both concepts in most software solutions currently being used, is only partly existent. This paper aims to demonstrate, how these currently existing limitations can be overcome, to reveal important factors to be considered and to showcase future possibilities of the aforementioned concepts. For this purpose, we developed a series of prototypes and conceptional visuals regarding creation of SCR, aggregation of microDocs and their visual appearance taking human perception into account.

Ontology Modelling and Standardized Information Exchange with Content Delivery Applications in Technical Communication

Ontologies are a technology recently used in technical communication (TC) to model information into a multidimensional net. They expand the modelling by taxonomy of metadata in TC. Any kind of relation between multiple classes and instances can be established. These ontologies can appear in the form of semantic correlation rules (SCR), which represent the connection between the metadata of the objects. SCR are used in connection with component content management systems (CCMS), semantic modelling systems (SMS) and content delivery portals (CDP) to deliver the appropriate amount of content in a more precise manner to the end user. In general, Ontology tools, CCMS and CDP are not based on the same ecosystem and therefore, they do not always work together effortlessly. A solution to this problem are exchange formats like the intelligent information Request and Delivery Standard (iiRDS), which enable a standardized information exchange between supported systems. Another solution would be compound information systems (CIS) like ONTOLIS, which combine a CCMS, CDP and SMS all in one. This paper aims to investigate the effect of SCR in the CDP of a CIS like ONTOLIS and to evaluate the use of exchange formats like iiRDS.