Summary
It has been observed that a good number of financial organizations often face a number of threats due to credit card fraud that affects consistently to the card holder as well as the ...organizations. This is one of the fastest‐growing frauds of its kind and the most emerging problems for the institutions to prevent. A number of researchers and analysts have shown interest to work on this area in order to identify such issues in an effective manner by applying various supervised as well as unsupervised learning approaches. In this assessment, three classification techniques such as support vector machine (SVM), k‐nearest neighbor (k‐NN), and extreme learning machine (ELM) that come under supervised learning category are applied to the BankSim data to categorize the normal and fraudulent class transactions in credit card. These algorithms are incorporated with the graph features extracted from the dataset by using a database tool Neo4j. The nodes of the graph represent the transactional data samples and the edges create relationships among the nodes to find the patterns of data using connected data analysis. k‐fold cross validation approach in Gaussian mixture model (GMM) has been applied for classification of the credit card transaction data in a single distribution. Further, a combined graph‐based Gaussian mixture model (CGB‐GMM) has been proposed to effectively detect the fraudulent instances in credit card transactions with the application of graph algorithms such as degree centrality, LPA, page rank, and so forth. Each of the learning algorithms are implemented with and without the application of graph algorithms and their performances are assessed empirically for analysis.
The study answers the question which database is the best choice for efficient data storage of UML models. Three products were considered: MongoDB, PostgreSQL and Neo4J. The effectiveness test ...consists of measurement the response time of queries that save and load data. This study also take into account the memory increase ratio during data insertion and the level of complexity of the implementation of the test data mappers used in database queries.
•An architecture based on CPS is proposed to network the resources on the shop floor and extend these to the customer.•An ontology-based model is provided in Protégé to reflect the relation of ...customer, product, and manufacturing process.•A graph model is established and implemented in Neo4j based on a semantic model.•The capability for dynamic product ordering and DVSM is established and evaluated after implementation within the model.
Smart manufacturing is characterized as transparent shop floor production, rapid and intelligent responses to dynamic changes, and a utilization of high-performance inter-cooperation networks. Smart manufacturing and a global appetite for personalized products have transitioned industry from mass production into the age of mass customization. Increased autonomy is slowly changing customer expectations as well, enabling customers to modify a product design not only during an order, but sometimes even long after placing an order. In this context, this paper fills a gap by presenting a data-centric infrastructure to enable interaction with a “global, virtual data space,” which overcomes the problems with traditional direct access methods such as interoperability and compatibility. Using a Cyber-Physical System (CPS), resource monitoring on the shopfloor as well as multiple parities beyond the enterprise boundary will be interconnected through this data-centric infrastructure. A semantic knowledge management system, which encompasses product lifecycle knowledge and manufacturing process ontology, is developed as the data schema in the data-centric infrastructure. In comparison to relational databases which are effective at handling paper forms and tabular structure, the flexible schema of graph databases enable these to handle dynamic and uncertain variables. These capabilities are deemed critical for a platform supporting real-time information exchange between customer, manufacturer and collaborators. One advantage of such a system allowing for real-time information exchange is that it enables last minute order changes by the customer, allowing for product design changes even after production has started on the order. The other advantage is that it allows manufacturing managers to monitor the productivity of customer-directed, dynamic manufacturing processes by utilizing Dynamic Value Stream Mapping (DVSM) methods.
Big Data is a research area where many different disciplines work together. Social media has grown in popularity as a tool for disseminating and gathering information. However, the success of social ...media like Twitter, Facebook, etc., has not only attracted genuine users but also spammers who utilize social graphs, famous phrases, and hashtags to spread malware. This study uses several social network analysis and visualization methods based on bibliometric data from the Web of Science to look at the structure and patterns of interdisciplinary collaborations and the latest emerging overall practice. For a better understanding of spamming behaviors on Twitter, the Twitter data set is thoroughly analyzed, and categorized into Spam and Non-Spam classifications. Earlier studies confined their scope to investigating the most negatively influential spammers by blocking the most influential spammers. However, the cumulative impact of other spammers having low individual negative influence values but higher impact values was neglected. In this article, we develop an algorithm for detecting social spam using Node Rank-based Influence Minimization (NRIM), which integrates Node Rank with the impact value of spam. The proposed spam influence minimization model also identifies spam-influential users and aids in limiting the flow of spam tweets within the Twitter network. Additionally, a detection algorithm for influential communities has been proposed to limit the spread of spam content through influential communities on the Twitter network. The primary focus of this paper is to reduce the spam impact on Twitter data by identifying influential spammers using the Node_Rank-based Influence Minimization (NRIM) algorithm. To begin, the tweets are classified into spam and non-spam using a machine learning algorithm. Furthermore, the spam observed in the Graph is analyzed, and the Spammer is passed through the NRIM algorithm to find the influential Spammers. In addition to this, the negative impact of the Spammer is reduced on the Twitter graph, and its impact is analyzed on query processing executed on Graph. The technique used for the minimization of the Spammer’s negative effect on the graph reduces the query execution time by 12%.
•A novel taxonomy and thorough review providing classification of spam and non spam.•Spam are discovered and the impact of influence on their spread is the main emphasis.•The spam influential value is determined using the NRIM algorithm.•The impact of the spam is reduced by Influence-based Spam reduction strategy.•Real-time statistics used in assessment, proposed method outperforms earlier method.
The MolProbity web service provides macromolecular model validation to help correct local errors, for the structural biology community worldwide. Here we highlight new validation features, and also ...describe how we are fighting back against outside developments which compromise that mission. Our new tool called UnDowser analyzes the properties and context of clashing HOH “waters” to diagnose what they might actually represent; a dozen distinct scenarios are illustrated and described. We now treat alternate conformations more thoroughly, and switching to the Neo4j database (graphical rather than relational) enables cleaner, more comprehensive, and much larger reference datasets. A problematic outside change is that refinement software now increasingly restrains traditional validation criteria (geometry, clashes, rotamers, and even Ramachandran) in order to supplement the sparser experimental data at 3–4 Å resolutions typical of modern cryoEM. But unfortunately the broad density allows model optimization without fixing underlying problems, which means these structures often score much better on validation than they really are. CaBLAM, our tool designed for evaluating peptide orientations at lower resolutions, was described in the previous Tools issue, and here we demonstrate its effectiveness in diagnosing local errors even when other validation outliers have been artificially removed. Sophisticated hacking of the MolProbity server has required continual monitoring and various security measures short of restricting user access. The deprecation of Java applets now prevents KiNG interactive online display of outliers on the 3D model during a MolProbity run, but that important functionality has now been recaptured with a modified version of the Javascript NGL Viewer.
Actualmente en cada segundo y minuto en el mundo son generados grandes cantidades de datos que necesitan ser almacenados y procesados, esta información es muy variada en cuanto a la forma de ...almacenamiento y fuente de procedencia así como son muchos y variados los problemas que surgen a la hora de procesar esta información. Uno de estos problemas a solucionar son los de conexión o ruta a través de herramientas graficas como Neo4j, donde, utilizando el método comparativo en el presente trabajo, se realizara una investigación con el objetivo de demostrar las diferencias entre velocidad de ejecución de consultas y eficiencia entre las base de datos orientada a grafo (Neo4j) y las base de datos relacionales (PostgreSQL).
Graph data management systems are designed for managing highly interconnected data. However, most of the existing work on the topic does not take into account the temporal dimension of such data, ...even though they may change over time: new interconnections, new internal characteristics of data (etc.). For decision makers, these data changes provide additional insights to explain the underlying behaviour of a business domain. The objective of this paper is to propose a complete solution to manage temporal interconnected data. To do so, we propose a new conceptual model of temporal graphs. It has the advantage of being generic as it captures the different kinds of changes that may occur in interconnected data. We define a set of translation rules to convert our conceptual model into the logical property graph. Based on the translation rules, we implement several temporal graphs according to benchmark and real-world datasets in the Neo4j data store. These implementations allow us to carry out a comprehensive study of the feasibility and usability (through business analyses), the efficiency (saving up to 99% query execution times comparing to classic approaches) and the scalability of our solution.
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