Change point detection is crucial for identifying state transitions and anomalies in dynamic systems, with applications in network security, health care, and social network analysis. Dynamic systems are represented by dynamic graphs with spatial and temporal dimensions. As objects and their relations in a dynamic graph change over time, detecting these changes is essential. Numerous methods for change point detection in dynamic graphs have been developed, but no systematic review exists. This paper addresses this gap by introducing change point detection tasks in dynamic graphs, discussing two tasks based on input data types: detection in graph snapshot series (focusing on graph topology changes) and time series on graphs (focusing on changes in graph entities with temporal dynamics). We then present related challenges and applications, provide a comprehensive taxonomy of surveyed methods, including datasets and evaluation metrics, and discuss promising research directions.

An exponential growth in the literature in general and the medical literature in particular raises a need for effective intelligent analysis strategies and tools to provide valuable insights to researchers about the current evolving literature. While existing applications provide more specific approaches to the problem, such as focusing on particular genome or protein information, in this paper, the proposed application provides effective and detailed analysis of PubMed. The developed tool, named KoExPubMed, follows a more generalized and holistic way by taking into consideration different types of information such as authors, countries, genes, and the interactions between them. The developed application consists of four main components; (1) keyword search and ID extraction, (2) PubMed article information and abstract retrieval, (3) country and address extraction, and (4) gene information extraction. In addition to the fundamental components, the tool provides a variety of visualization options for showing the extracted information and the related associations, including line charts for densities and countries, chord charts for collaborations of authors, network graphs for the genes mentioned together, bubble charts for gene frequencies, etc. By addressing the need for a generalized data mining tool, we propose a comprehensive application which is capable of employing data mining and machine learning techniques to extract from PubMed knowledge valuable to researchers and practitioners who are interested in closely investigating the achievements of others.

Molecular subtyping is a method of separating tumor clusters in a cancer type with common features according to molecular data and classification models. Genome datasets are taken from many different people and some genetic material, more precisely genetic markers, are obtained to predict the presence of a disease. In addition, breast cancer occurs due to mutation or modification observed in cells. miRNAs and lncRNAs take participation in cell cycle, regulation, and even chromatic inhibition of cell. For example, miRNAs function in cell cycle regulation as the degradation of mRNAs. Therefore, the aim of this work is to investigate the roles of miRNAs and lncRNAs in prognosis and characterizing the subtypes of Breast Cancer.

The aim of this work is to employ face recognition for creating learning profiles of the analysed persons who are students in this study. Generating education profiles will help experts in the diagnosis of Attention Deficit Hyperactivity Disorder (ADHD), which is a serious problem in children. Children with ADHD often have the ability and potential to learn. However, it may be difficult to reveal their capabilities and skills. Accordingly, a suffering child may have a hard time succeeding in real life when he/she is ignored and expected to mix with other children. The unrealized gap and deficiency may lead to other problems and more complicated situation with unpredictable consequences. Thanks to the system developed in this study, and the like, which will help in diagnosing the ADHD disease, and hence suffering individuals will be able to recognize their deficiencies, understand their ability to learn and adapt when approached differently in a way which suits his/her situation. This personalized handling of infected students will be an excellent guide to advance their potential and integration within the society carefully and smoothly. The system analyzes the face of a student to inspire his/her emotional state. The reported test results demonstrate how the system works well and produces high accuracy under a variety of severe conditions such as skewed angle, less illumination, accessories etc.

The development in technology has led to the generation of huge amounts of data from various sources, including biological data, social networking data, etc. Accordingly, social network analysis has received considerable attention with the availability of more raw datasets which could be realized using a network structure. Most of the datasets can be represented as a social network which is a graph consisting of actors having relationships. Many tools exist for social network analysis inspired to extract knowledge from the networks. NetDriller has been developed as a social network extraction, manipulation and analysis tool to cover the lack that exists in other tools. It is capable of constructing social networks from raw data by employing a variety of data mining and machine learning techniques. In this paper, we describe an extend version of NetDriller, which has some new essential functions, including social network construction using data collection from Twitter, DBLP and IEEE. We also added (1) a new chart for viewing the network property and metrics, and (2) new graph manipulation techniques using GUI to keep the tool up to date with the huge volume of networks and the different types of raw data available on the web.

The initial announcement for the fourteenth ASONAM Conference invited the submission of research papers and special sessions proposals to the Social Networks Analysis and Mining (ASONAM 2022), Hague, Netherlands / 3-6 August 2022. However, the coronavirus epidemic is still affecting every human activity that included gatherings of people and travel is still limited for international conferences for many researchers. It was therefore decided to move the conference to a blended conference later in the 2022 and change the conference to a hybrid in-person conference and on-line conference at a different location. The final announcement was therefore ASONAM 2022-The 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 10-13 November 2022, Istanbul and Virtual on Zoom.

Monitoring and management of large scale applications is already a complex task because of syntactic and unstructured nature of execution data. Traditional application monitoring and management solutions focused on employing analysis techniques on unstructured and syntactic log information become limited as unstructured information cannot be well utilized to find out related events information or correlate such information with other related information from applications. Our proposed solution of semantically formalized logging fills this gap by bringing formal semantics and combining it in a meaningful way to enable automated monitoring and management of applications. Such formalized and well-structured log information helps analytical solution to maximally automate the process of monitoring and management of applications. However, while formalizing and structuring the log information, we came across several missing and incomplete data which causes hindrance in this process. In this paper, we tackle this problem and propose a social network analysis based solution to handle incomplete and missing data from application execution, possibly compute it and use it by our proposed solution of semantically formalizing and structured logs with adapted data mining techniques to enable automated and effective application monitoring and management. We demonstrate from an industrial use-case application that how historical data from application execution is stored using semantic logging and utilized with standard social-network analysis techniques to find out missing values in incomplete data and perform application monitoring and management.

The Middleware is a piece of software lying between the operating system and the application layer. Distributed applications are gaining popularity with the widespread of reliable communication services. It is affordable to have data accessible 24/7 from almost anywhere, thanks to the well-developed mobile technology and handheld devices. Healthcare domain is one of the very demanding application areas that highly benefits from these developments. Actually, health clinic system is an evolving and promising area in which clients can easily log into a distant health clinic server and retrieve, add, or update the data and diagnosis of the patients using Common Object Request Broker Architecture (CORBA) Internet Inter-Orb Protocol (IIOP) middleware. In this paper, we describe the development and implementation of a middleware model that utilizes the effective connection between two different programming languages (java and .Net) to send and receive requested patients' data in a very efficient response time. This is achieved by using the reference of the object in the server. Based on our knowledge, our approach has the best response time compared to the existing works in the area. Our approach has been successfully tested and evaluated.

Frequent pattern mining and consequently association rule mining is a useful technique for discovering relationships between items in databases. However, as the size of the data to be analyzed increases or the values of the pruning thresholds decrease, larger number of frequent pattern and more association rules will be generated with little information about the association rules in relation to each other. This research paper discusses a method to segment rules into different sets with no internal conflicts. The goal is to establish an effective method to reduce the difficulty for businesses to review the association rules of different customer segments, and track the behaviors of market segments based on their buying behaviors. The method established in this paper has the advantage of not needing customer information, thus removing the need for businesses to obtain customer information. This removes the threat of intrusions into customer privacy. The method also generates the rule sets based on conflicting rules, and dividing rules based on customer behaviors is more accurate than customer characteristics. The proposed method has been validated by running some tests.

Matchmaking services connecting consumers and providers on the internet have become phenomenally important in today's world. Whereas the matchmaking was by traditional media such as print and television in the past it is now expected to include the internet. Consumers expect these services to be readily available as this can only be accomplished via the internet Providers that do not have an internet presence are therefore severely disadvantage in the competition for customers (consumers). The decline of the traditional physical music store and the ascendancy of the virtual iTunes store is a perfect example of this. As the traditional stores go bankrupt and go out of business has also become the largest music retailer in the United States of America. Consumers simply do not want to spend extra energy to get what they want. If the music can be purchased and downloaded from the comfort of their own home, the consumers (customers) will do just that. Therefore online matchmaking services are a hot topic of discussion for many companies, as they are finding ways to provide the fastest, cheapest and most efficient methods for consumers to easily access their services.

The motivation for the study described in this paper is realizing the fact that organizational structure of a group is a key indicator in determining its strengths and weaknesses. A general knowledge of the prevalent models of terrorist organizations leads to a better understanding of their capabilities. Knowledge of the different labels and systems of classification that have been applied to groups and individuals aid us in discarding useless or irrelevant terms, and in understanding the purposes and usefulness of different terminologies. Previous studies in network analysis have mostly dealt with legal networks with transparent structures. Terrorist networks share some features with conventional (real world) networks, but they are harder to identify because they mostly hide their illicit activities. In this paper we describe a novel approach for extracting structural patterns of terrorist networks with the help of social network analysis measures and techniques. We propose a global measure for estimating the degree of organization of social networks; the measure is global in terms of being applied to the whole network as an entity and being extracted from the major well-known SNA measures. The importance of such research comes from the fact that individuals in organized intellectual networks and especially terrorist networks tend to hide their individual rules and thus there is a need to deal with such networks as a whole, discovering the degree of organization and thus its strengths and weaknesses.

Searching for the right information over the Web is not straight-forward. In the era of high speed internet, high capacity networks, and interactive Web applications, it has become even easier for the users to publish data online. A huge amount of data is published over the internet; every data is in the form of web pages, news, blogs and other material, etc. Similarly, for search engines like Google and Yahoo, it becomes rather hard to find out the right information, i.e., as per user's preferences; search results for same query differ in priority for different users. In this paper, we proposed a way to prioritize search results of search engines like Google, based on the personal interests and context of users. In order to find out personal interest and context, we follow a unique approach of (1) finding out activities of a user of his/her social-network, (2) finding out what information does the social networks (i.e., friends and community) provide to the user. Based on this information, we have developed a methodology that takes into account the information about social networks and prioritize search results from Web search engine.

This paper presents a comprehensive approach for the transformation of ODL Schemas into XML Schemas. The approach starts with an incomplete set of rules described in the literature to assist in the transformation process. The fact that the rules provided a solid foundation for expansion, as well as the fact that the rules only cover a small subset of ODL, was our main motivation for continuing the study of this topic. In this paper, we first analyze an existing set of nine transformation rules. After evaluating the correctness and completeness of the rules, we proceed to propose some improvements and extensions into a more complete set of rules that cover the whole transformation process. By modifying the existing rule set, we are able to handle a much wider variety of ODL. Finally, we discuss some ODL scenarios that the original rule set cannot handle. This is meant to justify the need for the proposed extension as described in this paper. The presented more complete rule set is capable of handling a larger subset of ODL (including dictionaries, global and local scope enumerations, and most importantly, inheritance).

In this paper, we investigate and explore the process of analyzing log data of website visitor traffic in order to assist the owner of a website in understanding the behavior of its visitors. The developed approach involves the review of statistical data on the types of visitors that come to the website, as well as the steps they take to reach and satisfy the goal of their visit. The value added from the analysis of this data provides eCommerce and commercial website owners with the information needed to display targeted advertisements or messages to their customers. In the long run, this is expected to allow for the increase in sales and overall customer loyalty.

Automated text summarization is important to for humans to better manage the massive information explosion. Several machine learning approaches could be successfully used to handle the problem. This paper reports the results of our study to compare the performance between neural networks and support vector machines for text summarization. Both models have the ability to discover non-linear data and are effective model when dealing with large datasets.

The great increase in online transactions and the thousands of online retailers has created a great demand for companies to gain competitive advantage. An easy way for a company to gain customer advantage is through the use of data mining. Due to this high demand we have developed a prototypical tool to help with the analysis of these online transactions. From the raw data generated by running these transactions we are able to find consumer trends and shopping patterns by using hierarchical clustering and association rules mining algorithm. The focus of this research is to demonstrate how this development can be useful and effective in a business situation for companies to gain competitive advantage.

The distance measures used to determine the dissimilarities between high-dimensional feature vectors are often expensive to compute. To reduce the number of expensive distance calculations in the search process, Korn, et al [5] proposed a multi-step algorithm, which involves two stages: filtering and refinement. This algorithm was later improved by Seidl and Kriegel [8] to produce optimal-sized candidate set in the filtering stage; the improved algorithm is said to be filtering optimal, but can not produce the result incrementally in the refinement stage. In this paper, we propose an extended version of the algorithm that can produce the nearest neighbors incrementally in an optimal way. Our algorithm is both filtering and refinement optimal, and well serves real applications. We proved the optimality of the proposed extended algorithm.

The sensitivity of computed results to implementations of algorithms in GIS is considered in this paper on the example of a precisely defined recursive variant of the Ramer-Douglas-Peucker line simplification algorithm (called the R-D-P algorithm). We establish a robust version of the R-D-P algorithm where the determination of the simplification is rounding error free if the data are already machine numbers. Under these assumptions, the results are reproducible which is not the case with other versions of the algorithm.