Web Domain Research Articles

WSEM QT : a novel approach for quality‐based evaluation of web data sources for a data warehouse

The incorporation of suitable external data from the World Wide Web offers an effective solution for enriching the data in the data warehouse (DW). However, the main challenge is the quality-aware selection of web data sources to maintain the quality of the DW. In the previous works, the quality evaluation of web sources is through expert evaluation only, which makes it a very lengthy process. Also, since the quality model consists of mixed quality factors from diverse domains of Web, DW and underlying business, finding an expert possessing an expertise of all these domains is a huge bottleneck in the evaluation process. In order to overcome these existing issues, this study proposes a novel multi-level approach web source evaluation with multi-criteria decision-making and web quality testing tools (WSEMQT) and underlying quality model web quality model for evaluating web sources for the DW. The authors introduce automated web source quality evaluation in the first level of web source based evaluation and multiple dimensions of quality evaluation at the second level of expert-based evaluation. At both the levels, multi-criteria decision-making methods are applied to the evaluation scores obtained to ascertain the ranked list of Web sources. The authors present a real-world academic web data case study which shows that the proposed approach can be executed successfully for real-world problems.

A Sentiment Analysis System for the Hindi Language by Integrating Gated Recurrent Unit with Genetic Algorithm

The growing availability and popularity of opinion rich resources such as blogs, shopping websites, review portals, and social media platforms have attracted several researchers to perform the sentiment analysis task. Unlike English, Chinese, Spanish, etc. the availability of Indian languages such as Hindi, Telugu, Tamil, etc., over the web have also been increased at a rapid rate. This research work understands the growing popularity of Hindi language in the web domain and considered it for the task of sentiment analysis. The research work analyses the hidden sentiments from the movie reviews collected from the review section of Hindi language e-newspapers. The reviews are multilingual, which makes sentiment analysis a challenging task. To overcome the challenges, this research work proposes a deep learning based approach where a Gated Recurrent Unit network is combined with the Hindi word embedding model. The strategy enables the network to efficiently capture the semantic and syntactic relation between Hindi words and accurately classify them into the sentiment classes. Gated Recurrent Unit network's performance is profoundly dependent upon the selection of its hyper-parameters; therefore, this research work also utilizes a Genetic Algorithm to automatically build a gated recurrent network architecture enabling it to select the best optimal hyper-parameters. It has been observed that the proposed Genetic Algorithm-Gated Recurrent Unit (GA-GRU) model is effective and achieves breakthrough performance results on the Hindi movie review dataset as compared to other traditional resource-based and machine learning approaches

Generating knowledge graphs by employing Natural Language Processing and Machine Learning techniques within the scholarly domain

The continuous growth of scientific literature brings innovations and, at the same time, raises new challenges. One of them is related to the fact that its analysis has become difficult due to the high volume of published papers for which manual effort for annotations and management is required. Novel technological infrastructures are needed to help researchers, research policy makers, and companies to time-efficiently browse, analyse, and forecast scientific research. Knowledge graphs i.e., large networks of entities and relationships, have proved to be effective solution in this space. Scientific knowledge graphs focus on the scholarly domain and typically contain metadata describing research publications such as authors, venues, organizations, research topics, and citations. However, the current generation of knowledge graphs lacks of an explicit representation of the knowledge presented in the research papers. As such, in this paper, we present a new architecture that takes advantage of Natural Language Processing and Machine Learning methods for extracting entities and relationships from research publications and integrates them in a large-scale knowledge graph. Within this research work, we (i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, (ii) describe an approach for integrating entities and relationships generated by these tools, (iii) show the advantage of such an hybrid system over alternative approaches, and (vi) as a chosen use case, we generated a scientific knowledge graph including 109,105 triples, extracted from 26,827 abstracts of papers within the Semantic Web domain. As our approach is general and can be applied to any domain, we expect that it can facilitate the management, analysis, dissemination, and processing of scientific knowledge.

A Survey on the Use of Computer Vision to Improve Software Engineering Tasks

Software engineering (SE) research has traditionally revolved around engineering the source code. However, novel approaches that analyze software through computer vision have been increasingly adopted in SE. These approaches allow analyzing the software from a different complementary perspective other than the source code, and they are used to either complement existing source code-based methods, or to overcome their limitations. The goal of this manuscript is to survey the use of computer vision techniques in SE with the aim of assessing their potential in advancing the field of SE research. We examined an extensive body of literature from top-tier SE venues, as well as venues from closely related fields (machine learning, computer vision, and human-computer interaction). Our inclusion criteria targeted papers applying computer vision techniques that address problems related to any area of SE. We collected an initial pool of 2,716 papers, from which we obtained 66 final relevant papers covering a variety of SE areas. We analyzed what computer vision techniques have been adopted or designed, for what reasons, how they are used, what benefits they provide, and how they are evaluated. Our findings highlight that visual approaches have been adopted in a wide variety of SE tasks, predominantly for effectively tackling software analysis and testing challenges in the web and mobile domains. The results also show a rapid growth trend of the use of computer vision techniques in SE research.

Continuous Delivery of Customized SaaS Edge Applications in Highly Distributed IoT Systems

Edge computing is a reality for the current IoT systems that need fast processing and quick response time to make real-time decisions and IoT systems without permanent connectivity to the cloud (e.g., car manufacturing, precision agriculture, or cattle raising). Additionally, these industries are facing the need for rapid and continuous innovation by accelerating the delivery of over-the-air (OTA) software updates in edge devices. DevOps promotes collaboration between development and operation teams and automation at all steps of software construction to achieve continuous delivery (CD) of business value. Although DevOps has demonstrated numerous successful cases in the Web domain, in the IoT domain and, more specifically, at the edge, there are few reported cases. This work presents a success case of CD of customized software as a service software as a service (SaaS) updates at the IoT Edge. This may enable new business models at the IoT Edge. This article presents an architectural model of a highly distributed (cloud and edge) IoT system and a CD process flow for customized SaaS applications in edge nodes. Both the architectural model and the CD process flow are instantiated in a case study for precision agriculture.

The state of practice of COVID-19 tracking systems: An inventory study.

In this poster, we report the preliminary results of an inventory of 149 publicly accessible active COVID‐19 tracking systems. Key findings include the frequency distribution of the systems' web domain names, the countries where the systems were created, the languages they support, the visual display format, the map platforms, and the data sources. These findings help to advance the knowledge of the data characteristics and design of pandemic surveillance/tracking systems.

Multi-criteria analysis of WWW domain efficiency on social behavior in cyber space

The level of technological development, as well as technology, allows a contemporary individual to put any possible files, photos or multimedia contents on his internet-connected computer. As a result, nowadays we practically have an enormous amount of data, available to almost any possible individual worldwide. People make connections over Web service throughout internet as visible communication. World Wide Web represents the most prominent internet field thus partly influencing internet users in contemporary world. Defining efficiency of World Wide Web domain within cyber space means a lot to social behavior. This paper deals with estimating efficiency of World Wide Web domain on social affairs in cyber space with the use of multi-criteria analysis. Based on the criteria chosen, World Wide Web domain efficiency assessment in cyber space has been conducted, with the emphasis on the influences towards efficiency in the domain of fulfilled influences on social affairs. Identification of such World Wide Web fields facilitates the process of technological progress on one hand or facilitates recognition, prevention and protection of human and material resources on the other hand. World Wide Web domain efficiency in cyber space analysis has been performed through the method of Analytic Hierarchy Process (AHP method), while the efficiency expertise of World Wide Web domain on social behavior in cyber space has been performed within a software tool “Super Decision 2.6.0 – RC1“. For the sake of the comparative data analysis, an “on–line“ survey has been made on a representatvie sample of 148 individuals, applying a five-degree Likert Scale of attitudes as well as the analysis of obtained data within a software tool used for statistical data processing “Statistical Package for the Social Sciences“. Upon a completion of performed analysis based on an influence significance, the following World Wide Web domains were singled out: Facebook, Youtube, Wikipedia and Twitter.

Brief history of top-level domains and challenges for information professionals

Top-level domains, in addition to fulfilling the technical function of enabling access to network resources, allow information professionals in generalóand content managers in particularóto work with domain names for branding and search engine optimization (SEO) purposes. Likewise, they facilitate the performance of metric analyses thanks to their hierarchical structure. The massive creation of new TLDs that was started in the early 21st century by the Internet Corporation for Assigned Names and Numbers (ICANN) opened the possibility of being able to select (or even register) a wide variety of web domains. However, the sources of information about TLDs as well as their management and usefulness for both the commercial sector (SEO, web content management, and digital analytics) and scholarly community (webometrics) are not well known among information professionals. The objective of this text is to explain the meaning and function of TLDs, to highlight their different categories, and to show their evolution over time, in order to provide useful information for professionals dedicated to the generation, dissemination, storage/retrieval, and analysis of online content.

Optimizing Ontology Alignment through Improved NSGA-II

Over the past decades, a large number of complex optimization problems have been widely addressed through multiobjective evolutionary algorithms (MOEAs), and the knee solutions of the Pareto front (PF) are most likely to be fitting for the decision maker (DM) without any user preferences. This work investigates the ontology matching problem, which is a challenge in the semantic web (SW) domain. Due to the complex heterogeneity between two different ontologies, it is arduous to get an excellent alignment that meets all DMs’ demands. To this end, a popular MOEA, i.e., nondominated sorting genetic algorithm (NSGA-II), is investigated to address the ontology matching problem, which outputs the knee solutions in the PF to meet diverse DMs’ requirements. In this study, for further enhancing the performance of NSGA-II, we propose to incorporate into NSGA-II’s evolutionary process the monkey king evolution algorithm (MKE) as the local search algorithm. The improved NSGA-II (iNSGA-II) is able to better converge to the real Pareto optimum region and ameliorate the quality of the solution. The experiment uses the famous benchmark given by the ontology alignment evaluation initiative (OAEI) to assess the performance of iNSGA-II, and the experiment results present that iNSGA-II is able to seek out preferable alignments than OAEI’s participators and NSGA-II-based ontology matching technique.

Feature Selection us ing Normalized Weight Method f or Tamil Text Classification

The Feature Selection process simplify the Tamil text classification work at present we are in the information age, in this period all the applications has great growth in the domain of World Wide Web, so regional language like Tamil materials such as web pages, e-mails, e-books, and digital data has grown enormously so the retrieval of this Tamil digital document is more wanted by Tamil Document searcher. For quick retrieval of needed Tamil digitized documents among the millions of Tamil web documents, these documents should be classified by content according to their classes. The Tamil Text classification is a background work for many Tamil NLP applications such as query response, information extraction, information summarization, etc. the implementation of text categorization is very important in the information retrieval field. The text categorization assigns a document an appropriate category from a predefined group of categories. Tamil Text Classification classifies the documents based on Tamil text in a Document. Tamil language words are very rich in morphology and hence Tamil language consists of very large set of word forms. So it is important to reduce the features of Tamil text. This paper discusses about Feature selection Using Normalized weight from the huge set of key words from the preprocessed corpus. The Feature selection done by Term Weighting (TF*IDF) normalized method is reducing the size of the key word list which is very useful for training and testing Tamil text classification algorithms.

ROLE OF ADVANCED WEB BASED CONTENT MANAGEMENT SYSTEM AND ITS SIGNIFICANCE IN LIBRARIES MANAGEMENT SYSTEM

Libraries are vaults of learning and the enormous development in computerized assets has constrained library experts to utilize different data innovation tools to oversee and render management to the clients in Chinese education and research sector. To accomplish more noteworthy proficiency in the quickly evolving economic condition, libraries are progressively searching for new standards to convey management to clients in their work areas to innovate their knowledge. Library gateways assume a significant job by making a web domain where clients can without much of a stretch access data. It offers benefits as well as improving insightful correspondence and research among the supporters of the library. This paper discuses the entry of Advanced Web based Content Management System (AW-CMS) using linked list based iterations for database management has been made here to clarify the different supervisionclients, so it mayimprove the successful data searchers and assess accurate data proficiently and reliability in an effective and efficient manner. It likewise features, how it attempts to meet client desires for data on interest in library management and its experimental validation in accordance with existing software systems.

Optimizing Biomedical Ontology Alignment through a Compact Multiobjective Particle Swarm Optimization Algorithm Driven by Knee Solution

Nowadays, most real-world decision problems consist of two or more incommensurable or conflicting objectives to be optimized simultaneously, so-called multiobjective optimization problems (MOPs). Usually, a decision maker (DM) prefers only a single optimum solution in the Pareto front (PF), and the PF’s knee solution is logically the one if there are no user-specific or problem-specific preferences. In this context, the biomedical ontology matching problem in the Semantic Web (SW) domain is investigated, which can be of help to integrate the biomedical knowledge and facilitate the translational discoveries. Since biomedical ontologies often own large-scale concepts with rich semantic meanings, it is difficult to find a perfect alignment that could meet all DM’s requirements, and usually, the matching process needs to trade-off two conflict objectives, i.e., the alignment’s recall and precision. To this end, in this work, the biomedical ontology matching problem is first defined as a MOP, and then a compact multiobjective particle swarm optimization algorithm driven by knee solution (CMPSO-K) is proposed to address it. In particular, a compact evolutionary mechanism is proposed to efficiently optimize the alignment’s quality, and a max-min approach is used to determine the PF’s knee solution. In the experiment, three biomedical tracks provided by Ontology Alignment Evaluation Initiative (OAEI) are used to test CMPSO-K’s performance. The comparisons with OAEI’s participants and PSO-based matching technique show that CMPSO-K is both effective and efficient.

GUI information-based interaction logging and visualization for asynchronous usability testing

Asynchronous usability testing is efficient and promising usability testing methodology. However, evaluators are required to dedicate a considerable amount of effort for identifying usability problems in asynchronous testing. Evaluators cannot observe user interactions directly; only the collected data is available to them for identifying the usability problems. To reduce the amount of effort required, this paper presents a framework and tool for interaction visualization. This framework tracing user interactions based on meaningful GUI changes. The collected interaction log is visualized in a way that is similar to the visualization of traversed path used in web domains. Using our approach, the evaluator shall be able to understand user behavior more easily. In addition, we designed a black-box-based interaction logging for minimizing the implementation costs. Our tool especially helpful when testing has a large number of interaction data to analyze. To evaluate the proposed framework and tool, user interaction data were collected, and the results were evaluated by mobile application experts. The evaluation results demonstrate that the user interaction data traced by the proposed framework accurately reflect the user interactions and that the visualization results are valid.

Big data experiments with the archived Web: Methodological reflections on studying the development of a nation's Web

This article outlines how the 'digital geography' of a nation can be studied, that is the online presence of one nation. The entire Danish Web domain and its development from 2006 to 2015 is used as a case, based on the holdings in the Danish national Web archive. The following research questions guide the investigation: What has the Danish Web domain looked like in the past, and how has it developed in the period 2006-2015? Methodologically, we investigate to what extent one can delimit 'a nation' on the Web, and what characterizes the archived Web as a historical source for academic studies, as well as the general characteristics of our specific data source. Analytically, the article introduces a design for how this type of big data analyses of an entire national Web domain can be performed. Our findings show some of the ways in which a nation's digital landscape can be mapped, ie. on size, content types and hyperlinks. On a broader canvas, this study demonstrates that with hard- and software as well as human competencies from different disciplines it is possible to perform large-scale historical studies of one of the biggest media sources of today, the World Wide Web.

Application of rural web in analyzing the economic performance of rural areas in Johor

Planning for sustainable rural development in Malaysia has entered a phase where rural areas are no longer tied merely to food production as an economic activity. The increasing demand for rural amenities, ecosystem services and preservation of cultural landscapes generates the idea of a much diverse option for an alleviation of the local economy. Due to the variation of local actors, resources, and activities, sustainable rural planning requires the understanding of the complex development processes occurring in its territories. The rural web is an analytical framework that can be used to understand the dynamic relationships between different domains of rural development. In this paper, the rural web is used as a tool to comprehensively analyze the economic performance of the village’s development and economic activity of rural areas in Johor. This paper discusses the study areas of six villages from six different rural areas surrounding the state of Johor. The finding shows that KampungParitPuteriMenangis has the highest economic performance while KampungPayaBesar was identified as the lowest economic performance compared to other villages. Market governance was identified as the major domain of the rural web in the twenty economic projects identified in six selected villages for this study.

Webometric Analysis and Link Relationship of Computer Science and Information Technology Websites of Malaysia Universities

This paper aims to investigate 18 web domains of computer science and information technology academic websites of Malaysia universities.We collected more than two million web pages. A webometric analysis was used to explore the number of web pages, inbound links, the web impact factor (WIF) and link relationships. The results show Fakulti Teknologi dan Sains Maklumat (FTSM), Universiti Kebangsaan Malaysia (UKM) has the highest number of webpages while Fakulti Teknologi Kreatif dan Warisan (FTKW), Universiti Malaysia Kelantan (UMK) has the largest WIF score. Pearson’s rank correlation coefficient was used to detect the relationship between institutions subdomain age and WIF. Correlations point out that there is scant relationship between subdomain age and WIF score across all 18 Malaysia selected schools [r =−.076, n = 18, p < .0005]. This is due to WIF are highly dependent on the quality of the content to attract backlinks and Google crawler algorithm that changes from time to time for the number of web pages. Subdomain age is independent to the year of establishment of the schools. These findings can be used as a guide to the implementation of university web content strategy.

Web Domains Ranking with Real User Traffic Based on the Big Data Platform

Web Domains Ranking with Real User Traffic Based on the Big Data Platform

SOADIWA: A Service-oriented Architecture for Data Interoperability in Web Applications

The realisation of Service-Oriented Architecture (SOA) to communicate data between systems running on different platforms lack an organised framework to capture the essential elements required for successful interoperability between web applications and their services. In this work, a SOA for Data Interoperability in Web Applications (SOADIWA) was designed. The architecture of SOADIWA was based on five layers, namely Web Application Layer (WAL), Quality of Service Assurance Certifier Layer (QoSACL), Web Service Layer (WSL), Visualization Input Layer (VIL) and Visualization Output Layer (VOL). In WAL, the Service Requester (SR) initiates a request for data from the Service Provider (SP) through the QoSACL to provide appropriate website via WSL for rendering of services which must be accepted, processed and returned for a particular need in VIL. The requested data is filtered in VIL for data exploration and analysis in VOL using context-sensitive visualization techniques. The purpose of QoSACL is to check and verify the claims made by the SP about its quality of service. This enabled the SR to choose the service that satisfied its needs. The implementation comprised of Java Script, Microsoft Visual Studio 2017 and NuGet packages; while the experiment was simulated on LoadUI pro application. Standard metrics such as Optimal Performance (OP) and Phased Effort Distribution (PED) were developed to test SOADIWA. These results conformed to basic web service interoperability. The work led to the integration of a host of techniques towards the creation of a novel tool that is useful in web domain using SOA approach.

Data Pre-Processing and Customized Onto-Graph Construction for Knowledge Extraction in Healthcare Domain of Semantic Web

Present electronic world produces enormous amount of data every second in various formats, especially in healthcare units. To efficiently utilize the available data by representing it in the machine readable form, the concept of Semantic web stepped in progressing towards automated knowledge discovery process. In this paper, comprehensive pre-processing techniques have been proposed for preparing the raw data to be presentable in structured format so as to construct the onto-graph for selected features in a health care domain. Cluster based Missing Value Imputation Algorithm (CMVI) has been proposed to enhance the quality of the imputed data which is the most important step during data pre-processing. Missing values were randomly induced into the Pima Indian Diabetic dataset with the missing ratio of 1%, 3% and 5% for each attribute up to 50% of the attributes in the original diabetic dataset. The experimental observations reveal that the quality of the pre-processed data is better compared to raw, unprocessed data in terms of imputation accuracy measured against coefficient of determination (R2), Index of agreement (d2) and Root Mean Square Error (RMSE).Documented results proved that the proposed techniques are comparatively superior than the traditional approaches with increased R2 & d2 and decreased RMSE scores. Further, importance of knowledge graph and various ontological representation types are discussed in short as construction of .owl file is the first step towards automation in semantic web.

Query Extension Suggestions for Visual Query Systems Through Ontology Projection and Indexing

Ontology-based visual query formulation is a viable alternative to textual query editors in the Semantic Web domain for extracting data from structured data sources in terms of the skills and knowledge required. A visual query system is at any moment responsible for providing the user with query extension suggestions; however, suggestions leading to empty results are often not useful. To this end, in this article, we first present an approach for projecting OWL 2 ontologies into navigation graphs to be used for query formulation and then a solution where an efficient finite index is used to calculate non-ranked approximated extension suggestions for ontology-based visual query systems using navigation graphs. The results of our experiments suggest that one can efficiently project an ontology into a navigation graph, query it for running an interactive user interface, and suggest query extensions that do not lead to dead-ends.